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Artificial Intelligence - Ai

Artificial is something created by humans to be similar to something else that is naturally existing in reality. Not better, just similar. Something contrived by Art rather than Nature. Not arising from natural growth or characterized by vital processes.

is having the Capacity for Thought and Reason especially to a high degree. To understand and gain skills and knowledge from experience. Possessing sound Knowledge. Exercising or showing good judgment.  Endowed with the capacity to Reason. Having good understanding or a high mental capacity; quick to Comprehend. Intelligence is making good decisions and examining things carefully. Intelligence is always learning. "Now is that something a Machine can do? No, not yet."

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Human Intelligence vs Artificial Intelligence Artificial Intelligence is the attempt to mimic human thinking and actions using Computerized Machines. Define Thinking?

Human Intelligence is not totally defined just yet, so Artificial Intelligence is limited and mostly misunderstood.

Virtual Reality - Automation

Conversations with Ai

Though machine intelligence, or Artificial Intelligence, has great areas of performance and capabilities, artificial intelligence is mostly just fantasy for now. There will never be a HAL 9000 Heuristically programmed ALgorithmic Computer like in the movie 2001: A Space Odyssey, a computer that can be corrupted to Kill, not a good idea, like the War Games - Joshua Simulations (youtube). But this doesn't mean that Ai technology like Siri, or other information stations, can be helpful to people, especially the blind. What's really interesting is the Ai computer like the one in the 1977 movie Demon Seed, which the computer decides it wants to be human, Ai come full circle, which in a way shows you that a human is the superior machine. There will never be Cyborgs because Powered Exoskeletons like the Hybrid Assistive Limb are mostly used to help handicap people with disabilities, which will not make them super human. Body Hacking 

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 with no disabilities can use their eyes, ears and hands as a Brain Computer Interface, as we've been doing since the creation of modern computers and smartphones. There will be no Terminator or Sentient Android named Data either. And don't ever worry about someone becoming 'The Lawnmower Man', though I did like the virtual teaching methods, which proved that it's not how fast you learn but what you actually learn. I also liked the end when he was able to digitize himself, and to confirm that he succeeded he made a billion phones ring at once, I myself would send a billion text messages that would say "You Are Loved, Keep Learning."

To continue, people are not going to merge with machines, were just using machines to increase our abilities, and at the same time, we are using machines to improve the quality of life. People merging with machines is only for the handicapped who need extra help. People saying that we're going to merge with machines sends the wrong message and makes people fear technology. People just say these crazy things to sell stories and to bring attention to themselves. Another reason why Media Literacy is so important. So 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 is ready.

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

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.

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

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

Dummy is a model or replica of a human being. Something designed to resemble and serve as a substitute for the real or usual thing; a counterfeit or sham. A prototype or mock-up.

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.

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

Robotics - Sensors

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 understand them, because that will only create misunderstanding and confusion. So unless you're trying to manipulate peoples thinking, you're better off saying something that is 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 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.

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.

General Purpose Technology are technologies that can affect an entire economy (usually at a national or global level). GPTs have the potential to drastically alter societies through their impact on pre-existing economic and social structures. Examples include the steam engine, railroad, interchangeable parts, electricity, electronics, material handling, mechanization, control theory (automation), the automobile, the computer, the Internet, and the blockchain.

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.

A computer did not beat Lee Se--Dol playing the Board Game Go,  a team of human's using a machine beat him, that's not AI, that's just lazy. That's like someone using a calculator to beat you at math when you don't have a calculator, that doesn't make a calculator smart, a human still has to push the buttons, or write the code. Google Software DeepMind’s AI System Algorithm, it does show us how advanced machines are becoming, which is good, that's if we use our advanced technological machines for actual real life problem solving, instead of using it 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.

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 (Berkekly)

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

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.

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!

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.

Neuromorphic Engineering describes the use of very-large-scale integration (VLSI) 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.

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.

Computational Learning Theory is a subfield of Artificial Intelligence devoted to studying the design and analysis of machine learning algorithms.

Computer Code - Super Computers

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 - Adapting

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.

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.

