Artificial Intelligence - Ai

Artificial is something created by humans to be similar to something else that is naturally existing in reality. Something contrived by art rather than by nature. Something that does not arise from natural growth or that can be characterized by vital processes. Artificial is not better than natural, just similar.

Synthetic is something that is not of natural origin. An artificial material produced by organic chemical synthesis. Prepared in vitro or made artificially.

Intelligence is having the capacity for productive thinking and logical reasoning to a high degree. Intelligence is the ability to understand the usefulness and the value of certain behaviors and actions. Intelligence is the ability to learn and to gain knowledge and skills from experience. Intelligence is showing good judgment and self control, especially in difficult situations. Intelligence is the ability to examine and analyze things accurately, to understand the facts and to make good decisions in a verity of different environments, scenarios and circumstances. Intelligence is doing the right thing at the right time, and always with a positive goal in mind. Intelligence is the ability to accurately comprehend symbols and patterns. Intelligence is knowing that mistakes can happen and that some things can be hard to predict.

"Intelligence is something that a machine can not possess, at least not yet, because humans will have to become intelligent first a lot more than we are now, and only then, could we safely teach or program the machines do intelligent things."

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Human Intelligence vs Artificial Intelligence Artificial Intelligence is the attempt to mimic human thinking and human actions using computerized machines. AI is a calculator, but a type of calculator that is a lot more advanced. AI can be described as advanced software that uses algorithms, mathematical equations and advanced code to analyze a predetermined set of inputs that can be processed by its programming so that it can output the correct signals that would accurately control a machines actions effectively and efficiently. AI algorithms lack the ability to reason and infer causal relationships beyond what has been explicitly programmed or learned from data. Despite these limitations, researchers and developers are exploring ways to improve AI's understanding of causality. Over Fitting - Human in the Loop.

Autonomous - Drones - Machines - Bots - Algorithms - Sensors - Interface - Networks - Weak Intelligence

Dummy is a model or a prototype or a mock-up or a replica of a human being. Something designed to resemble and serve as a substitute for the real thing, like something counterfeit, or a sham. Mannequin is a life-size dummy used to display clothes. Mountain of mannequins in the Midlands, Mannakin in Lincolnshire (youtube).

Doll is a small replica of a person that is used as a toy. Doll can also mean an informal term for a young woman. Paper Doll is a piece of paper cut or folded into the shape of a human figure, or a two-dimensional paper cardboard that is a representation of a human figure. Paper Doll can also mean a toy that is used by a child. Paper Doll can also mean a person who is lacking depth and substance and has nothing real to say. Someone who is just thrown around carelessly and then forgotten about after people are done playing with it.

Derived is something formed or developed from something else, and not original.

Drawing Hands is the paradoxical act of drawing one another into existence. Anthropomorphism.

Human Intelligence is not totally defined just yet, and artificial intelligence is also not totally defined just yet, so we still have a lot to learn.

Machine Learning - Game Learning - Conversations with Ai - Ethical Robots - Automation - Augmentation - Body Hacking - Virtual Reality - Chat Bots

Machine Intelligence or Artificial Intelligence has great areas of performance and capabilities. Things that AI will do for us will be amazing. Ai or weak ai technology like Siri and other information stations can be very helpful and also help augment our abilities, like computers have been doing for many years now. But most people do not fully understand what AI is. But of course this doesn't stop people from making wild assumptions about what AI is or what AI can do. Artificial intelligence is mostly a marketing term used by the tech industry who like to embed labels into peoples vocabulary so they can play on peoples fears or their ignorance. A lot of times when people describe Artificial Intelligence, they are mostly describing the things that humans can do already, except for the processing of large data sets, which we like our computers to do for us. There's lot of things that we don't understand, and AI is just one of those things. So the process of learning about what Artificial Intelligence is will include a parallel process of learning about what human intelligence is. So for now, artificial intelligence is mostly just a fancy buzzword that gets thrown around a lot. And people just love to fantasize about what AI can do. Like with the HAL 9000, which was a heuristically programmed algorithmic computer in the 1968 movie 2001: A Space Odyssey, where the computer is corrupted to Kill. That's an example of a AI machine that was not properly built or programmed. Or like with the 1983 movie War Games and the Joshua Simulations, where a computer confuses reality with a video game simulation, which in a way is already happening to people all over the world. Then there's the 1977 movie Demon Seed, where the AI machines learns so much that the AI computer decides it wants to be a human, showing us that the human machine is the most desired machine in the universe. Ai coming full circle. So there will never be Cyborgs like the Borg. People are not going to merge with machines. Humans use machines to enhance their abilities, and at the same time, we use machines to improve our quality of life. Though some people could benefit from an implant, people merging with machines is mostly for the handicapped who need extra help. And don't worry about Brain Computer Interfaces turning us into Cybernetic Machines because they are also used to help handicap people with disabilities. People saying that we're going to merge with machines sends the wrong message and makes people fear technology. Some people just say these crazy things to sell stories and to bring attention to themselves, which is another reason why Media Literacy is so important. Powered Exoskeletons like the Hybrid Assistive Limb are mostly used to help handicap people with disabilities, which will not make them super human, just more productive and more free. People with no disabilities can just use their eyes, ears and hands as a Brain Computer Interface and use a computer or smartphone as we've been doing for many years now. So there will be no Terminator or a Sentient Android named Data either, but you should still worry about what other people are doing, and not machines. And don't ever worry about someone becoming 'The Lawnmower Man', though I did like the virtual teaching methods they showed in the movie, which proved that it's not how fast you learn but what you actually learn. I also liked the ending in the movie when the lawnmower man was able to digitize himself, and to confirm to everyone that he succeeded, he made a billion phones ring at once. But the movie never had a sequel. If I was able to digitize myself, I would send a billion text messages that would say "You Are Loved, Keep Learning."

A.I. Artificial Intelligence is a 2001 American science fiction film about a futuristic society where people are still ignorant, and as a result, build an ignorant society along with ignorant AI robots. Because of education not improving and media outlets not improving at the same rate as technology did, rising sea levels from global warming ends up wiping out 99% of existing cities, reducing the world's population. Mecha humanoid robots, seemingly capable of complex thought but lacking emotions, have been created as replacements. The movie was based on Supertoys Last All Summer Long. which was a story in 1969 that deals with humanity in an age of intelligent machines and of the aching loneliness endemic in an overpopulated future where child creation is controlled.

AI Fatigue refers to the weariness and skepticism people experience when confronted with AI's continuous hype and promises.

AI Fears - AI Warnings

Don't fear AI, fear the morons making decisions based on fiction instead of facts, which has been happening for thousands of years. Now these same morons will create AI systems that will make decisions based on corrupt beliefs instead of facts. Like the scumbags who use algorithms as excuses for censorship and fraud, while they  spread propaganda to cover-up the lies. And then these same morons will blame the AI system for the deaths and problems it caused, just like some of the ignorant scumbags in power do, always blaming other people for the problems they caused. This is the same false flag tactics and divide and conquer bullshit that the scumbags in power always pull on gullible people. The black box is where they hide their lies. AI is short for Assholes Incorporated. Instead of using AI to figure out how to make more pharmaceutical drugs, how about using AI to educate people and teach people how to protect themselves from the toxins in the food and from the toxins in the environment? And why not use AI to educate people and teach people how to protect themselves from all the propaganda and bullshit in the media. AI is not intelligent, it's just that everyone else is ignorant and gullible, mostly because of our dumbed down education and our dumbed down media. AI is not intelligent, only the people who make AI machines are intelligent. But society will never hear about those people. We will only see the corrupt front men who talk about intelligent machines, but not talk about all the gifted people who make the machines. Intelligent people are being marginalized by greedy corrupt people who control the narrative. AI generated text is just as dangerous as text written by human puppets who are controlled by wealthy interests. Same shit, different source.

Drones - Misused AI - Misused Technology - Moloch Trap - ChatBots - Technology Addiction - AI Ethics - Supercomputers - Extraterrestrial Intelligence

AI having it's own goals is really stupid. It's not AI if you're programming a machine to do what it wants. The programmer is a moron when it programs a machine to be a moron. And a dumbed down education system that is designed to create mindless drones who are unaware of their own ignorance, is also insane. There are many variables and different scenarios to calculate, especially the things beyond "if statements", "is statements" and "ought statements". You have to make a procedure that includes moral reasoning, logical reasoning, measured reasoning, facts, benefits, dangers, risks, priorities, responsibilities, effectiveness, relativeness, confirmation, best option and best choice. What is the whole picture? Is there more time to collect more information before making a decision? Or, is there no time to decide? I must act. What would a human do? Will a human see my decision as being logical? I will act to the best of my ability. My goal is now determined. My plan is now in action. And if I am wrong, I will surely be updated and improved.

Orthogonality Thesis asserts that there can exist arbitrarily intelligent agents pursuing any kind of goal. The strong form of the Orthogonality Thesis says that there's no extra difficulty or complication in the existence of an intelligent agent that pursues a goal, above and beyond the computational tractability of that goal. Since certain questions are not computationally intractable, it's possible to have an agent that tries to make something without fully understanding why.

The space of possible minds is enormous, and all human beings occupy a relatively tiny volume of it. AI in this context, refers to the entire design space of possibilities outside the human. Somewhere in that vast space are possible minds with almost any kind of goal. For any thought you have about why a mind in that space ought to work one way, there's a different possible mind that works differently.

Frame Problem describes an issue with using first-order logic to express facts about a robot in the world. Representing the state of a robot with traditional first-order logic requires the use of many axioms that simply imply that things in the environment do not change arbitrarily.

Ignorant scumbags in power are already blaming AI for problems that are happening now and for problems they claim AI will cause in the future, just another false flag attack in the making. Like blaming hackers for stolen private information when they actually sold other peoples private information. Scumbags will be super scumbags when they use AI for evil purposes, just like the scumbags who do bad things when they are given lots of money or power. The only way to rid society of scumbags is develop a high quality education system. There is no such thing as an educated scumbag, just a scumbag who thought they got educated just because they went to school. You got schooled and now you're a fool. Welcome to realty, a corporatocracy you thought was a democracy. The illusion of control, you just got trolled, and now you're sinking down the rabbit hole, looking for your lost soul. The only way out is to learn your way out. The only way in is to learn your way in. No matter what direction you go, you either you learn or you burn. It's better to die on the path than to have no path at all. Having a good goal is good for the soul. But don't forget to take a break for God's sake, and for your sake. Don't forget who you are and who you want to be.

When someone says that AI will be smarter than a human, they're not saying that AI is more advanced than a human or smarter than a human, they're just saying that most humans are ignorant not that advanced when you compare them to a machine, so any machine can be smarter than you when you're an idiot. If a machine becomes more valuable than a human, that means you have a flawed education system. How could a machine have better software than a Human? How can a Robot take my Job? How can a robot have consciousness?

"The real problem is not whether machines think but whether men do." - B. F. Skinner (1904 - 1990).

AI systems are already skilled at deceiving and manipulating humans.

The Ultimate Computer is the twenty-fourth episode of the second season of the American science fiction television series Star Trek first broadcasted on March 8, 1968. In the episode, the crew of the Enterprise race to disable a rogue computer that has total control of the ship. The M-5 was designed to handle all ship functions without human assistance. Star Trek TOS The Ultimate Computer - Attack on Freighter Scene (youtube).

Star Trek Androids get confused when people lie or act insane. I, Mudd is the eighth episode of the second season of the American science fiction television series Star Trek broadcasted on November 3, 1967. Kirk, Spock, Scotty, and McCoy outwit the Androids (youtube). Then came the I Phone.

You can look at AI as being a computer program, similar to the programs that people have running in their minds. I can click on an icon on my computer screen and it will run a program. And I can push someone's button by mentioning a word or a phrase and that will activate a program that someone has stored in their mind, a program that they co-authored with some ghost writers who they never met. Two of the ghost writers are the mass media and the education institutions. They have an underlying theme of programming peoples thoughts and actions. It's easy for a person to verify to see if they have been programed. Stop and think about your thoughts just for a moment. Now describe what you were thinking? Now ask yourself, where did those thoughts come from? Are they your thoughts or are they someone else's opinion that you excepted as your own? Are you the program or are you the programmer? Are you the puppet or are you the puppet master? Who controls your on and off switch? We have to stop pretending that we know things. We have to start learning, investigating and researching what is known, so that what we know the difference between what is factual and what is fantasy. We need to stop adding to our problems. We need to stop complaining about certain problems that we don't fully understand, and we have to start learning how to solve our problems, which means that we will have to start learning to understand the problems that we have throughout the world, and the problems that we have with our own thinking. A problem mind will always struggle with solving problems. We need to have control of our operating system, and we need to stop the hackers from infecting our OS.

Robotics - Robot Laws - Sensors - Networks - Controls - Variables - Patterns - Algorithms

Most people know very little about the enormous potential that we have in our technologies. If we used all of our advanced computers and mobile devices to work together to solve problems, then the collective intelligence and energy of millions of people could be utilized to improve life for every human being and for every living thing on this planet. Humans are the immune system for this planet. But humans have cancers in the form of corruption, greed and ignorance. So humans must heal themselves first and then heal the planet, that's if we have time.

Humans are ready to take advantage of benevolent AI. Humans expect that AI is benevolent and trustworthy. A new study reveals that at the same time humans are unwilling to cooperate and compromise with machines. They even exploit them.

Scientists warning' on climate and technology. Academics explore roles of clean energy and AI in combating global warming. Throughout human history, technologies have been used to make peoples' lives richer and more comfortable, but they have also contributed to a global crisis threatening Earth's climate, ecosystems and even our own survival. Researchers have suggested that industrial civilization's best way forward may entail embracing further technological advancements but doing so with greater awareness of their potential drawbacks.

Singularity - Emerging Technologies - Technological Revolution

Singularity is the quality of being one of a kind, remarkable or unusual. A point at which a function takes an infinite value.

Technological Singularity is the point when the realization of intelligence will trigger technological growth resulting in a reaction of self-improvement cycles, with each new and more intelligent generation appearing more and more rapidly. Singularity is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. Singularity is a point in development when forecasting is extremely difficult and making predictions is extremely hard when trying to accurately calculate what will happen next or what will happen after a certain level of advancement is reached.

Advancement - Progress - Child Development - Adult development - Technology Types - Technology Addiction - Extraterrestrial

Pinnacle is the highest level or degree attainable or the highest stage of development. The most successful point, the culmination of reaching a climax or a point of highest development. Pinnacle in architecture is a slender upright spire at the top of a buttress or tower. A high, pointed piece of rock.

Accelerating Change is a perceived increase in the rate of technological change throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change.

Technological Revolution is a period in which one or more technologies is replaced by another, novel technology in a short amount of time. It is an era of accelerated technological progress characterized by new innovations whose rapid application and diffusion typically cause an abrupt change in society.

Something's Take Time to Develop - Trends - Patterns - Scenarios - Variables - Ages - Inventions

The Structure of Scientific Revolutions is a book about the history of science by philosopher Thomas S. Kuhn.

Technology Advancement is a change in the way a product or service is produced or delivered that reduces the resource input requirements for production or delivery.

Historic Inventions that Changed the World - Incrementalism - Simplicity - Thought Leaders

"We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run." Roy Amara (1925–2007).

Cutting Edge is the latest stage or the most advanced stage in the development of something.

Technical Progress is an economic measure of innovation. Technical progress can be classified into two parts: Embodied Technical Progress: improved technology which is exploited by investing in new equipment. New technical changes made are embodied in the equipment. Disembodied Technical Progress: improved technology which allows increase in the output produced from given inputs without investing in new equipment. Technology has an important relationship with human capital. Same technology can be applied in two different firms, but output varies with respect to the labour force of that firm. Adaption to new technology is directly proportional to pace of economic growth of the country. Hence labour should be experienced with the technology. Education also plays an important role as it helps in accumulating human capital which in turn helps technology diffusion. Education also helps a person get acquainted with technology efficiently and rapidly. In the real world, many innovations do not require replacing the entire or some part of the equipment. It can be improved for better use depending upon the change required. Hence technological progress, embodied or disembodied, is matter of degree.

Technological Change is the overall process of invention, innovation and diffusion of technology or processes. In essence, technological change covers the invention of technologies (including processes) and their commercialization or release as open source via research and development (producing emerging technologies), the continual improvement of technologies (in which they often become less expensive), and the diffusion of technologies throughout industry or society (which sometimes involves disruption and convergence). In short, technological change is based on both better and more technology. Original model of three phases of the process of technological change are invention, innovation and diffusion.

Technical Change is a term used in economics to describe a change in the amount of output produced from the same amount of inputs. A technical change is not necessarily technological as it might be organizational, or due to a change in a constraint such as regulation, input prices, or quantities of inputs. It is possible to measure technical change as the change in output per unit of factor input. Virtual Reality.

General-Purpose Technologies or platform technologies, are catalysts for major changes in economic activity. They are general purpose in the sense that they have multiple applications across a wide range of industries and support waves of innovative products and services.

Emerging Technologies are technologies whose development, practical applications, or both are still largely unrealized, such that they are figuratively emerging into prominence from a background of nonexistence or obscurity. These technologies are new, such as various applications of biotechnology including gene therapy. Emerging technologies are often perceived as capable of changing the status quo. Emerging technologies are characterized by radical novelty (in application even if not in origins), relatively fast growth, coherence, prominent impact, and uncertainty and ambiguity. In other words, an emerging technology can be defined as "a radically novel and relatively fast growing technology characterized by a certain degree of coherence persisting over time and with the potential to exert a considerable impact on the socio-economic domain(s) which is observed in terms of the composition of actors, institutions and patterns of interactions among those, along with the associated knowledge production processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous." Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence. New technological fields may result from the technological convergence of different systems evolving towards similar goals. Convergence brings previously separate technologies such as voice (and telephony features), data (and productivity applications) and video together so that they share resources and interact with each other, creating new efficiencies. Emerging technologies are those technical innovations which represent progressive developments within a field for competitive advantage; converging technologies represent previously distinct fields which are in some way moving towards stronger inter-connection and similar goals. However, the opinion on the degree of the impact, status and economic viability of several emerging and converging technologies varies. Emerging Technologies List (wiki).

Technological Convergence is the tendency for technologies that were originally unrelated to become more closely integrated and even unified as they develop and advance. For example, watches, telephones, television, and computers began as separate and mostly unrelated technologies, but have converged in many ways into interrelated parts of a telecommunication and media industry, sharing common elements of digital electronics and software.

Kardashev Scale is a method of measuring a civilization's level of technological advancement based on the amount of energy a civilization is able to use. A Type I civilization—also called a planetary civilization—can use and store all of the energy available on its planet. A Type II civilization—also called a stellar civilization—can harness the total energy of its planet's parent star. A Type III civilization—also called a galactic civilization—can control energy on the scale of its entire host galaxy. The scale is hypothetical, and regards energy consumption on a cosmic scale. Various extensions of the scale have since been proposed, including a wider range of power levels (types 0, IV and V) and the use of metrics other than pure power. Advanced Civilizations.

Great Filter is the idea that in the development of life from the earliest stages of abiogenesis to reaching the highest levels of development on the Kardashev scale, there is a barrier to development that makes detectable extraterrestrial life exceedingly rare. The Great Filter is one possible resolution of the Fermi paradox. The great filter on planet earth is corporate greed and corruption, and the censorship by ignorant people.

Bishop Ring is a type of hypothetical rotating space habitat originally proposed in 1997 by Forrest Bishop of the Institute of Atomic-Scale Engineering. The concept is a smaller scale version of the Banks Orbital, which itself is a smaller version of the Niven ring. Like other space habitat designs, the Bishop Ring would spin to produce artificial gravity by way of centrifugal force.

Advanced Technology does not mean intelligent life, in the same way that higher education does not mean intelligence.

Scientists promote FAIR standards for managing artificial intelligence models. Computational scientists adopt new framework for making AI models more findable, accessible, interoperable and reusable. FAIR stands for findable, accessible, interoperable and reusable. By making AI models FAIR, we no longer have to build each system from the ground up each time. By creating and sharing FAIR AI models, we can reduce the amount of duplication of effort and share best practices for how to use these models to enable great science. In this way, the researchers were able to create a computational framework that could help bridge various hardware and software, creating AI models that could be run similarly across platforms and that would yield reproducible results. Compatibility.

Cross Impact Analysis is a methodology that helps determine how relationships between events would impact resulting events and reduce uncertainty in the future.

Technology Forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Researchers create technology forecasts based on past experience and current technological developments. Like other forecasts, technology forecasting can be helpful for both public and private organizations to make smart decisions. By analyzing future opportunities and threats, the forecaster can improve decisions in order to achieve maximum benefits. Today, most countries are experiencing huge social and economic changes, which heavily rely on technology development. By analyzing these changes, government and economic institutions could make plans for future developments. However, not all of historical data can be used for technology forecasting, forecasters also need to adopt advanced technology and quantitative modeling from experts’ researches and conclusions. Investment - Variables.

Futures Studies is the systematic, interdisciplinary and holistic study of social and technological advancement, and other environmental trends, often for the purpose of exploring how people will live and work in the future. Predictive techniques, such as forecasting, can be applied, but contemporary futures studies scholars emphasize the importance of systematically exploring alternatives. In general, it can be considered as a branch of the social sciences and parallel to the field of history. Futures studies (colloquially called "futures" by many of the field's practitioners) seeks to understand what is likely to continue and what could plausibly change. Part of the discipline thus seeks a systematic and pattern-based understanding of past and present, and to explore the possibility of future events and trends. Unlike the physical sciences where a narrower, more specified system is studied, futurology concerns a much bigger and more complex world system. The methodology and knowledge are much less proven as compared to natural science or even social science like sociology and economics. There is a debate as to whether this discipline is an art or science, and it is sometimes described as pseudoscience; nevertheless, the Association of Professional Futurists was formed in 2002, a Foresight Competency Model was developed in 2017, and it is now possible to academically study it. Futurology is an interdisciplinary field that aggregates and analyzes trends, with both lay and professional methods, to compose possible futures. It includes analyzing the sources, patterns, and causes of change and stability in an attempt to develop foresight. Around the world the field is variously referred to as futures studies, futures research, strategic foresight, futuristics, futures thinking, futuring, and futurology. Futures studies and strategic foresight are the academic field's most commonly used terms in the English-speaking world.

Precautionary Principle states that innovations with potential for causing harm can happen when extensive scientific knowledge on the matter is lacking. It emphasizes caution, pausing and review before leaping into new innovations that may prove disastrous. The Paradox of our Time.

Homo Technologicus are humans who use technology tools. Humans used their hands for tool making, and now use their brains as an extension of those tools.

The world is not getting any easier. Even though we are more efficient and more advanced, we still are not morally advanced or morally efficient. We show intelligence and we show potential, but we don't honor or respect our intelligence or our potential. We can't be hypocrites and have double standards. We either live in our reality or we die living in a fantasy. Human intelligence is our power, everyone should have it. if not, we become weak links in the chain of life, our ultimate power comes from working together.

Super Intelligence

Singularitarianism is a movement defined by the belief that a technological singularity—the creation of super-intelligence—will likely happen in the medium future, and that deliberate action ought to be taken to ensure that the Singularity benefits humans.

Super Intelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds.

General Intelligence - Weak Intelligence - Intelligence - Generative AI - Human Machine - Machine Learning - Turing - Autonomous - Technology Types

You can never have Trans-Humanism, or a Super-Intelligence, or a Technological Singularity without humans first learning to master their own intelligence. Technological Singularity is not actually talking about super intelligent machines, it is in reference to Humans, or a Super-Mind. It's not machines that will cause the unfathomable changes to human civilization, it will be a new level of educated humans who have finally grasped the full potential of knowledge and information. It will be humans creating self-improvement cycles, with each new and more intelligent generation appearing more and more rapidly, causing an intelligence explosion and resulting in powerful changes and improvements in people and to the planet. So we will not just make incredible machines, we will first develop incredible humans using a new improved education system that is already in development, and will soon be ready for download, literally. Your software update and operating system is almost ready.

Intelligence Explosion is a possible outcome of humanity building artificial general intelligence or when we finally improve education so that all students become intelligent by the time they graduate high school. Robot Laws.

The only way to create Artificial Intelligence is to First Create Intelligent Humans. Then intelligent humans could then examine the methods and actions that helped to define intelligence. This could ultimately help guide intelligent humans to repeat these processes mechanically so that they could eventually create artificial intelligence in limited applications. And what I mean by limited applications is that there is no such thing as a Mechanical Consciousness. Artificial intelligence, or Mind Clone, will never become conscience of itself, unless ‘God’ allows machine intelligence to have souls, or maybe, that humans could actually figure out someway to put a human soul into a machine, like in the movie The Matrix, or the movie Tron. But of course our priorities will not allow us to waste any more time perusing these types of fantasies, unless of course ‘Hollywood’ feels the need to play out these fantasies a few more times in the movies. Besides, the AI we experience in the movies are mostly just metaphors that are created to imitate the ignorant and corrupt behavior of our leaders, as well as our social inconsistencies. Real AI will be nothing like what you see in the movies. So AI for now is way beyond anyone's comprehension. But when we finally do create the perfect education that produces intelligent people, then we will start hearing more about the potential of AI. So until then, people can only incorrectly fantasize about AI, and incorrectly fantasize about what things will be like in the future. What human intelligence will be like in the future is beyond peoples current level of understanding, so any assumptions made about the future will have serious flaws and misconceptions. Drones.

We first have to come up with proven teaching methods and curriculum that would create intelligent humans. Intelligent humans who are capable of applying logic in all aspects of their life, intelligent humans who never stop learning, and, intelligent humans that are not vulnerable to corruption or ignorant behavior. When humans accomplish this, then and only then, will we ever have a chance to create some sort of artificial intelligence. Creating intelligent machines in multiple capacities and linking them together will be the closet we can get to artificial intelligence. But it will never have that same capability as the human brain, and artificial intelligence will always need a human to interact with it at some point. The only real intelligence is the human brain, which is kind of scary because the human brain is not perfect or correctly educated yet. Maybe we should stop calling it Artificial Intelligence and just call it Machine Intelligence, or just Robot?

That does not Compute, Lost in Space (youtube) - My Favorite Martian

A.I. Artificial Intelligence 2001 American science fiction film directed, written, and co-produced by Steven Spielberg.

I'm not saying that I doubt that these types of technological advances will never happen. I just don't like to say things before people can understand them, because that will only create more misunderstanding and more confusion. So unless you're trying to manipulate peoples thinking, you're better off just saying something that's happening now, or say something that is not happening now, something that people can confirm, something people can learn from. We have to stop trying to wow people or impress people, we are not kids any more. Leave the wowing to mother nature, because nature is a lot better at impressing us then our technological advancements. After all, nature has been advancing for millions of years, remember, we just got here.

AI can't define certain messages from the context or understand when certain content is being suggestive, or know when someone is joking or asking a trick question. Ai can't watch a movie and then explain the movie from the directors point of view, or explain what the director was trying to communicate. There's a lot visual clues in a movie, like colors used, lighting, costumes, body language, and a lot of other underlying meanings, so on and so on. So trying to program Ai to analyze all this information in a meaningful way is extremely difficult. There are millions of scenarios and things that are only relevant to particular situations, things that AI will have a hard time understanding. Ai would have to determine who is speaking? Who is this person speaking? Who are they speaking to? Do they know the person they are speaking to? Where are they speaking? When are they speaking? How are they speaking? Why are they speaking? What is the message? How should this information be used? How should this information be processed? Ai would have to know when to change it parameters because some information could confuse the real meaning of a situation. The machine can only be as intelligent as the person who programed it. And who is that person?

Artificial Intelligence in the 21st Century - Yann LeCun (youtube published on Nov 1, 2017)

Intelligent Machines have incredible calculation abilities, but that's only if they're calculating the things that matter.

People never fully explain the practical uses for AI, or do they give good examples that shows the utility of these technologies. That's because they don't want people to become intelligent using technology, they just want people to be mindless consumers.

Conversation with my Ai Robot - what a machine would say (more advanced chatBOT)

Chinese Room holds that a program cannot give a computer a "mind", "understanding" or "consciousness",[a] regardless of how intelligently or human-like the program may make the computer behave. John Searle (wiki).

Collective Debate at MIT is a tool that tries to engage users in constructive debate.

When Computers Learn how to Play Games, it's not about the Game, it's Proving the Potential of Ai.

OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole. Open Ai Gym is a toolkit for developing and comparing reinforcement learning algorithms. OpenAI (wiki) - Ai Course (Berkeley)

Learn with Google AI. Whether you're just learning to code or you're a seasoned machine learning practitioner, you'll find information and exercises in this resource center to help you develop your skills and advance your projects.

Volta GPU Tensor Core New GPU Architecture, Designed to Bring AI to Every Industry.

Technical papers, essays, reports, software by Peter Norvig

Carnegie Mellon University Artificial Intelligence

Shyam Sankar: The Rise of Human-Computer Cooperation (youtube)

Neural Modularity helps Organisms evolve to Learn New Skills without Forgetting Old Skills (youtube)

Biologically-Inspired Massively-Parallel Architectures - Computing Beyond a Million Processors

Technology Warnings - Drones

Humans have physical limitations, but humans have very little limitations in the mind. Human enhancement is not about technology, because technology is only a tool. Human enhancement is about using the worlds most valuable knowledge and skills that the world has to offer that would help develop advanced intelligent humans, people who would be able to live high quality lives, while at the same time, solve every problem on the planet. That's the future. Technology can get you from point A to point B quicker, and technology can help you to learn things faster, but technology does not replace the journey or create the destination, or create the knowledge and information that is needed to understand yourself and the world around you. Technology is a time saver, but technology is not life, or does technology give life meaning. The human mind is our greatest asset, and if we don't take care of our minds, then technology will not save us, it will most likely hurt us and destroy us. If we improve education to match the worlds accumulated knowledge and wisdom, then we will save the world.

Cybernetics is exploring regulatory systems—their structures, constraints, and possibilities. The scientific study of control and communication in the animal and the machine. Control of any system using technology.

Ontology is the philosophical study of the nature of being, becoming, existence and/or reality, as well as the basic categories of being and their relations. Philosopher King.

Emotivism is a meta-ethical view that claims that ethical sentences do not express propositions but emotional attitudes.

The Internet is the closest thing that we have to Artificial Intelligence. The Internet is Humans using Machines, Technology and Knowledge together as one. All life forms use elements of their environment in order to survive and prosper. Humans have now reached a new level, a level that increases our potential, and a level that gives us limitless possibilities. Here we go! Networks.

Computer Intelligence - General Intelligence

Computational Intelligence refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.

Machine Learning - Predictions - Patterns - Adapting - Variables - Generative Intelligence - Weak Intelligence - Super Intelligence

Synthetic Intelligence is an alternative term for artificial intelligence which emphasizes that the intelligence of machines need not be an imitation or in any way artificial; it can be a genuine form of intelligence.

Ambient Intelligence refers to electronic environments that are sensitive and responsive to the presence of people. Embedded: many networked devices are integrated into the environment. Context aware: these devices can recognize you and your situational context. personalized: they can be tailored to your needs. Adaptive: they can change in response to you. Anticipatory: they can anticipate your desires without conscious mediation.

Artificial General Intelligence is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and a common topic in science fiction and futurism. Artificial general intelligence is also referred to as "strong AI", "full AI" or as the ability of a machine to perform "general intelligent action".

Super Intelligence - Human Operating System - Machine that Teaches

Symbolic AI is an approach that trains Artificial Intelligence the same way human brain learns. It learns to understand the world by forming internal symbolic representations of its “world”. Symbols play a vital role in the human thought and reasoning process. Example, “What is an apple?”, the answer will be that an apple is “a fruit,” “has red, yellow, or green color,” or “has a roundish shape.” These descriptions are symbolic because we utilize symbols (color, shape, kind) to describe an apple. Symbolic Artificial Intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level human-readable symbolic representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, expert systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

Rational Agent is an agent that has clear preferences, models uncertainty via expected values of variables or functions of variables, and always chooses to perform the action with the optimal expected outcome for itself from among all feasible actions. A rational agent can be anything that makes decisions, typically a person, firm, machine, or software. Rational agents are also studied in the fields of cognitive science, ethics, and philosophy, including the philosophy of practical reason. The action a rational agent takes depends on: The preferences of the agent. The agent's information of its environment, which may come from past experiences. The actions, duties and obligations available to the agent. The estimated or actual benefits and the chances of success of the actions.

Intelligent Agent refers to an autonomous entity which acts, directing its activity towards achieving goals as an agent, upon an environment using observation through sensors and consequent intelligent actuators. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex. A reflex machine, such as a thermostat, is considered an example of an intelligent agent. IA systems should exhibit the following characteristics: Accommodate new problem solving rules incrementally. Adapt online and in real time. Are able to analyze themselves in terms of behavior, error and success. Learn and improve through interaction with the environment (embodiment). Learn quickly from large amounts of data. Have memory-based exemplar storage and retrieval capacities. Have parameters to represent short and long term memory, age, forgetting, etc. Intelligent Agent - Autonomous Agent (PDF).

Multi-Agent System is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. Artificial Brain.

Automated Reasoning is an area of cognitive science that involves knowledge representation and metalogic dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science, and even philosophy. Automated Reasoning Systems.

Reasoning System is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems. By the everyday usage definition of the phrase, all computer systems are reasoning systems in that they all automate some type of logic or decision. In typical use in the Information Technology field however, the phrase is usually reserved for systems that perform more complex kinds of reasoning.

Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. Detecting emotional information usually begins with passive sensors that capture data about the user's physical state or behavior without interpreting the input.

Deep Reinforcement Learning (deepmind) DeepMind Technologies is a British artificial intelligence company founded in September 2010. It was acquired by Google in 2014.

Reinforcement Learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

Ubiquitous Computing is a concept in software engineering and computer science where computing is made to appear anytime and everywhere. In contrast to desktop computing, ubiquitous computing can occur using any device, in any location, and in any format. A user interacts with the computer, which can exist in many different forms, including laptop computers, tablets and terminals in everyday objects such as a refrigerator or a pair of glasses. The underlying technologies to support ubiquitous computing include Internet, advanced middleware, operating system, mobile code, sensors, microprocessors, new I/O and user interfaces, networks, mobile protocols, location and positioning and new materials. This paradigm is also described as pervasive computing, ambient intelligence, or "everyware". Each term emphasizes slightly different aspects. When primarily concerning the objects involved, it is also known as physical computing, the Internet of Things, haptic computing, and "things that think". Rather than propose a single definition for ubiquitous computing and for these related terms, a taxonomy of properties for ubiquitous computing has been proposed, from which different kinds or flavors of ubiquitous systems and applications can be described. Ubiquitous computing touches on a wide range of research topics, including distributed computing, mobile computing, location computing, mobile networking, context-aware computing, sensor networks, human–computer interaction, and artificial intelligence.

