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Artificial Intelligence - Ai


Artificial is something contrived by Art rather than Nature. Not arising from natural growth or characterized by vital processes.
Virtual Reality

Intelligence
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.

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. 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 Transhumanism, 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 supermind. 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.

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 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)
Robotics

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

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.

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.

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

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)

Tensor Flow Open Source Software Library for Machine Intelligence.

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.

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.

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

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

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.

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".

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.

Ai Course (Berkekly)
Is Ai vulnerable to Viruses?
DeepMind Technologies is a British artificial intelligence company founded in September 2010. It was acquired by Google in 2014.

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

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
The Human Brain
Memory
Associations
Transmitting Data using Light

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

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

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

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

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

Decision Making
Computing
Parallel Computing

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

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

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

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

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

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

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

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

Knowledge Management - Internet

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

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


Neural Network


Unsupervised Learning with Artificial Neurons

Stochastic Phase-Change Neurons

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.

1:
Architecture Rule specifies what variables are involved in the network and their topological relationships—for example the variables involved in a neural network might be the weights of the connections between the neurons, along with activities of the neurons
2: Activity Rule states that most neural network models have short time-scale dynamics: local rules define how the activities of the neurons change in response to each other. Typically the activity rule depends on the weights (the parameters) in the network.
3: Learning Rule specifies the way in which the neural network's weights change with time. This learning is usually viewed as taking place on a longer time scale than the time scale of the dynamics under the activity rule. Usually the learning rule will depend on the activities of the neurons. It may also depend on the values of the target values supplied by a teacher and on the current value of the weights.

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

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.

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.

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.

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.

Networks
Human Brain
Internet

"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." 

Natural Language Processing
Natural Language Toolkit

"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?

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.

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


Controls  -  Automation


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.

Control Engineering is the engineering discipline that applies control theory to design systems with desired behaviors. The practice uses sensors to measure the output performance of the device being controlled and those measurements can be used to give feedback to the input actuators that can make corrections toward desired performance. When a device is designed to perform without the need of human inputs for correction it is called automatic control (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 systems of a diverse range.

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.

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.

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)
Placebos

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

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. 

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.


Drones


"The danger is not Artificial Intelligence, the danger is the ignorant criminals in power who will use AI incorrectly, like they are now with Drones. When crazy people make machines that kill humans, that's not artificial intelligence, that's just ignorance.

There is no future in War. Just like all ignorant behaviors, war will become obsolete. 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.

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)

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?

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.

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
Robots

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 (automata or automatons) 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.

Impulsivity

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
Auto-Pilot - Subconscious
Software

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

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.

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.

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 still it needs to be programmed to learn, I would like to see that process.

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.

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

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.

Robot Learning (PDF)
Robotics

Internet of Things

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
 
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.

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.

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.


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)



Algorithms


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.

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.

Genetic Algorithm - 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 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

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

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

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

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
Patterns
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

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
PHP
If Function (excel)
If Statement (excel)

Learning to Build Robots

Computer Knowledge

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

Subroutine

Variable
Software Design Pattern

Learning to Write Code

Compiler
Source Code

Iteration
Batch File

Internet

Conditional Computer Programming

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

Memory Cell
Programmable Logic Controller
Python Programming Language

Gottfried Wilhelm Leibniz
Alphabet of Human Thought
Characteristica Universalis
Calculus Ratiocinator

Philosophiae Naturalis Principia Mathematica
Networks

Problem of Induction
Mathematical Induction
Induction

Modulo Operation
Modular Arithmetic
Cybernetics
Ontology
Emotivism

Mathematical Biophysics
Expression-Oriented Programming Language
Command Query Separation
Computer Programming

Observer Effect (Information Technology) (wiki)
Observer
 
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.


Variables


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

Scenario - Cause and Effect

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

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.


Scenarios


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.

Reasoning
Patterns
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
ROS

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

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

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.
 

Patterns


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

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." 

Time
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.

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.

Statistics
Cycle Detection is the algorithmic problem of finding a cycle in a sequence of iterated function values.
Vibrations
Trypophobia 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 that allow the discovery of patterns or correlations in large quantities of data.

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.

Vision:

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. 

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.

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.

Visual Search Engine App
Gif

Arxiv Full Resolution Image Compression with Recurrent Neural Networks.

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.

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).

Translations
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

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

Hearing:
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.

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.

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

Smell:
And, finally, 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.

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.

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 (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.

Wearable Sensors - Flexible Electronics

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

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.

Food Sensors

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.

Smartphone Accessories
Medical Sensors



Brain Computer Interface


Do you need a Computer Chip implanted in your Brain?


You don't need a Computer Chip implanted in your Body or Brain, you can still use jump drives, cellphones, paper and other devices to carry important information with you that you need to remember. External Memory Devices are amazing tools. Jumpdrives can be made to look like jewelry or credit cards. You can even get a mini tattoo of your SS# in the form of a QR Code that could link to your information. There are only a few very unique circumstances that would require the need for a person to have a human-implantable microchip, like people who have disabilities. But the body does produce the necessary voltage to run an extra memory device, so I guess it's just a matter of Sensory Substitution. Blending human brains with computers has already been happening for over 20 years, so this is Your Brain on Computers. But you don't need to stick a computer in your head, you can just carry one around in your pocket.

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).
Mind-Controlled Device helps stroke patients retrain brains to move Paralyzed Hands

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

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 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

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

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."


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.



The Thinker Man