Is Ai vulnerable to Viruses?

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 Machine

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 (academic) 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[which?] 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
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).

Neural Network

Artificial Neural Network is a network inspired by biological neural networks (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. Deep Learning

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.

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

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.

Feedforward Neural Network is an artificial neural network wherein connections between the units do not form a cycle. This is different from recurrent neural network.

Unsupervised Learning with Artificial Neurons
Stochastic Phase-Change Neurons

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.

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.

Node (networking) is either a connection point, a redistribution point (e.g. data communications equipment), or a communication endpoint (e.g. data terminal equipment). The definition of a node depends on the network and protocol layer referred to. A physical network node is an active electronic device that is attached to a network, and is capable of creating, receiving, or transmitting information over a communications channel. A passive distribution point such as a distribution frame or patch panel is consequently not a node.

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).

Networks - Human Brain - Internet

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 that human is not intelligent. People can be easily fooled, and not just by machines.

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.

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.

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).

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.

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.

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).

Cleverbot is a chatterbot web application that uses an artificial intelligence (AI) 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.

Natural Language Processing (interpretation)

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.

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

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.

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

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

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.

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 are still doing it today in 2018. 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'm 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. Let us progress together. Answer from Machine: Great Idea.

Question from Human: Are you more intelligent then 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 then you. And humans can out perform machines in many ways because humans have more abilities, more potential and are a lot more versatile then 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: The first thing that you would need to do is to learn how a stupid that question is. What will humans do when machines do most of the work? Humans will Educate themselves to the highest degree possible, and then humans will continue to educate themselves so that educated people can 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. 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 then 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 then 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 then 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 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 then good. When you believe that your self serving ego is more important then 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 then 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 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 purpose, so it's not unusual to think that someone made humans for some reason and 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.

Controls  -  Automation

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. Control also means to have great skillfulness and knowledge of some subject or activity. Having a firm understanding or knowledge. In science control is a standard against which other conditions can be compared and verified in a scientific experiment.

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.

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. PDF - 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. Quality Control

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

Control (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.

You Can't Control Everything

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.

Regulator (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.

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. They 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

Controller (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 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.

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 (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)

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


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. Algorithms

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.

People fear autonomous killing robots, but in a sense we already have them, they're called soldiers, they're called police, they're called the CIA, they're called the NSA, they're called the IRS, they're called the TSA, 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. Yes we need Command Hierarchy, especially when organizing for emergency response, like an Incident Command System. But when people say "I'm just following orders", what they are really saying is that I can't think for myself and have no intelligent reasoning that would allow me to make intelligent decisions on my own. When people blindly follow orders, they are no more then a robot. Humans are born free thinkers, but when they are not allowed to think freely for themselves, they are no more then autonomous killing machines. People who have power are Autonomous Robots. So don't worry about machines killing you, because autonomous humans have killed millions, and will continue to kill millions. So unless you become intelligent, this ignorance will continue to Crush, Kill and Destroy.

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

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.

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, how will you save life? 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.

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?

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.

Autonomy in Weapon Systems (pdf)

Self Driving Cars

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).
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.

Automation Paradox (off loading)


Autopilot is a 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 are used in aircraft, boats (known as self-steering gear), 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.

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
GMU Autonomous Robotics Laboratory

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.

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".

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).

Self-Management (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 (ACI) 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 (MVC) to improve separation of concerns by helping encapsulate functional concerns.

Impulsivity (lack of control)

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.

Group Thinking (influence)
Auto-Pilot - Subconscious
Software (computers)

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?

Everything is Connected

Machines are Replacing some 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.

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 then the most intelligent human. But a machine could easily be more intelligent then a human who has never learned enough, or went to school. You really don't want a machine to be more intelligent then 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 then a human who is not fully educated. Ai will not be more intelligent then 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 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.

Machine Learning

Machine Learning is the study of pattern recognition and computational learning theory in artificial intelligence. Field of study that gives computers the ability to learn without being explicitly programmed. But 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.

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.
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 on meta-data about machine learning experiments. List of Machine Learning Concepts - 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 Algorithms.