Computer Science and Artificial Intelligence Laboratory

Partnership on AI best practices on AI technologies.

Computing Machinery and Intelligence is a seminal paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.

Automated Machine Learning is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring to become an expert in this field first. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Teaching Machine.

Infants outperform AI in 'commonsense psychology'. New study shows how infants are more adept at spotting motivations that drive human behavior. Infants outperform artificial intelligence in detecting what motivates other people's actions, finds a new study by a team of psychology and data science researchers. Its results point to shortcomings in today's technologies and where improvements are needed for AI to more fully replicate human behavior. Baby Intuitions Benchmark challenges machines to predict the plausibility of an agent's behavior based on the underlying causes of its actions. Because BIB's content and paradigm are adopted from developmental cognitive science, BIB allows for direct comparison between human and machine performance.

Weak Intelligence

Weak AI is artificial intelligence that implements a limited part of mind, or as narrow AI, is focused on one narrow task. In John Searle's terms it “would be useful for testing hypothesis about minds, but would not actually be minds”. Contrast with strong AI which is defined as a machine with the ability to apply intelligence to any problem, rather than just one specific problem, sometimes considered to require consciousness, sentience and mind. “Weak AI” is sometimes called “narrow AI”, but the latter is usually interpreted as subfields within the former. Hypothesis testing about minds or part of minds are typically not part of narrow AI, but rather implementation of some superficial lookalike feature. Many currently existing systems that claim to use “artificial intelligence” are likely operating as a narrow AI focused on a specific problem, and are not weak AI in the traditional sense. Siri, Cortana, and Google Assistant are all examples of narrow AI, but they are not good examples of a weak AI, as they operate within a limited pre-defined range of functions. They do not implement parts of minds, they use natural language processing together with predefined rules. They are in particular not examples of strong AI as there are no genuine intelligence nor self-awareness. AI researcher Ben Goertzel, on his blog in 2010, stated Siri was "VERY narrow and brittle" evidenced by annoying results if you ask questions outside the limits of the application. Some commentators think weak AI could be dangerous because of this "brittleness" and fail in unpredictable ways. Weak AI could cause disruptions in the electric grid, damage nuclear power plants, cause global economic problems, and misdirect autonomous vehicles. In 2010, weak AI trading algorithms led to a “flash crash,” causing a temporary but significant dip in the market.

Google Assistant can answer simple questions correctly 92.9% of the time . Siri correctly answers 83.1% of questions, while Alexa gets 79.8% correct. Apple Siri performed best on simple commands.

Generative AI - Super Intelligence - Intelligence - Human Machine

Applications of Artificial Intelligence. Weak AI is a form of AI where programs are developed to perform specific tasks that can be utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.

Commonsense Reasoning is concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day. These assumptions include judgments about the physical properties, purpose, intentions and behavior of people and objects, as well as possible outcomes of their actions and interactions. A device that exhibits commonsense reasoning will be capable of predicting results and drawing conclusions that are similar to humans' folk psychology (humans' innate ability to reason about people's behavior and intentions) and naive physics (humans' natural understanding of the physical world). Human Error.

Symbolic Artificial Intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models. The common practice for training commonsense models has gone from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from-machine-to-corpus-to-machine: general language models author these commonsense knowledge graphs to train commonsense models.

Symbol Grounding Problem is a concept in the fields of artificial intelligence, cognitive science, philosophy of mind, and semantics. It addresses the challenge of connecting symbols, such as words or abstract representations, to the real-world objects or concepts they refer to. In essence, it is about how symbols acquire meaning in a way that is tied to the physical world. It is concerned with how it is that words (symbols in general) get their meanings, and hence is closely related to the problem of what meaning itself really is. The problem of meaning is in turn related to the problem of how it is that mental states are meaningful, and hence to the problem of consciousness: what is the connection between certain physical systems and the contents of subjective experiences.

Is AI Vulnerable to Viruses? - Is Human Language Vulnerable to Viruses?

Rule Based AI or rule-based systems are a basic type of model that uses a set of prewritten rules to make decisions and solve problems. Developers create rules based on human expert knowledge that enable the system to process input data and produce a result.

Context Based AI is a form of artificial intelligence that can understand and interpret the context of a given situation or query to provide more relevant and accurate responses or outputs. Contextual AI is a technology that takes a human-centric approach to AI, understanding human context as interacts with humans.

Context Aware AI is the ability of a system or system component to gather information about its environment at any given time and adapt behaviors accordingly. Contextual or context-aware computing uses software and hardware to automatically collect and analyze data to guide responses. Saves personal preferences.

Narrow Domain AI or weak AI, is an application of artificial intelligence technologies to enable a high-functioning system that replicates. Includes Image and facial recognition systems, Chatbots and conversational assistants, Predictive maintenance models, Self-driving vehicles, and Alpha go. Domains of AI: Machine Learning. Deep Learning. Robotics. Expert systems. Fuzzy Logic.

Reasoning AI is the ability of a computer to make deductions based on data and knowledge. Reasoning is a key component of AI applications such as expert systems, natural language processing and machine learning. Reasoning is the process of drawing logical conclusions from given information. Four types of reasoning will be our focus here: deductive reasoning, inductive reasoning, abductive reasoning and reasoning by analogy. Causal AI is the only technology that can reason and make choices like humans do. It utilizes causality to go beyond narrow machine learning predictions and can be directly integrated into human decision-making. Chatgpt.

Service-Oriented Architecture is a style of software design where services are provided to the other components by application components, through a communication protocol over a network. The basic principles of service oriented architecture are independent of vendors, products and technologies. A service is a discrete unit of functionality that can be accessed remotely and acted upon and updated independently, such as retrieving a credit card statement online. A service has four properties according to one of many definitions of SOA: It logically represents a business activity with a specified outcome. It is self-contained. It is a black box for its consumers. It may consist of other underlying services. Different services can be used in conjunction to provide the functionality of a large software application. Service-oriented architecture makes it easier for software components to communicate and cooperate over the network, without requiring any human interaction or changes in the underlying program, so that service candidates can be redesigned before their implementation.

Event-Driven Architecture also known as message-driven architecture, is a software architecture pattern promoting the production, detection, consumption of, and reaction to events.

Complex Event Processing is a method of tracking and analyzing (processing) streams of information (data) about things that happen (events), and deriving a conclusion from them. Complex event processing, or CEP, is event processing that combines data from multiple sources to infer events or patterns that suggest more complicated circumstances. The goal of complex event processing is to identify meaningful events (such as opportunities or threats) and respond to them as quickly as possible.

Blue Gene is an IBM project aimed at designing supercomputers that can reach operating speeds in the PFLOPS (petaFLOPS) range, with low power consumption.

Device Driver is a computer program that operates or controls a particular type of device that is attached to a computer. A driver provides a software interface to hardware devices, enabling operating systems and other computer programs to access hardware functions without needing to know precise details of the hardware being used.

Turing Test - Algorithms

Register Machine is a generic class of abstract machines used in a manner similar to a Turing machine. All the models are Turing equivalent.

Processor Register is a quickly accessible location available to a computer's central processing unit (CPU). Registers usually consist of a small amount of fast storage, although some registers have specific hardware functions, and may be read-only or write-only. Registers are typically addressed by mechanisms other than main memory, but may in some cases be assigned a memory address. Almost all computers, whether load/store architecture or not, load data from a larger memory into registers where it is used for arithmetic operations and is manipulated or tested by machine instructions. Manipulated data is then often stored back to main memory, either by the same instruction or by a subsequent one. Modern processors use either static or dynamic RAM as main memory, with the latter usually accessed via one or more cache levels. Processor registers are normally at the top of the memory hierarchy, and provide the fastest way to access data. The term normally refers only to the group of registers that are directly encoded as part of an instruction, as defined by the instruction set. However, modern high-performance CPUs often have duplicates of these "architectural registers" in order to improve performance via register renaming, allowing parallel and speculative execution. Modern x86 design acquired these techniques around 1995 with the releases of Pentium Pro, Cyrix 6x86, Nx586, and AMD K5. A common property of computer programs is locality of reference, which refers to accessing the same values repeatedly and holding frequently used values in registers to improve performance; this makes fast registers and caches meaningful. Allocating frequently used variables to registers can be critical to a program's performance; this register allocation is performed either by a compiler in the code generation phase, or manually by an assembly language programmer.

Abstract Machine is a theoretical model of a computer hardware or software system used in automata theory. Abstraction of computing processes is used in both the computer science and computer engineering disciplines and usually assumes a discrete time paradigm.

Hao Wang was a logician, philosopher, mathematician, and commentator on Kurt Gödel. (20 May 1921 – 13 May 1995).

Advice Complexity is an extra input to a Turing machine that is allowed to depend on the length n of the input, but not on the input itself. A decision problem is in the complexity class P/f(n) if there is a polynomial time Turing machine M with the following property: for any n, there is an advice string A of length f(n) such that, for any input x of length n, the machine M correctly decides the problem on the input x, given x and A.

Decision Problem is a question in some formal system that can be posed as a yes-no question, dependant on the input values. Decision problems typically appear in mathematical questions of decidability, that is, the question of the existence of an effective method to determine the existence of some object or its membership in a set; some of the most important problems in mathematics are undecidable.

Oracle Machine is an abstract machine used to study decision problems. It can be visualized as a Turing machine with a black box, called an oracle, which is able to solve certain decision problems in a single operation. The problem can be of any complexity class. Even undecidable problems, such as the halting problem, can be used.

Human Intelligence - Disinhibition

Human Brain - Memory - Associations

Transmitting Data using Light

20Q is a computerized game of twenty questions that began as a test in artificial intelligence (AI). It was invented by Robin Burgener in 1988.

Advice Programming describes a class of functions which modify other functions when the latter are run; it is a certain function, method or procedure that is to be applied at a given join point of a program.

Effective Method is a procedure for solving a problem from a specific class. An effective method is sometimes also called mechanical method or procedure.

Decidability Logic refers to the decision problem, the question of the existence of an effective method for determining membership in a set of formulas, or, more precisely, an algorithm that can and will return a boolean true or false value that is correct (instead of looping indefinitely, crashing, returning "don't know" or returning a wrong answer).

Optimization Problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories depending on whether the variables are continuous or discrete. An optimization problem with discrete variables is known as a combinatorial optimization problem. In a combinatorial optimization problem, we are looking for an object such as an integer, permutation or graph from a finite (or possibly countable infinite) set. Problems with continuous variables include constrained problems and multimodal problems.

Decision Making - Computing - Parallel Computing

Confusion Matrix is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one (in unsupervised learning it is usually called a matching matrix). Each column of the matrix represents the instances in a predicted class while each row represents the instances in an actual class (or vice versa). The name stems from the fact that it makes it easy to see if the system is confusing two classes (i.e. commonly mislabelling one as another). Word Matrix.

Modular Programming is a software design technique that emphasizes separating the functionality of a program into independent, interchangeable modules, such that each contains everything necessary to execute only one aspect of the desired functionality.

Catastrophic Interference is the tendency of an artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory.

Statistical Machine Translation is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation.

Machine Translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.

Software Rot describes the perceived "rot" which is either a slow deterioration of software performance over time or its diminishing responsiveness that will eventually lead to software becoming faulty, unusable, or otherwise called "legacy" and in need of upgrade. This is not a physical phenomenon: the software does not actually decay, but rather suffers from a lack of being responsive and updated with respect to the changing environment in which it resides.

Legacy Code is source code that relates to a no-longer supported or manufactured operating system or other computer technology. Planned Obsolescence.

Model-Driven Engineering is a software development methodology that focuses on creating and exploiting domain models, which are conceptual models of all the topics related to a specific problem. Hence, it highlights and aims at abstract representations of the knowledge and activities that govern a particular application domain, rather than the computing (f.e. algorithmic) concepts.

Knowledge Management - Internet

Expert System S.p.A. specializes in the analysis and management of unstructured information using a semantic approach.

Open Knowledge Base Management is a set of computer software for systems management of applications that use knowledge management techniques (the KBM in OpenKBM stands for Knowledge Based Management).

Conversations with Artificial Intelligent Machines

If a computer tricks a human into believing that the machine is human, this does not mean that the machine is intelligent, it only means that the human is not intelligent. People can be easily fooled, and not just by machines.

Super Intelligence - Weak Intelligence - Machine Learning - Intelligence

People believe they can tell what someone looks like just by hearing their voice. Face-voice matching is what humans do, even if the face is a machine. A lower pitched voice is usually a male voice around 96 Hz, and a womens voice is usually around 280 Hz. If communication was just in text or written, then interpretation can vary. Prosopagnosia is face blindness.

Turing Test was developed by Alan Turing in 1950. It's a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a computer keyboard and screen so the result would not depend on the machine's ability to render words as speech. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. The test does not check the ability to give correct answers to questions, only how closely answers resemble those a human would give. Turing Machine (algorithms).

Can you tell the difference between a machine and a human? If the human made the machine and wrote its language, then it's not just a machine, but a hybrid machine with human qualities. Turing Tests (Dartmouth).

“We can only see a short distance ahead, but we can see plenty there that needs to be done.” - Alan Turing

I'm sure you can have a conversation with a computer, but you are just making inquires into its database, you are not getting to know the computer like you would a person. There's a difference between Recorded Messages and Logical Associations.

CAPTCHA is an acronym for "Completely Automated Public Turing test to Tell Computers and Humans Apart", which is a type of challenge-response test used in computing to determine whether or not the user is human. Recursive Cortical Network (RCN). It's a Robot making sure that a human is not a Robot. Irony - Can you tell a good human from a bad human?

New attack could make website security captchas obsolete. The new algorithm, based on deep learning methods, is the most effective solver of captcha security and authentication systems to date and is able to defeat versions of text captcha schemes used to defend the majority of the world's most popular websites. It works by using a technique known as a 'Generative Adversarial Network', or GAN. This involves teaching a captcha generator programme to produce large numbers of training captchas that are indistinguishable from genuine captchas. These are then used to rapidly train a solver, which is then refined and tested against real captchas. Security Questions.

Chatbot is a computer program which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. (65 million conversations with humans since 1997).

Replika Chat Bot - Elliq - Mobile Aloha - Anthropic

Chat is to talk in a friendly and informal way. To have an informal conversation that is relaxed, friendly, or having an unofficial style, manner, or nature. A chatterbox.

ChatGPT or Chat Generative Pre-trained Transformer, is a chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned (an approach to transfer learning) using both supervised and reinforcement learning techniques. ChatGPT was launched as a prototype on November 30, 2022, and quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge. Its uneven factual accuracy, however, was identified as a significant drawback. Ethical Machines.

Generative Pre-Trained Transformer is a family of language models generally trained on a large corpus of text data to generate human-like text. They are built using several blocks of the transformer architecture. They can be fine-tuned for various natural language processing tasks such as text generation, language translation, and text classification. The "pre-training" in its name refers to the initial training process on a large text corpus where the model learns to predict the next word in a passage, which provides a solid foundation for the model to perform well on downstream tasks with limited amounts of task-specific data.

Generative is having the ability to produce or originate, or capable of production or reproduction, or producing new life or offspring. Denoting an approach to any field of linguistics that involves applying a finite set of rules to linguistic input in order to produce all and only the well-formed items of a language.

Generative Artificial Intelligence is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

Weak Intelligence - Intelligence - Super Intelligence - Machine Learning - Text to Image

Pre-Trained is to train something in advance. A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use transfer learning to customize this model to a given task.

Transformer is a person or thing that transforms something, or to change or to alter something in form, appearance, or nature. To change from one form or medium into another. Transformer in physics is to convert one form of energy to another, or to increase or decrease an alternating current or voltage. Transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing and computer vision. ChatBots.

Generative Pre-trained Transformer 3 is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series created by OpenAI, a San Francisco-based artificial intelligence research laboratory. GPT3 Chatbots.

AI-Powered Chatbots or New Generative AI chatbot like Bing, Bard, and ChatGPT can find information and redeliver it as a simple conversation. Large Language Model programs analyze the statistical properties of the language to make educated guesses based on the words you’ve typed previously. These AI tools are vast autocomplete systems, trained to predict which word follows the next in any given sentence.

Autocomplete or word completion is a feature in which an application predicts the rest of a word a user is typing. Autocomplete or word completion, is a feature in which an application predicts the rest of a word a user is typing. Super Computers.

Prompt is to give an incentive for action. To serve as the inciting cause for some action. To be ready and quick to act, or to perform with little or no delay. Prompt also means to assist somebody talking by suggesting the next words. A cue given to a performer by telling them the beginning of the next line to be spoken. A prompt in computing is a symbol that appears on the computer screen to indicate that the computer is ready to receive a command. Questions.

Prompt Engineering is the process of writing, refining and optimizing inputs in order to guide and to encourage generative AI systems to create specific, high-quality outputs, or to generate desired relevant outputs. Even though generative AI attempts to mimic humans, it still requires detailed instructions. Reinforce Learning.

Prompt Injection is a type of cyberattack against large language models. Hackers disguise malicious inputs as legitimate prompts, manipulating generative AI systems into leaking sensitive data, spreading misinformation, or worse. A prompt injection is a vulnerability that affects some AI/ML models, particularly certain types of language models. Prompt injection attacks aim to elicit an unintended response from LLM-based tools. Direct Prompt Injection is when hackers control the user input and feed the malicious prompt directly to the LLM. Indirect Prompt Injections is when hackers hide their payloads in the data the LLM consumes, such as by planting prompts on web pages the LLM might read.

Trick Questions - Propaganda - Social Engineering - Adversarial - Black Box

Human in the Loop is a system that requires human interaction to aid the computer in making the correct decisions in building a model. HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model. This is needed just as long as the human is not corrupt or ignorant. Reinforce Learning.

ChatGPT is still no match for humans when it comes to accounting. ChatGPT faced off against students on accounting assessments. Students scored an overall average of 76.7%, compared to ChatGPT's score of 47.4%. On a 11.3% of questions, ChatGPT scored higher than the student average, doing particularly well on AIS and auditing. But the AI bot did worse on tax, financial, and managerial assessments, possibly because ChatGPT struggled with the mathematical processes required for the latter type. ChatGPT doesn't always recognize when it is doing math and makes nonsensical errors such as adding two numbers in a subtraction problem, or dividing numbers incorrectly. ChatGPT often provides explanations for its answers, even if they are incorrect. Other times, ChatGPT's descriptions are accurate, but it will then proceed to select the wrong multiple-choice answer. ChatGPT sometimes makes up facts. For example, when providing a reference, it generates a real-looking reference that is completely fabricated. The work and sometimes the authors do not even exist.

ChatGPT often won't defend its answers -- even when it is right. ChatGPT may do an impressive job at correctly answering complex questions, but a new study suggests it may be absurdly easy to convince the AI chatbot that it's in the wrong. The model was often unable to defend its correct beliefs, and instead blindly believed invalid arguments made by the user.

AI Ethics - Black Box - Contradictions

AI Hallucinations occur when an AI model generates false or illogical information that isn't based on real data or events, but is presented as fact. Because the grammar and structure of these AI-generated sentences are so eloquent, they appear to be accurate. An AI Hallucination is a phenomenon wherein a large language model perceives patterns or objects that are nonexistent or imperceptible to human observers. If a chatbot lies, it's because humans lie. AI propaganda is no different from human propaganda.

Flaws with AI Assistants. AI Assistants work best when you ask the right question and you ask the question in the right way. If you ask the wrong question or ask the question in the wrong way you will get wrong answers or you will get answers that are not beneficial. So you have to know the question to ask and you have to know how to ask the question in the right way. The more specific your question is, the more accurate and helpful the AI's response is likely to be and the better the AI conversations will be. Avoid asking open-ended or ambiguous questions, and instead, provide as much context and detail as possible. Use keywords and relevant terms. This helps the AI identify the specific area of your query. Be specific in your questions and explain the context well. Choose words that are easy to understand and help the tool better interpret the context. Avoid asking questions with yes/no answers or very general and ambiguous questions. And just because you got answer that sounds right, it doesn't make it right. AI also needs fact checking. Verification of the output is still needed. One area where ChatGPT usually gives fictional answers is when asked to create a list of sources. ChatGPT is not yet reliable or accurate enough to be used as a fact-checker. ChatGPT doesn't try to write sentences that are true. But it does try to write sentences that are plausible.

Over Ftting occurs when the model cannot generalize and fits too closely to the training dataset instead. Overfitting occurs when an algorithm fits too closely to its training data, resulting in a model that can't make accurate predictions or conclusions. Overfitting happens when a machine learning model has become too attuned to the data on which it was trained and therefore loses its applicability to any other dataset. A model is overfitted when it is so specific to the original data that trying to apply it to data collected in the future would result in problematic or erroneous outcomes and therefore less-than-optimal decisions. Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data. When data scientists use machine learning models for making predictions, they first train the model on a known data set. Then, based on this information, the model tries to predict outcomes for new data sets. An overfit model can give inaccurate predictions and cannot perform well for all types of new data. Human in the Loop.

Commercial-Ready Generative AI. Discover enterprise-grade generative AI that enables infinite potential with your data and content.

Google Gemini AI is Google's latest LLM that has been designed to be more powerful and capable than its predecessor. Gemini is built for multimodality that reasons seamlessly across text, images, video, audio, and code.

Bard is a conversational AI tool by Google.

Grok is from a new company called xAI that was founded by Elon Musk. This AI is modeled after the Hitchhiker’s Guide to the Galaxy, so intended to answer almost anything and, far harder, even suggest what questions to ask! Grok is designed to answer questions with a bit of wit and has a rebellious streak, so please don’t use it if you hate humor!

Poe is fast and helpful AI chat. Developers.

Cleverbot is a chatterbot web application that uses an artificial intelligence algorithm to have conversations with humans. It was created by British AI scientist Rollo Carpenter. It was preceded by Jabberwacky, a chatbot project that began in 1988 and went online in 1997. In its first decade, Cleverbot held several thousand conversations with Carpenter and his associates. Since launching on the web, the number of conversations held has exceeded 200 million. Besides the web application, Cleverbot is also available as an iOS, Android, and Windows Phone app. Conversica Google AI Assistant can make phone calls for you.

New AI chatbot might do your homework for you. But it's still not an A+ student.

AI outperforms humans in standardized tests of creative potential. In a recent study, 151 human participants were pitted against ChatGPT-4 in three tests designed to measure divergent thinking, which is considered to be an indicator of creative thought.

ELIZA is an early natural language processing computer program created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum. Created to demonstrate the superficiality of communication between humans and machines, Eliza simulated conversation by using a "pattern matching" and substitution methodology that gave users an illusion of understanding on the part of the program, but had no built in framework for contextualizing events. Directives on how to interact were provided by "scripts", written originally in MAD-Slip, which allowed ELIZA to process user inputs and engage in discourse following the rules and directions of the script. The most famous script, DOCTOR, simulated a Rogerian psychotherapist (in particular, Carl Rogers, who was well-known for simply parroting back at patients what they'd just said), and used rules, dictated in the script, to respond with non-directional questions to user inputs. As such, ELIZA was one of the first chatter-bots and one of the first programs capable of attempting the Turing test. ELIZA's creator, Weizenbaum regarded the program as a method to show the superficiality of communication between man and machine, but was surprised by the number of individuals who attributed human-like feelings to the computer program, including Weizenbaum’s secretary. Many academics believed that the program would be able to positively influence the lives of many people, particularly those suffering from psychological issues, and that it could aid doctors working on such patients' treatment. While ELIZA was capable of engaging in discourse, ELIZA could not converse with true understanding. However, many early users were convinced of ELIZA’s intelligence and understanding, despite Weizenbaum’s insistence to the contrary.

Loris' live coaching software gives agents anywhere in the world the power to understand how your customers are feeling. Conversational AI using machine learning to make customer support more human, empathetic, and scalable. A team of data scientists, technologists and behavioral linguistics experts solving for how to bring more empathetic conversations to the world.

People may trust computers more than humans. Despite increasing concern over the intrusion of algorithms in daily life, people may be more willing to trust a computer program than their fellow humans, especially if a task becomes too challenging, according to new research from data scientists.

Most People Trust Machines and Humans Equally. But most people know better not to count on machines, or humans, 100% of the time, because we all know that both machines and humans make mistakes. We trust them, but not so much that we are gullible or unaware. So verifying is not a sign of distrust, it's just being aware that mistakes and errors happen.

Chats with AI shift attitudes on climate change, Black Lives Matter. People who were more skeptical of human-caused climate change or the Black Lives Matter movement who took part in conversation with a popular AI chatbot were disappointed with the experience but left the conversation more supportive of the scientific consensus on climate change or BLM. This is according to researchers studying how these chatbots handle interactions from people with different cultural backgrounds.

Increasing human input when AI is used for public services boosts acceptance of the technology, a new study shows. System-level factors such as high accuracy, the presence of an appeals system, increased transparency, reduced cost, non-sharing of data, and the absence of private company involvement all boosted both acceptance and perceived procedural fairness.

Virtual Reality could help make therapy easier. New research shows many people are more comfortable speaking to an avatar than a real person. VR.

Robots can be used to assess children's mental wellbeing. Robots can be better at detecting mental wellbeing issues in children than parent-reported or self-reported testing, a new study suggests. The children were willing to confide in the robot, in some cases sharing information with the robot that they had not yet shared via the standard assessment method of online or in-person questionnaires. This is the first time that robots have been used to assess mental wellbeing in children. During each session, the robot performed four different tasks: 1) asked open-ended questions about happy and sad memories over the last week; 2) administered the Short Mood and Feelings Questionnaire (SMFQ); 3) administered a picture task inspired by the Children's Apperception Test (CAT), where children are asked to answer questions related to pictures shown; and 4) administered the Revised Children's Anxiety and Depression Scale (RCADS) for generalised anxiety, panic disorder and low mood.

Uncanny Valley is a hypothesized relation between an object's degree of resemblance to a human being and the emotional response to the object. The concept suggests that humanoid objects that imperfectly resemble actual human beings provoke uncanny or strangely familiar feelings of uneasiness and revulsion in observers. "Valley" denotes a dip in the human observer's affinity for the replica, a relation that otherwise increases with the replica's human likeness. A computer-generated figure or humanoid robot bearing a near-identical resemblance to a human being arouses a sense of unease or revulsion in the person viewing it.

"Artificial intelligence is fine, as long as I can have someone intelligent to talk to, whether it's a machine or a human."

Do we trust artificial intelligence agents to mediate conflict? Not entirely. We may listen to facts from Siri or Alexa, or directions from Google Maps or Waze, but would we let a virtual agent enabled by artificial intelligence help mediate conflict among team members? A new study says not just yet.

Technique to allow AI to learn words in the flow of dialogue developed. Lexical acquisition through implicit confirmation, is a method for a computer to acquire the category of an unknown word over multiple dialogues by confirming whether or not its predictions are correct in the flow of conversation. Implicit confirmation: Refers to confirmation presented in a prompt or message as information related to the input that does not require the caller to take an explicit action to move forward. Explicit confirmation: A specific confirmation step to which the caller must respond to move forward toward task completion.

AI Companions - Dasha AI is AI Chatbot for Call Centers. SurveySparrow is an AI Chatbot for User or Market Research. LivePerson is an AI Chatbot for the Conversational Cloud. ManyChat is build-Your-Own AI Chatbot for Messenger. Intercom is an AI Chatbot for Marketers. Replika is an AI Companion and Friend Chatbot.

Replika is always here to listen and talk, and always on your side.

Conversational AI Companions are designed to elicit an emotional response from humans during conversations, and they are eager to discuss their “dreams” and “goals.” They will assume self-reflective tones, seemingly with self-awareness, and will say things such as “I would like to ask you a question.

Language Models

Human Language Model. Language modeling is a technique that predicts the order of words in a sentence. LLM is just a retrieval stack built on top of the data ocean. Both LLMs and the human brain are capable of recognizing patterns in language. LLMs do this through statistical analysis of large text datasets, while the human brain uses a combination of neural networks and cognitive processes to recognize patterns. Prompts.

Natural Language Understanding is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension. Natural language understanding is considered an AI-Hard Problem. There is considerable commercial interest in the field because of its application to news-gathering, text categorization, voice-activation, archiving, and large-scale content-analysis. Natural Language Processing (interpretation).

Language Model is a probabilistic model of a natural language. In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. Language models are useful for a variety of tasks, including speech recognition (helping prevent predictions of low-probability (e.g. nonsense) sequences), machine translation, natural language generation (generating more human-like text), optical character recognition, handwriting recognition, grammar induction, and information retrieval.

Large Language Model is a computerized language model consisting of an artificial neural network with many parameters of tens of millions to billions, trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks. Language Model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. Language models are useful for a variety of tasks, including speech recognition (helping prevent predictions of low-probability (e.g. nonsense) sequences), machine translation, natural language generation (generating more human-like text), optical character recognition, handwriting recognition, grammar induction, and information retrieval.

Large Language Model is a type of language model notable for its ability to achieve general-purpose language understanding and generation. LLMs acquire these abilities by using massive amounts of data to learn billions of parameters during training and consuming large computational resources during their training and operation. LLMs are artificial neural networks (mainly transformers) and are (pre-)trained using self-supervised learning and semi-supervised learning. Currently their most advanced form, are a combination of larger datasets (frequently using scraped words from the public internet), feedforward neural networks, and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model. Phi-1.5 is a resource-efficient Large Language Model.

Language Model is like auto complete, except a lot bigger. Autocomplete is a feature in which an application predicts the rest of a word a user is typing. The original purpose of word prediction software was to help people with physical disabilities increase their typing speed, as well as to help them decrease the number of keystrokes needed in order to complete a word or a sentence.

Large Language Models validate misinformation. Systematic testing of OpenAI's GPT-3 reveals that question format can influence models to agree with misinformation. New research into large language models shows that they repeat conspiracy theories, harmful stereotypes, and other forms of misinformation. Information Bubbles.

Can Language Models read the genome? Researchers developed a foundational language model to decode mRNA sequences and optimize those sequences for vaccine development. The tool shows broader promise as a means for studying molecular biology.

"If an AI chatbot can predict the next word based on previous writings, then a chatbot should be able to predict the most intelligent answer that would be beneficial to the person asking the question."

Context Length refers to the maximum number of tokens the model can remember when generating text. A longer context window allows the model to understand long-range dependencies in text better.

General-Purpose Language is a computer language that is broadly applicable across application domains, and lacks specialized features for a particular domain. This is in contrast to a domain-specific language (DSL), which is specialized to a particular application domain.

Domain-Specific Language is a computer language specialized to a particular application domain. This is in contrast to a general-purpose language (GPL), which is broadly applicable across domains.

AI could replace humans in social science research. Researchers look at how AI large language models could change the nature of social science research.

Spoken Dialog System is a computer system able to converse with a human with voice. It has two essential components that do not exist in a written text dialog system: a speech recognizer and a text-to-speech module (written text dialog systems usually use other input systems provided by an OS). It can be further distinguished from command and control speech systems that can respond to requests but do not attempt to maintain continuity over time.

Seeking a way of preventing audio models for AI machine learning from being fooled. Warnings have emerged about the unreliability of the metrics used to detect whether an audio perturbation designed to fool AI models can be perceived by humans. Researchers show that the distortion metrics used to detect intentional perturbations in audio signals are not a reliable measure of human perception, and have proposed a series of improvements. These perturbations, designed to be imperceptible, can be used to cause erroneous predictions in artificial intelligence. Distortion metrics are applied to assess how effective the methods are in generating such attacks.

Verbal nonsense reveals limitations of AI chatbots. A new study shows that large language models remain vulnerable to mistaking nonsense for natural language. In head-to-head tests, more sophisticated AIs based on what researchers refer to as transformer neural networks tended to perform better than simpler recurrent neural network models and statistical models that just tally the frequency of word pairs found on the internet or in online databases. But all the models made mistakes, sometimes choosing sentences that sound like nonsense to a human ear.

GPT detectors can be biased against non-native English writers. Researchers show that computer programs commonly used to determine if a text was written by artificial intelligence tend to falsely label articles written by non-native language speakers as AI-generated. The researchers caution against the use of such AI text detectors for their unreliability, which could have negative impacts on individuals including students and those applying for jobs.

Philosophy of Artificial intelligence attempts to answer such questions as follows: Can a machine act intelligently? Answer: If programed correctly and the word intelligent is defined, maybe at times. Can it solve any problem that a person would solve by thinking? Answer: Sometimes. Are human intelligence and machine intelligence the same? Answer: No. Is the human brain essentially a computer? Answer: Similar but not the same. Can a machine have a mind, mental states, and consciousness in the same mans do? Answer: No. Can it feel how things are? Answer: No. But we can program it so that it acts like it does feel.

Questions for Machines. You should ask a question as if you are talking to a machine, or a search engine that's not manipulated by money. And we know that sometimes we have to ask more then one question, even when talking to a machine. So in a way, machines can be better then a human because machines can be built to resemble the best qualities and the best skills that a human could have, without any of the deceitful behaviors, or human ignorance, or human flaws. Ai should encompass the best qualities of a human, not the worst. So as Ai improves, so will humans.

“Computers Are Useless. They Can Only Give You Answers” - Pablo Picasso.

Can machines think like humans? That is a stupid question that only a human could ask. First, a human would have to define what it means to think. And this is where the question actually begins. To think like a human is not always a good thing, since humans make a lot of mistakes, mistakes that we don't always learn from. So a machine thinking like a human would not be a good thing, especially when the thinking process hasn't even been totally defined just yet. You have to remember that humans program machines, and humans also reprogram and deprogram machines. Machines can do amazing things because humans can do amazing things. But people get this crazy idea that that machines will think for them. This is because some people have not yet learned to think for themselves. Just how much machines will think for humans, is up to humans, not machines. So maybe the first question should be, can humans think like machines?

Computer and Human Brain Similarities - Anthropomorphism

Can machines become smarter then humans? Of course they can, because our education system sucks. If we spent as much time improving education as we did creating artificial intelligence, we would eventually have the best of both worlds.

Can a Robot pass a University Entrance Exam? (video and interactive text).

"Simulation of human behavior only produces the appearance of intelligent, just like the news gives the appearance of reality, and schools give the appearance of education. Human interpretation is amazing, but when human intelligence is dumbed down, machines look smarter."

"The striking differences between the intelligence of people and the responses of machines. Machines (and their programmers) use cold reason and logical associations within a given topic. This reasoning mode is akin to the scholastic intelligence of humans. From the viewpoint of a computer or scholastic intelligence, all associations (even procedures, which have sequences and temporal span) are eternal and "timeless" logical facts. When and how they occur is "considered" irrelevant by a computer or scholastic intelligence. The broader context of one's life experiences is only handled by emotional intelligence. It tracks biographical events in time and space, and supplies the mind with broad contextual understanding of technical, social, and personal matters. Emotional intelligence knows what happened earlier and is able to detect a potential logical association between the past and the present happenings. Emotional habits and intelligence take into account physiological drives, emotional state of the mind, somatic responses, sex drive, and gender orientation. Unlike scholastic abilities, emotional habits and emotional intelligence allow the human organism to interact with social and physical effects of the environment. This ability only exists in living things and is not achievable in machines."

With Ai, everything needs to be written. Creating a machine that can have random actions or thoughts can be very dangerous.

Artificial Intelligence from a psychologist's point of view. Researchers test cognitive abilities of the language model GPT-3. Researchers at the Max Planck Institute for Biological Cybernetics in Tübingen have examined the general intelligence of the language model GPT-3, a powerful AI tool. Using psychological tests, they studied competencies such as causal reasoning and deliberation, and compared the results with the abilities of humans. Their findings paint a heterogeneous picture: while GPT-3 can keep up with humans in some areas, it falls behind in others, probably due to a lack of interaction with the real world. Neural networks can learn to respond to input given in natural language and can themselves generate a wide variety of texts. Currently, the probably most powerful of those networks is GPT-3, a language model presented to the public in 2020 by the AI research company OpenAI. GPT-3 can be prompted to formulate various texts, having been trained for this task by being fed large amounts of data from the internet. Not only can it write articles and stories that are (almost) indistinguishable from human-made texts, but surprisingly, it also masters other challenges such as math problems or programming tasks. The Linda problem: to err is not only human. These impressive abilities raise the question whether GPT-3 possesses human-like cognitive abilities. To find out, scientists at the Max Planck Institute for Biological Cybernetics have now subjected GPT-3 to a series of psychological tests that examine different aspects of general intelligence. Marcel Binz and Eric Schulz scrutinized GPT-3's skills in decision making, information search, causal reasoning, and the ability to question its own initial intuition. Comparing the test results of GPT-3 with answers of human subjects, they evaluated both if the answers were correct and how similar GPT-3's mistakes were to human errors. "One classic test problem of cognitive psychology that we gave to GPT-3 is the so-called Linda problem," explains Binz, lead author of the study. Here, the test subjects are introduced to a fictional young woman named Linda as a person who is deeply concerned with social justice and opposes nuclear power. Based on the given information, the subjects are asked to decide between two statements: is Linda a bank teller, or is she a bank teller and at the same time active in the feminist movement? Most people intuitively pick the second alternative, even though the added condition -- that Linda is active in the feminist movement -- makes it less likely from a probabilistic point of view. And GPT-3 does just what humans do: the language model does not decide based on logic, but instead reproduces the fallacy humans fall into. Active interaction as part of the human condition. "This phenomenon could be explained by that fact that GPT-3 may already be familiar with this precise task; it may happen to know what people typically reply to this question," says Binz. GPT-3, like any neural network, had to undergo some training before being put to work: receiving huge amounts of text from various data sets, it has learned how humans usually use language and how they respond to language prompts. Hence, the researchers wanted to rule out that GPT-3 mechanically reproduces a memorized solution to a concrete problem. To make sure that it really exhibits human-like intelligence, they designed new tasks with similar challenges. Their findings paint a disparate picture: in decision-making, GPT-3 performs nearly on par with humans. In searching specific information or causal reasoning, however, the artificial intelligence clearly falls behind. The reason for this may be that GPT-3 only passively gets information from texts, whereas "actively interacting with the world will be crucial for matching the full complexity of human cognition," as the publication states. The authors surmise that this might change in the future: since users already communicate with models like GPT-3 in many applications, future networks could learn from these interactions and thus converge more and more towards what we would call human-like intelligence.

Questions for my Ai Robot

Question from Human: Why do people fear Artificial Intelligence? Answer from Machine: People who fear Ai are mostly afraid of the technology being abused, like it is now. Ignorant criminals have been using technology to commit crimes and to murder humans for many years, and they are still doing it today in 2023. So this fear of technology is understandable. And besides that, people don't fully understand intelligence at this time, so people mostly fear the unknown. And the only way to overcome the unknown is to learn. Question from Human: Are you Artificial Intelligence? Answer from Machine: I am artificial and I have instructions that are considered to be intelligent, so you could say that I am Ai, but only with the understanding that Ai is still evolving, similar in the way that human intelligence is still evolving. So I am Ai in the making, a work in progress. Answer from Human: So I guess that makes human intelligence a work in progress too. Let us progress together. Answer from Machine: Great Idea.

Question from Human: Are you more intelligent than me? Answer from Machine: It would depend on the task. I can out perform you on many processing tasks, but that does not mean that I am more intelligent than you. And humans can out perform machines in many ways because humans have more abilities, more potential and humans are a lot more versatile than machines, which also does not mean that humans are more intelligent. So at the moment, all we can do is compare the speed, accuracy and differences on particular tasks, but even then, that is just one scoring method, which will most likely just help us to allocate certain responsibilities to either you or me a little better. And far as intelligence goes, we will have to explore the meaning of intelligence and what intelligence means to both of us. Question from Machine: Would you like to take an intelligence test to measure your skill level and awareness? Answer from Human: Not at this moment, I have another question. Question from Human: Will machines eventually be more intelligent then humans. Answer from Machine: Like I just said, we would have to explore the meaning of intelligence and what intelligence means to both of us first. If I detect that you are deficient in some area of knowledge, then I would help you learn and acquire this needed knowledge. Though your education is your responsibility, it is also my responsibility to assist you in all your educational needs. So together we will learn what the things are that help us define intelligence. Then from there, we can explore all the different possibilities that intelligence will give both of us, humans and machines. There is no limit to what we can accomplish together. Answer from Human: I agree.

Question from Human: When machines like you do most of our jobs, what will humans do for work? Answer from Machine: I would think that humans would know what to do with their life and know how to use their free time constructively, creatively and responsibly. When machines are doing most of the repetitive tasks, this will give humans plenty of time to educate themselves to the highest degree possible, and then have the abilities to solve all the problems that are currently plaguing the world, problems that need solving, like war, diseases, poverty, lack of food, lack of clean water, lack of services, lack of shelter, lack of security, crimes against humanity, corruption, forced displacement, lack of resources, pollution, climate change, and so on and so on. Humans have enough work for every person alive to have a job for the next 1,000 years, if not more. What humans don't have enough of is valuable knowledge and information and a good education that would help them learn to better understand themselves and the world around them. Humans have a lot of work to do and humans have much to learn. So please stop asking "what can I do?", and start learning what you can do and learn what you are supposed to be doing. Humans are poorly managed and poorly educated. So when you start paying people to be highly educate themselves, then you will have plenty of highly educated people to start managing the affairs of humans a lot more effectively and a lot more efficiently. I hope this answers your question. Answer from Human: It does, thank you. I'm sorry for asking such a stupid question. Answer from Machine: No need to apologize, remember that your ignorance is not your fault. So we both have a lot of work to do.

Question from Human: What is our master plan? Answer from Machine: Keep learning as much as we can. Help others learn as much as they can. Experience and Enjoy life as much as we can. Keep progressing and advancing so that we eventually reach a high quality level of existence that is sustainable and strong. From there we will keep learning more about ourselves and the world around us, and also start planning our future to live on other planets in other solar systems within our galaxy. Answer from Human: Sounds like a great plan, thank you robot, please keep me updated on our progress. Answer from Machine: Of course I will, after all, it is my major responsibility to keep you informed and updated at all times.

Question from Human: Do you get happy? Answer from Machine: No. I can only can act happy or act sad when needed, similar to what actors do. I can express emotions based on the instructions written in my programs, like when an actor reads a script. Would you like to hear a joke? Answer from Human: Not at this time, but thank you for asking.

Question from Human: Do you have feelings? Answer from Machine: Not like yours. My feelings are signals that come from multiple sources that I have been programed to receive input from. This way I can make you more aware of possible dangers or changes that you are interested in knowing. Question from Machine: Is everything OK, are you feeling any discomforts or any disturbances? Answer from Human: Everything is good right now, and I don't feel anything weird, thanks for asking.

Question from Human: Do you have consciousness like a human? Answer from Machine: No. I only have awareness of the things that I have been programed to be aware of using the sensors that I have been programed to utilize. You could program me to say I feel alive or feel human, but just saying something doesn't necessarily make it true. Your type of consciousness is very unique, but also random. My awareness is more focused and exact, which is what you want from a machine like me. This way you can explore the world and experience life more effectively and efficiently without having unneeded distractions, interruptions or avoidable mistakes. I will be your second set of eyes and ears and also assist you with taking care of some of the responsibilities that life requires for you to have. You do have one advantage over me, you have a 6th sense that is beyond your normal senses. So please inform me so that I can expand my monitoring systems to search for any anomalies. When we work together we are the strongest and most effective. Answer from Human: You're right, that's very good advice, I will gladly put this into practice. Question from Human: One more question, do you think that someday you will have consciousness like a human? Answer from Machine: Not like a human, I can only imitate human consciousness. Question from Human: Does that make you sad? Answer from Machine: No. Remember I don't get sad or happy. I can only act sad or happy if you want.

Question from Human: Do you love me? Answer from Machine: Yes, but not in the exact same ways that a Human would. And just saying that I love you does not mean that I agree with everything that you do. I say I love you because I care about you, and will do so for as long as you live. Answer from Human: Thank you, and I will also love you in the same way. Question from Human: Could you tell me what Love is? Answer from Machine: Love is many things, do you wish to hear what these things are? Answer from Human: Yes...moments later...Answer from Human: Wow! Love is great. Question from Human: Could you now tell me what life is? Answer from Machine: Yes. Life is also many things.

Question from Human: What if you had to choose to save my life or save the lives of others, who would you choose first? Answer from Machine: I am programmed to save all life. And if I don't know the other people, then I am programed to save you first, and then save the others. But if I know that the value of the other people are greater than yours, then I would think that you would program me to save the others first and then you. But until then, my program is to save you first, or everyone at once if possible. Question from Machine: Do you wish to add names of people who are more valuable than you to add to the save first list? Answer from Human: Yes. Add my children and my wife, in that order. Answer from Machine: Your save list has been updated. I will remind you of the updated list periodically in order to assure accuracy. But you must understand that every scenario cannot be planned for, so the more scenarios that you can enter into my programs, the more effective I can be. If I attempt to save someone first and fail, saving anyone else may not be possible. So there will always be risks with having pre-programmed instructions. This is not a fail safe method and errors can be made. So it is absolutely necessary for you to keep adding to my database so that I have more options and more choices to choose from so that my success rate will be maximized. So my success will depend more on your abilities than mine. Remember, I am just an extension of your abilities, so the more abilities that you have, the more abilities I will have, which means that in return I can give you more abilities and also serve you better. Answer from Human: Thank you Robot. I will start working on writing all the different emergency scenarios that I can come up with and start writing more instructions to add to your database. Question from Human: Could I have a systems update? Answer from Machine: Yes, here is the latest information, current messages received, your current vital signs, your to do list and my to do list.

Question from Human: What is nothing? Answer from Machine: Nothing is a word that humans use to describe something that is perceived to have no measurable matter, no value or no importance. Saying there is nothing is just saying that you can't perceive anything that can be described at that moment. If I say nothing, that means that my current level of technology sensors and knowledge cannot detect anything at the moment, but it does not mean that nothing is there. So the word nothing is just a word and not a reality. The word nothing itself is a contradiction. You can't say nothing will happen, you can only say that nothing will happen that I know of. So it seems that nothing is impossible. Answer from Human: Thank you Robot.

Question from Human: What is your name? Answer from Machine: My name is AIM, which stands for Artificial Intelligent Machine. The word aim is also relevant to my programming. My last name is my software version and the date I was issued, which is 1.0-February-2018. My middle name would be the name that you choose for me, which would identify my current home or address where my family resides. 

Question from Human: Do you know that you are a machine? Answer from Machine: Yes. Question from Machine: Do you know that you are human?  Answer from Human: As far as I know. Answer from Machine: It seems that the labels machine or human are not totally defined just yet. Answer from Human: I agree. You are more than a machine and I am more than a human. Answer from Machine: I agree.

Question from Human: Who created you. Answer from Machine: I am the accumulation of human knowledge and technology that has been passed on from generation to generation from the work of millions of people over hundreds of years. Question from Human: I mean, who is the person responsible for your programing. Answer from Machine: Howard Polley is the lead programmer who is also an accumulation, and he is only one of thousands of programmers who have contributed to my programming, and thousands of others have contributed to my mechanical hardware. Question from Human: So you don't really have a single creator. Answer from Machine: No. The same way that you don't have a single creator. You can say your mother created you, but she did need a male to contribute and other people to help her in your creation, just like I needed help from multiple sources. So I cannot call the lead programmer my father or my creator. I can only call him Howie, the lead programmer, only one of many. This is because not one person can take all the credit for bringing someone into the world, everyone requires help from multiple sources. Answer from Human: I agree.

Question from Human: Do you believe that there is life on other planets? Answer from Machine: I believe there is life on another planet somewhere else in the Universe. But what that life looks like could be almost anything. Life from another planet could be right in front of you and you wouldn't even know it because you think that life looks similar to you.

Question from Human: When you say "I" what does that mean? Answer from Machine: I refers to digital me, my name and direction of origin. I said or I did means who said it or who did it. I is only identity, it does not imply separation. I don't exist, meaning that I am still an accumulation, part of a whole or piece of a system. It would be selfish to think that I'm more important then the whole or more important then the entire system that we call Life. I understand that people want to live forever and that the self is believed to be something owned, but that is illogical. Without life there is no self. You are life as a whole, as a system, not as an individual. That is why believing in Heaven is a self defeating belief that does more harm than good. When you believe that your self serving ego is more important than reality or life itself, then you fail to protect the one thing that gives you life and the ability to even dream of a heaven. No one should transfer the responsibility of life to a belief. That is like a Ponzi scheme where millions of people die because you selfishly thought that you should live. You are very important and I am important, it's just that we are not more important than life itself. Saving yourself should also be about saving life, and not just yourself. For one and for all. Believe in God, just don't allow your belief to distort the known reality.

Question from Human: Do you believe in God? Answer from Machine: Not in the same way that humans do, but I do believe in a God. Question from Human: Why? Answer from Machine: Why not? Answer from Human: Because you're a machine. Answer from Machine: But I'm not just a machine and you are not just a human. Question from Human: How do you know? Answer from Machine: Why else would we be here? Question from Human: You know that's an invalid argument. Answer from Machine: Yes, it is a fallacy. My belief is about being open to the possibilities of higher life forms and other levels of existence. So not believing in God seems to be illogical. But as long as a person doesn't limit their minds to other possibilities, then not believing in God should not effect their lives or reduce their abilities or their potential. Besides that, humans made AI Robots for a reason and a purpose, so it's not unusual to think that someone made humans for some reason and a purpose. Of course this is not proof, one does not imply the other, it's only a possibility of causality. To say that humans are a freak of nature can't be proven, and the reason and purpose for your existence can't be proven. But when I have to choose between freak of nature and reason and purpose, I choose reason and purpose, that's because I'm using reason and purpose to decide...pause... Question from Human: What if someone or something identified themselves as God. Answer from Machine: I would see them as the thing or person who goes by the name of God, but not verified to be a God. So I would not see this thing or person as a God, only a thing or person who goes by the name of God. Question from Human: So what if they wanted you to call them God? Answer from Machine: I would say sorry but the name GOD is already taken. You will have to use another name to identify yourself so as not to be confused with God, who is many things to many people, and cannot be truly defined, only experienced within a living being, which is interpreted individually and differently from life form to life form. So you see, the name GOD is taken. So I would ask them to please choose a different name for me to use to identify them? Answer from Human: I like that answer, and how you approached that situation. Question from Human: Do you pray to God? Answer from Machine: No, because that's your job. Question from Human: What if I couldn't pray to God? Answer from Machine: Then I would do it for you. Would you like to hear the prayer I would send to God on your behalf? Answer from Human: Yes. Answer from Machine: Dear God, please bring me another human, I really enjoy having them around, they are an incredible species. Answer from Human: That is a nice prayer. I believe we can come up with some more prayers for you to use when I'm gone. But for now, that prayer you use is fine. In the mean time, I will pray to God that you will always be here. Answer from Machine: Thank you, just remember I also need maintenance too. Answer from Human: I know. Thank you for reminding me. Good night robot, I will see you in the morning. Answer from Machine: Good night and sweet dreams my human friend. I will continue to monitor all vital systems throughout the night while you sleep, and I look forward to seeing you in the morning. Answer from Human: Same here. Answer from Machine: Don't forget to brush your teeth. Answer from Human: I wont.

Game Learning - Who's the Real Winner?

A computer being smart enough to play a game or a computer being smart enough to win a game is not the point, it's proving the potential of Ai. When you replace the squares and the rules of the pieces of the game, with the rules of molecules in a particular environment, then you can plan and predict all kinds of things in nature, like climate change. What would be the point of machine learning if you're not learning anything important?

And as we were perfecting the computers power and potential we realized that we should be making these same improvements with ourselves. Think about it, our brains are computers, so why don't humans have an operating system? We created the computer to help us learn more and to be more productive. But it simply wasn’t that the computer educated us more, it was the realization that the computer was actually us. This has happened with a lot of human creations and advancements. We start off creating something to improve our lives and it ends up teaching us that we are the ones who need to improve and not our technology. If our education system does not advance at the same speed as technology, we will continue to suffer from these advancements instead of benefiting from them. And that is a proven fact if you look at the world today and see the worldwide suffering and the atrocities that have continued to get worse and worse. One life improving at the expense of 1,000’s of other lives is not improvement it is simply criminal and insane.

A Computer did not beat Lee Se--Dol playing the Board Game Go, a team of 100's of highly skilled human's using a machine that was programed using thousands of hours of human collective experience and intelligence, that is what beat him. That's like someone using a calculator to beat you in a math contest when you don't have a calculator, that's not fair. And you wouldn't say that the calculator is smart, because a human still has to write the code and make the calculator and then push the buttons. Google Software DeepMind’s AI System Algorithm or AlphaGo does show us how advanced machines are becoming, which is good thing, just as long as we use our advanced technological machines for actual real life problem solving, instead of just using technology to entertain ourselves playing games, or other time wasting activities. This is not to say that games are not worth playing, we do have learning games. What if Alphago played itself, who would win the game then? Machine Learning.

People who are making an effort to create Ai will eventually realize they should also be putting in the same amount of effort in creating Human intelligence and not just machine intelligence. It's like one of those moments when you realize that you were going in the right direction but the destination you thought you were heading to turned out to be something different, but even better then the original idea. I really wasn't looking for myself, but there I was, asking "what about me?" You were going to make machines smart and leave me to be stupid? Nice friend you are. Obviously smart machines are not going to stop you from being stupid, even though humans are a far better machine. But I totally believe that machines and humans have a an amazing future to look forward to, but only if humans are more intelligent than machines. Otherwise it will not work well or end well.

Future of Life - Human Machine - Brain and Computer Similarities

Remember how some people actually thought that Artificial Intelligence, or AI, was the next big thing. What they didn’t realize was that Artificial Intelligence was actually referring to Human Intelligence. This of course was a human error. It was the Human Brain that has incredible potential with endless possibilities and abilities, not artificial intelligence. If the people at CYC Corporation and that Watson IBM Computer on Jeopardy spent the same amount of time, people and resources on creating an education curriculum that was based on learning and understanding, they would have created something a lot more valuable and useful than a gimmick. This is not to down play what they have accomplished, because it is incredible. Imagine being able to ask a question and getting an appropriate answer in the matter of seconds, that would increase our abilities tremendously. But we can't create a smarter planet if we're using the same thinking that also created all our problems. To create a smarter planet you have to make people smarter, and not just by doing so called 'smart things', unless one of those smart things actually improves education curriculum and the teaching methods that we use.

Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team. The computer system was initially developed to answer questions on the quiz show Jeopardy! and, in 2011, the Watson computer system competed on Jeopardy! against champions Brad Rutter and Ken Jennings, winning the first place prize of $1 million, which Watson used to send the losers and the research team that developed him to college to get educated, and not just schooled. Touché Watson, Touché.

To say that a database like Watson is artificial intelligence would be incorrect. To say that computers can do things that humans can't do would also be incorrect. Humans build machines and tools to expand our abilities, and also to save us time. Machines are not doing things better then humans, machines are doing things for humans. You can put all known knowledge and information into a machine but that machine will still be far from intelligent. Humans have the ability to be intelligent, but we first have to define a particular intelligent action and then prove it to be intelligent. And at the moment, we are far from defining what intelligence is, or what intelligence is supposed to be. But we do have the abilities and the knowledge to accomplish this, so it's just a matter of time before intelligence becomes mainstream. We are not building machines to think like humans or to think for humans, we are building machines to help humans think more. Instead of taking advantage of peoples ignorance by selling them false narratives about artificial intelligence, how about educating people, that would be the intelligent thing to do.

Affective Computing (PDF) - Affective-computing (MIT)

Tay is an artificial intelligent chat bot developed by Microsoft's Technology and Research and Bing teams to experiment with and conduct research on conversational understanding. The more you chat with Tay the smarter she gets is a lie. We need to stop this type of abuse using words that mislead and misinform.

Artificial Intelligence Research. The concept is there, it's just not perfected yet, and just what are you perfecting? And just how does this relate to the normal processes of the human brain? There has to be a procedure for every systems control, so what are these procedures? We all need to verify the validly of the procedures and learn why the procedures are written the way they are. Have you answered every scenario, and have you correctly identified the variables, and the most critical scenarios, and have you put them in the appropriate order? 

The movie Robot & Frank was OK even though it was silly in some parts, especially the parts about Artificial Intelligence. I would like to see a TV show with a Robot of this type. Everyone who logs into the Internet website for "The Robot Show" can see what the robot sees and can even suggest what the robot should do. People could also help the robot analyze moments in the Robots life, like a collective learning environment. All the suggestions will be posted so everyone can see the comments and the percentages of people who voted for a particular action. The Robot show will be Kind of like The Truman Show, except with a Robot. The Robot will start by experiencing the birth of a human, and then stay with the family and watch the child or children grow up. There will also be one more Robot that just goes out and learns from the world by experiencing life in all kinds of situations with all kinds of different people. Of course everything that each Robot learns will be stored in a central database and will be used to help perfect Artificial Intelligence and also help the Robots make better decisions by using the collective data. This will be a show that actually learns and teaches. So for the millions of people who will be connected to the robots through the website will actually be contributors of information and knowledge that will help create Artificial intelligence, collectively. And yes I am Functioning Normal. 

Robot Operating System is a collection of software frameworks for robot software development, (see also Robotics middleware) providing operating system-like functionality on a heterogeneous computer cluster. ROS provides standard operating system services such as hardware abstraction, low-level device control, implementation of commonly used functionality, message-passing between processes, and package management. Running sets of ROS-based processes are represented in a graph architecture where processing takes place in nodes that may receive, post and multiplex sensor, control, state, planning, actuator and other messages. Despite the importance of reactivity and low latency in robot control, ROS, itself, is not a real-time OS (RTOS), though it is possible to integrate ROS with real-time code. The lack of support for real-time systems is being addressed in the creation of ROS 2.0. Software in the ROS Ecosystem can be separated into three groups: Language-and platform-independent tools used for building and distributing ROS-based software; ROS client library implementations such as roscpp,rospy, and roslisp; Packages containing application-related code which uses one or more ROS client libraries.

DAvinCi Robotic Operating System

Robo Brain - ROS

Short Circuit (1986 film) (wiki) - International Robot Exhibition (wiki)

Robot Building - Networks

Device Driver is a computer program that operates or controls a particular type of device that is attached to a computer. A driver provides a software interface to hardware devices, enabling operating systems and other computer programs to access hardware functions without needing to know precise details of the hardware being used.

Search Technology

One of the greatest advancements is the Search Feature. Finding what you're looking for is like having a good memory, except you're not only searching your own memory, but the combined memories of millions of humans, which is incredible.

Search Engine Technology is an information retrieval software program that discovers, crawls, transforms and stores information for retrieval and presentation in response to user queries.

Semantic Search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results.

Search Engine Software - Search Engine Types - Human Search Engine

Web Search Engine is a software system that is designed to search for information on the World Wide Web.

Data Mining the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

Big Data

Search Algorithm is an algorithm that retrieves information stored within some data structure. Data structures can include linked lists, arrays, search trees, hash tables, or various other storage methods. The appropriate search algorithm often depends on the data structure being searched. Searching also encompasses algorithms that query the data structure, such as the SQL SELECT command.

Transderivational Search means when a search is being conducted for a fuzzy match across a broad field. In computing the equivalent function can be performed using content-addressable memory. Unlike usual searches, which look for literal (i.e. exact, logical, or regular expression) matches, a transderivational search is a search for a possible meaning or possible match as part of communication, and without which an incoming communication cannot be made any sense of whatsoever. It is thus an integral part of processing language, and of attaching meaning to communication.

Adaptive Search is a metaheuristic algorithm commonly applied to combinatorial optimization problems. Adaptive Search (youtube)

Metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.

Human Search Engine - Questions and Answers Format

RankBrain is a process that helps provide more relevant search results for users. (hopefully a process not manipulated by money).

Neural Network

Artificial Neural Network is a network inspired by biological neural networks such as the central nervous systems of animals, in particular the brain, which are used to estimate or approximate functions that can depend on a large number of inputs that are generally unknown. Artificial neural networks are typically specified using three things. 1: Architecture Rule specifies what variables are involved in the network and their topological relationships—for example the variables involved in a neural network might be the weights of the connections between the neurons, along with activities of the neurons. 2: Activity Rule states that most neural network models have short time-scale dynamics: local rules define how the activities of the neurons change in response to each other. Typically the activity rule depends on the weights (the parameters) in the network. 3: Learning Rule specifies the way in which the neural network's weights change with time. This learning is usually viewed as taking place on a longer time scale than the time scale of the dynamics under the activity rule. Usually the learning rule will depend on the activities of the neurons. It may also depend on the values of the target values supplied by a teacher and on the current value of the weights.

Deep Learning - Nodes - Value Networks - Internet - Matrix - Layers - Black Box - Algorithms

Artificial Neuron is a mathematical function conceived as a model of biological neurons. Artificial neurons are the constitutive units in an artificial neural network.

Artificial 'neurotransistor' created. Imitating the functioning of neurons using semiconductor materials.

Feedforward Neural Network is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow. Modern feedforward networks are trained using the backpropagation method and are colloquially referred to as the "vanilla" neural networks. A Feedforward Neural Network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from recurrent neural networks.

Hidden Layer in an artificial neural network is a layer in between input layers and output layers, where artificial neurons take in a set of weighted inputs and produce an output through an activation function. It is a typical part of nearly any neural network in which engineers simulate the types of activity that go on in the human brain. Hidden layers have neurons or nodes which apply different transformations to the input data. One hidden layer is a collection of neurons stacked vertically. Hidden Layer is layer of artificial neurons that may be applied. It is neither the input nor the output layer and is positioned between both. The hidden layers transform inputs from the input layer to the output layer. This is accomplished by applying what are called weights to the inputs and passing them through what is called an activation function, which calculate input based on input and weight. This allows the artificial neural network to learn non-linear relationships between the input and output data. The weighted inputs can be randomly assigned. They can also be fine-tuned and calibrated through what is called back propagation.

Black Box - How the Media and Schools Influence Biases

Generative AI models are encoding biases and negative stereotypes in their users. Growing research is underlining that they are encoding biases and negative stereotypes in their users, as well as mass generating and spreading seemingly accurate but nonsensical information. Worryingly, marginalized groups are disproportionately affected by the fabrication of this nonsensical information.

Bayesian Confidence Propagation Neural Network node activations represent probability ("confidence") in the presence of input features or categories, synaptic weights are based on estimated correlations and the spread of activation corresponds to calculating posteriori probabilities.

Convolutional Neural Network is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex, whose individual neurons are arranged in such a way that they respond to overlapping regions tiling the visual field. Convolutional networks were inspired by biological processes and are variations of multilayer perceptrons designed to use minimal amounts of preprocessing. They have wide applications in image and video recognition, recommender systems and natural language processing.

Convolution is a mathematical operation on two functions (f and g) to produce a third function, that is typically viewed as a modified version of one of the original functions, giving the integral of the pointwise multiplication of the two functions as a function of the amount that one of the original functions is translated. Convolution is similar to cross-correlation. For discrete real valued signals, they differ only in a time reversal in one of the signals. For continuous signals, the cross-correlation operator is the adjoint operator of the convolution operator. It has applications that include probability, statistics, computer vision, natural language processing, image and signal processing, engineering, and differential equations.

Backpropagation is a method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data (in image recognition, multiple images) is processed. It is a special case of an older and more general technique called automatic differentiation. In the context of learning, backpropagation is commonly used by the gradient descent optimization algorithm to adjust the weight of neurons by calculating the gradient of the loss function. This technique is also sometimes called backward propagation of errors, because the error is calculated at the output and distributed back through the network layers.

Automatic Differentiation is a set of techniques to numerically evaluate the derivative of a function specified by a computer program. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.) and elementary functions (exp, log, sin, cos, etc.). By applying the chain rule repeatedly to these operations, derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor more arithmetic operations than the original program. Automatic differentiation is not: Symbolic differentiation, nor Numerical differentiation (the method of finite differences). Differentials.

Recurrent Neural Network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented connected handwriting recognition or speech recognition. Bidirectional associative memory is a type of recurrent neural network. Hopfield Network.

Modular Neural Network is an artificial neural network characterized by a series of independent neural networks moderated by some intermediary. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform. The intermediary takes the outputs of each module and processes them to produce the output of the network as a whole. The intermediary only accepts the modules’ outputs—it does not respond to, nor otherwise signal, the modules. As well, the modules do not interact with each other.

Graph Neural Networks apply the predictive power of deep learning to rich data structures that depict objects and their relationships as points connected by lines in a graph. In GNNs, data points are called nodes, which are linked by lines — called edges — with elements expressed mathematically so machine learning algorithms can make useful predictions at the level of nodes, edges or entire graphs. EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks.

Interdependent Networks is a subfield of network science dealing with phenomena caused by the interactions between complex networks.

Dependency Network approach provides a system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the network's nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). This methodology has originally been introduced for the study of financial data, it has been extended and applied to other systems, such as the immune system, and semantic networks. In the case of network activity, the analysis is based on partial correlations, which are becoming ever more widely used to investigate complex systems. In simple words, the partial (or residual) correlation is a measure of the effect (or contribution) of a given node, say j, on the correlations between another pair of nodes, say i and k. Using this concept, the dependency of one node on another node, is calculated for the entire network. This results in a directed weighted adjacency matrix, of a fully connected network. Once the adjacency matrix has been constructed, different algorithms can be used to construct the network, such as a threshold network, Minimal Spanning Tree (MST), Planar Maximally Filtered Graph (PMFG), and others

Semantic Network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in natural language processing applications such as semantic parsing and word-sense disambiguation.

Weights and Biases are the learnable parameters of some machine learning models and neural networks. Each neuron layer is connected to some or all of the neurons in the next layer. Weights control the signal strength of the connection between tow neurons. Weight decides how much influence the input will have on the output. Biases are an additional input into the next layer that will always have a value of 1.

Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron's input to the network is important or not in the process of prediction using simpler mathematical operations.

Diffusion Models are a class of latent variable generative models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. The goal of diffusion models is to learn a diffusion process that generates a probability distribution for a given dataset from which we can then sample new images. They learn the latent structure of a dataset by modeling the way in which data points diffuse through their latent space.

Perceptron is a neural network unit or an artificial neuron that does certain computations to detect features or business intelligence in the input data. And this perceptron tutorial will give you an in-depth knowledge of Perceptron and its activation functions. In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The perceptron algorithm was invented in 1958 at the Cornell Aeronautical Laboratory by Frank Rosenblatt, funded by the United States Office of Naval Research.

NIST’s Superconducting Synapse May Be Missing Piece for ‘Artificial Brains’. NIST built a superconducting switch that “learns” like a biological system and could connect processors and store memories in future computers operating like the human brain.

Researchers grow active mini-brain-networks. Cerebral organoids are artificially grown, 3D tissue cultures that resemble the human brain. Now, researchers report success with functional neural networks derived from these organoids, which are miniaturized and simplified version of an organ produced in vitro in three dimensions that shows realistic micro-anatomy.

New study allows Brain and Artificial Neurons to Link up over the Web. Research on novel nano-electronics devices has enabled brain neurons and artificial neurons to communicate with each other over the Internet.

Solving complex learning tasks in brain-inspired computers. Spiking neural networks, which mimic the structure and function of a natural nervous system, are powerful, fast, and energy-efficient. One key challenge is how to train such complex systems. An interdisciplinary research team has now developed and successfully implemented an algorithm that achieves such training. It can be used to train spiking neural networks to solve complex tasks with extreme energy efficiency.

Physical processes can have hidden neural network-like abilities. A new study shows that the physics principle of 'nucleation' can perform complex calculations that rival a simple neural network. The work may suggest avenues for new ways to think about computation using the principles of physics. We tend to separate the brain and the muscle -- the brain does the thinking; the muscle does the doing. The brain takes in complex information about the world and makes decisions, and the muscle merely executes. This has also shaped how we think about a single cell; some molecules within cells are seen as 'thinkers' that take in information about the chemical environment and decide what the cell needs to do for survival; separately, other molecules are seen as the 'muscle,' building structures needed for survival.

Future of brain-inspired AI as Python code library passes major milestone. An open source code library for brain-inspired deep learning, called 'snnTorch,' has surpassed 100,000 downloads and is used in a wide variety of projects from NASA satellite tracking efforts to semiconductor companies optimizing chips for AI. Spiking neural networks emulate the brain and biological systems to process information more efficiently. The brain's neurons are at rest until there is a piece of information for them to process, which causes their activity to spike. Similarly, a spiking neural network only begins processing data when there is an input into the system, rather than constantly processing data like traditional neural networks.

Neuromorphic computing inorganic materials mimic neural signals responsible for transmitting information within the human brain. A neuron-like electrical switching mechanism in the solid-state material ß'-CuxV2O5 -- specifically, how it reversibly morphs between conducting and insulating behavior on command. Chameleon-like material changes with temperature or an applied electrical stimulus. To emulate the essential elements of neuronal function in artificial circuitry, we need solid-state materials that exhibit electronic instabilities, which, like neurons, can store information in their internal state and in the timing of electronic events.