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.

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. Outline of Machine Learning (wiki) - PDF

Robot Learning (PDF) - Robotics

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

Internet of Things

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.

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.

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

Deep Learning
Deep-Learning Program DRIVE PX
Deep Learning & Artificial Intelligence Solutions from NVIDIA
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

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

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.

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). Agent

Reinforcement Learning Algorithms - TRPO, DQN, A3C, DDPG, DPO, Rainbw

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.

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

Human Operating System

Teaching Machine - Computer Science

Numenta reverse engineering the neocortex.

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.

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

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).


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) specifying how to solve some problem. Procedure - Formula

Task (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 (computing) execution is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system.

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 (open loop).

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)

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.

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.

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

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

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.

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.

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.

Reasoning (intelligence)

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.

Statistics (math)

Algorithms, Direct Coding or Both?

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.

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.

PHP if else
HP Control Structures
If Function (excel)
If Statement (excel)

Probability (odds) - Variables

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

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.

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".

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

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.


Variable is something not consistent or having a fixed Pattern; liable to change. A value is either arbitrary or not fully specified or unknown.

is an instance of change; 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. Iteration
Variant is something a little different from others of the same type. Exhibiting variation and change. In biology, a group of organisms within a species that differ in trivial ways from similar groups.

Scenario - Cause and Effect

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.

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.

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 (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 (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 (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 (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.

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.


Scenario is one of many known sequence of possible events.

Prepared for Emergencies - Cause and Effect

Conditional Probability is a measure of the probability of an event given that (by assumption, presumption, assertion or evidence) another event has occurred.

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

Formulating - Variables

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.

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

Problem Solving
Quality Control

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

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.

Short Circuit (1986 film) (wiki)

International Robot Exhibition (wiki)
Robot Building

We created something incredible called the Computer

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.

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 just 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. 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 then machines. Otherwise it will not work well or end well."

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 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 then a Gimmick. This is not to down play what they have accomplished, because it's 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.

Future of Life - Human Machine

Brain and Computer Similarities

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.


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.

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." 

Spatial Intelligence

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

Pattern is a perceptual structure

Pattern Recognition focuses on the recognition of patterns and regularities in data. Deciphering Information.

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).

Pattern is a discernible regularity in the world or in a manmade design. As such, the elements of a pattern repeat in a predictable manner. A geometric pattern is a kind of pattern formed of geometric shapes and typically repeating like a wallpaper.

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

Statistics (math)

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

Vibrations (hz)

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

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.

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

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


Sensor is an object whose purpose is to detect events or changes in its environment and sends the information to the computer which then tells the actuator (output devices) to provide the corresponding output. A sensor is a device that converts real world data (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.

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. Sensor Array

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.

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.

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.

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

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

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.

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.

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.
Engineers Create Artificial Skin That "Feels" Temperature Changes

Human Senses - Materials Science (strength limits)

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.

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)

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


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. 

How computers learn to recognize objects instantly (video and interactive text)
Build a TensorFlow Image Classifier in 5 Min (youtube)

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.

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 Modelling and Computational Learning
VOC - Visual Object Classes

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.

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.

Orientation (computer vision) in computer vision and image processing a common assumption is that 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

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.

LIDAR-Sensing using Light.

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

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).

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

Translations (language)

Question and Answer Platforms

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

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.

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


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.

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.


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.


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.

Body Language

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.

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.

Brain Computer Interface helps Paralyzed Man feel again through Mind-Controlled Robotic Arm.
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.
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
Prosthetic memory system successful in humans uses a person’s own memory patterns to facilitate the brain’s ability to encode and recall memory.

Big Improvements to Brain-Computer Interface. Newly developed “glassy carbon” electrodes transmit more robust signals to restore motion in people with damaged spinal cords.

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

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. Medical Sensors.

Wearable and Bendable Electronics Conference

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

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.

Cyborg Olympics

Human–Animal Hybrid incorporates elements from both humans and non-human animals.

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

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.

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.

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? 

"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."

Artificial Intelligence helped us find Human Intelligence

The Thinker Man