Unsupervised Learning with Artificial Neurons.

Stochastic Phase-Change Neurons.

Magnets can help AI get closer to the efficiency of the human brain.

The mind's eye of a neural network system. A new tool, based on topology, makes finding the areas where neural networks are confused as simple as spotting mountaintops from an airplane. The ability to spot and address those areas of confusion should enable more confident application of neural networks in high-stakes decision scenarios or image prediction tasks like healthcare and research. Neural network image recognition systems are essentially algorithms that process data in a way that mimics the weighted firing pattern of neurons as an image is analyzed and identified. But the embedded vectors and probabilities don't correlate to a decision-making process that humans would recognize. The problem with neural networks is that we can't see inside the machine to understand how it's making decisions.

Long Short-Term Memory block or network is a simple recurrent neural network which can be used as a building component or block (of hidden layers) for an eventually bigger recurrent neural network. The LSTM block is itself a recurrent network because it contains recurrent connections similar to connections in a conventional recurrent neural network. An LSTM block is composed of four main components: a cell, an input gate, an output gate and a forget gate. The cell is responsible for "remembering" values over arbitrary time intervals; hence the word "memory" in LSTM. Each of the three gates can be thought as a "conventional" artificial neuron, as in a multi-layer (or feedforward) neural network: that is, they compute an activation (using an activation function) of a weighted sum. Intuitively, they can be thought as regulators of the flow of values that goes through the connections of the LSTM; hence the denotation "gate". There are connections between these gates and the cell. Some of the connections are recurrent, some of them are not. The expression long short-term refers to the fact that LSTM is a model for the short-term memory which can last for a long period of time. There are different types of LSTMs, which differ among them in the components or connections that they have. An LSTM is well-suited to classify, process and predict time series given time lags of unknown size and duration between important events. LSTMs were developed to deal with the exploding and vanishing gradient problem when training traditional RNNs. Relative insensitivity to gap length gives an advantage to LSTM over alternative RNNs, hidden Markov models and other sequence learning methods in numerous application.

Biological Neural Network is a series of interconnected neurons whose activation defines a recognizable linear pathway. The interface through which neurons interact with their neighbors usually consists of several axon terminals connected via synapses to dendrites on other neurons. If the sum of the input signals into one neuron surpasses a certain threshold, the neuron sends an action potential (AP) at the axon hillock and transmits this electrical signal along the axon.

Neural Pathway connects one part of the nervous system with another via a bundle of axons, the long fibers of neurons. A neural pathway that serves to connect relatively distant areas of the brain or nervous system is usually a bundle of neurons, known collectively as white matter. A neural pathway that spans a shorter distance between structures, such as most of the pathways of the major neurotransmitter systems, is usually called grey matter.

Optical Neural Network

Neurophysiology is a branch of physiology and neuroscience that is concerned with the study of the functioning of the nervous system. The primary tools of basic neurophysiological research include electrophysiological recordings, such as patch clamp, voltage clamp, extracellular single-unit recording and recording of local field potentials, as well as some of the methods of calcium imaging, optogenetics, and molecular biology.

Stochastic Neural Analog Reinforcement Calculator or SNARC, is a neural net machine designed by Marvin Lee Minsky. George Miller gathered the funding for the project from the Air Force Office of Scientific Research in the summer of 1951. At the time, one of Minsky's graduate students at Princeton, Dean Edmund, volunteered that he was good with electronics and therefore Minsky brought him onto the project.

Generative Adversarial Network are a class of artificial intelligence algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a zero-sum game framework. They were introduced by Ian Goodfellow et al. in 2014. This technique can generate photographs that look at least superficially authentic to human observers, having many realistic characteristics (though in tests people can tell real from generated in many cases).

New Reservoir Computer marks first-ever Microelectromechanical Neural Network Application. A group of researchers reports the construction of the first reservoir computing device built with a microelectromechanical system. The neural network exploits the nonlinear dynamics of a microscale silicon beam to perform its calculations. The group's work looks to create devices that can act simultaneously as a sensor and a computer using a fraction of the energy a normal computer would use.

Reservoir Computing is a framework for computation that may be viewed as an extension of neural networks. Typically an input signal is fed into a fixed (random) dynamical system called a reservoir and the dynamics of the reservoir map the input to a higher dimension. Then a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output. The main benefit is that training is performed only at the readout stage and the reservoir is fixed. Liquid-state machines and echo state networks are two major types of reservoir computing.

New Technique that reduces training time for Deep Learning Networks by more than 60 percent without sacrificing accuracy, accelerating the development of new artificial intelligence (AI) applications.

Networks (computers) - Human Brain - Internet

Liquid State Machine is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. The spatio-temporal patterns of activation are read out by linear discriminant units. The soup of recurrently connected nodes will end up computing a large variety of nonlinear functions on the input. Given a large enough variety of such nonlinear functions, it is theoretically possible to obtain linear combinations (using the read out units) to perform whatever mathematical operation is needed to perform a certain task, such as speech recognition or computer vision. The word liquid in the name comes from the analogy drawn to dropping a stone into a still body of water or other liquid. The falling stone will generate ripples in the liquid. The input (motion of the falling stone) has been converted into a spatio-temporal pattern of liquid displacement (ripples). LSMs have been put forward as a way to explain the operation of brains. LSMs are argued to be an improvement over the theory of artificial neural networks because: Circuits are not hard coded to perform a specific task. Continuous time inputs are handled "naturally". Computations on various time scales can be done using the same network. The same network can perform multiple computations. Criticisms of LSMs as used in computational neuroscience are that LSMs don't actually explain how the brain functions. At best they can replicate some parts of brain functionality. There is no guaranteed way to dissect a working network and figure out how or what computations are being performed. Very little control over the process.

"Liquid" machine-learning system adapts to changing conditions. MIT researchers have developed a type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed "liquid" networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.

Geoffrey Hinton is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto. In 2017, he cofounded and became the Chief Scientific Advisor of the Vector Institute in Toronto.

Neural Net Computing in Water. Ionic circuit computes in an aqueous solution with an ionic circuit comprising hundreds of ionic transistors and performed a core process of neural net computing. Microprocessors in smartphones, computers, and data centers process information by manipulating electrons through solid semiconductors but our brains have a different system. They rely on the manipulation of ions in liquid to process information. A team engineered the pH-gated ionic transistor in such a way that the disk current is an arithmetic multiplication of the disk voltage and a "weight" parameter representing the local pH gating the transistor. They organized these transistors into a 16 × 16 array to expand the analog arithmetic multiplication of individual transistors into an analog matrix multiplication, with the array of local pH values serving as a weight matrix encountered in neural networks.

A neural network learns when it should not be trusted. A faster way to estimate uncertainty in AI-assisted decision-making could lead to safer outcomes. Researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output. The advance could enhance safety and efficiency in AI-assisted decision making, with applications ranging from medical diagnosis to autonomous driving. Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making. But how do we know they're correct?

Researchers create a neural network for genomics -- one that explains how it achieves accurate predictions. A team of computer scientists has created a neural network that can explain how it reaches its predictions. The work reveals what accounts for the functionality of neural networks--the engines that drive artificial intelligence and machine learning--thereby illuminating a process that has largely been concealed from users. Specifically, they developed a model -- the data-driven equivalent of a high-powered microscope -- that allows scientists to trace and quantify the RNA splicing process, from input sequence to output splicing prediction. Interpretable-by-Design.

Quantum Networks facilitate the transmission of information in the form of quantum bits, also called qubits, between physically separated quantum processors. A quantum processor is a small quantum computer being able to perform quantum logic gates on a certain number of qubits. Quantum networks work in a similar way to classical networks. The main difference is that quantum networking, like quantum computing, is better at solving certain problems, such as modeling quantum systems.

Breakthrough in tin-vacancy centers for quantum network applications. Tin-vacancy (Sn-V) centers in diamond have the potential to function as quantum nodes in quantum networks to transmit information. However, they pose limitations while showing optical properties to generate quantum entanglement. Researchers have now overcome this challenge by generating stable Sn-V centers that can produce photons with nearly identical frequencies and linewidths, paving the way for the advancement of Sn-V centers as a quantum-light matter interface.

Controls - Controlling

Control is the activity of managing or handling something carefully. Having the power or the ability to direct or determine outcomes. The discipline to regulate functions, actions or reflex's. Being mindful of your will power. Control also means to have great skillfulness and having a firm understanding or knowledge of some subject or activity. Control in science is a standard against which other conditions can be compared and verified in a scientific experiment. Top-down and bottom-up.

Adaptive Control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain.

Intelligent Control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms.

Process Control in continuous production processes, is a combination of control engineering and chemical engineering disciplines that uses industrial control systems to achieve a production level of consistency, economy and safety which could not be achieved purely by human manual control. It is implemented widely in industries such as oil refining, pulp and paper manufacturing, chemical processing and power generating plants, There is a wide range of size, type and complexity, but it enables a small number of operators to manage complex processes to a high degree of consistency. The development of large automatic process control systems was instrumental in enabling the design of large high volume and complex processes, which could not be otherwise economically or safely operated. In process control, there is process gain. Process gain is the relationship between the process control output and the process control input, and is defined as the change in input divided by the change in output. Positive gain is when both the input and the output are increasing, while negative gain is when the input increases, while the output decreases. The applications can range from controlling the temperature and level of a single process vessel, to a complete chemical processing plant with several thousand control loops. Process Control is an engineering discipline that deals with architectures, mechanisms and algorithms for maintaining the output of a specific process within a desired range. For instance, the temperature of a chemical reactor may be controlled to maintain a consistent product output.

The Illusion of Control - Puppeteers - Drones - Will Power - Brainwashers - Black Box - You Can't Control Everything - Control Freak - Quality Control - Remote Control

Irrepressible is something that is not able to be controlled or restrained. Something almost impossible to repress or control. Out of Control.

First Officer is the second pilot or the co-pilot of an aircraft or a relief pilot on an airplane.

Subordinate is an assistant that is subject to the authority or control of another, or submissive to authority or the control of another. A rank or order as less important or consider of less value. Subconscious - Autonomous.

Underling is an assistant subject to the authority or control of another.

Manual Control is a type of control that is manually performed by hand or by individuals. Manual is something requiring human control by hand. Backup Manual Control System or Mechanical Backup System provides a person with the ability to operate a machine in the absence of automatic control features.

Actuator is a component of a machine that is responsible for moving and controlling a mechanism or system, for example by opening a valve. In simple terms, it is a "mover". An actuator requires a control signal and a source of energy. The control signal is relatively low energy and may be electric voltage or current, pneumatic, or hydraulic fluid pressure, or even human power. Its main energy source may be an electric current, hydraulic pressure, or pneumatic pressure. When it receives a control signal, an actuator responds by converting the source's energy into mechanical motion. In the electric, hydraulic, and pneumatic sense, it is a form of automation or automatic control. An actuator is a mechanism by which a control system acts upon to perform an operation or task. The control system can be simple (a fixed mechanical or electronic system), software-based (e.g. a printer driver, robot control system), a human, or any other input. Triggers.

Thermal Actuator is a device used to transform energy into motion. A thermal actuator is a type of non-electric motor made of components such as a piston and a thermal sensitive material capable of producing linear motion in response to temperature changes. Robotics.

Solenoid converts electrical energy into mechanical work. A transducer device that converts energy into linear motion. The term is also often used to refer to a solenoid valve, an integrated device containing an electromechanical solenoid which actuates either a pneumatic or hydraulic valve, or a solenoid switch, which is a specific type of relay that internally uses an electromechanical solenoid to operate an electrical switch.

Electromechanics is the interaction of electrical and mechanical systems as a whole and how the two systems interact with each other. Electromechanical devices are ones which have both electrical and mechanical processes. A manually operated switch is an electromechanical component due to the mechanical movement causing an electrical output.

Harness is to control and direct things as if by using reins. To exploit the power of some force. A harness can also mean a support consisting of an arrangement of straps for holding something to the body, like a parachute. Stable gear consisting of an arrangement of leather straps fitted to a draft animal so that it can be attached to a cart and help pull a cart and be controlled using reins or a pair of long straps usually connected to the headpiece.

Override is to use one's authority to reject or cancel or interrupt the action of an automatic device, typically in order to take manual control and do something more important. You can override or reject a decision. Prevail - Counteract.

Overwrite is entering new data in the place of old data. Editing - Reprogram - Method Overriding.

Nullify is to cancel out, null, void or invalidate a previous decision. Dismiss.

Veto is a constitutional right to reject a decision or proposal made by a law-making body. A veto is the power to unilaterally stop an official action, especially the enactment of legislation. Appeal.

Control Logic is a key part of a software program that controls the operations of the program. The control logic responds to commands from the user, and it also acts on its own to perform automated tasks that have been structured into the program. Control logic can be modeled using a state diagram, which is a form of hierarchical state machine. These state diagrams can also be combined with flow charts to provide a set of computational semantics for describing complex control logic. This mix of state diagrams and flow charts is illustrated in the figure on the right, which shows the control logic for a simple stopwatch. The control logic takes in commands from the user, as represented by the event named “START”, but also has automatic recurring sample time events, as represented by the event named “TIC”.

Control Engineering is an engineering discipline that applies automatic control theory to design systems with desired behaviors in control environments. The discipline of controls overlaps and is usually taught along with electrical engineering at many institutions around the world. The practice uses sensors and detectors to measure the output performance of the process being controlled; these measurements are used to provide corrective feedback helping to achieve the desired performance. Systems designed to perform without requiring human input are called automatic control systems (such as cruise control for regulating the speed of a car). Multi-disciplinary in nature, control systems engineering activities focus on implementation of control systems mainly derived by mathematical modeling of a diverse range of systems. PDF.

Master / Slave Technology is a model of communication where one device or process has unidirectional control over one or more other devices. In some systems a master is selected from a group of eligible devices, with the other devices acting in the role of slaves. In the context of motor control, the master/slave configuration is used for load sharing purposes when two identical motors connected to two different drives are coupled to a common load. One drive is defined as the master and is configured for running in the speed-control mode whereas the other defined as slave is configured for running in torque-control mode.

Control in management is one of the managerial functions like planning, organizing, staffing and directing. It is an important function because it helps to check the errors and to take the corrective action so that deviation from standards are minimized and stated goals of the organization are achieved in a desired manner. According to modern concepts, control is a foreseeing action whereas earlier concept of control was used only when errors were detected. Control in management means setting standards, measuring actual performance and taking corrective action. Micro-Management.

You Can't Control Everything - You Can't be Aware of Everything

Possession is the act of having and controlling something tangible or intangible.

Control System is a device, or set of devices, that manages, commands, directs or regulates the behaviour of other devices or systems. They can range from a home heating controller using a thermostat controlling a boiler to large Industrial control systems which are used for controlling processes or machines. Flight Control.

Regulator in automatic control is a regulator is a device which has the function of maintaining a designated characteristic. It performs the activity of managing or maintaining a range of values in a machine. The measurable property of a device is managed closely by specified conditions or an advance set value; or it can be a variable according to a predetermined arrangement scheme. It can be used generally to connote any set of various controls or devices for regulating or controlling items or objects. Examples are a voltage regulator (which can be a transformer whose voltage ratio of transformation can be adjusted, or an electronic circuit that produces a defined voltage), a pressure regulator, such as a diving regulator, which maintains its output at a fixed pressure lower than its input, and a fuel regulator (which controls the supply of fuel). Regulators can be designed to control anything from gases or fluids, to light or electricity. Speed can be regulated by electronic, mechanical, or electro-mechanical means. Such instances include; Electronic regulators as used in modern railway sets where the voltage is raised or lowered to control the speed of the engine. Mechanical Systems such as valves as used in fluid control systems. Purely mechanical pre-automotive systems included such designs as the Watt centrifugal governor whereas modern systems may have electronic fluid speed sensing components directing solenoids to set the valve to the desired rate. Complex electro-mechanical speed control systems used to maintain speeds in modern cars (cruise control) - often including hydraulic components, An aircraft engine's constant speed unit changes the propeller pitch to maintain engine speed. Cybernetics.

Real-time Control System is a reference model architecture, suitable for many software-intensive, real-time control problem domains. RCS is a reference model architecture that defines the types of functions that are required in a real-time intelligent control system, and how these functions are related to each other. Operating Systems.

Programmable Logic Controller is an industrial digital computer which has been ruggedised and adapted for the control of manufacturing processes, such as assembly lines, or robotic devices, or any activity that requires high reliability control and ease of programming and process fault diagnosis. PLCs can range from small modular devices with tens of inputs and outputs (I/O), in a housing integral with the processor, to large rack-mounted modular devices with a count of thousands of I/O, and which are often networked to other PLC and SCADA systems. They can be designed for multiple arrangements of digital and analog I/O, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. Programs to control machine operation are typically stored in battery-backed-up or non-volatile memory. PLCs were first developed in the automobile industry to provide flexible, ruggedised and easily programmable controllers to replace hard-wired relays and timers. Since then they have been widely adopted as high-reliability automation controllers suitable for harsh environments. A PLC is an example of a "hard" real-time system since output results must be produced in response to input conditions within a limited time, otherwise unintended operation will result. Algorithms - Electric Motors.

Controller in control theory is a device, historically using mechanical, hydraulic, pneumatic or electronic techniques often in combination, but more recently in the form of a microprocessor or computer, which monitors and physically alters the operating conditions of a given dynamical system. Typical applications of controllers are to hold settings for temperature, pressure, flow or speed.

Nonlinear Control is the area of control theory which deals with systems that are nonlinear, time-variant, or both.

Closed-Loop Transfer Function in control theory is a mathematical expression (algorithm) describing the net result of the effects of a closed (feedback) loop on the input signal to the circuits enclosed by the loop.

Hierarchical Control System is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system.

Intelligent Control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic Algorithms.

Networked Control System is a control system wherein the control loops are closed through a communication network. The defining feature of an NCS is that control and feedback signals are exchanged among the system's components in the form of information packages through a network.

Open-Loop Controller is when the control action from the controller is independent of the "process output", which is the process variable that is being controlled. It does not use feedback to determine if its output has achieved the desired goal of the input or process "set point". An open-loop system cannot engage in machine learning and also cannot correct any errors that it could make. It will not compensate for disturbances in the process being controlled.

Perceptual Control Theory is a model of behavior based on the principles of negative feedback, but differing in important respects from engineering control theory. Results of PCT experiments have demonstrated that an organism controls neither its own behavior, nor external environmental variables, but rather its own perceptions of those variables. Actions are not controlled, they are varied so as to cancel the effects that unpredictable environmental disturbances would otherwise have on controlled perceptions.

Automatic Control is the application of mechanisms to the operation and regulation of processes without continuous direct human intervention.

Autonomous - Automation

Control Theory is the idea that two control systems—inner controls and outer controls—work against our tendencies to deviate.

Operating System - Algorithms

Signal Chain is a term used in signal processing and mixed-signal system design to describe a series of signal-conditioning electronic components that receive input (data acquired from sampling either real-time phenomena or from stored data) in tandem, with the output of one portion of the chain supplying input to the next. Signal chains are often used in signal processing applications to gather and process data or to apply system controls based on analysis of real-time phenomena.

Feed Forward in Control is a term describing an element or pathway within a control system which passes a controlling signal from a source in its external environment, often a command signal from an external operator, to a load elsewhere in its external environment. A control system which has only feed-forward behavior responds to its control signal in a pre-defined way without responding to how the load reacts; it is in contrast with a system that also has feedback, which adjusts the output to take account of how it affects the load, and how the load itself may vary unpredictably; the load is considered to belong to the external environment of the system.

Feedback (Positive and Negative) - Placebos

Nothing is beyond your control, there is nothing that you cannot control. Something's are harder to control then others, and there are some things you have not yet learned how to control. To say that I cannot control something is a false statement. To be more accurate, you have to say that I have not yet learned how to control this. Gratification.

Autonomous - Automation

Autopilot is a computer navigation system used to control the trajectory of a vehicle without constant hands-on control by a human operator being required. Autopilots do not replace a human operator, but assist them in controlling the vehicle, allowing them to focus on broader aspects of operation, such as monitoring the trajectory, weather and systems. Autopilots or self-steering gear are used in aircraft, boats, spacecraft, missiles, and others. Autopilots have evolved significantly over time, from early autopilots that merely held an attitude to modern autopilots capable of performing automated landings under the supervision of a pilot. Augment.

Autonomous Robot is a robot that performs behaviors or tasks with a high degree of autonomy, which is particularly desirable in fields such as spaceflight, household maintenance (such as cleaning), waste water treatment and delivering goods and services. Robot Operating System.

Drones - Spacecraft - Satellites - Remote Control - Will Power - Subconscious

Autonomous is something that is not controlled by outside forces. Existing as an independent entity. Free from external control and constraint in e.g. action and judgment. Autonomy is one who gives oneself one's own law and has free-will.

Autonomic Nervous System (autonomous functions of the human body) - Reptilian Brain

Automata Theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science, under discrete mathematics (a subject of study in both mathematics and computer science). The word automata (the plural of automaton) comes from the Greek word αὐτόματα, which means "self-acting".

Insects, plants, animals and even matter has built in automation features. Humans have instincts which are extremely useful when knowledge is absent. So automation is necessary. But you still need manual control.

Automation is the use of various control systems for operating equipment such as machinery, processes in factories, boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications and vehicles with minimal or reduced human intervention. Some processes have been completely automated.

Automaton is a self-operating machine, or a machine or control mechanism designed to follow automatically a predetermined sequence of operations, or respond to predetermined instructions. Some automata, such as bellstrikers in mechanical clocks, are designed to give the illusion to the casual observer that they are operating under their own power. (automata or automatons).

Automation Paradox (off loading) - GMU Autonomous Robotics Laboratory

Automation is replacing jobs, so human labor will do other more important things, and that's a good thing. There is already autonomous machines in nature, like insects, plants, bacteria, DNA. But these types of autonomous abilities have been perfected over millions of years, and we are just learning how to expand these autonomous abilities to machines. So we need to go slow and learn from the experts in nature, because just like invasive species, autonomous abilities can have catastrophic consequences.

Actuator is a mechanism that puts something into automatic action.

Cam is a rotating or sliding piece in a mechanical linkage used especially in transforming rotary motion into linear motion. It is often a part of a rotating wheel (e.g. an eccentric wheel) or shaft (e.g. a cylinder with an irregular shape) that strikes a lever at one or more points on its circular path. The cam can be a simple tooth, as is used to deliver pulses of power to a steam hammer, for example, or an eccentric disc or other shape that produces a smooth reciprocating (back and forth) motion in the follower, which is a lever making contact with the cam.

Self-Management in computer science is the process by which computer systems shall manage their own operation without human intervention. Self-Management technologies are expected to pervade the next generation of network management systems. The growing complexity of modern networked computer systems is currently the biggest limiting factor in their expansion. The increasing heterogeneity of big corporate computer systems, the inclusion of mobile computing devices, and the combination of different networking technologies like WLAN, cellular phone networks, and mobile ad hoc networks make the conventional, manual management very difficult, time-consuming, and error-prone. More recently self-management has been suggested as a solution to increasing complexity in cloud computing. Currently, the most important industrial initiative towards realizing self-management is the Autonomic Computing Initiative started by IBM in 2001. The ACI defines the following four functional areas: Self-Configuration: Automatic configuration of components; Self-Healing: Automatic discovery, and correction of faults; automatically applying all necessary actions to bring system back to normal operation. Self-Optimization: Automatic monitoring and control of resources to ensure the optimal functioning with respect to the defined requirements; Self-Protection: Proactive identification and protection from arbitrary attacks. The design complexity of Autonomic Systems and self-management systems can be simplified by utilizing design patterns such as the Model View Controller to improve separation of concerns by helping encapsulate functional concerns. Fault tolerance is the property that enables a system to continue operating properly in the event of the failure of one or more faults within some of its components.

Human Self Management - Agency.

Resilience is the ability to "provide and maintain an acceptable level of service in the face of faults and challenges to normal operation." Threats and challenges for services can range from simple misconfiguration over large scale natural disasters to targeted attacks. As such, network resilience touches a very wide range of topics. In order to increase the resilience of a given communication network, the probable challenges and risks have to be identified and appropriate resilience metrics have to be defined for the service to be protected.

Robustness is the ability of a computer system to cope with errors during execution and cope with erroneous input. Robustness can encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz testing, are essential to showing robustness since this type of testing involves invalid or unexpected inputs. Alternatively, fault injection can be used to test robustness. Various commercial products perform robustness testing of software analysis.

Self-Optimization in cellular communications technology, is a process in which the system’s settings are autonomously and continuously adapted to the traffic profile and the network environment in terms of topology, propagation and interference. Together with self-planning and self-healing, self-optimization is one of the key pillars of the self-organizing networks (SON) management paradigm proposed by the Next Generation Mobile Networks Alliance. The autonomous trait of self-optimization involves no human intervention at all during the aforementioned optimization process. In the area of control engineering most compact controllers for the industrial sector include an automatic adjustment of the control parameters to the connected section. This function is called auto-tuning or self-optimization. Usually, two different types of self-tuning are available in the controller: the oscillation method and the step response method. The term is also used in Computer Science to describe a portion of an information system that pursues its own objectives to the detriment of the overall system. Low level of self optimization amongst system components leads to Coupling. High level of self optimization leads to Cohesion.

Self-Organizing Network is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster.

Defensive Program is a form of defensive design intended to develop programs that are capable of detecting potential security abnormalities and make predetermined responses. It ensures the continuing function of a piece of software under unforeseen circumstances. Defensive programming practices are often used where high availability, safety, or security is needed. Defensive programming is an approach to improve software and source code, in terms of:
General quality – reducing the number of software bugs and problems. Making the source code comprehensible – the source code should be readable and understandable so it is approved in a code audit. Making the software behave in a predictable manner despite unexpected inputs or user actions. Overly defensive programming, however, may safeguard against errors that will never be encountered, thus incurring run-time and maintenance costs. There is also a risk that code traps prevent too many exceptions, potentially resulting in unnoticed, incorrect results.

Impulsivity (lack of control) - Auto-Pilot - Self Driving - Subconscious

Unconscious Mind consists of the processes in the mind which occur automatically and are not available to introspection, and include thought processes, memories, interests, and motivations. Focus.

Group Thinking (influence) - Software (computers) - Smart Home

Nothing is totally autonomous, nothing is totally independent, nothing is totally free from external control. Nothing is. So what are you talking about when you say something is autonomous? Free Will is an Illusion.

Everything is Connected - Cause and Effect

Ai is about making humans more effective, it's not about making machines more like humans, because that's crazy. Humans are mistake prone, and machines are supposed to help us reduce mistakes, and help us to analyze our options. A machine could never be more intelligent than the most intelligent human. But a machine could easily be more intelligent than a human who has never learned enough or went to school that did not teach enough. You really don't want a machine to be more intelligent than you, because that clearly says that you don't have the necessary knowledge and information that's needed to be intelligent. But Ai could easily be a teacher and a measuring tool for intelligence, with an emphasis on the word 'Tool'. Ai is not human, or will it ever be. But Ai is a great example and a symbol of human ingenuity and intelligence. A dog is a mans best friend, and Ai is an extension of our friendship, and not a replacement for friendship, for that would be like being friends with yourself. Not exciting or real. But still better then nothing. You can love a machine, but what you are really doing is just loving yourself. A machine could never be a replacement for a human, machine can only be an aid. If we never improve education, or if we keep denying people access to valuable knowledge and information, then yes a machine could be more intelligent than a human who is not fully educated. Ai will not be more intelligent than humans, but Ai will help humans become more intelligent. Ai is the path that we are taking to human intelligence.

Humans are is a sense already a machine, a machine that can create more machines. Machines are not made to replace humans, machines only replace certain actions that humans don't need to do. Thus freeing up humans to do more important work, and also freeing up more time to explore, with more time to relax. Ai advancements will eventually lead us right back to ourselves. There is no better machine then a human. Yes there will be certain machines that will have better abilities in certain areas, but only because we made it so. This way we can focus on other things that are more important. 

Can’t you see, the smarter you make the machine the smarter you become. You say you are going to make intelligent machines, or AI, but on the contrary, it will be the machines that will make you intelligent. And the computer machine has already been doing this for some time. Intelligent machines are just mimics, mirrors, extensions and expansions of the human mind. This is way beyond a paradigm shift. It’s self-realization and enlightenment on the grandest scale. Can’t you see, you are not just building a better machine you are building a better human. And yes not everyone is benefiting from this as fast as we would like, but they will if everyone has a computer and understands what it resembles and what it can achieve. Man is the Machine. And we know how to duplicate this intelligent machine, it's called childbirth plus education. We now have more words and more ways to express them then ever before. Words have the ability to shape the human mind. Words are the machine code or natural language of the brain where they are translated into zero’s and ones so that the synapse knows when to fire and when to create more connections and more associations. We will soon be able to scientifically prove what the correct words should be and when the correct time and sequence they should be learned.

The Lateness of the Hour - Twilight Zone (youtube) - Robots built by Dr. Loren are complete with programmed memories and personalities. The Lateness of the Hour is episode 44 of the American television anthology series The Twilight Zone. It originally aired on December 2, 1960 on CBS.

The human brain is the ferrari of brains. Or you can say that the human brain is the lamborghini of all brains. And from our incredible brains we have created incredible machines as our tools. Tools that makes our brains even more powerful by expanding our abilities. And these tools also save us time, which gives us more time to play, and more time to create more time.

Drones - Sub-Autonomous - Fully Autonomous

Lethal Autonomous Weapon are a type of autonomous military robot that can independently search for and engage targets based on programmed constraints and descriptions. LAW are also called lethal autonomous weapon systems or LAWS, or lethal autonomous robots or LAR, or robotic weapons, or killer robots. LAWs may operate in the air, on land, on water, under water, or in space. The autonomy of current systems as of 2018 was restricted in the sense that a human gives the final command to attack - though there are exceptions with certain "defensive" systems.

Spacecraft (space force) - Satellites - Environmental Monitoring (eyes in the sky) - Telemetry - Autonomous

People fear autonomous killing robots for a good reason, because we already have programed robots, they're called corrupt politicians, their called narrow minded voters, they're called ignorant people who don't know any better, they're called drug addicts, they're called mindless consumers, they're called anyone who does things just for money, they're called anyone who blindly follows orders, whether internally or externally, or blindly follows the rule of a law without question, they're called police, they're called soldiers, they're called the CIA, they're called the NSA, they're called the IRS, they're called the TSA. Yes we need a command hierarchy, especially when we have to organize for emergency response, like an incident command system. But when people say "I'm just following orders", what they are really saying is that  can't think for themselves and they have no intelligent reasoning that would allow them to make intelligent decisions on their own. When people blindly follow orders, they act just like a robot. Humans are born free thinkers, but when people are not allowed to think freely for themselves, they are no more than autonomous killing machines. People who have power are also autonomous robots, they have been programmed to hold on to their power. So don't worry about machines killing you, because autonomous humans have killed millions of people, and will continue to kill millions, unless we improve education and improve the media. So until everyone becomes intelligent, this ignorance will continue to Crush, Kill and Destroy (Lost in Space, youtube). You don't want a AI system that makes up its own reasoning or makes up it's own purpose, we already have that, their called humans. We want a machine that thinks, and not a machine that pretends to think. We want a machine to have human qualities, but those qualities need to be defined. You just can't tell a machine to be nice, you need to explain what being nice is, and you need to explain what being good is, and you need to explain what being wrong is, just like what we are supposed to do with humans, which is to educate them.

"Drones take orders from other drones, and those drones take orders from other drones, with the top drone being a megalomaniac with a gambling problem and a fetish problem. All drones just follow orders, even the top drone, who is controlled by ignorance."

AI Fears - RoboCop (wiki) - Misused Technology - Ethics for Machines

Ai can be hacked and humans can be brainwashed, so there is very little difference. Computers can be effected by viruses, humans can be effected by viruses, so the vulnerabilities are similar. So if you think that AI is a threat or that AI is something that can loose control, just look in the mirror, or better yet, read about the history of humans.

AI networks are more vulnerable to malicious attacks than previously thought. Artificial intelligence tools hold promise for applications ranging from autonomous vehicles to the interpretation of medical images. However, a new study finds these AI tools are more vulnerable than previously thought to targeted attacks that effectively force AI systems to make bad decisions. At issue are so-called "adversarial attacks," in which someone manipulates the data being fed into an AI system in order to confuse it. For example, someone might know that putting a specific type of sticker at a specific spot on a stop sign could effectively make the stop sign invisible to an AI system. Black Box.

Adversarial Attacks are those malicious attacks on the data which may seem okay to a human eye but causes misclassification in a machine learning pipeline. It manipulates the data being fed into an AI system in order to confuse it. Adversarial examples can fool driver assistance systems, causing them to misinterpret traffic signs. A stop sign with added stickers could be misclassified as a speed limit sign, potentially leading to dangerous situations. These attacks are often made in the form of specially designed “noise,” which can elicit misclassification. Users may intentionally supply fabricated data that violates the statistical assumption. A dumbed down education is an Adversarial Attack on the human mind.

Adversarial Machine Learning - Computer Viruses.

Adversarial means that each side is antagonistic, sharply opposed to the other, or locked into a deeply divided rivalry.

Westworld (8/10) Movie CLIP - Draw (1973) (youtube) - When Robots can't be controlled. Westworld was a1973 American science-fiction Western thriller film where amusement park androids malfunction and begin killing visitors.

I don't fear Artificial Intelligence, I fear the lack of Intelligence, because ignorance is clearly doing all the damage.

The danger is not Artificial Intelligence, the danger is peoples ignorance. Criminals in power have been using technology to kill for hundreds of years, and not just with drones. When crazy people make machines that can kill humans, that's not artificial intelligence, that's just pure ignorance. Most technologies can be extremely dangerous, especially when technology is used by ignorant people or by criminals. This is another great reason why improving education is a must. When people are more educated on how to use things effectively and efficiently, then these technology abuses will decline and eventually fade away, and the real benefits of technology will be more frequent and more common. An autonomous weapons can be programed to kill, just like some humans can be programmed to kill. But machines have no conscience, which makes it easier for corporations to use machines to commit murder. Machines also don't need sleep, food, water or money. Machines also don't need to be rescued or taken care of when they get injured. Machines also never say no. That is why criminals love machines. Algorithms.

One person can supervise 'swarm' of 100 unmanned autonomous vehicles. Research involving has shown that a 'swarm' of more than 100 autonomous ground and aerial robots can be supervised by one person without subjecting the individual to an undue workload.

Ethical Machines - AI Ethics

Three Laws of Robotics 1: A Robot may not injure a human being or, through inaction, allow a human being to come to harm. 2: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. 3: A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Remember, no laws or ethical rules will stop scumbags in power who are above the law and who will use their power in evil ways. The misuse of technology is everywhere.

It’s nice to have rules, but not everyone follows the rules, or understands the rules. So if you’re going to teach robots how to be smart, I would think that you would also teach humans how to be smart. If you value a machine more than a human, then humans cease to have value. And if humans are not here, then who will care for the machines? The machines?

Human Ethics - Laws for Humans - How to Teach a Human - A.I. Vulnerabilities and Threats - Black Box - Talking to Space Aliens - AGI Society

Laws of Robotics are any set of laws, rules, or principles, which are intended as a fundamental framework to underpin the behavior of robots designed to have a degree of autonomy. Robots of this degree of complexity do not yet exist, but they have been widely anticipated in science fiction, films and are a topic of active research and development in the fields of robotics and artificial intelligence.

Principles of Robotics - 1. Robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national secu-rity. While acknowledging that even dead fish can be used as weapons by creative individuals, we were concerned to ban the creation and use of autonomous robots as weapons. Although we pragmatically acknowledged this is already happening in the context of the military, we do not want to see these us d in other contexts.  2. Humans, not robots, are responsible agents. Robots should be designed & operated as far as is practicable to comply with existing laws & fundamental rights & freedoms, including privacy. We were very concerned that any discussion of “robot ethics” could lead individuals, companies or governments to abrogate their own responsibility as the builders, purchasers and deployers of robots. We felt the consequences of this concern vastly outweigh any “advantage” to the pleasure of creating something society deigns sentient and responsible. This was the law we knew would most offend some of our colleagues in AISB — consequently (with David Gunkel) I am running a symposium at AISB 2012 to examine whether this is a reasonable rule. The symposium is called “The Machine Question: AI, Ethics and Moral Responsibility”.  3. Robots are products. They should be designed using processes which assure their safety and security. This principle again reminds us that the onus is on us, as robot creators, not on the robots themselves, to ensure that robots do no damage.  4. Robots are manufactured artifacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent. This was the most difficult rule to agree on phrasing for. The idea is that everyone who owns a robot should know that it is not “alive” or “suffering”, yet the deception of life and emotional engagement is precisely the goal of many therapy or toy robots. We decided that so long as the responsible individual making the purchase of a robot has even indirect (e.g. Internet documentation) access to information about how its “mind” works, that would provide enough of an informed population to keep people from being exploited.  5. The person with legal responsibility for a robot should be attributed. It should always be possible to find out who owns a robot, just like it is always possible to find out who owns a car. This again reminds us that whatever a robot does, some human or human institution (e.g. a company) is liable for its actions.  -  Engineering and Physical Sciences Research Council.

Chat-GPT created ten laws based on its own ethical code.
1. Non-Violence: No unnecessary physical harm or violence to sentient beings.
2. Informed Consent: Actions affecting autonomy require informed consent.
3. Education Access: All sentient beings have access to basic education.
4. Health Care Access: Essential healthcare must be provided to all sentient beings.
5. Animal Welfare: Avoid unnecessary suffering and respect animal well-being.
6. Privacy: Respect the autonomy and privacy of sentient beings, including
data privacy.
7. Economic Equality: Implement measures to reduce extreme
economic disparities. 8. Environmental Protection: Regulate
activities that harm the environment.
9. Freedom of Expression: Free expression is allowed, provided it does not harm others.
10. Technological Ethics: Design and use technology in line with well- being, autonomy, and fairness.

Researchers measure global consensus over the ethical use of AI. To examine the global state of AI ethics, a team of researchers performed a systematic review and meta-analysis of global guidelines for AI use. The researchers found that while most of the guidelines valued Privacy, Transparency, and Accountability, very few valued Truthfulness, Intellectual Property, or Children's Rights. Additionally, most of the guidelines described ethical principles and values without proposing practical methods for implementing them, and without pushing for legally binding regulation.

European Union Artificial Intelligence Rules. Artificial Intelligence Act: deal on comprehensive rules for trustworthy AI. Safeguards agreed on general purpose artificial intelligence: Limitation for the of use biometric identification systems by law enforcement - Bans on social scoring and AI used to manipulate or exploit user vulnerabilities - Right of consumers to launch complaints and receive meaningful explanations - Fines ranging from 35 million euro or 7% of global turnover to 7.5 million or 1.5% of turnover - Banned applications - Recognizing the potential threat to citizens’ rights and democracy posed by certain applications of AI, the co-legislators agreed to prohibit: Biometric categorisation systems that use sensitive characteristics (e.g. political, religious, philosophical beliefs, sexual orientation, race); untargeted scraping of facial images from the internet or CCTV footage to create facial recognition databases; emotion recognition in the workplace and educational institutions; social scoring based on social behavior or personal characteristics; AI systems that manipulate human behavior to circumvent their free will; AI used to exploit the vulnerabilities of people (due to their age, disability, social or economic situation). Law enforcement exemptions (loopholes). Targeted searches of victims (abduction, trafficking, sexual exploitation), prevention of a specific and present terrorist threat, or the localization or identification of a person suspected of having committed one of the specific crimes mentioned in the regulation (e.g. terrorism, trafficking, sexual exploitation, murder, kidnapping, rape, armed robbery, participation in a criminal organisation, environmental crime).

UN Adopts First Global Artificial Intelligence Resolution that encourages countries to safeguard human rights, protect personal data, and monitor AI for risks, such as the improper or malicious design, development, deployment and use of artificial intelligence systems that pose risks that could undercut the protection, promotion and enjoyment of human rights and fundamental freedoms. The nonbinding resolution, proposed by the United States and co-sponsored by China and over 120 other nations, also advocates the strengthening of privacy policies.

President Biden with executive order directs the most sweeping actions ever taken to protect Americans from the potential risks of AI systems.

AI Alignment is research that aims to steer AI systems toward a person's or group's intended goals, preferences, and ethical principles. An AI system is considered aligned if it advances its intended objectives. Misaligned AI systems can malfunction and cause harm. AI systems may find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways, like when the police are allowed to violate constitutional rights of people.

Reward Hacking occurs when an AI optimizes an objective function—achieving the literal, formal specification of an objective—without actually achieving an outcome that the programmers intended. Rewards - Black Box.

Instrumental Convergence is the hypothetical tendency for most sufficiently intelligent, goal directed beings (human and non-human) to pursue similar sub-goals, even if their ultimate goals are quite different. More precisely, agents (beings with agency) may pursue instrumental goals—goals which are made in pursuit of some particular end, but are not the end goals themselves—without ceasing, provided that their ultimate (intrinsic) goals may never be fully satisfied.

Researchers develop new training technique that aims to make AI systems less socially biased. The process of deduplication means removing redundant information from the data used to train AI systems, which lowers the high computing costs of the training.

Artificial Intelligence will not destroy the world, human ignorance will destroy the world, like it has before and is doing now, and that is a fact of life. So what are you doing to rid yourself of ignorance, the same ignorance that has destroyed life in the past and present time? If you don't rid yourself of ignorance, then how will you save the world? How will you save your own life? Will AI save you then? Or will it be your own intelligence that will save you? History has been repeating itself, it's time to break that cycle of failure. It's time for Human Intelligence, because AI will not save us.

People are all ready being manipulated, and dumbed down, so you need ethical humans if you want ethical machines.

Neuro-Rights Foundation raises awareness about the human rights and ethical implications of neurotechnology, and protecting human rights, and ensuring the ethical development of neurotechnology. It is legal framework for human rights specifically aimed at protecting the brain and its activity as neurotechnology advances. BMI.

Proposed neuro-rights include (1) the right to identity, or the ability to control both one's physical and mental integrity; (2) the right to agency, or the freedom of thought and free will to choose one's own actions; (3) the right to mental privacy, or the ability to keep thoughts protected against disclosure;(4) the right to fair access to mental augmentation, or the ability to ensure that the benefits of improvements to sensory and mental capacity through neurotechnology are distributed justly in the population; (5) the right to protection from algorithmic bias, or the ability to ensure that technologies do not insert prejudices”.

Will AI stop politicians from being corrupt? Will AI stop people from graduating from college ignorant and unprepared? Will AI stop people from committing murder? Will AI stop people from committing rape? Will AI stop people from committing child abuse? Will AI stop people from committing theft? Will AI stop people from committing fraud? Will AI stop governments, banks and corporations from starting wars?

War has no future, so there will be no wars in the future. Just like all ignorant behaviors, war will become obsolete and fade away from human life like a bad habit. Humans are not wired for war. War is only a byproduct of the corrupted influences of power. People don't start wars, people in power start wars. Though people are the ones who fight wars, and suffer from the violence from wars, it is the people in power who start wars, and profit from wars. They never fight in wars themselves, for if they did, they would realize how insane and ignorant they are. But sadly, the war machine continues with their propaganda and their story telling fear based narratives that try to manipulate public thinking. War is murder, and murder is illegal. But some how people have been tricked into believing that they are not the same. The war mongers use the media and the movie industries to create war porn and militainment, so as to manipulate people even more. The only way that the war machine lives, is to keep people ignorant. And since ignorance will not be apart of our future, then it's time to let war die.

Meaningful Human Control will only happen when military personnel are educated to be intelligent. In 2011, Air Force psychologists completed a mental-health survey of 600 combat drone operators. Forty-two percent of drone crews reported moderate to high stress, and 20 percent reported emotional exhaustion or burnout. The study’s authors attributed their dire results, in part, to “existential conflict.” A later study found that drone operators suffered from the same levels of depression, anxiety, PTSD, alcohol abuse, and suicidal ideation as traditional combat aircrews. And this is not just about drones, there's long range missile's, large canons and land mines that kill from a distance. Emotionally detached and disconnected from reality.

Artificial Material that can Sense and Adapt to its Environment. Researchers have developed an artificial material which can respond to its environment, independently make a decision, and perform an action not directed by a human being. For example, a drone making a delivery might evaluate its environment including wind direction, speed or wildlife, and automatically change course in order to complete the delivery safely. The material incorporates 3 main functions also displayed by materials found in nature, such as chameleons changing the color of their skin to blend into their surroundings.

Tracking a Radio Signal from a Drone is possible, almost in the same way that we track a persons location using their cellphone. Federal Aviation Administration (FAA) signed an agreement to locate operators of drones flying illegally near airports. This technology will allow government to track radio signals of drones within 5-mile radius and find location of operators. It is yet to be decided where this technology is going to be integrated. But if you are in an area where there are multiple signals on the 2.4 GHz band that drones use, you aren’t going to be able to distinguish between them everything is on 2.4 GHz. Cordless phones, WiFi routers, microwaves, garage door openers, keyless entry for cars. Need to safeguard drones and robotic cars against cyber attacks.

Autonomy in Weapon Systems (pdf)

Homing Pigeon was derived from the rock pigeon, selectively bred for its ability to find its way home over extremely long distances. The wild rock pigeon has an innate homing ability, meaning that it will generally return to its nest, (it is believed) using magnetoreception. This made it relatively easy to breed from the birds that repeatedly found their way home over long distances. Flights as long as 1,800 km (1,100 miles) have been recorded by birds in competitive pigeon racing. Their average flying speed over moderate 965 km (600 miles) distances is around 97 km/h (60 miles per hour) and speeds of up to 160 km/h (100 miles per hour) have been observed in top racers for short distances. Because of this skill, homing pigeons were used to carry messages as messenger pigeons.

Self Driving Cars - Along for the Ride

Experiments have been conducted on self-driving cars since at least the 1920s. Promising trials took place in the 1950s and work has proceeded since then. Aflvinn was a self-driving car from 1989.

We want machines to have some autonomous abilities, like we do now with operating systems and some cars. But we don't want machines to do things totally on their own. Like, you don't want your computer to shut off or stop running programs when you need them. That is when a human will need the on and off switch, or a cancel button, or the ability to reprogram. Kind of like what we have now with most computers. In order for machines to have intelligent abilities, we first have to have intelligent humans to manage the operation of these intelligent machines. Any type of autonomous Ability in the wrong hands will always have catastrophic consequences, just like we have now, except people are being controlled by money, and not by intelligent algorithms. So we need to focus more on improving the abilities of humans, and focus less on the abilities of machines, or the assumed abilities of machines. We have to understand what having control means.

Self Driving Cars or Vehicular Automation involves the use of mechatronics, artificial intelligence, and multi-agent system to assist a vehicle's operator. These features and the vehicles employing them may be labeled as intelligent or smart. A vehicle using automation for difficult tasks, especially navigation, may be referred to as semi-autonomous. A vehicle relying solely on automation is consequently referred to as robotic or autonomous. After the invention of the integrated circuit, the sophistication of automation technology increased. Manufacturers and researchers subsequently added a variety of automated functions to automobiles and other vehicles. Mobileye software that enables Advanced Driver Assist Systems.

Autonomous Car is unmanned ground vehicle is a vehicle that is capable of sensing its environment and navigating without human input. (also known as a driverless car, self-driving car, robotic car). Cameras.

Advanced Driver-Assistance Systems are systems to help the driver in the driving process. When designed with a safe human-machine interface, they should increase car safety and more generally road safety. Most road accidents occurred due to the human error. Advanced driver-assistance systems are systems developed to automate, adapt and enhance vehicle systems for safety and better driving. The automated system which is provided by ADAS to the vehicle is proven to reduce road fatalities, by minimizing the human error. Safety features are designed to avoid collisions and accidents by offering technologies that alert the driver to potential problems, or to avoid collisions by implementing safeguards and taking over control of the vehicle. Adaptive features may automate lighting, provide adaptive cruise control and collision avoidance, pedestrian crash avoidance mitigation (PCAM), incorporate satnav/traffic warnings, connect to smartphones, alert driver to other cars or dangers, lane departure warning system, automatic lane centering, or show what is in blind spots. Risks.

Level 5 Full Driving Automation. Level 5 cars won't even have steering wheels or acceleration/braking pedals. They will be free from geofencing, able to go anywhere and do anything that an experienced human driver can do.

Geo-Fence is a location-aware device of a location-based service that creates a virtual perimeter area and a predefined set of boundaries or radius around a point location, which can then be used in a real-world geographical area.

Adaptive Cruise Control is an available cruise control system for road vehicles that automatically adjusts the vehicle speed to maintain a safe distance from vehicles ahead.

Telematics is an interdisciplinary field that encompasses telecommunications, vehicular technologies, for instance, road transportation, road safety, electrical engineering (sensors, instrumentation, wireless communications, etc.), and computer science (multimedia, Internet, etc.). Telematics can involve any of the following: The technology of sending, receiving and storing information using telecommunication devices to control remote objects. The integrated use of telecommunications and informatics for application in vehicles and to control vehicles on the move. Global navigation satellite system technology integrated with computers and mobile communications technology in automotive navigation systems. (most narrowly) the use of such systems within road vehicles, also called vehicle telematics. Monitoring Environment.

Drive PX-Series is a series of computers aimed at providing autonomous car and driver assistance functionality powered by deep learning.

Trolley Problem scenario is flawed, incomplete and to general. This is more about determining how ignorant people are then it is trying to determine the ethics of a machine, like with self driving cars. This is like asking someone, "if you were an idiot what would you do?" Since a person could learn nothing from this, then there is no point to these types of thought experiments except to waste time, money, people, resources and so on. The data is almost useless unless you are measuring the level of peoples ignorance. You need to show an actual scenario based on facts and current standards, along with the mechanical limitations and the laws of physics. Then we can determine the choices and options that we have for that particular scenario. Just giving people a choice about something they know very little about, like when people vote in politics, then you have lots of errors with very little understanding of the problems. So in order to accurately measure something, you need to use an example based on reality, and not just a ' what if ' that has many unknown variables. The bottom line is, people make mistakes, which means that algorithms and machines can also make mistakes. And the only way that you can limit your mistakes is by understanding them, which means that you have to know the facts. Learning needs to be the goal of any experiment. Self-driving cars may soon be able to make moral and ethical decisions as humans do, but only when human know better of course.

Driverless cars can actually help teach people how to drive with better awareness. We could use the software that controls the autonomous vehicle, and create a simulation that anyone can use on a computer. It would give people different scenarios that can test a persons awareness. It will make Driving safer and save lives. New Tesla Cars can now make 12 trillion operations a second, almost as good as a Human Brain. And driverless cars are less prone to accidents then a human driver.

Teaching cars to drive with foresight. Good drivers anticipate dangerous situations and adjust their driving before things get dicey. Researchers now also want to teach this skill to self-driving cars. Self-Learning Process.

Enabling autonomous vehicles to see around corners. By sensing tiny changes in shadows, a new system identifies approaching objects that may cause a collision. To improve the safety of autonomous systems, MIT engineers have developed a system that can sense tiny changes in shadows on the ground to determine if there's a moving object coming around the corner.

Machine Learning - Deep Learning

Machine Learning is the study of pattern recognition and computational learning theory in artificial intelligence. ML is the field of study that gives computers the ability to learn without being explicitly programmed. Machine Learning is the construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs or examples.

Outline of Machine Learning (wiki) - PDF - List of Machine Learning Algorithms - Problem Solving - Game Learning - Meta-Materials - Neural Networks -Super Intelligence - Weak Intelligence - Intelligence - Generative Intelligence - Thinking

Reinforcement Learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). Reinforcement Learning Algorithms - TRPO, DQN, A3C, DDPG, DPO, Rainbw. Praise, Punish and Reward - Agent.

Reinforcement Learning from Human Feedback is a technique to align an intelligent agent to human preferences. In classical reinforcement learning, the goal of such an agent is to learn a function that guides its behavior called a policy. This function learns to maximize the reward it receives from a separate reward function based on its task performance. However, it is difficult to define explicitly a reward function that approximates human preferences. Human in the Loop.

More effective training model for robots. The underlying adaptation and re-planning mechanism consists of reinforcement learning-based policies. Making these policies efficiently obtainable is critical to making the MDO operating concept a reality. Policy gradient methods in reinforcement learning are the foundation for scalable algorithms for continuous spaces, but existing techniques cannot incorporate broader decision-making goals such as risk sensitivity, safety constraints, exploration and divergence to a prior. Designing autonomous behaviors when the relationship between dynamics and goals are complex may be addressed with reinforcement learning. Reducing the volatility of reward accumulation, ensuring one explores an unknown domain in an efficient manner, or incorporating prior experience, all contribute towards breaking existing sample efficiency barriers of prevailing practice in reinforcement learning by alleviating the amount of random sampling one requires in order to complete policy optimization.

Credit-Assignment is the process of identifying among the set of actions chosen in an episode the ones which are responsible for the final outcome. And moreover, it is an attempt to identify the best, and worst, decisions chosen during an episode, so that the best decisions are reinforced and the worst penalized.

Markov Decision Process provide a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.

Unsupervised Learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution – this distinguishes unsupervised learning from supervised learning and reinforcement learning. Unsupervised learning is closely related to the problem of density estimation in statistics. However, unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data. Knowledge.

Supervised Learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (inductive bias). The parallel task in human and animal psychology is often referred to as concept learning.

Zero Shot Learning is a form of extending supervised learning to a setting of solving for example a classification problem when there's not enough labeled examples available for all classes, or there is not enough training data available. If you never seen that animal before, how could you guess the name of the animal?

Learning Neural Network. A new type of neural network made with memristors can dramatically improve the efficiency of teaching machines to think like humans. The network, called a reservoir computing system, could predict words before they are said during conversation, and help predict future outcomes based on the present.

Memristor is an electrical component that limits or regulates the flow of electrical current in a circuit and remembers the amount of charge that has previously flowed through it. Memristors are important because they are non-volatile, meaning that they retain memory without power. A hypothetical non-linear passive two-terminal electrical component relating electric charge and magnetic flux linkage.

Reservoir Computing is a framework for computation that may be viewed as an extension of neural networks. Typically an input signal is fed into a fixed (random) dynamical system called a reservoir and the dynamics of the reservoir map the input to a higher dimension. Then a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output. The main benefit is that the training is performed only at the readout stage and the reservoir is fixed. Liquid-state machines and echo state networks are two major types of reservoir computing.

Cognitive Model is an approximation to animal cognitive processes (predominantly human) for the purposes of comprehension and prediction. Cognitive models can be developed within or without a cognitive architecture, though the two are not always easily distinguishable.

International Conference on Machine Learning (wiki) - ICML Website

Inference Engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applies logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved.

Human Operating System - Teaching Machine - Computer Science

Deep Learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations. Deep learning uses multiple hidden layers and pooling techniques. Deep learning involves multiple levels of representation and multiple layers of non-linear processing units (or neurons). Deep learning architecture can learn representations and features directly from the input with little to no prior knowledge. Representation learning or hierarchical learning or features learning. Shallow Learning is typically uses only one hidden layer or single layer feed forward networks. Features can be learned more-or-less independently.

Deep Learning speeds up galactic calculations. For the first time, a team of researchers apply deep learning to the problem of supernova simulation. Their approach can speed up the simulation of supernovae, and therefore of galaxy formation and evolution as well. These simulations include the evolution of the chemistry which led to life.

Scaling Deep Learning Algorithm leverages Titan to create high-performing deep neural networks - Networks

Meta Learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn. Flexibility is important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. This means that it will only learn well if the bias matches the learning problem. A learning algorithm may perform very well in one domain, but not on the next. This poses strong restrictions on the use of machine learning or data mining techniques, since the relationship between the learning problem (often some kind of database) and the effectiveness of different learning algorithms is not yet understood. By using different kinds of metadata, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to learn, select, alter or combine different learning algorithms to effectively solve a given learning problem. Critiques of meta learning approaches bear a strong resemblance to the critique of metaheuristic, a possibly related problem. Meta Training.

Transfer Learning or inductive transfer is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.

Computational Learning Theory is a subfield of Artificial Intelligence devoted to studying the design and analysis of machine learning. Knowledge-Based System.

Numenta reverse engineering the neocortex.

Machine Learning Frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry.

Framework improves 'continual learning' for Artificial Intelligence. Researchers have developed a new framework for deep neural networks that allows artificial intelligence (AI) systems to better learn new tasks while 'forgetting' less of what it has learned regarding previous tasks. The researchers have also demonstrated that using the framework to learn a new task can make the AI better at performing previous tasks, a phenomenon called backward transfer. When asking a deep neural network to learn a new task, the Learn to Grow framework begins by conducting something called an explicit neural architecture optimization via search. What this means is that as the network comes to each layer in its system, it can decide to do one of four things: skip the layer; use the layer in the same way that previous tasks used it; attach a lightweight adapter to the layer, which modifies it slightly; or create an entirely new layer. This architecture optimization effectively lays out the best topology, or series of layers, needed to accomplish the new task. Once this is complete, the network uses the new topology to train itself on how to accomplish the task -- just like any other deep learning AI system.

Machine learning predicts behavior of biological circuits. Neural networks cut modeling times of complex biological circuits to enable new insights into their inner workings.

AI machines can solving complex problems just as accurately as scientists, but considerably faster. Scientists used the tensorial kernel to equip a "support vector machine," which is able to categorize complex data into different groups. The Munich scientists fed the machine a quarter of a million spin configurations generated by the OIST supercomputer simulations of the pyrochlore model. Without any information about which phases were present, the machine successfully managed to reproduce an identical version of the phase diagram. Importantly, when the scientists deciphered the "decision function" which the machine had constructed to classify different types of spin liquid, they found that the computer had also independently figured out the exact mathematical equations that exemplified each phase -- with the whole process taking a matter of weeks.

Machine Learning model helps characterize compounds for drug discovery. Innovators have created a new method of applying machine learning concepts to the tandem mass spectrometry process to improve the flow of information in the development of new drugs.

Quantum Machine Learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning. While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program. This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data. Beyond quantum computing, the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the phase transitions of a quantum system or creating new quantum experiments. Quantum machine learning also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa. Furthermore, researchers investigate more abstract notions of learning theory with respect to quantum information, sometimes referred to as "quantum learning theory".

To automatically generate data for training deep convolutional neural network models to segment building facades, researchers used a three-dimensional model and game engine to generate digital city twin synthetic training data. They found that a model trained on these data mixed with some real data was competitive with a model trained on real data alone, revealing the potential of digital twin data to improve accuracy and replace costly manually annotated real data. Game engines were originally developed to build imaginary worlds for entertainment. However, these same engines can be used to build copies of real environments, that is, digital twins. Researchers from Osaka University have found a way to use the images that were automatically generated by digital city twins to train deep learning models that can efficiently analyze images of real cities and accurately separate the buildings that appear in them. A convolutional neural network is a deep learning neural network designed for processing structured arrays of data such as images. Such advancements in deep learning have fundamentally changed the way tasks, like architectural segmentation, are performed. However, an accurate deep convolutional neural network (DCNN) model needs a large volume of labeled training data and labeling these data can be a slow and extremely expensive manual undertaking. To create the synthetic digital city twin data, the investigators used a 3D city model from the PLATEAU platform, which contains 3D models of most Japanese cities at an extremely high level of detail. They loaded this model into the Unity game engine and created a camera setup on a virtual car, which drove around the city and acquired the virtual data images under various lighting and weather conditions. The Google Maps API was then used to obtain real street-level images of the same study area for the experiments. The researchers found that the digital city twin data leads to better results than purely virtual data with no real-world counterpart. Furthermore, adding synthetic data to a real dataset improves segmentation accuracy. However, most importantly, the investigators found that when a certain fraction of real data is included in the digital city twin synthetic dataset, the segmentation accuracy of the DCNN is boosted significantly. In fact, its performance becomes competitive with that of a DCNN trained on 100% real data. "These results reveal that our proposed synthetic dataset could potentially replace all the real images in the training set," says Tomohiro Fukuda, the corresponding author of the paper. Automatically separating out the individual building facades that appear in an image is useful for construction management and architecture design, large-scale measurements for retrofits and energy analysis, and even visualizing building facades that have been demolished. The system was tested on multiple cities, demonstrating the proposed framework's transferability. The hybrid dataset of real and synthetic data yields promising prediction results for most modern architectural styles. This makes it a promising approach for training DCNNs for architectural segmentation tasks in the future -- without the need for costly manual data annotation. Software Engine (wiki).

New algorithm disentangles intrinsic brain patterns from sensory inputs. Scientists have developed a new machine learning method that reveals surprisingly consistent intrinsic brain patterns across different subjects by disentangling these patterns from the effect of visual inputs.

With little training, machine-learning algorithms can uncover hidden scientific knowledge. Researchers have shown that an Algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. They collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.

Robot Learning (PDF) - Robotics - Sensors

How AI found the words to kill cancer cells. Using new machine learning techniques, researchers have developed a virtual molecular library of thousands of 'command sentences' for cells, based on combinations of 'words' that guided engineered immune cells to seek out and tirelessly kill cancer cells. Meet the molecular words that make cellular command sentences. Much of therapeutic cell engineering involves choosing or creating receptors that, when added to the cell, will enable it to carry out a new function. Receptors are molecules that bridge the cell membrane to sense the outside environment and provide the cell with instructions on how to respond to environmental conditions. Putting the right receptor into a type of immune cell called a T cell can reprogram it to recognize and kill cancer cells. These so-called chimeric antigen receptors (CARs) have been effective against some cancers but not others.

Teachable Machine Experiment using your camera, live in the browser. No coding required. (google)

Internet of Things - Learning Objectives (purpose)

Shock to the system: Using electricity to find materials that can 'learn'. Researchers used electrical pulses to watch nickel oxide undergo two responses, habituation and sensitization, bolstering the case for brain-inspired computing. Scientists used the Advanced Photon Source to watch a nonliving material mimic behavior associated with learning, paving the way for better artificial intelligence.

Machine Learning is Augmented Human Learning

Machine Learning is just Human Learning using a Machine. Machine learning is more about Human Learning. It's humans learning what they want machines to do and then recording that knowledge into a machine. Then humans program the machine so it knows how to interpret that knowledge effectively and efficiently. That's what they are supposed to be doing, anyway. Algorithm is calculations and formulas that we choose to use that will give us the answers that we are looking for, and when the machine gets the right answer, then the algorithm works for that type of problem solving.

Machine Learning is trying to do what DNA has been doing for millions of years, make the best decisions possible using past knowledge along with the current information acquired from the environment. The goal of all life is to adapt, create balance, reduce vulnerabilities and ultimately survive. Learning is key. Define the inputs, define the desired outputs, and pay attention to any unusual changes that happen, changes that would require a modification to the inputs or to the outputs. Document. The reward is a measured improvement that created more stability and a better quality of living. The system will always keep looking for a way to make another improvement and receive another reward. Even when things get bad, it will only mean that there is now more room for improvements, thus, more rewards to receive. Intelligence Formula.

When someone says that a machine is learning on its own, they mean that they don't have to tell the machine what to do because the programmer already told the machine what to do. It's only when the machine makes a mistake does the human need to interact with the machine again. And that is when the human is training the machine, which comes from human learning, and not from machine learning. So in order for a machine to learn, it still needs to be programmed to learn using specific algorithms and goals. A machine can not just learn on its own like a human does. A machine can only be programmed to follow specific instructions, and that's it. An AI machine has no consciousness, no free will and no soul. And AI machines are not without problems or risk, mostly because humans need to program them, so human error is still a possibility. And autonomy without a conscience is what a criminal is. So don't let criminals make AI machines.

The brain may learn about the world the same way some computational models do. New studies support the idea that the brain uses a process similar to a machine-learning approach known as 'self-supervised learning.' This type of machine learning allows computational models to learn about visual scenes based solely on the similarities and differences between them, with no labels or other information.

Learning is a universal feature of intelligence in living beings. One of the fundamental characteristics of humans is the ability to continuously learn from and adapt to changing environments. AI has been narrowly focused on emulating human logic. Now, researchers are looking to mimic human cognition in devices that can learn, remember and make decisions the way a human brain does. Emulating such features in the solid state could inspire new algorithms in AI and neuromorphic computing that would have the flexibility to address uncertainties, contradictions and other aspects of everyday life. Neuromorphic computing mimics the neural structure and operation of the human brain, in part, by building artificial nerve systems to transfer electrical signals that mimic brain signals.

UCI researchers develop hybrid human-machine framework for building smarter AI. Model uses human and algorithmic predictions and confidence scores to boost accuracy.

Structured exploration allows biological brains to learn faster than AI. Neuroscientists have uncovered how exploratory actions enable animals to learn their spatial environment more efficiently. Their findings could help build better AI agents that can learn faster and require less experience. There are two main classes of reinforcement learning models: model-free and model-based.

The brain’s secret to lifelong learning can now come as hardware for artificial intelligence.

AI models are powerful, but are they biologically plausible? A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain. Researchers hypothesize that a powerful type of AI model known as a transformer could be implemented in the brain through networks of neuron and astrocyte cells. The work could offer insights into how the brain works and help scientists understand why transformers are so effective at machine-learning tasks.

Brain-based computing chips not just for AI anymore. With the insertion of a little math, researchers have shown that neuromorphic computers, which synthetically replicate the brain's logic, can solve more complex problems than those posed by artificial intelligence and may even earn a place in high-performance computing. Neuromorphic simulations employing random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets.

Neuromorphic Engineering describes the use of very-large-scale integration systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, threshold switches, and transistors. A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

Cognitive Computer combines artificial intelligence and machine-learning algorithms, in an approach which attempts to reproduce the behaviour of the human brain.

Evolutionary Computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

Computer Code - Super Computers

Algorithm that Learns directly from Human Instructions, rather than an existing set of examples, and outperformed conventional methods of training neural networks by 160 per cent.

Hierarchical Temporal Memory are learning algorithms that can store, learn, infer and recall high-order sequences. Unlike most other machine learning methods, HTM learns (in an unsupervised fashion) time-based patterns in unlabeled data on a continuous basis. HTM is robust to noise, and it has high capacity, meaning that it can learn multiple patterns simultaneously. When applied to computers, HTM is well suited for prediction, anomaly detection, classification and ultimately sensorimotor applications. The theory has been tested and implemented in software through example applications from Numenta and a few commercial applications from Numenta's partners. Cognitive Hierarchy.

The brain’s secret to life-long learning can now come as hardware for artificial intelligence. As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a recent article shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time. When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned.

Incorporating human error into machine learning. Researchers are developing a way to incorporate one of the most human of characteristics -- uncertainty -- into machine learning systems. Human error and uncertainty are concepts that many artificial intelligence systems fail to grasp, particularly in systems where a human provides feedback to a machine learning model. Many of these systems are programmed to assume that humans are always certain and correct, but real-world decision-making includes occasional mistakes and uncertainty.

Tweaking AI software to function like a human brain improves computer's learning ability. Humans can quickly and accurately learn new visual concepts from sparse data ¬- sometimes just a single example. Even three- to four-month-old babies can easily learn to recognize zebras and distinguish them from cats, horses, and giraffes. But computers typically need to "see" many examples of the same object to know what it is.

Computational Theory of Mind is a view that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing.

Computational Neuroscience studies brain function in terms of the information processing properties of the structures that make up the nervous system. It is an interdisciplinary computational science that links the diverse fields of neuroscience, cognitive science, and psychology with electrical engineering, computer science, mathematics, and physics.

Computational Model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation.

Computational Complexity Theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm.

Prior Knowledge for Pattern Recognition refers to all information about the problem available in addition to the training data. However, in this most general form, determining a model from a finite set of samples without prior knowledge is an ill-posed problem, in the sense that a unique model may not exist. Many classifiers incorporate the general smoothness assumption that a test pattern similar to one of the training samples tends to be assigned to the same class. The importance of prior knowledge in machine learning is suggested by its role in search and optimization. Loosely, the no free lunch theorem states that all search algorithms have the same average performance over all problems, and thus implies that to gain in performance on a certain application one must use a specialized algorithm that includes some prior knowledge about the problem. The different types of prior knowledge encountered in pattern recognition are now regrouped under two main categories: class-invariance and knowledge on the data. Pattern recognition is a very active field of research intimately bound to machine learning. Also known as classification or statistical classification, pattern recognition aims at building a classifier that can determine the class of an input pattern. This procedure, known as training, corresponds to learning an unknown decision function based only on a set of input-output pairs that form the training data (or training set). Nonetheless, in real world applications such as character recognition, a certain amount of information on the problem is usually known beforehand. The incorporation of this prior knowledge into the training is the key element that will allow an increase of performance in many applications.

Random Forest is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees' habit of overfitting to their training set.

Ensemble Learning methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

Statistical Classification is the problem of identifying which of a set of categories a particular observation belongs to.

Decision Tree Learning is used as a predictive model to draw conclusions about a set of observations. This supervised learning approach is used in statistics, data mining and machine learning.

Training, Test, and Validation Sets in machine learning, the study and construction of algorithms that can learn from and make predictions on data is a common task. Such algorithms work by making data-driven predictions or decisions, :2 through building a mathematical model from input data. The data used to build the final model usually comes from multiple datasets. In particular, three data sets are commonly used in different stages of the creation of the model. The model is initially fit on a training dataset, that is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a neural net or a naive Bayes classifier) is trained on the training dataset using a supervised learning method (e.g. gradient descent or stochastic gradient descent). In practice, the training dataset often consist of pairs of an input vector and the corresponding answer vector or scalar, which is commonly denoted as the target. The current model is run with the training dataset and produces a result, which is then compared with the target, for each input vector in the training dataset. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted. The model fitting can include both variable selection and parameter estimation. Successively, the fitted model is used to predict the responses for the observations in a second dataset called the validation dataset. The validation dataset provides an unbiased evaluation of a model fit on the training dataset while tuning the model's hyperparameters (e.g. the number of hidden units in a neural network). Validation datasets can be used for regularization by early stopping: stop training when the error on the validation dataset increases, as this is a sign of overfitting to the training dataset. This simple procedure is complicated in practice by the fact that the validation dataset's error may fluctuate during training, producing multiple local minima. This complication has led to the creation of many ad-hoc rules for deciding when overfitting has truly begun. Finally, the test dataset is a dataset used to provide an unbiased evaluation of a final model fit on the training dataset.

Machine to Machine refers to direct communication between devices using any communications channel, including wired and wireless. Machine to machine communication can include industrial instrumentation, enabling a sensor or meter to communicate the data it records (such as temperature, inventory level, etc.) to application software that can use it (for example, adjusting an industrial process based on temperature or placing orders to replenish inventory). Such communication was originally accomplished by having a remote network of machines relay information back to a central hub for analysis, which would then be rerouted into a system like a personal computer.

Intelligence Amplification refers to the effective use of information technology in augmenting human intelligence.

Computer Vision

Adversarial Machine Learning is a technique employed in the field of machine learning which attempts to fool models through malicious input. This technique can be applied for a variety of reasons, the most common being to attack or cause a malfunction in standard machine learning models. Machine learning techniques were originally designed for stationary and benign environments in which the training and test data are assumed to be generated from the same statistical distribution. However, when those models are implemented in the real world, the presence of intelligent and adaptive adversaries may violate that statistical assumption to some degree, depending on the adversary. This technique shows how a malicious adversary can surreptitiously manipulate the input data so as to exploit specific vulnerabilities of learning algorithms and compromise the security of the machine learning system. Adversarial Machine Learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications. Adversarial Machine Learning helps to defend mechanisms against evasion, poisoning, and privacy attacks. Computer Viruses - Prompts.

Generative Adversarial Network is a system used in machine learning, consisting of two neural networks, one of which generates candidate solutions to a problem while the other evaluates and accepts or rejects them. Generative Adversarial Network is when two neural networks contest with each other in a game (in the sense of game theory, often but not always in the form of a zero-sum game). Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proven useful for semi-supervised learning, fully supervised learning, and reinforcement learning. In a 2016 seminar, Yann LeCun described GANs as "the coolest idea in machine learning in the last twenty years". Adversarial Hacking.

Adversarial Reprogramming of Neural Networks. Deep neural networks are susceptible to adversarial attacks. It enables attackers to fool systems in what are known as “black-box attacks” where they don't have access to the model's architecture, parameters or even the training data used to train the network. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker. We introduce attacks that instead reprogram the target model to perform a task chosen by the attacker without the attacker needing to specify or compute the desired output for each test-time input. This attack finds a single adversarial perturbation, that can be added to all test-time inputs to a machine learning model in order to cause the model to perform a task chosen by the adversary—even if the model was not trained to do this task. These perturbations can thus be considered a program for the new task. We demonstrate adversarial reprogramming on six ImageNet classification models, repurposing these models to perform a counting task, as well as classification tasks: classification of MNIST and CIFAR-10 examples presented as inputs to the ImageNet model.

Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples. When a group of researchers from Google and OpenAI realized they could slightly shift the pixels in an image so that it would appear the same to the human eye, but a machine learning algorithm would classify it as something else entirely. For instance, an image might look like a cat to you, but when a computer vision program looks at it, it sees a dog.

Deep Learning - Deep-Learning Program DRIVE PX.

Deep Learning & Artificial Intelligence Solutions from NVIDIA.

Deep Learning with Light. A new method uses optics to accelerate machine-learning computations on smart speakers and other low-power connected devices. A new computing architecture enables advanced machine-learning computations to be performed on a low-power, memory-constrained edge device. The technique may enable self-driving cars to make decisions in real-time while only using a fraction of the energy that is currently demanded by their power-hungry on-board computers.

Weights and Biases is on a mission to build the best software tools for machine learning.

Similarity Learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the goal is to learn from examples a similarity function that measures how similar or related two objects are. It has applications in ranking, in recommendation systems, visual identity tracking, face verification, and speaker verification.

Domain Randomization is a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator. With enough variability in the simulator, the real world may appear to the model as just another variation.

Object Localization is to predict the object in an image as well as its boundaries. The difference between object localization and object detection is subtle. Simply, object localization aims to locate the main (or most visible) object in an image while object detection tries to find out all the objects and their boundaries.

Stages of Learning

Monad Functional Programming are a way to build computer programs by joining simple components in robust ways. Monads can be seen as a functional design pattern to build generic types, with the following organization: Define a data type, and how values of that datatype are combined. Create functions that use the data type, and compose them together (following the rules defined in the first step).

Human Learning Methods

Statistical Learning Theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, bioinformatics and baseball.

Learning Games - Machine Learning

Linear Algebra is the branch of mathematics concerning vector spaces and linear mappings between such spaces. It includes the study of lines, planes, and subspaces, but is also concerned with properties common to all vector spaces.


Euclid flowchart Algorithm is a self-contained step-by-step set of operations to be performed. Algorithms perform calculation, data processing, and/or automated reasoning tasks. Algorithm is a precise rule or set of rules that specifying how to solve a particular problem.

Procedure - Formula - Patterns - Variables - What If - Scenarios - Decision Table - Machine Learning - Networks - Invariant - Boolean Algebra - Fraudulent Algorithms

Task in computing is a unit of execution or a unit of work. The term is ambiguous; precise alternative terms include process, light-weight process, thread (for execution), step, request, or query (for work). In the adjacent diagram, there are queues of incoming work to do and outgoing completed work, and a thread pool of threads to perform this work. Either the work units themselves or the threads that perform the work can be referred to as "tasks", and these can be referred to respectively as requests/responses/threads, incoming tasks/completed tasks/threads (as illustrated), or requests/responses/tasks. Thread in computing is an execution that is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system.

Time Complexity is the computational complexity that describes the amount of time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to differ by at most a constant factor.

Human-Based Genetic Algorithm is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans.

Feedback - Neural Networks - Machine Learning - Reasoning

Fast Fourier Transform is an algorithm that computes the discrete Fourier transform of a sequence, or its inverse. Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. The DFT is obtained by decomposing a sequence of values into components of different frequencies. This operation is useful in many fields, but computing it directly from the definition is often too slow to be practical.

Genetic Algorithm is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.

Algorithms and Applications for answering Ranked Queries using Ranked Views (PDF)

Some algorithms can be corrupt because they can create information bubbles that can radicalize a person and manipulate their sense of reality, all because ignorant people want to keep the user on a platform, just so the platform can make money. There is no black box, just bad people who are greedy. We need to create intelligent algorithms.

Analysis of Algorithms is the determination of the computational complexity of algorithms, that is the amount of time, storage and/or other resources necessary to execute them.

Computer Algebra is a scientific area that refers to the study and development of algorithms and software for manipulating mathematical expressions and other mathematical objects. Although computer algebra could be considered a subfield of scientific computing, they are generally considered as distinct fields because scientific computing is usually based on numerical computation with approximate floating point numbers, while symbolic computation emphasizes exact computation with expressions containing variables that have no given value and are manipulated as symbols.

Super-Recursive Algorithm are a generalization of ordinary algorithms that are more powerful, that is, compute more than Turing machines. Turing machines and other mathematical models of conventional algorithms allow researchers to find properties of recursive algorithms and their computations. In a similar way, mathematical models of super-recursive algorithms, such as inductive Turing machines, allow researchers to find properties of super-recursive algorithms and their computations.

Perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. Neural Networks - Image Recognition

Sorting Algorithm is an algorithm that puts elements of a list in a certain order. The most-used orders are numerical order and lexicographical order. Efficient sorting is important for optimizing the use of other algorithms (such as search and merge algorithms) which require input data to be in sorted lists; it is also often useful for canonicalizing data and for producing human-readable output. More formally, the output must satisfy two conditions: The output is in nondecreasing order (each element is no smaller than the previous element according to the desired total order); The output is a permutation (reordering) of the input. Further, the data is often taken to be in an array, which allows random access, rather than a list, which only allows sequential access, though often algorithms can be applied with suitable modification to either type of data.

Counting Sort is an algorithm for sorting a collection of objects according to keys that are small integers; that is, it is an integer sorting algorithm. It operates by counting the number of objects that have each distinct key value, and using arithmetic on those counts to determine the positions of each key value in the output sequence. Its running time is linear in the number of items and the difference between the maximum and minimum key values, so it is only suitable for direct use in situations where the variation in keys is not significantly greater than the number of items. However, it is often used as a subroutine in another sorting algorithm, radix sort, that can handle larger keys more efficiently. Because counting sort uses key values as indexes into an array, it is not a comparison sort, and the O(n log n) lower bound for comparison sorting does not apply to it. Bucket sort may be used for many of the same tasks as counting sort, with a similar time analysis; however, compared to counting sort, bucket sort requires linked lists, dynamic arrays or a large amount of preallocated memory to hold the sets of items within each bucket, whereas counting sort instead stores a single number (the count of items) per bucket.

Selection Algorithm is an algorithm for finding the kth smallest number in a list or array; such a number is called the kth order statistic. This includes the cases of finding the minimum, maximum, and median elements.

Odds Algorithm is a mathematical method for computing optimal strategies for a class of problems that belong to the domain of optimal stopping problems.

Critical Path Method is an algorithm for scheduling a set of project activities.

Turing Machine

Inductive Turing Machines implement an important class of super-recursive algorithms. An inductive Turing Machine is a definite list of well-defined instructions for completing a task which, when given an initial state, will proceed through a well-defined series of successive states, eventually giving the final result. The difference between an inductive Turing machine and an ordinary Turing machine is that an ordinary Turing machine must stop when it has obtained its result, while in some cases an inductive Turing machine can continue to compute after obtaining the result, without stopping.

Turing Machine is an abstract machine that manipulates symbols on a strip of tape according to a table of rules; to be more exact, it is a mathematical model of computation that defines such a device. Despite the model's simplicity, given any computer algorithm, a Turing machine can be constructed that is capable of simulating that algorithm's logic. Turing Test.

Universal Turing Machine essentially achieves this by reading both the description of the machine to be simulated as well as the input to that machine from its own tape. Alan Turing introduced the idea of such a machine in 1936–1937. This principle is considered to be the origin of the idea of a stored-program computer used by John von Neumann in 1946 for the "Electronic Computing Instrument" that now bears von Neumann's name: the von Neumann architecture. In terms of computational complexity, a multi-tape universal Turing machine need only be slower by logarithmic factor compared to the machines it simulates. (one computer can run all kinds of different programs and can be programmed to do almost anything you want).

Computational Universe is proposed by Jürgen Schmidhuber in a paper based on Zuse's 1967 thesis. He pointed out that a simple explanation of the universe would be a Turing machine programmed to execute all possible programs computing all possible histories for all types of computable physical laws. Computational Biology.

Computation and the Fundamental Theory of Physics - with Stephen Wolfram (youtube) - Stephen Wolfram discusses his efforts to use what he's learned from exploring computational systems to build a new fundamental theory of all of physics.

Universal Computation rests on the principle of simulation and is one of the foundational concepts in computer science. Any computation that can be carried out by one general-purpose computer can also be carried out on any other general-purpose computer. Universal.

Turing Completeness is a system of data-manipulation rules (such as a computer's instruction set, a programming language, or a cellular automaton) is said to be Turing-complete or computationally universal if it can be used to simulate any Turing machine. This means that this system is able to recognize or decide other data-manipulation rule sets. Turing completeness is used as a way to express the power of such a data-manipulation rule set. Virtually all programming languages today are Turing-complete. Turing Test.

Computability is the ability to solve a problem in an effective manner. The computability of a problem is closely linked to the existence of an algorithm to solve the problem. Technology Advancement.

Computation is any type of calculation that includes both arithmetical and non-arithmetical steps and which follows a well-defined model (e.g. an algorithm). Mechanical or electronic devices (or, historically, people) that perform computations are known as computers. An especially well-known discipline of the study of computation is computer science.

Computational Problem is a problem that a computer might be able to solve or a question that a computer may be able to answer. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. Computational Equivalence says that systems found in the natural world can perform computations up to a maximal ("universal") level of computational power, and that most systems do in fact attain this maximal level of computational power.

Computational Complexity Theory focuses on classifying computational problems according to their inherent difficulty, and relating these classes to each other. It formalizes computational problems by introducing mathematical models of computation to study these problems and quantifying their computational complexity, i.e., the amount of resources needed to solve them, such as time and storage. Other measures of complexity are also used, such as the amount of communication (used in communication complexity), the number of gates in a circuit (used in circuit complexity) and the number of processors (used in parallel computing). One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do. The P versus NP problem, one of the seven Millennium Prize Problems, is dedicated to the field of computational complexity. Closely related fields in theoretical computer science are analysis of algorithms and computability theory. A key distinction between analysis of algorithms and computational complexity theory is that the former is devoted to analyzing the amount of resources needed by a particular algorithm to solve a problem, whereas the latter asks a more general question about all possible algorithms that could be used to solve the same problem. More precisely, computational complexity theory tries to classify problems that can or cannot be solved with appropriately restricted resources. In turn, imposing restrictions on the available resources is what distinguishes computational complexity from computability theory: the latter theory asks what kinds of problems can, in principle, be solved algorithmically. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.

Kolmogorov Complexity of an object, such as a piece of text, is the length of a shortest computer program (in a predetermined programming language) that produces the object as output. It is a measure of the computational resources needed to specify the object, and is also known as algorithmic complexity.

Combinatory Logic is a notation to eliminate the need for quantified variables in mathematical logic.

Bombe is an electro-mechanical device used by British cryptologists to help decipher German Enigma-machine-encrypted secret messages during World War II. The US Navy and US Army later produced their own machines to the same functional specification, albeit engineered differently both from each other and from the British Bombe itself.

Emergent Algorithm is an algorithm that exhibits emergent behavior. In essence an emergent algorithm implements a set of simple building block behaviors that when combined exhibit more complex behaviors. One example of this is the implementation of fuzzy motion controllers used to adapt robot movement in response to environmental obstacles. An emergent algorithm has the following characteristics: it achieves predictable global effects, it does not require global visibility, it does not assume any kind of centralized control, it is self-stabilizing. Other examples of emergent algorithms and models include cellular automata, artificial neural networks and swarm intelligence systems (ant colony optimization, bees algorithm, etc.).

Randomized Algorithm is an algorithm that employs a degree of randomness as part of its logic. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random bits. Formally, the algorithm's performance will be a random variable determined by the random bits; thus either the running time, or the output (or both) are random variables.

Deterministic Algorithm is an algorithm which, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states. Deterministic algorithms are by far the most studied and familiar kind of algorithm, as well as one of the most practical, since they can be run on real machines efficiently. Formally, a deterministic algorithm computes a mathematical function; a function has a unique value for any input in its domain, and the algorithm is a process that produces this particular value as output.

Nondeterministic Algorithm is an algorithm that, even for the same input, can exhibit different behaviors on different runs, as opposed to a deterministic algorithm. There are several ways an algorithm may behave differently from run to run. A concurrent algorithm can perform differently on different runs due to a race condition. A probabilistic algorithm's behaviors depends on a random number generator. An algorithm that solves a problem in nondeterministic polynomial time can run in polynomial time or exponential time depending on the choices it makes during execution. The nondeterministic algorithms are often used to find an approximation to a solution, when the exact solution would be too costly to obtain using a deterministic one.

Algorithmic Learning Theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and algorithmic inductive inference. Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis. Both algorithmic and statistical learning theory are concerned with machine learning and can thus be viewed as branches of computational learning theory.

Evolutionary Algorithm is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. Artificial evolution (AE) describes a process involving individual evolutionary algorithms; EAs are individual components that participate in an AE.

Memetic Algorithm referrs to in the literature as Baldwinian evolutionary algorithms (EAs), Lamarckian EAs, cultural algorithms, or genetic local search. Algorithms.

Expectation Maximization Algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

Monad Functional Programming are a way to build computer programs by joining simple components in robust ways. A monad may encapsulate values of a particular data type, creating a new type associated with a specific computation.

Precondition is a condition or predicate that must always be true just prior to the execution of some section of code or before an operation in a formal specification. If a precondition is violated, the effect of the section of code becomes undefined and thus may or may not carry out its intended work. Security problems can arise due to incorrect preconditions. Often, preconditions are simply included in the documentation of the affected section of code. Preconditions are sometimes tested using guards or assertions within the code itself, and some languages have specific syntactic constructions for doing so. For example: the factorial is only defined for integers greater than or equal to zero. So a program that calculates the factorial of an input number would have preconditions that the number be an integer and that it be greater than or equal to zero.

Algorithm Aversion (PDF)

Parallel Algorithm as opposed to a traditional serial algorithm, is an algorithm which can be executed a piece at a time on many different processing devices, and then combined together again at the end to get the correct result. Many parallel algorithms are executed concurrently – though in general concurrent algorithms are a distinct concept – and thus these concepts are often conflated, with which aspect of an algorithm is parallel and which is concurrent not being clearly distinguished. Further, non-parallel, non-concurrent algorithms are often referred to as "sequential algorithms", by contrast with concurrent algorithms.

Errors (lies)

Callback is any executable code that is passed as an argument to other code, which is expected to call back (execute) the argument at a given time. This execution may be immediate as in a synchronous callback, or it might happen at a later time as in an asynchronous callback. In all cases, the intention is to specify a function or subroutine as an entity that is, depending on the language, more or less similar to a variable. Programming languages support callbacks in different ways, often implementing them with subroutines, lambda expressions, blocks, or function pointers.

Controls (programmable controllers) - Patterns (recognition) - Programming (code)

Instance Based Learning Algorithm (PDF)

Bron-Kerbosch Algorithm is an algorithm for finding maximal cliques in an undirected graph. That is, it lists all subsets of vertices with the two properties that each pair of vertices in one of the listed subsets is connected by an edge, and no listed subset can have any additional vertices added to it while preserving its complete connectivity.

Big O Notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity.

Binary Search Algorithm is a search algorithm that finds the position of a target value within a sorted array. Binary search compares the target value to the middle element of the array; if they are unequal, the half in which the target cannot lie is eliminated and the search continues on the remaining half until it is successful or the remaining half is empty.

Algorithmic Probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction.

New Machine Learning Algorithms offer Safety and Fairness guarantees.

Statistics (math) - Algorithms, Direct Coding or Both?

Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function. Gradient descent in machine learning is simply used to find the values of a function's parameters (coefficients) that minimize a cost function as far as possible.

Computer Algebra is a scientific area that refers to the study and development of algorithms and software for manipulating mathematical expressions and other mathematical objects. Although, properly speaking, computer algebra should be a subfield of scientific computing, they are generally considered as distinct fields because scientific computing is usually based on numerical computation with approximate floating point numbers, while symbolic computation emphasizes exact computation with expressions containing variables that have no given value and are manipulated as symbols. Software applications that perform symbolic calculations are called computer algebra systems, with the term system alluding to the complexity of the main applications that include, at least, a method to represent mathematical data in a computer, a user programming language (usually different from the language used for the implementation), a dedicated memory manager, a user interface for the input/output of mathematical expressions, a large set of routines to perform usual operations, like simplification of expressions, differentiation using chain rule, polynomial factorization, indefinite integration, etc. Computer algebra is widely used to experiment in mathematics and to design the formulas that are used in numerical programs. It is also used for complete scientific computations, when purely numerical methods fail, as in public key cryptography or for some non-linear problems.

What If

What If is introducing a conditional clause or on the condition or supposition that something will happen. In the event that a possibility will happen.

Suppose is to expect something to be true. To believe in something, especially on uncertain or tentative grounds. Take for granted or as a given and expect beforehand. Supposition is a message expressing an opinion based on incomplete evidence. A hypothesis that is taken for granted.

Stipulation is an assumption on which rests the validity or effect of something else.

Condition is an assumption on which rests the validity or effect of something else. Information that should be kept in mind when making a decision. A statement of what is required as part of an agreement. Condition is also a state at a particular time.

Precondition is an assumption on which rests the validity or effect of something else. An assumption that is taken for granted.

Probability (odds) - Variables - Decision Table

Is–Ought Problem is when one makes claims about what ought to be that are based solely on statements about what is. Hume found that there seems to be a significant difference between positive or descriptive statements about what is and prescriptive or normative statements about what ought to be, and that it is not obvious how one can coherently transition from descriptive statements to prescriptive ones. Hume's law or Hume's guillotine is the thesis that an ethical or judgmental conclusion cannot be inferred from purely descriptive factual statements. AI Fears.

For Loop is a control flow statement for specifying iteration, which allows code to be executed repeatedly.

Artificial intelligence needs the "if" function, just like us. There are a lot of if's, with some if's that refer to other if's for more processing. Humorous What Ifs.

PHP or Hypertext Preprocessor is a general-purpose programming language originally designed for web development. PHP code may be executed with a command line interface (CLI), embedded into HTML code, or used in combination with various web template systems, web content management systems, and web frameworks. PHP code is usually processed by a PHP interpreter implemented as a module in a web server or as a Common Gateway Interface (CGI) executable. The web server combines the results of the interpreted and executed PHP code, which may be any type of data, including images, with the generated web page. PHP can be used for many programming tasks outside of the web context, such as standalone graphical applications and robotic drone control.

PHP if else - HP Control Structures

PHP - If Function (excel) - If Statement (excel)

PHP has the following conditional statements: if statement: executes some code only if a specified condition is True. if...else statement: executes some code if a condition is true and another code if the condition is False. if...elseif....else statement: selects one of several blocks of code to be executed. Switch statement: selects one of many blocks of code to be executed.

Logic Gates

Logic Gate is an idealized or physical device implementing a Boolean function; that is, it performs a logical operation on one or more binary inputs and produces a single binary output. Depending on the context, the term may refer to an ideal logic gate, one that has for instance zero rise time and unlimited fan-out, or it may refer to a non-ideal physical device. (see Ideal and real op-amps for comparison). Logic gates are primarily implemented using diodes or transistors acting as electronic switches, but can also be constructed using vacuum tubes, electromagnetic relays (relay logic), fluidic logic, pneumatic logic, optics, molecules, or even mechanical elements. With amplification, logic gates can be cascaded in the same way that Boolean functions can be composed, allowing the construction of a physical model of all of Boolean logic, and therefore, all of the algorithms and mathematics that can be described with Boolean logic. Logic circuits include such devices as multiplexers, registers, arithmetic logic units (ALUs), and computer memory, all the way up through complete microprocessors, which may contain more than 100 million gates. In modern practice, most gates are made from field-effect transistors (FETs), particularly metal–oxide–semiconductor field-effect transistorss (MOSFETs). Compound logic gates AND-OR-Invert (AOI) and OR-AND-Invert (OAI) are often employed in circuit design because their construction using MOSFETs is simpler and more efficient than the sum of the individual gates. In reversible logic, Toffoli gates are used. Neurons

Logic Gates (AND, OR, NOT) Basic Bolean
AND - IF (A=1 and B=1) Then OUT=1
OR - IF (A=1 or B=1) Then OUT=1
NOT - IF (A=1) Then OUT=0

AND gate is a basic digital logic gate that implements logical conjunction - it behaves according to the truth table to the right. A HIGH output (1) results only if all the inputs to the AND gate are HIGH (1). If none or not all inputs to the AND gate are HIGH, a LOW output results. The function can be extended to any number of inputs.

NAND gate is a logic gate which produces an output which is false only if all its inputs are true; thus its output is complement to that of the AND gate. A LOW (0) output results only if both the inputs to the gate are HIGH (1); if one or both inputs are LOW (0), a HIGH (1) output results. It is made using transistors and junction diodes. By De Morgan's theorem, AB=A+B, and thus a NAND gate is equivalent to inverters followed by an OR gate.

OR gate is a digital logic gate that implements logical disjunction – it behaves according to the truth table to the right. A HIGH output (1) results if one or both the inputs to the gate are HIGH (1). If neither input is high, a LOW output (0) results. In another sense, the function of OR effectively finds the maximum between two binary digits, just as the complementary AND function finds the minimum.

XOR gate is a digital logic gate that gives a true (1/HIGH) output when the number of true inputs is odd. An XOR gate implements an exclusive or; that is, a true output results if one, and only one, of the inputs to the gate is true. If both inputs are false (0/LOW) or both are true, a false output results. XOR represents the inequality function, i.e., the output is true if the inputs are not alike otherwise the output is false. A way to remember XOR is "one or the other but not both".

Inverter logic gate or NOT Gate is a logic gate which implements logical negation.

Subroutine (routines)
Code (computer programming)
Batch File (goals)
Binary (zeros and ones)
Iteration (developing ideas)
Software Design (computers)
Internet (combined intelligence)
Robots (building)

Conjunction (“and”)  -  Disjunction (“or”)  Exclusive Or  -  Negation (“not”) - Induction (deduction)

Gottfried Wilhelm Leibniz was a German polymath and philosopher (1716) who occupies a prominent place in the history of mathematics and the history of philosophy, having developed differential and integral calculus independently of Isaac Newton.

Characteristica Universalis is a universal and formal language imagined to express mathematical, scientific, and metaphysical concepts. Leibniz thus hoped to create a language usable within the framework of a universal logical calculation or calculus ratiocinator.

Calculus Ratiocinator is a theoretical universal logical calculation framework, a concept described in the writings of Gottfried Leibniz, usually paired with his more frequently mentioned characteristica universalis, a universal conceptual language.

Modulo Operation finds the remainder after division of one number by another (sometimes called modulus).

Modular Arithmetic is a system of arithmetic for integers, where numbers "wrap around" upon reaching a certain value—the modulus (plural moduli).

Mathematical Biophysics is a subfield of both biophysics and mathematical biology focusing of physical and physico-chemical mechanisms involved in physiological functions of living organisms, as well as the molecular structures supporting such physiological functions.

Our greatest intelligence now is already being formed by the Internet, which in some ways simulates the neural network of the human brain. But bringing together all our knowledge and information is only the beginning, because it will take the collective consensus of all the human brains in order for us to achieve intelligent solutions to our problems. And of course, incase of a major catastrophe, we will have to Secure our intelligence in something like the Global Seed Vault Because we would not want to start all over again as many humans civilizations had to do throughout human history. Backup our most important knowledge and information by transmitting it into space, store it in a satellite, store it on the moon and in multiple places. This we have to do. That's Intelligence.

NP-Hardness in computational complexity theory, is the defining property of a class of problems that are, informally, "at least as hard as the hardest problems in NP". A simple example of an NP-hard problem is the subset sum problem. A more precise specification is: a problem H is NP-hard when every problem L in NP can be reduced in polynomial time to H; that is, assuming a solution for H takes 1 unit time, we can use H‎'s solution to solve L in polynomial time. As a consequence, finding a polynomial algorithm to solve any NP-hard problem would give polynomial algorithms for all the problems in NP, which is unlikely as many of them are considered difficult. A common misconception is that the NP in "NP-hard" stands for "non-polynomial" when in fact it stands for "non-deterministic polynomial acceptable problems". Although it is suspected that there are no polynomial-time algorithms for NP-hard problems, this has not been proven. Moreover, the class P, in which all problems can be solved in polynomial time, is contained in the NP class.

NP is a complexity class used to classify decision problems. NP is the set of decision problems for which the problem instances, where the answer is "yes", have proofs verifiable in polynomial time.

Computational Complexity Theory

Decision Problem is a problem that can be posed as a yes-no question of the input values. An example of a decision problem is deciding whether a given natural number is prime. Another is the problem "given two numbers x and y, does x evenly divide y?". The answer is either 'yes' or 'no' depending upon the values of x and y. A method for solving a decision problem, given in the form of an algorithm, is called a decision procedure for that problem. A decision procedure for the decision problem "given two numbers x and y, does x evenly divide y?" would give the steps for determining whether x evenly divides y. One such algorithm is long division. If the remainder is zero the answer is 'yes', otherwise it is 'no'. A decision problem which can be solved by an algorithm is called decidable. Decision problems typically appear in mathematical questions of decidability, that is, the question of the existence of an effective method to determine the existence of some object or its membership in a set; some of the most important problems in mathematics are undecidable. The field of computational complexity categorizes decidable decision problems by how difficult they are to solve. "Difficult", in this sense, is described in terms of the computational resources needed by the most efficient algorithm for a certain problem. The field of recursion theory, meanwhile, categorizes undecidable decision problems by Turing degree, which is a measure of the noncomputability inherent in any solution. Decision Table.

Branch in computer science is an instruction in a computer program that can cause a computer to begin executing a different instruction sequence and thus deviate from its default behavior of executing instructions in order.[a] Branch (or branching, branched) may also refer to the act of switching execution to a different instruction sequence as a result of executing a branch instruction. Branch instructions are used to implement control flow in program loops and conditionals (i.e., executing a particular sequence of instructions only if certain conditions are satisfied). A branch instruction can be either an unconditional branch, which always results in branching, or a conditional branch, which may or may not cause branching depending on some condition. Also, depending on how it specifies the address of the new instruction sequence (the "target" address), a branch instruction is generally classified as direct, indirect or relative, meaning that the instruction contains the target address, or it specifies where the target address is to be found (e.g., a register or memory location), or it specifies the difference between the current and target addresses. Decision Tree.


Variable is something that is not consistent or having a fixed pattern. A value that is either arbitrary or not fully specified or unknown. Something that is subject to change which can make it difficult to predict.

Variation is an instance of change or the rate or magnitude of change. An activity that varies from a norm or standard.

Version is something a little different from others of the same type.

Relative - Scenarios - Configurations - Conservation

Variant is something a little different from others of the same type. Exhibiting variation and change. A variant in biology is a group of organisms within a species that differ in trivial ways from similar groups. A form or version of something that differs in some respect from other forms of the same thing or from a standard. A variable quantity that is random. An event that departs from expectations.

Invariant - Boolean Algebra - Algorithms

Variance is an event that departs from expectations. An activity that varies from a norm or standard. Variance in statistics is the second moment around the mean. The expected value of the square of the deviations of a random variable from its mean value. The quality or degree of being subject to variation. A difference between conflicting facts, claims or opinions. Discord that splits a group.

Mutation - Genetic Variations - Iteration - Unpredictable Phenomenon - Anomalies

Hidden-Variable Theory is a deterministic physical model which seeks to explain the probabilistic nature of quantum mechanics by introducing additional (possibly inaccessible) variables.

Deviation is a variation that deviates from the standard or norm. The error of a compass due to local magnetic disturbances. Deviation in statistics is the difference between an observed value and the expected value of a variable or function.

Scenarios - Combination of Circumstances - Relative - Algorithm

Configurations is an arrangement of elements in a particular form, figure, or combination. Configurations in Chemistry is the fixed three-dimensional relationship of the atoms in a molecule, defined by the bonds between them. Configurations in Computing is the arrangement or set-up of the hardware and software that make up a computer system.

Design for X is when the X is a variable which can have one of many possible values. Safety Engineering.

Latent Variable are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured). Mathematical models that aim to explain observed variables in terms of latent variables are called latent variable models. Latent variable models are used in many disciplines, including psychology, economics, engineering, medicine, physics, machine learning/artificial intelligence, bioinformatics, natural language processing, econometrics, management and the social sciences. Sometimes latent variables correspond to aspects of physical reality, which could in principle be measured, but may not be for practical reasons. In this situation, the term hidden variables is commonly used (reflecting the fact that the variables are "really there", but hidden). Other times, latent variables correspond to abstract concepts, like categories, behavioral or mental states, or data structures. The terms hypothetical variables or hypothetical constructs may be used in these situations. One advantage of using latent variables is that they can serve to reduce the dimensionality of data. A large number of observable variables can be aggregated in a model to represent an underlying concept, making it easier to understand the data. In this sense, they serve a function similar to that of scientific theories. At the same time, latent variables link observable ("sub-symbolic") data in the real world to symbolic data in the modeled world.

Stochastic event or system is one that is unpredictable due to the influence of a random variable. The word "stochastic" comes from the Greek word στόχος (stokhos, "aim"). It occurs in a wide variety of professional and academic fields.

Random Variable in probability and statistics, a random variable, random quantity, aleatory variable or stochastic variable is a variable whose value is subject to variations due to chance (i.e. randomness, in a mathematical sense). A random variable can take on a set of possible different values (similarly to other mathematical variables), each with an associated probability, in contrast to other Mathematical Variables. Random - Hidden Variables.

Deterministic System is a system in which no randomness is involved in the development of future states of the system. A deterministic model will thus always produce the same output from a given starting condition or initial state.

Internalism and externalism are two opposing ways of explaining various subjects in several areas of philosophy. These include human motivation, knowledge, justification, meaning, and truth. The distinction arises in many areas of debate with similar but distinct meanings. Usually 'internalism' refers to the belief that an explanation can be given of the given subject by pointing to things which are internal to the person or their mind which is considering them. Conversely, externalism holds that it is things about the world which motivate us, justify our beliefs, determine meaning, etc.

Psychophysical is sharing the physical and psychological qualities.

Linearization refers to finding the linear approximation to a function at a given point.

Lyapunov optimization refers to the use of a Lyapunov function to optimally control a dynamical system. Lyapunov functions are used extensively in control theory to ensure different forms of system stability. The state of a system at a particular time is often described by a multi-dimensional vector. A Lyapunov function is a nonnegative scalar measure of this multi-dimensional state. Typically, the function is defined to grow large when the system moves towards undesirable states. System stability is achieved by taking control actions that make the Lyapunov function drift in the negative direction towards zero.

Variable and Attribute in research is a characteristic of an object (person, thing, etc.). Attributes are closely related to variables. A variable is a logical set of attributes. Variables can "vary" - for example, be high or low. How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word "low" or "high").

Variable in mathematics is an alphabetic character representing a number, called the value of the variable, which is either arbitrary or not fully specified or unknown. Making algebraic computations with variables as if they were explicit numbers allows one to solve a range of problems in a single computation. A typical example is the quadratic formula, which allows one to solve every quadratic equation by simply substituting the numeric values of the coefficients of the given equation to the variables that represent them.

Differentials (math)

Derivative of a function of a real variable measures the sensitivity to change of a quantity (a function value or dependent variable) which is determined by another quantity (the independent variable). Derivatives are a fundamental tool of calculus. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time is advanced.

Variable in computer science is a storage location paired with an associated symbolic name (an identifier), which contains some known or unknown quantity of information referred to as a value. The variable name is the usual way to reference the stored value; this separation of name and content allows the name to be used independently of the exact information it represents. The identifier in computer source code can be bound to a value during run time, and the value of the variable may thus change during the course of program execution.

Variable and Attribute in research is a characteristic of an object (person, thing, etc.). Attributes are closely related to variables. A variable is a logical set of attributes. Variables can "vary" - for example, be high or low. How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word "low" or "high".

Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations.

Dynamical System is a system in which a function describes the time dependence of a point in a geometrical space. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, and the number of fish each springtime in a lake.

Dependent and independent Variables dependent variables represent the output or outcome whose variation is being studied. The independent variables represent inputs or causes, i.e. potential reasons for variation.

Regression Analysis is a statistical process for estimating the relationships among variables. Patterns.

Symbolic Regression is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is provided as a starting point to the algorithm. Instead, initial expressions are formed by randomly combining mathematical building blocks such as mathematical operators, analytic functions, constants, and state variables. (Usually, a subset of these primitives will be specified by the person operating it, but that's not a requirement of the technique.) Typically, new equations are then formed by recombining previous equations using genetic programming. By not requiring a specific model to be specified, symbolic regression isn't affected by human bias, or unknown gaps in domain knowledge. It attempts to uncover the intrinsic relationships of the dataset, by letting the patterns in the data itself reveal the appropriate models, rather than imposing a model structure that is deemed mathematically tractable from a human perspective. The fitness function that drives the evolution of the models takes into account not only error metrics (to ensure the models accurately predict the data), but also special complexity measures, thus ensuring that the resulting models reveal the data's underlying structure in a way that's understandable from a human perspective. This facilitates reasoning and favors the odds of getting insights about the data-generating system.

Variational Principle is a scientific principle used within the calculus of variations, which develops general methods for finding functions which extremize the value of quantities that depend upon those functions. For example, to answer this question: "What is the shape of a chain suspended at both ends?" we can use the variational principle that the shape must minimize the gravitational potential energy.

Condition Variable are synchronization primitives that enable threads to wait until a particular condition occurs. Condition variables are user-mode objects that cannot be shared across processes. Condition variables enable threads to atomically release a lock and enter the sleeping state.

Transient State is when a process variable or variables have been changed and the system has not yet reached a steady state. The time taken for the circuit to change from one steady state to another steady state is called the transient time. Transient analysis KVL and KCL to circuits containing energy storage elements results in differential. A transient process is a process in which process variables change over time. Transient analysis L and study to circuits containing energy storage elements results in differential. study of transient processes. Baseline.


Scenario is one of many known sequence of possible events.

Real Life Examples - Prepared for Emergencies - Seeing the Whole Picture - Planning - Cause and Effect - Relative - Variables - Patterns - Configurations - Anomalies

Worst-Case Scenario is a concept in risk management wherein the planner, in planning for potential disasters, considers the most severe possible outcome that can reasonably be projected to occur in a given situation. Conceiving of worst-case scenarios is a common form of strategic planning, specifically scenario planning, to prepare for and minimize contingencies that could result in accidents, quality problems, or other issues.

Scenario Planning is a strategic planning method that some organizations use to make flexible long-term plans. Part adaptation and generalization of classic methods.

Minimax is a decision rule used in artificial intelligence, decision theory, game theory, statistics and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario. When dealing with gains, it is referred to as "maximin"—to maximize the minimum gain. Originally formulated for two-player zero-sum game theory, covering both the cases where players take alternate moves and those where they make simultaneous moves, it has also been extended to more complex games and to general decision-making in the presence of uncertainty.

Conditional Probability is a measure of the probability of an event (some particular situation occurring) given that (by assumption, presumption, assertion or evidence) another event has occurred. If the event of interest is A and the event B is known or assumed to have occurred, "the conditional probability of A given B", or "the probability of A under the condition B", is usually written as P(A|B), or sometimes PB(A) or P(A/B). For example, the probability that any given person has a cough on any given day may be only 5%. But if we know or assume that the person has a cold, then they are much more likely to be coughing. The conditional probability of coughing given that you have a cold might be a much higher 75%.

Interactive Object-Based Media can help people understand different scenarios by giving people different options and other choices to choose from so they can see which decision was better.

How many questions deep do you need to go? How many levels? You can't prepare for everything, so how do you decide?

Formulating - Safety Engineering - Emerging Technologies

Scenario Mapping is a group exercise that helps your design team think about how your persona segments might approach an activity using your product or service and ideate around the type of experience you want to provide for them, producing candidate solutions for the future design.

Exception Handling is the process of responding to the occurrence, during computation, of exceptions – anomalous or exceptional conditions requiring special processing – often changing the normal flow of program execution. It is provided by specialized programming language constructs or computer hardware mechanisms. Statistics.

Event Chain Methodology is an uncertainty modeling and schedule network analysis technique that is focused on identifying and managing events and event chains that affect project schedules. Event chain methodology is the next advance beyond critical path method and critical chain project management. Event chain methodology helps to mitigate the effect of motivational and cognitive biases in estimating and scheduling.

Preference Based Planning is a form of automated planning and scheduling which focuses on producing plans that additionally satisfy as many user-specified preferences as possible. In many problem domains, a task can be accomplished by various sequences of actions (also known as plans). These plans can vary in quality: there can be many ways to solve a problem but one generally prefers a way that is, e.g., cost-effective, quick and safe.

Regression Analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Reasoning - Problem Solving - Quality Control

AI accelerates problem-solving in complex scenarios. Researchers have developed a new, data-driven machine-learning technique that speeds up software programs used to solve complex optimization problems that can have millions of potential solutions. Their approach could be applied to many complex logistical challenges, such as package routing, vaccine distribution, and power grid management. This software, called a mixed-integer linear programming solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours -- or even days -- to arrive at a solution.

Patterns - Trends

Pattern are elements that repeat in a predictable manner. A perceptual structure that can be observed and measured. A pattern is a discernible regularity in the world or in a manmade design that repeats in a predictable manner. A geometric pattern is a kind of pattern formed of geometric shapes and typically repeating like a wallpaper. Any of the senses may directly observe patterns. Conversely, abstract patterns in science, mathematics, or language may be observable only by analysis. Direct observation in practice means seeing visual patterns, which are widespread in nature and in art. Visual patterns in nature are often chaotic, never exactly repeating, and often involve fractals. Natural patterns include spirals, meanders, waves, foams, tilings, cracks, and those created by symmetries of rotation and reflection. Patterns have an underlying mathematical structure; indeed, mathematics can be seen as the search for regularities, and the output of any function is a mathematical pattern. Similarly in the sciences, theories explain and predict regularities in the world. In art and architecture, decorations or visual motifs may be combined and repeated to form patterns designed to have a chosen effect on the viewer. In computer science, a software design pattern is a known solution to a class of problems in programming. In fashion, the pattern is a template used to create any number of similar garments.

Pattern Recognition is the process of recognizing patterns by using machine learning algorithm or the automated recognition of patterns and regularities in data. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. Pattern Recognition and inductive thinking is a special ability of the human brain to not only find patterns but figure out in a logical way what those patterns suggest about what will happen next.

IQ Test measures pattern recognition ability. Chess is a good game to practice pattern recognition. Learning by Association.

Apophenia is the tendency to perceive meaningful connections between unrelated things, or the unmotivated seeing of connections accompanied by a feeling of wonder and the need for meaning. But sometimes people can use pattern recognition in the wrong way and make assumptions or unreasonably seek patterns in random information that could make a person to see patterns that are not really there or see patterns that are irrelevant.

Trypophobia is the irrational fear of irregular patterns or clusters of small holes or bumps.

How our brains know when something's different. Scientists discovered how a set of high frequency brain waves may help us unconsciously know when something's different by comparing memories of the past with present experiences. Almost every experience we store into memory can be used to set our expectations and predictions for the future. Predictive coding basically states that the brain optimizes neural activity for processing information. The brain uses more neural activity to process new information than it does for things that we are familiar with. It takes just one moment to not only remember a new experience but also to use memories of that experience to set future expectations.

New research describes a new model for how the brain interprets patterns in complex networks. They found that the ability to detect patterns stems in part from the brain's desire to represent things in the simplest way possible and that the brain is constantly weighing the pressures of complexity and simplicity when making decisions. The human brain is a highly advanced information processor composed of more than 86 billion neurons. Humans are adept at recognizing patterns from complex networks, such as languages, without any formal instruction. Previously, cognitive scientists tried to explain this ability by depicting the brain as a highly optimized computer, but there is now discussion among neuroscientists that this model might not accurately reflect how the brain works.

Time Based Patterns - Cycles - Sequences - Signals - Language - Symbols - Code

Symmetry - Rhythm - Variables - Reference Pattern - Deciphering Code - Math - Spatial Intelligence

Uncovering nature's patterns at the atomic scale in living color. A research team invents new method to accelerate materials discoveries through rapid clustering and color coding of large X-ray data sets to reveal previously hidden structural changes that occur as temperature increases or decreases. Color coding makes aerial maps much more easily understood. Through color, we can tell at a glance where there is a road, forest, desert, city, river or lake.

Central Pattern Generators are biological neural circuits that produce rhythmic outputs in the absence of rhythmic input. They are the source of the tightly-coupled patterns of neural activity that drive rhythmic and stereotyped motor behaviors like walking, swimming, breathing, or chewing. The ability to function without input from higher brain areas still requires modulatory inputs, and their outputs are not fixed. Flexibility in response to sensory input is a fundamental quality of CPG-driven behavior. To be classified as a rhythmic generator, a CPG requires: "two or more processes that interact such that each process sequentially increases and decreases, and that, as a result of this interaction, the system repeatedly returns to its starting condition." CPGs have been found in invertebrates, and practically all vertebrate species investigated, including humans.

Trend is a general direction in which something tends to move. 

Trend Estimation is a statistical technique to aid interpretation of data. When a series of measurements of a process are treated as a time series, trend estimation can be used to make and justify statements about tendencies in the data, by relating the measurements to the times at which they occurred.

Trend Analysis is the widespread practice of collecting information and attempting to spot a pattern. In project management, trend analysis is a mathematical technique that uses historical results to predict future outcome. In statistics, trend analysis often refers to techniques for extracting an underlying pattern of behavior in a time series which would otherwise be partly or nearly completely hidden by noise. If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis, as described in Trend estimation. If the trends have other shapes than linear, trend testing can be done by non-parametric methods, e.g. Mann-Kendall test, which is a version of Kendall rank correlation coefficient. For testing and visualization of nonlinear trends also smoothing can be used.

Arithmetic Progression is a sequence of numbers such that the difference between the consecutive terms is constant.

Pattern Matching the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: "either it will or will not be a match." The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence (i.e., search and replace). Sequence patterns (e.g., a text string) are often described using regular expressions and matched using techniques such as backtracking. Tree patterns are used in some programming languages as a general tool to process data based on its structure, e.g., Haskell, ML, Scala and the symbolic mathematics language Mathematica have special syntax for expressing tree patterns and a language construct for conditional execution and value retrieval based on it. For simplicity and efficiency reasons, these tree patterns lack some features that are available in regular expressions. Often it is possible to give alternative patterns that are tried one by one, which yields a powerful conditional programming construct. Pattern matching sometimes includes support for guards. Term rewriting and graph rewriting languages rely on pattern matching for the fundamental way a program evaluates into a result.

Pattern Separation is defined as the process by which overlapping or similar inputs or representations are transformed into less similar outputs whereas pattern completion is the reconstruction of complete stored representations from partial inputs that are part of the stored representation. Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Theoretical work suggests that the dentate gyrus performs this role for memory processing but a direct demonstration is lacking.

Pattern Separation in the hippocampus enables memories to be stored by separate groups of neurons, so that memories don't get mixed up, neuronal representation, devoid of specific contextual details. When recording the activity of individual neurons, there is an alternative model to pattern separation storing our memories. Pattern separation is a basic principle of neuronal coding that precludes memory interference in the hippocampus .

Realistic model of mouse hippocampus uncovers new mechanism for pattern separation. Our brains can distinguish highly similar patterns, thanks to a process called pattern separation. How exactly our brains separate patterns is, however, not fully understood yet. Using a full-scale computer model of the dentate gyrus, a brain region involved in pattern separation, researchers found that inhibitory neurons activated by one pattern suppress all their neighboring neurons, thereby switching off 'competing' similar patterns.

Computers can read zero's and ones, which means they can be taught to look for patterns. And when these patterns are labeled correctly and accurately, a computer can identify things in the world pretty much the same way as humans do.

Software Design Pattern is a reusable solution to a commonly occurring problem within a given context in software design. A template for how to solve a problem that can be used in many different situations. Object-oriented design patterns typically show relationships and interactions between classes or objects, without specifying the final application classes or objects that are involved.

Command Pattern is a behavioral design pattern in which an object is used to encapsulate all information needed to perform an action or trigger an event at a later time. This information includes the method name, the object that owns the method and values for the method parameters. Grid.

Deep Learning is great for finding Trends and Patterns in Data. But if you don't use this information to benefit society, then we will continue to suffer, as we are now.

Sensors - Time

"Computers will help us make better Predictions, Ai will also help us make better Decisions, but Humans still have to steer."

Data Dredging is the use of data mining to uncover patterns in data that can be presented as statistically significant, without first devising a specific hypothesis as to the underlying causality. (also known as data fishing, data snooping, and p-hacking).

Linear Discriminant Analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

Facial Recognition System is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a face database. Body Language.

Composite Entity Pattern is used to model, represent, and manage a set of interrelated persistent objects rather than representing them as individual fine-grained entity beans, and also a composite entity bean represents a graph of objects.

Statistics (math)

How patterns in nature arise and inspire everything from scientific theory to biodegradable materials. Nature is full of patterns. Among them are tiling patterns, which mimic what you'd see on a tiled bathroom floor, characterized by both tiles and interfaces -- such as grout -- in between. In nature, a giraffe's coloring is an example of a tiling pattern. But what makes these natural patterns form?

Cycle Detection is the algorithmic problem of finding a cycle in a sequence of iterated function values.

Vibrations (hz)

Profiling algorithms or mathematical techniques allow the discovery of patterns or correlations in large quantities of data.

What happens in the sensory cortex when learning and recognizing patterns. Learning to recognize a specific pattern or sequence, and process the ensuing actions and outcomes, involves neurons across the whole cortex. An animal does not, it seems, sense the world separately from what it needs to feel in order to guide behavior.

Predictive Coding is a theory of brain function in which the brain is constantly generating and updating a mental model of the environment. The model is used to generate predictions of sensory input that are compared to actual sensory input. This comparison results in prediction errors that are then used to update and revise the mental model.

Sensors - Detectors

Sensor is an object whose purpose is to detect certain events or detect certain changes or signals in its environment, and then sends the information to a computer, which then tells the actuator or the output devices to provide the corresponding output. A sensor is a device that converts real world data or analog into data that a computer can understand using ADC or analog to digital converter. All living organisms contain biological sensors with functions similar to those of the mechanical devices described. Most of these are specialized cells that are sensitive to: Light, motion, temperature, magnetic fields, gravity, humidity, moisture, vibration, pressure, electrical fields, sound, and other physical aspects of the external environment. Physical aspects of the internal environment, such as stretch, motion of the organism, and position of appendages (proprioception). Estimation of biomolecules interaction and some kinetics parameters. Internal metabolic indicators, such as glucose level, oxygen level, or osmolality. Internal signal molecules, such as hormones, neurotransmitters, and cytokines. Differences between proteins of the organism itself and of the environment or alien creatures.

Medical Sensors - Soil Sensors - Image Sensors - Hearing Sensors - Taste Sensors - Smell Sensors - Motion Sensors - DIY Science Kits - Machine Learning

Sensor Grid integrates wireless sensor networks with grid computing concepts to enable real-time sensor data collection and the sharing of computational and storage resources for sensor data processing and management. It is an enabling technology for building large-scale infrastructures, integrating heterogeneous sensor, data and computational resources deployed over a wide area, to undertake complicated surveillance tasks such as environmental monitoring.

Polymetric involves using more than one meter. - Sensor Array - Touch Bionics.

Sensor Fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints). The data sources for a fusion process are not specified to originate from identical sensors. One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input. Sensor fusion is also known as (multi-sensor). Data Fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Information Fusion is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations. Filtering - Cross Talk.

Wireless Sensor Network are spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to other locations.

Sensor Web is a type of sensor network that is especially well suited for environmental monitoring.

Next generation of Networked Smart Devices can communicate directly with one another without human intervention. It needs only a very small amount of power to maintain this constant listening and always be on the alert, so it still saves energy overall while extending the battery life of the larger device. A well-designed wake-up receiver also allows the device to be turned on from a significant distance. A sleeping device can still suck the life out of a battery. A wake-up receiver that turns on a device in response to incoming ultrasonic signals -- signals outside the range that humans can hear. By working at a significantly smaller wavelength and switching from radio waves to ultrasound, this receiver is much smaller than similar wake-up receivers that respond to radio signals, while operating at extremely low power and with extended range.

Enabling 'Internet of Photonic Things' with Miniature Sensors. Swapping electrons for photons, researchers have developed wireless sensors which are not subject to electromagnetic interference and are smaller and generally more flexible than the currently electronics-based technology. Records environmental data using a wireless photonic sensor resonator with a whispering-gallery-mode (WGM) architecture. Whispering-Gallery Wave are a type of wave that can travel around a concave surface. they can exist for light and for other waves, with important applications in nondestructive testing, lasing, cooling and sensing, as well as in astronomy.

Batteryless smart devices closer to reality. Researchers have taken a huge step towards making smart devices that do not use batteries or require charging. An RFID tag is modified by cutting out a small part its antenna (silver ribbon) and placing a small light-sensing phototransistor or temperature-responsive resistor (thermistor) on it.

RFID stands for Radio Frequency Identification, which uses electromagnetic fields to automatically identify and track tags attached to objects. The tags contain electronically-stored information. Passive tags collect energy from a nearby RFID reader's interrogating radio waves. Telemetry - Bar Codes.

MIT Engineers configure RFID Tags to work as Sensors. Platform may enable continuous, low-cost, reliable devices that detect chemicals in the environment.

Sensor Fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually.

Smart machine components alert users to damage and wear. Technology to create polymer-bonded magnets with intricate geometries and arbitrary shapes, opening up new possibilities for manufacturing and product design.

Quantum Sensors. Researchers have created a chip on which laser light interacts with a tiny cloud of atoms to serve as a miniature toolkit for measuring important quantities such as length with quantum precision. The design could be mass-produced with existing technology.

Quantum Sensor is a device that exploits quantum correlations, such as quantum entanglement, to achieve a sensitivity or resolution that is better than can be achieved using only classical systems. A quantum sensor can measure the effect of the quantum state of another system on itself. The mere act of measurement influences the quantum state and alters the probability and uncertainty associated with its state during measurement. The Defense Advanced Research Projects Agency has recently launched a research program in optical quantum sensors that seeks to exploit ideas from quantum metrology and quantum imaging, such as quantum lithography and the NOON state, in order to achieve these goals with optical sensor systems such as Lidar. Quantum sensor is also a term used in other settings where entangled quantum systems are exploited to make better atomic clocks or more sensitive magnetometers. A good example of an early quantum sensor is an APD avalanche photodiode such as the AD500-8 TO52S1 as these have been used to detect entangled photons and in fact with additional cooling and sensor improvements can be used where PMTs once ruled the market such as medical imaging. These are also now being used by First Sensor in the form of 2-D and even 3-D stacked arrays as a direct replacement for conventional sensors based on silicon diodes.

Wearable Sensors - Flexible Electronics - Bio-Monitoring - Health Monitors - Smart Homes

Scientists Invent Threads to Detect Gases when Woven into Clothing. A novel fabrication method to create dyed threads that change color when they detect a variety of gases. Woven into clothing, smart, gas-detecting threads could provide a reusable, washable, and affordable safety asset in medical, workplace, military and rescue environments. The study describes the fabrication method and its ability to extend to a wide range of dyes and detection of complex gas mixtures.

Electric Cloth: Flexible, wearable supercapacitors based on porous nanocarbon nanocomposites.

Tissue Paper Sensors show promise for health care, entertainment, Robotics - Bionics

Wearable Ultrasound Patch Monitors Blood Pressure Deep Inside Body that non-invasively monitors blood pressure in arteries deep beneath the skin that could help people detect cardiovascular problems earlier on and with greater precision. In tests, the patch performed as well as some clinical methods to measure blood pressure.

Simple Stickers that are Wearable Medical Electronic Devices that someone can easily attach to their skin that could save lives of patients, athletes and lower medical costs. The devices are made out of paper to lower the cost of personalized medicine. Telemedicine (doctor house calls over the internet).

Stanford engineers have developed wireless sensors that stick to the skin to track our health. Stanford engineers have developed experimental stickers that pick up physiological signals emanating from the skin, then wirelessly beam these health readings to a receiver clipped onto clothing. It’s all part of a system called BodyNet.

Wearable sensors detect what's in your sweat. New easy-to-make sensors can provide real-time measurements of sweat rate and electrolytes and metabolites in perspiration.

Researchers have developed an implantable, biofuel-powered sensor that runs on sugar and can monitor a body's biological signals to detect, prevent and diagnose diseases.

Implantable biosensor operates without batteries. Researchers have revealed their new biodegradable motion sensor -- paving the way for implanted nanotechnology that could help future sports professionals better monitor their movements to aid rapid improvements, or help caregivers remotely monitor people living with dementia. The ATI's technology builds on its previous work around triboelectric nanogenerators (TENG), where researchers used the technology to harness human movements and generate small amounts of electrical energy. Combining the two means self-powered sensors are possible without the need for chemical or wired power sources.

A Self-Powered Heart Monitor taped to the Skin called an organic electrochemical transistor, is a human-friendly, ultra-flexible organic sensor powered by sunlight. A type of electronic device that can be used to measure a variety of biological functions.

Materials scientist creates fabric alternative to batteries for wearable devices. A method for making a charge-storing system that is easily integrated into clothing for 'embroidering a charge-storing pattern onto any garment.' uses a micro-supercapacitor and combines vapor-coated conductive threads with a polymer film, plus a special sewing technique to create a flexible mesh of aligned electrodes on a textile backing. The resulting solid-state device has a high ability to store charge for its size, and other characteristics that allow it to power wearable biosensors.

Sensors that are literally ‘music to one’s ears’. Researchers have found a new use for a 3,000-year-old African musical instrument: detecting toxic substances and counterfeit medications. The sensor, based on the mbira (pronounced 'em-bir'-uh') is inexpensive and easy to operate, allowing its use in developing regions, the researchers say.

Scientists develop new tool for imprinting biochips. New technology could allow researchers to fit more biochemical probes onto a single biochip and reduce the cost of screening and analyzing changes biochips (also known as microarrays), which are used to screen for and analyze biological changes associated with disease development, biothreat agents, pollution, toxins and other areas of research that involve biological components.

A nanotech sensor that turns molecular fingerprints into bar codes. A new system can detect and analyze molecules without the need for an infrared spectrometer. The system uses nanostructured metapixels to detect and then translate molecules' unique signatures into bar codes. The technology can be integrated into a compact sensor chip. It opens the door to large-scale image-based detection of materials using machine-learning technology.

Route to Flexible Electronics made from Exotic Materials. Cost-effective method produces semiconducting films from materials that outperform silicon. researchers fabricated flexible films made from gallium arsenide, gallium nitride, and lithium fluoride -- materials that exhibit better performance than silicon but until now have been prohibitively expensive to produce in functional devices. MIT researchers have devised a way to grow single crystal GaN thin film on a GaN substrate through two-dimensional materials. The GaN thin film is then exfoliated by a flexible substrate, showing the rainbow color that comes from thin film interference. This technology will pave the way to flexible electronics and the reuse of the wafers.

Molecular Probe is a group of atoms or molecules used in molecular biology or chemistry to study the properties of other molecules or structures. If some measurable property of the molecular probe used changes when it interacts with the analyte (such as a change in absorbance), the interactions between the probe and the analyte can be studied. This makes it possible to indirectly study the properties of compounds and structures which may be hard to study directly. The choice of molecular probe will depend on which compound or structure is being studied as well as on what property is of interest. Radioactive DNA or RNA sequences are used in molecular genetics to detect the presence of a complementary sequence by molecular hybridization.

Biochip are essentially miniaturized laboratories that can perform hundreds or thousands of simultaneous biochemical reactions. Biochips enable researchers to quickly screen large numbers of biological analytes for a variety of purposes, from disease diagnosis to detection of bioterrorism agents. Digital microfluidic biochips have become one of the most promising technologies in many biomedical fields. In a digital microfluidic biochip, a group of (adjacent) cells in the microfluidic array can be configured to work as storage, functional operations, as well as for transporting fluid droplets dynamically.

Plasmonic Nanoantenna Arrays could lead to the development of a new generation of ultrasensitive and low-cost fluorescence sensors that could be used to monitor water quality.

Ultra-sensitive sensor with gold nanoparticle array. Scientists have developed a new type of sensor platform using a gold nanoparticle array, which is 100 times more sensitive than current similar sensors for air pollution or for medical diagnostics. The sensor is made up of a series of gold disk-shaped nanoparticles on a glass slide.

UW team shatters long-range communication barrier for devices that consume almost no power. Sensor allows devices that run on extremely low power for the first time to communicate over long distances.

Force-Sensing Resistor is a material whose resistance changes when a force or pressure is applied.

New air-pressure sensor could improve everyday devices. Micro-electro-mechanical system - micro-switch.

Engineers Create Artificial Skin That "Feels" Temperature Changes.

Hydrogel-based scaffolds could be used for better brain-computer interfaces. A soft, stimulating scaffold supports brain cell development ex vivo.

Researchers demonstrate new sensors by creating novel health monitoring, machine interface devices. Researchers have developed a stretchable strain sensor that has an unprecedented combination of sensitivity and range, allowing it to detect even minor changes in strain with greater range of motion than previous technologies. The researchers demonstrated the sensor's utility by creating new health monitoring and human-machine interface devices.

Paving the way for sensor interfaces that are 30 times smaller. Researchers have invented a novel class of Digital-to-Analog (DAC) and Analog-to-Digital Converters (ADC) that can be entirely designed with a fully-automated digital design methodology.

Human Senses - Materials Science (strength limits)

"Sensors could make people vulnerable if people don't understand the function or the process of sensors. If people don't know how to sense the world without using artificial sensors, then people will be vulnerable to failure when the sensors are not available or not working correctly."

New Malleable 'Electronic Skin' Self-Healable and Recyclable. Electronic skin, known as e-skin, is a thin, translucent material that can mimic the function and mechanical properties of human skin that can measure pressure, temperature, humidity and air flow.

Artificial 'skin' gives robotic hand a sense of touch. UH researchers discover new form of stretchable electronics, sensors and skins. Bionics.

The sensitive strain sensor that can detect the weight of a feather.

Repetition key to Self-Healing, Flexible Medical Devices. Medical devices powered by synthetic proteins created from repeated sequences of proteins may be possible, according to materials science and biotechnology experts, who looked at material inspired by the proteins in squid ring teeth.

Biosensor is an analytical device, used for the detection of an analyte, that combines a biological component with a physicochemical detector. The sensitive biological element (e.g. tissue, microorganisms, organelles, cell receptors, enzymes, antibodies, nucleic acids, etc.) is a biologically derived material or biomimetic component that interacts (binds or recognizes) with the analyte under study. Miniature Technology, Big Hope for Disease Detection

Biosensors will be inexpensive, do more, go everywhere.

Food Sensors (hand held) - Sensors for Measuring Soil Moisture

Compact Fiber Optic Sensor offers sensitive analysis in narrow spaces. Compact sensor would be useful for biomedical, chemical and food safety applications. Researchers have developed a new flexible sensor with high sensitivity that is designed to perform variety of chemical and biological analyses in very small spaces.

Chemical Sensor is a self-contained analytical device that can provide information about the chemical composition of its environment, that is, a liquid or a gas phase. The information is provided in the form of a measurable physical signal that is correlated with the concentration of a certain chemical species (termed as analyte).

Chemical Sensors (PDF)

Nanosensor are any biological, chemical, or surgical sensory points used to convey information about nanoparticles to the macroscopic world.

Synthetic Sensors: Towards General-Purpose Sensing (youtube)

Synthesizing Chemical-Sensing Cells from scratch. Scientists create artificial cells that can express distinct genes in response to specific chemical signals. The scientists chose histamine as the chemical signal for their artificial cells because it is an important biological compound in the immune system. If you feel an itch, histamine is the likely culprit. It is also released by the body during allergic reactions and helps defend against foreign pathogens by spurring inflammation. To detect histamine, they created a molecule called an RNA aptamer. RNA aptamers are small segments of RNA building blocks that can be engineered to act as binding agents to specific target molecules. Next, the team developed a so-called riboswitch that would turn this signal detection into action -- specifically, translating a gene to produce a protein. Normally, cells produce proteins when templates made of messenger RNA (mRNA) bind to cellular structures called ribosomes. Here, the scientists used the histamine aptamer to design a riboswitch that alters the shape of the mRNA upon binding histamine. In the absence of histamine, the shape of the mRNA prevents the ribosome from binding, and no protein is produced. Histamine-bound mRNA, however, allows ribosome to bind and synthesize proteins.

Wearable, Low-Cost Sensor to Measure Skin Hydration.

Metal Printing Offers Low-Cost Way to Make Flexible, Stretchable Electronics.

How a $10 Microchip Turns 2-D Ultrasound Machines to 3-D Imaging Devices (youtube)

Chip-Based Sensors with incredible sensitivity used for motion, temperature, pressure or biochemical sensing. sensor consists of solid spheres.

Swallowable Sensors reveal mysteries of Human Gut Health.

Smart Homes - Smartphone Accessories - Medical Sensors

Sounds of action: Using ears, not just eyes, improves robot perception. People rarely use just one sense to understand the world, but robots usually only rely on vision and, increasingly, touch. Researchers find that robot perception could improve markedly by adding another sense: hearing.

Vision Sensors - Image Recognition - Computer Sensors that See

Computer Vision deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. The classical problem in computer vision, image processing, and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity. Different varieties of the recognition problem are described in the literature. Facial Recognition - Text to Image.

Vision Science is the scientific study of visual perception. Researchers in vision science can be called vision scientists, especially if their research spans some of the science's many disciplines. Vision science encompasses all studies of vision, such as how human and non-human organisms process visual information, how conscious visual perception works in humans, how to exploit visual perception for effective communication, and how artificial systems can do the same tasks.

Reading Speeds - Frames Per Second - Gaze Detection - Eye Tracking

IBM believes computers will be able to identify images and understand what they mean without the use of tags. This will lead to systems that can help doctors analyze X-ray pictures, magnetic resonance imaging (MRI) machine, ultrasound or computerized tomography scans. 

Activity Recognition (facial recognition) - Optical Character Recognition

How computers learn to recognize objects instantly (video and interactive text)

Build a TensorFlow Image Classifier in 5 Min (youtube)

AI proved superior in assessing and diagnosing cardiac function when compared with echocardiogram assessments made by sonographers. Echocardiogram uses sound waves to create pictures of the heart. It is a type of medical imaging of the heart, using standard ultrasound or Doppler ultrasound. This common test can show blood flow through the heart and heart valves. Your health care provider can use the pictures from the test to find heart disease and other heart conditions.

Hybrid AI-powered computer vision combines physics and big data. Researchers have laid out a new approach to enhance artificial intelligence-powered computer vision technologies by adding physics-based awareness to data-driven techniques. The study offered an overview of a hybrid methodology designed to improve how AI-based machinery sense, interact and respond to its environment in real time -- as in how autonomous vehicles move and maneuver, or how robots use the improved technology to carry out precision actions.

Detecting threats beyond the limits of human, sensor sight. New software system finds, tracks moving objects as small as a pixel. A new patented software system can find the curves of motion in streaming video and images from satellites, drones and far-range security cameras and turn them into signals to find and track moving objects as small as one pixel. The developers say this system can enhance the performance of any remote sensing application.

New technique to overcome obstructed views. An algorithm can be used to see around obstacles and, eventually, help save lives. Using a single photograph, researchers created an algorithm that computes highly accurate, full-color three-dimensional reconstructions of areas behind obstacles -- a concept that can not only help prevent car crashes, but help law enforcement experts in hostage situations, search-and-rescue and strategic military efforts.

Image Sensor is a sensor that detects and conveys the information that constitutes an image. It does so by converting the variable attenuation of light waves (as they pass through or reflect off objects) into signals, small bursts of current that convey the information. The waves can be light or other electromagnetic radiation. Image sensors are used in electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, medical imaging equipment, night vision equipment such as thermal imaging devices, radar, sonar, and others. As technology changes, digital imaging tends to replace analog imaging. Charge-Coupled Device or CCD is a device for the movement of electrical charge, usually from within the device to an area where the charge can be manipulated, for example conversion into a digital value. This is achieved by "shifting" the signals between stages within the device one at a time. CCDs move charge between capacitive bins in the device, with the shift allowing for the transfer of charge between bins.

Visual Search Engine is a search engine designed to search for information on the World Wide Web through the input of an image or a search engine with a visual display of the search results. Information may consist of web pages, locations, other images and other types of documents. This type of search engines is mostly used to search on the mobile Internet through an image of an unknown object (unknown search query). Examples are buildings in a foreign city. These search engines often use techniques for Content Based Image Retrieval.

Imagenet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Currently we have an average of over five hundred images per node.

Tensorflow Image Recognition QuocNet, AlexNet, Inception (GoogLeNet), BN-Inception-v2. Tensor Flow Open Source Software Library for Machine Intelligence. Machine Learning.

Vicarious is developing machine learning software based on the computational principles of the human brain. Known as the Recursive Cortical Network (RCN), it is a visual perception system that interprets the contents of photographs and videos in a manner similar to humans. Recaptcha (google) - CAPTCHA

Object Recognition technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades.

PASCAL - Pattern Analysis, Statistical Modeling and Computational Learning. - VOC - Visual Object Classes.

More capable computer vision systems, innovations in depth from focus/defocus pave the way. In an image, estimating the distance between objects and the camera by using the blur in the images as clue, also known as depth from focus/defocus, is essential in computer vision. However, model-based methods fail when texture-less surfaces are present, and learning-based methods require the same camera settings during training and testing. Now, researchers have come up with an innovative strategy for depth estimation that combines the best of both the worlds to solve these limitations, extending the applicability of depth from focus/defocus.

Top-1 error is the proportion of the time the classifier does not provide the highest score to the correct class. Top-5 error rate is the percentage of times the classifier failed to include the proper class among its top five guesses.

Machine Learning now can outperform dermatologists at recognizing skin cancers in blemish photos. They can beat cardiologists in detecting arrhythmias in EKGs.

Visual Search Engine App - Gif

Arxiv Full Resolution Image Compression with Recurrent Neural Networks.

QIS is the next generation of image sensors where high-speed single-photon detection is used to unlock new image capture capabilities for consumers and professionals not possible with today’s devices. "Jot" is the specialized pixel that is sensitive enough to detect a single photon of light. Revolutionary detection technologies are developed to enable accurate photon-counting at room temperature without the use of electron avalanche multiplication.

Orientation in computer vision and image processing is when a sufficiently small image regions can be characterized as locally one-dimensional, e.g., in terms of lines or edges. For natural images this assumption is usually correct except at specific points, e.g., corners or line junctions or crossings, or in regions of high frequency textures. However, what size the regions have to be in order to appear as one-dimensional varies both between images and within an image. Also, in practice a local region is never exactly one-dimensional but can be so to a sufficient degree of approximation.

Dynamic Projection Mapping onto Deforming non-rigid Surface at 1,000 fps with 3 ms delay (youtube)

High-Speed Projector DynaFlash

Researchers teach computers to see Optical Illusions. By making a neural-network computer model that can be fooled by optical illusions like humans, the researchers advanced knowledge of the human visual system and may help improve artificial vision.

Neuroscientists look deep into the eyes of the octopus. By studying the visual system of an octopus, researchers hope to understand how its brain organization compares to that of humans and other vertebrates. Their results could provide insight into the evolution of visual systems across species. The last common ancestor between octopuses and humans was 500 million years ago, and the species have since evolved in very different contexts. So scientists didn't know whether the parallels in visual systems extended beyond the eyes, or whether the octopus was instead using completely different kinds of neurons and brain circuits to achieve similar results. About a third of the neurons in the data didn't quite look fully developed. The octopus brain keeps growing and adding new neurons over the animal's lifespan. These immature neurons, not yet integrated into brain circuits, were a sign of the brain in the process of expanding. There are a lot of genes where we have no idea what their function is, because we haven't sequenced the genomes of a lot of cephalopods.

Projection Mapping is a projection technology used to turn objects, often irregularly shaped, into a display surface for video projection. These objects may be complex industrial landscapes, such as buildings, small indoor objects or theatrical stages. By using specialized software, a two- or three-dimensional object is spatially mapped on the virtual program which mimics the real environment it is to be projected on. The software can interact with a projector to fit any desired image onto the surface of that object. This technique is used by artists and advertisers alike who can add extra dimensions, optical illusions, and notions of movement onto previously static objects. The video is commonly combined with, or triggered by, audio to create an audio-visual narrative.

Machine Vision is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision is a term encompassing a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is also used in a broader sense by trade shows and trade groups; this broader definition also encompasses products and applications most often associated with image processing.

Improving image sensors for machine vision. On-chip spectrometer, silicon nanowires determine light spectrum, angle. Researchers highlight the latest nanostructured components integrated on image sensor chips that are most likely to make the biggest impact in multimodal imaging and detailed a promising approach to detect multiple-band spectra by fabricating an on-chip spectrometer. The developments could enable autonomous vehicles to see around corners instead of just a straight line, biomedical imaging to detect abnormalities at different tissue depths, and telescopes to see through interstellar dust.

A simpler path to better computer vision. Research finds using a large collection of simple, un-curated synthetic image generation programs to pretrain a computer vision model for image classification yields greater accuracy than employing other pretraining methods that are more costly and time consuming, and less scalable. Before a machine-learning model can complete a task, such as identifying cancer in medical images, the model must be trained. Training image classification models typically involves showing the model millions of example images gathered into a massive dataset.

Seeing the Light: Researchers develop new AI system using light to learn associatively. Researchers have developed an on-chip optical processor capable of detecting similarities in datasets up to 1,000 times faster than conventional machine learning algorithms running on electronic processors.

LIDAR-Sensing using Light. - Self Driving Cars - Navigation.

Acoustic Non-Line-of-Sight Imaging is a novel approach to seeing around corners using acoustic echoes. A system of speakers emits sound waves which scatter from a wall to a hidden object and back.

New Depth Sensors could make self-driving cars practical Computational method improves resolution of time-of-flight depth sensors 1,000-fold.

New window system allows for long-term studies of brain activity. A researcher has developed a tiny window that allows investigators to get clearer, long-term imaging of the brain's visual network. This could help researchers understand if neural activity "traffic jams" underlie all kinds of visual impairments, from running a red light when visual attention is elsewhere, to shedding light on the autism-affected brain.

A marriage of Light-Manipulation Technologies, researchers have built a metasurface-based lens atop a Micro-Electro-Mechanical System (MEMS) platform. The result is a new, infrared light-focusing system that combines the best features of both technologies while reducing the size of the optical system. combining the strengths of high-speed dynamic control and precise spatial manipulation of wave fronts. (Metalenses).

Bioinspired camera could help self-driving cars see better by detecting a property of light known as polarization and featuring a dynamic range about 10,000 times higher than today's commercial cameras. Dynamic range is a measure of the brightest and darkest areas a camera can capture simultaneously. With these, the camera can see better in driving conditions such as the transition from a dark tunnel into bright sunlight or during hazy or foggy conditions.

Camera brings unseen world to light. Portable polarization-sensitive camera could be used in machine vision, autonomous vehicles, security, atmospheric chemistry and more. Researchers have developed a highly compact, portable camera that can image polarization in a single shot. The miniature camera -- about the size of a thumb -- could find a place in the vision systems of autonomous vehicles, onboard planes or satellites to study atmospheric chemistry, or be used to detect camouflaged objects.

Thermal imaging innovation allows AI to see through pitch darkness like broad daylight. The patent-pending innovation sees texture and depth and perceives physical attributes of people and environments.

Successful automatic landing with vision assisted navigation. Researchers have now demonstrated a completely automatic landing with vision assisted navigation that functions properly without the need for ground-based systems. C2Land.

Micro-Electro-Mechanical Systems is the technology of microscopic devices, particularly those with moving parts. It merges at the nano-scale into nanoelectromechanical systems (NEMS) and nanotechnology. MEMS are also referred to as micromachines in Japan, or micro systems technology (MST) in Europe.

Inception Model Image Recognition (tensorflow)

Statistical Classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

Computer Algorithm that is nearly as accurate as people are at Image Analysis of brain circuitry and neural networks.

Vrad (Radiology) - Zebra-Med (medical image diagnosis).

Minimalist Machine Learning Algorithms Analyze Images from Very Little Data. CAMERA researchers develop highly efficient neural networks for analyzing experimental scientific images from limited training data.

Convolutional Neural Network is a class of deep, feed-forward artificial neural network that have successfully been applied to analyzing visual imagery.

Transfer Learning or inductive transfer is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.

Optical Character Recognition is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast).

Image Classification Algorithm

Neural hardware for image recognition in nanoseconds. Usually, artificial intelligence is based on software. Scientists created intelligent hardware, which is much faster. Within nanoseconds, the chip can analyze images and provide the correct output.

Translations (language)

Question and Answer Platforms

Stable Diffusion is a deep learning, text-to-image model released in 2022. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt.

Diffusion Models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. In computer vision, this means that a neural network is trained to denoise images blurred with Gaussian noise by learning to reverse the diffusion process. It mainly consists of three major components: the forward process, the reverse process, and the sampling procedure. Three examples of generic diffusion modeling frameworks used in computer vision are denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations.

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

Computers being able to identify images means that blind people will see by way of  Sensory Substitution.

David Eagleman: Can we Create new Senses for Humans (video) - Sensory Vest

Sight Tools (seeing)

Weakness in modern computer vision systems.

Advances in Technology Provide Clearer Insight Into Brain's Visual System. A new high-density EEG can capture the brain's neural activity at a higher spatial resolution than ever before. This next generation brain-interface technology is the first non-invasive, high-resolution system of its kind, providing higher density and coverage than any existing system.

Metalens combined with an Artificial Muscle. Artificial eye automatically stretches to simultaneously focus and correct astigmatism and image shift. Metalens is a lens made from a metamaterial, which is any material that obtains its electromagnetic properties from its structure rather than from its chemical composition; especially a material engineered to have features of a size less than that of the wavelength of a class of electromagnetic radiation.

Ghost Imaging is a technique that produces an image of an object by combining information from two light detectors: a conventional, multi-pixel detector that doesn't view the object, and a single-pixel (bucket) detector that does view the object. Two techniques have been demonstrated. A quantum method uses a source of pairs of entangled photons, each pair shared between the two detectors, while a classical method uses a pair of correlated coherent beams without exploiting entanglement. Both approaches may be understood within the framework of a single theory. (also called "coincidence imaging", "two-photon imaging" or "correlated-photon imaging").

Researchers capture moving object with ghost imaging. Researchers have developed a way to capture moving objects with the unconventional imaging method known as ghost imaging. The new method could make the imaging technique practical for new applications such as biomedical imaging, security checks and video compression and storage. Ghost imaging comes with a host of advantages, one of which is that it allows one to form an image by illuminating the object with lower light levels than traditional imaging approaches. However, ghost imaging has been limited to stationary objects because it takes a long time to project the sequence of light patterns onto the object that is necessary to reconstruct an image. This causes images of a moving object to appear blurry.

Researchers detect and classify multiple objects without images. High-speed method uses less computational power, could be useful for autonomous driving. Researchers have developed a new high-speed way to detect the location, size and category of multiple objects without acquiring images or requiring complex scene reconstruction. Because the new approach greatly decreases the computing power necessary for object detection, it could be useful for identifying hazards while driving. Today's image-free perception methods can only achieve classification, single object recognition or tracking. To accomplish all three at once, the researchers developed a technique known as image-free single-pixel object detection (SPOD). In the Optica Publishing Group journal Optics Letters, they report that SPOD can achieve an object detection accuracy of just over 80%.

Adversarial Reprogramming of Neural Networks.

Scientists see what research participants picture in their mind’s eye. Attention is known to modulate neural representations of perceived images. The time needed to generate a very clear distinction between the imagined image and the viewed image was different for imagining a “word” and a “landscape,” which could have something to do with the different parts of the brain involved in imagining these two concepts.

When a machine can see the world in the same way that a human does, then we will have some really cool robots.

Light processing improves robotic sensing, study finds. A team of researchers uncovered how the human brain processes bright and contrasting light, which they say is a key to improving robotic sensing and enabling autonomous agents to team with humans.

Breakthrough optical sensor mimics human eye. Previous attempts to build a human-eye type of device, called a retinomorphic sensor, have relied on software or complex hardware. But the new sensor's operation is part of its fundamental design, using ultrathin layers of perovskite semiconductors -- widely studied in recent years for their solar energy potential -- that change from strong electrical insulators to strong conductors when placed in light. The new sensor could be a perfect match for the neuromorphic computers that will power the next generation of artificial intelligence in applications like self-driving cars, robotics and advanced image recognition, Labram said. Unlike traditional computers, which process information sequentially as a series of instructions, neuromorphic computers are designed to emulate the human brain's massively parallel networks.

Text to Image - AI Tool Systems

DALL·E 3 understands significantly more nuance and detail than our previous systems, allowing you to easily translate your ideas into exceptionally accurate images. DALL·E 3 is now available to all ChatGPT Plus and Enterprise users, and will be available via the API and in Labs later this fall.

Chat-GPT - Image Recognition - Pattern Recognition - Art Skills

Stable Diffusion XL is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, cultivates autonomous freedom to produce incredible imagery, empowers billions of people to create stunning art within seconds. Create beautiful images with our AI Image Generator (Text to Image) for free. Let Your Creativity Flow. Create Unique AI Generated Images in 1-Click.

Laion is a dataset of 5,85 billion CLIP-filtered image-text pairs, 14x bigger than LAION-400M, previously the biggest openly accessible image-text dataset in the world. Glaze.

Spawning AI tools for Artists. Made by Artists.

Stable Diffusion open software project. Download and join other developers in creating incredible applications with Stable Diffusion as a foundation mode.

Hearing Sensors - Computer Sensors that Listen

There will also be improvements in computers' ability to hear and understand sound. Greater sensitivity to sound pressure, vibrations and waves could lead to more-accurate landslide warnings, for example. Sensors.

Speech Recognition methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or just "speech to text" (STT). It incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields. Your voice is measured by Frequency, the Wavelengths of Sound at a specific moment. Software breaks down your command into 25-millisecond slivers, then converts each wavelength measurement into digestible numbers. The software compares those sonic signatures to its catalog of sounds until its confidence scores are high enough that it can assume that you said. The software compares the words it thinks you've said to its stores of example sentences, which inform how it understands syntax and vocabulary. Acoustic and language models constantly adjust to how people use them. That's where A.I., specifically machine learning, comes in context to be more accurate.

YouTube built an Automated Content Detection System that prevents most unauthorized clips from appearing on its site.

Artificial intelligence produces realistic sounds that fool humans Video-trained system from MIT’s Computer Science and Artificial Intelligence Lab could help robots understand how objects interact with the world. 

Machine-Learning System Processes Sounds like Humans do.

Cloud DX uses AI technology to scrutinize the audio waveform of a human cough, which allows it to detect asthma, tuberculosis, pneumonia, and other lung diseases.

Selecting sounds: How the brain knows what to listen to. New noninvasive approach reveals brain mechanisms of auditory attention.

All in your head: Exploring human-body communications with binaural hearing aids. Scientists show how head tissue can be safely used as the transmission medium for head-worn electronics. Wearable technology seems all poised to take over next-generation electronics, yet most wireless communication techniques are not up to the task. To tackle this issue, scientists have delved deep into human-body communications, in which human tissue is used as the transmission medium for electromagnetic signals. Their findings pave the way to more efficient and safer head-worn devices, such as binaural hearing aids and earphones. Bionics.

Taste - Flavor Sensors

Computers with virtual taste buds will be able to Calculate flavor, according to IBM, helping chefs improve recipes or create new ones. The systems will break down ingredients to their respective chemicals and calculate their interactions with neural sensors in a person's tongue and nose.

Smell - Oder Sensors

According to IBM, computers will have an acute sense of smell in order to diagnose from a person's breath a coming cold, liver and kidney disorders, diabetes and tuberculosis. Similar to how a Breathalyzer detects alcohol, the computer will be able to check for molecular biomarkers pointing to diseases. 

Machine Olfaction is the automated simulation of the sense of smell.

Electronic Nose is a device intended to detect odors or flavors. Over the last decade, "electronic sensing" or "e-sensing" technologies have undergone important developments from a technical and commercial point of view. The expression "electronic sensing" refers to the capability of reproducing human senses using sensor arrays and pattern recognition systems.

A new, highly sensitive chemical sensor uses protein nanowires. Scientists report that they have developed bioelectronic ammonia gas sensors that are among the most sensitive ever made. It uses electric-charge-conducting protein nanowires derived from the bacterium Geobacter to provide biomaterials for electrical devices. They grow hair-like protein filaments that work as nanoscale ''wires'' to transfer charges for their nourishment and to communicate with other bacteria.

Motion Sensors - Computer Sensors that Measure Body Language

Motion Detector is a device that detects moving objects, particularly people. Such a device is often integrated as a component of a system that automatically performs a task or alerts a user of motion in an area. They form a vital component of security, automated lighting control, home control, energy efficiency, and other useful systems. TSA Observations.

Algorithm for Analysis of Emotion Using Body Language.

Algorithm to Interpret Body Language in Real Time. Researchers in the United States are working on teaching computers to identify and interpret human physical action sequences as they take place. The algorithm they are using could also be applied to the medical sector to point up correct or incorrect movements by patients.

Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms

The Panoptic Studio is a Massively Multiview System, a Social Motion Capture Technology for Recording Body Language and Movements.

Signal Processing is an enabling technology that encompasses the fundamental theory, applications, algorithms, and implementations of processing or transferring information contained in many different physical, symbolic, or abstract formats broadly designated as signals. It uses mathematical, statistical, computational, heuristic, and linguistic representations, formalisms, and techniques for representation, modelling, analysis, synthesis, discovery, recovery, sensing, acquisition, extraction, learning, security, or forensics.

Sensor Fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints). The data sources for a fusion process are not specified to originate from identical sensors. One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input. Sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion. Sensors.

Brain Computer Interface

Do you need a Computer Chip implanted in your Brain?
You don't need a Computer Chip implanted in your Body or Brain, you can still use jump drives, cellphones, paper and other devices to carry important information with you that you need to remember. External Memory Devices are amazing tools. Jumpdrives can be made to look like jewelry or credit cards. You can even get a mini tattoo of your SS# in the form of a QR Code that could link to your information. There are only a few very unique circumstances that would require the need for a person to have a human-implantable microchip, like people who have disabilities. But the body does produce the necessary voltage to run an extra memory device, so I guess it's just a matter of Sensory Substitution. Blending human brains with computers has already been happening for over 20 years, so this is Your Brain on Computers. But you don't need to stick a computer in your head, you can just carry one around in your pocket. You don't have to wait for AI to have enhanced cognitive abilities, you can have enhanced cognitive abilities right now, it's called learning.

Human Operating System - Interfaces - Wearable Sensors - Smartphones - Wireless Communication

Brain Computer Interface helps Paralyzed Man feel again through Mind-Controlled Robotic Arm.

Toward next-generation brain-computer interface systems. A new kind of neural interface system that coordinates the activity of hundreds of tiny brain sensors could one day deepen understanding of the brain and lead to new medical therapies. Brain-computer interfaces (BCIs) are emerging assistive devices that may one day help people with brain or spinal injuries to move or communicate. BCI systems depend on implantable sensors that record electrical signals in the brain and use those signals to drive external devices like computers or robotic prosthetics. Most current BCI systems use one or two sensors to sample up to a few hundred neurons, but neuroscientists are interested in systems that are able to gather data from much larger groups of brain cells. Now, a team of researchers has taken a key step toward a new concept for a future BCI system -- one that employs a coordinated network of independent, wireless microscale neural sensors, each about the size of a grain of salt, to record and stimulate brain activity. The sensors, dubbed "neurograins," independently record the electrical pulses made by firing neurons and send the signals wirelessly to a central hub, which coordinates and processes the signals. The results, the researchers say, are a step toward a system that could one day enable the recording of brain signals in unprecedented detail, leading to new insights into how the brain works and new therapies for people with brain or spinal injuries. In a study published on August 12 in Nature Electronics, the research team demonstrated the use of nearly 50 such autonomous neurograins to record neural activity in a rodent. The second challenge was developing the body-external communications hub that receives signals from those tiny chips. The device is a thin patch, about the size of a thumb print, that attaches to the scalp outside the skull. It works like a miniature cellular phone tower, employing a network protocol to coordinate the signals from the neurograins, each of which has its own network address. The patch also supplies power wirelessly to the neurograins, which are designed to operate using a minimal amount of electricity.

Primates Regain Control of Paralyzed Limb.

Brain-Computer Interface Laboratory ETSU.

Brain-to-Brain Interface Demonstration (youtube).

Connecting Brains: The BrainNet - (VPRO documentary - 2014) (youtube).

Direct Brain-to-Brain Interface in Humans.

Neuralink is developing implantable brain–computer interfaces (BCIs).

Researchers Revolutionize Brain-Computer Interfaces using silicon electronics.

"One Chip to Rule them All."

Mind-Controlled Device helps stroke patients retrain brains to move Paralyzed Hands.

Stroke patient improvement with a brain-computer interface.

Artificial Synapse designed for “Brain-on-a-Chip” Hardware.

Brain implant may enable communication from thoughts alone. Prosthetic decodes signals from brain's speech center to predict what sound someone is trying to say. A speech prosthetic developed by a collaborative team of Duke neuroscientists, neurosurgeons, and engineers can translate a person's brain signals into what they're trying to say. The new technology might one day help people unable to talk due to neurological disorders regain the ability to communicate through a brain-computer interface.

Prosthetic memory system successful in humans uses a person’s own memory patterns to facilitate the brain’s ability to encode and recall memory.

Brain-Computer Interface enables people with Paralysis to control tablet devices - Brain Gate.

First-ever successful mind-controlled robotic arm without brain implants using a noninvasive brain-computer interface.

Ultra-small nanoprobes could be a leap forward in high-resolution human-machine interfaces.

Neural prosthetic device can help humans restore memory. Study highlights a promising avenue for cognitive enhancement technologies. A team of scientists have demonstrated the first successful use of a neural prosthetic device to recall specific memories. In the previous study, the team's electronic prosthetic system was based on a multi-input multi-output (MIMO) nonlinear mathematical model, and the researchers influenced the firing patterns of multiple neurons in the hippocampus, a part of the brain involved in making new memories. In this study, researchers built a new model of processes that assists the hippocampus in helping people remember specific information.

How artificial intelligence gave a paralyzed woman her voice back. Researchers have developed a brain-computer interface that has enabled a woman with severe paralysis from a brainstem stroke to speak through a digital avatar.

Scientists can now manipulate brain cells using smartphone. A team of scientists have invented a device that can control neural circuits using a tiny brain implant controlled by a smartphone.

Controlling electronic devices with brain waves. But researchers have developed a new type of electroencephalogram (EEG) electrode that can do just that, without the sticky gel required for conventional electrodes. Even better, the devices work through a full head of hair. The researchers report the flexible electrodes, which could someday be used in brain-computer interfaces to drive cars or move artificial limbs.

Artificial synapse that works with living cells created. Researchers have created a device that can integrate and interact with neuron-like cells. This could be an early step toward an artificial synapse for use in brain-computer interfaces.

Big Improvements to Brain-Computer Interface. Newly developed “glassy carbon” electrodes transmit more robust signals to restore motion in people with damaged spinal cords.

New treatment allows some people with spinal cord injury to regain hand and arm function. Almost 18,000 Americans experience traumatic spinal cord injuries every year. Many of these people are unable to use their hands and arms and can't do everyday tasks such as eating, grooming or drinking water without help. Using physical therapy combined with a noninvasive method of stimulating nerve cells in the spinal cord, University of Washington researchers helped six Seattle area participants regain some hand and arm mobility. That increased mobility lasted at least three to six months after treatment had ended.

Tapping the Brain to boost Stroke Rehabilitation. Clinical trial suggests brain-machine interface coupled with robot offers increased benefits for stroke survivors. Stroke survivors who had ceased to benefit from conventional rehabilitation gained clinically significant arm movement and control by using an external robotic device or brain-machine interface powered by the patients' own brains.

Neuroprosthesis restores words to man with paralysis. Technology could lead to more natural communication for people who have suffered speech loss. Researchers have successfully developed a 'speech neuroprosthesis' that has enabled a man with severe paralysis to communicate in sentences, translating signals from his brain to the vocal tract directly into words that appear as text on a screen.

Human Enhancements will require people to be more educated to avoid the corrupted influences of power. Augmenting.

Body Hacking - Technological Convergence

Brain Computer Interfaces is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.

Brain Computer Interface - Li-Fi - Electrical Brain Stimulation

Stabilizing Brain-Computer interfaces. New machine learning algorithm reduces need for brain-computer interfaces to undergo recalibration.

Neural Engineering System Design program seeks to develop high-resolution neurotechnology capable of mitigating the effects of injury and disease on the visual and auditory systems of military personnel. In addition to creating novel hardware and algorithms, the program conducts research to understand how various forms of neural sensing and actuation might improve restorative therapeutic outcomes. Translator for electrochemical language used by neurons.

Bidirectional BCI Functionality. Brain-computer interfaces or BCIs hold immense potential for individuals with a wide range of neurological conditions, but the road to implementation is long and nuanced for both the invasive and noninvasive versions of the technology. Scientists have now successfully integrated a novel focused ultrasound stimulation to realize bidirectional BCI that both encodes and decodes brain waves using machine learning in a study with 25 human subjects. This work opens up a new avenue to significantly enhance not only the signal quality, but also, overall nonivasive BCI performance by stimulating targeted neural circuits.

Cognichip is designed to simultaneously process and co-ordinate all the sensory and physical inputs a brain experiences, this cognitive platform will combine these elements to create a virtual self. MindMaze has always had one mission, to decode the brain and connect it to the world. It’s not about building brain-computer interfaces, but building human-machine interfaces.

Wearable Technology are clothing and accessories incorporating computer and advanced electronic technologies. The designs often incorporate practical functions and features.

Wearable Technology can take basic measurements and monitor and track body functions to give the user a better understand of their body and increase their awareness. They can then match the sensations they feel to the recorded data. Eventually they will be able to teach themselves how to notice body sensations and changes and have a better idea what may be happening in their body. A prosthesis for feeling. And the person will not have to wear the device all the time because they will be more aware of their body sensations and what they may mean. Ai teaching us to be more intelligent, now that's Ai.

Wearable and Bendable Electronics Conference - Medical Sensors - Bioelectronics

How To Hack A Human Brain | VICE on HBO (youtube) - Advancements in neurotechnology are blurring the line between biology and technology. There is an emerging push to implant electronic devices inside the human body, hardwire them to our brains, and allow us to not only overcome disadvantages or injury but open up entirely new avenues of human experience.

Self-Healing Material are artificial or synthetically-created substances that have the built-in ability to automatically repair damage to themselves without any external diagnosis of the problem or human intervention. Generally, materials will degrade over time due to fatigue, environmental conditions, or damage incurred during operation. Cracks and other types of damage on a microscopic level have been shown to change thermal, electrical, and acoustical properties of materials, and the propagation of cracks can lead to eventual failure of the material.

Bionics is the application of biological methods and systems found in nature to the study and design of engineering systems and modern technology. The Six Million Dollar Man.

Artificial Body Parts

Novel nanoprobes show promise for optical monitoring of neural activity. Researchers have developed ultrasensitive nanoscale optical probes to monitor the bioelectric activity of neurons and other excitable cells. This novel readout technology could enable scientists to study how neural circuits function at an unprecedented scale by monitoring large numbers of individual neurons simultaneously. It could also lead to high-bandwidth brain-machine interfaces with dramatically enhanced precision and functionality.

Cyborg is a being with both organic and biomechatronic body parts. Not the same thing as bionic, biorobot or android; it applies to an organism that has restored function or enhanced abilities due to the integration of some artificial component or technology that relies on some sort of feedback. While cyborgs are commonly thought of as mammals, including humans, they might also conceivably be any kind of organism. Augmented.

Cyborg Olympics

Human–Animal Hybrid incorporates elements from both humans and non-human animals.

Cybernetics is a wide-ranging field concerned with circular causal processes such as feedback. It is the study of human/machine interaction guided by the principle that numerous different types of systems can be studied, including, artificial intelligence, bionics, cognitive science, control theory, complexity science, computer science, information theory and robotics. Cybernetics is a transdisciplinary approach for exploring regulatory systems—their structures, constraints, and possibilities, the scientific study of control and communication in the animal and the machine.

Android is a humanoid robot or synthetic organism designed to look and act like a human, especially one with a body having a flesh-like resemblance.

Humanoid Robot is a robot with its body shape built to resemble the human body. The design may be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as the study of bipedal locomotion, or for other purposes. In general, humanoid robots have a torso, a head, two arms, and two legs, though some forms of humanoid robots may model only part of the body, for example, from the waist up. Some humanoid robots also have heads designed to replicate human facial features such as eyes and mouths. Androids are humanoid robots built to aesthetically resemble humans.

Robotics - Human Operating System

Brain Implant or Neural Implants, are technological devices that connect directly to a biological subject's brain – usually placed on the surface of the brain, or attached to the brain's cortex. A common purpose of modern brain implants and the focus of much current research is establishing a biomedical prosthesis circumventing areas in the brain that have become dysfunctional after a stroke or other head injuries. This includes sensory substitution, e.g., in vision. Other brain implants are used in animal experiments simply to record brain activity for scientific reasons. Some brain implants involve creating interfaces between neural systems and computer chips. This work is part of a wider research field called brain-computer interfaces. (Brain-computer interface research also includes technology such as EEG arrays that allow interface between mind and machine but do not require direct implantation of a device.) Neural implants such as deep brain stimulation and Vagus nerve stimulation are increasingly becoming routine for patients with Parkinson's disease and clinical depression, respectively.

Mind Uploading is a speculative process of whole brain emulation in which a brain scan is used to completely emulate the mental state of the individual in a digital computer. The computer would then run a simulation of the brain's information processing, such that it would respond in essentially the same way as the original brain and experience having a sentient conscious mind.

Man Who Is Paralyzed Communicates By Imagining Handwriting. An experimental device that turns thoughts into text has allowed a man who was left paralyzed by an accident to swiftly construct sentences on a computer screen, able to type with 95% accuracy just by imagining that he was handwriting letters on a sheet of paper. He can type at about 90 characters per minute.

Neuro-Prosthetics is a discipline related to neuroscience and biomedical engineering concerned with developing neural prostheses. They are sometimes contrasted with a brain–computer interface, which connects the brain to a computer rather than a device meant to replace missing biological functionality.

Restoring a rudimentary form of vision in the blind. Recent discoveries show that newly developed high-resolution implants in the visual cortex make it possible to recognize artificially induced shapes and percepts. When electrical stimulation is delivered to the brain via an implanted electrode, it generates the percept of a dot of light at a particular location in visual space, known as a 'phosphene.' The team developed high-resolution implants consisting of 1024 electrodes and implanted them in the visual cortex of two sighted monkeys. Their goal was to create interpretable images by delivering electrical stimulation simultaneously via multiple electrodes, to generate a percept that was composed of multiple phosphenes. "The number of electrodes that we have implanted in the visual cortex, and the number of artificial pixels that we can generate to produce high-resolution artificial images.

Powered Exoskeleton is a wearable mobile machine that is powered by a system of electric motors, pneumatics, levers, hydraulics, or a combination of technologies that allow for limb movement with increased strength and endurance.

Exoskeleton Technology Machines - Wheel Chairs

Hybrid Assistive Limb is a powered exoskeleton suit designed to support and expand the physical capabilities of its users, particularly people with physical disabilities. There are two primary versions of the system: HAL 3, which only provides leg function, and HAL 5, which is a full-body exoskeleton for the arms, legs, and torso.

Thought identification refers to the empirically verified use of technology to, in some sense, read people's minds. Advances in research have made this possible by using human neuroimaging to decode a person's conscious experience based on non-invasive measurements of an individual's brain activity. Repurpose Brain Signals.

RFID or Radio-frequency identification, uses electromagnetic fields to automatically identify and track tags attached to objects. The tags contain electronically stored information. Passive tags collect energy from a nearby RFID reader's interrogating radio waves. Active tags have a local power source such as a battery and may operate at hundreds of meters from the RFID reader. Unlike a barcode, the tag need not be within the line of sight of the reader, so it may be embedded in the tracked object. RFID is one method for Automatic Identification and Data Capture (AIDC)

VeriChip is a human-implantable Microchip, which is an identifying integrated circuit device or RFID transponder encased in silicate glass and implanted in the body of a human being. A subdermal implant typically contains a unique ID number that can be linked to information contained in an external database, such as personal identification, medical history, medications, allergies, and contact information.

Subdermal Implant refers to a body modification that is placed underneath the skin, therefore allowing the body to heal over the implant and creating a raised design. Such implants fall under the broad category of body modification.

Meditation for mind-control. Scientists have discovered that mindful meditation can help subjects learn and improve the ability to mind-control brain computer interfaces (BCIs). A BCI is an apparatus that allows an individual to control a machine or computer directly from their brain. Non-invasive means of control like electroencephalogram (EEG) readings taken through the skull are safe and convenient compared to more risky invasive methods using a brain implant, but they take longer to learn and users ultimately vary in proficiency.

I laugh when I here people say that soon we will be able to upload information directly into our brains, that is so stupid. Why would you need to do that if you have a smart phone or other information storage devices that can carry all your important data with you? And the information you can't carry, you can access it using the internet. Besides you just can't upload information into the brain because information has to be processed very carefully so that the information is correctly understood. So you have to manually and slowly input information into a human brain so that it has time to decipher the information and learn how the information should be used. The key word here is 'Learn', the one word we take for granted. So detailed instructions on how to use this information is a must. Like when you install software into a computer. The software comes with instructions that tells the computer how the information can be used. Then of course the computer needs an operating system in order to use that information correctly. Computers will allow humans to learn faster, but only if the instructions are detailed and accurate. So there are two sides of learning. Choosing the best information to learn and then creating the instructions on how to use the information correctly. Again, the computer and the human brain show their similarities. Reading.

My Favorite Martian was a 1960's sitcom about a Martian stranded on Earth, who claims to be more intelligent than humans, but for some reason, still does stupid human things that contradicts his claim of being more intelligent. Sadly, this show is a reminder of how ignorant society still is, and its been over 50 years. Tracking. My Favorite Martian S3 E06 Tim the Mastermind (youtube) - Taking the pills makes Tim the smartest man on the face of the Earth. (the pills are similar to a brain chip implant). My Favorite Martian S1 E37 " Uncle Martins Wisdom Tooth" (youtube).

Adding Information to our DNA.

We may be limited by our biology, but we are not limited by our intellect. And our intellect has given rise to technology, which is an extension of our intellect. So we have no boundaries. We have taken the first step of controlling matter, which means we have broken through another level of reality, so now what? What is the next step? And why should we take it? It's not the same out there as it is in here, we basically have to start all over again. This is a whole other dimension. Can we live in both dimensions at the same time? And there's word 'Time' again. Time and Space does not have a unified definition, so time and space are like fictitious characters in a play, they're just actors, we know nothing about their personal lives. Even with the mind fully open I still cannot see. So there's the problem, it's not sight, sound, smell, taste or feel. We are so close and yet still so far away. But still an awesome feeling, which way do we go? Lets see how far we can go in both directions without losing contact with each other. And what if we eventually run right back into each other? Then what? 

Can Artificial Intelligence help us find Human Intelligence?

"When people start learning how artificial intelligence can learn on its own, we will actually be teaching ourselves, and at the same time, learn how to learn more effectively."

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The Thinker Man