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Shortcuts are decision rules that perform well on standard benchmarks but fail to transfer to more challenging testing conditions. Shortcut opportunities come in many flavors and are ubiquitous across datasets and application domains. A few examples are visualized here:At a principal level, shortcut learning is not a novel phenomenon: variants are known under different terms such as “learning under covariate shift”, “anti-causal learning”, “dataset bias”, the “tank legend” and the “Clever Hans effect”. We here discuss how shortcut learning unifies many of deep learning’s problems and what we can do to better understand and mitigate shortcut learning.What is a shortcut?In machine learning, the solutions that a model can learn are constrained by data, model architecture, optimizer and objective function. However, these constraints often don’t just allow for one single solution: there are typically many different ways to solve a problem. Shortcuts are solutions that perform well on a typical test set but fail under different circumstances, revealing a mismatch with our intentions.
Shortcut learning beyond deep learningOften such failures serve as examples for why machine learning algorithms are untrustworthy. However, biological learners suffer from very similar failure modes as well. In an experiment in a lab at the University of Oxford, researchers observed that rats learned to navigate a complex maze apparently based on subtle colour differences - very surprising given that the rat retina has only rudimentary machinery to support at best somewhat crude colour vision. Intensive investigation into this curious finding revealed that the rats had tricked the researchers: They did not use their visual system at all in the experiment and instead simply discriminated the colours by the odour of the colour paint used on the walls of the maze. Once smell was controlled for, the remarkable colour discrimination ability disappeared.Animals often trick experimenters by solving an experimental paradigm (i.e., dataset) in an unintended way without using the underlying ability one is actually interested in. This highlights how incredibly difficult it can be for humans to imagine solving a tough challenge in any other way than the human way: Surely, at Marr’s implementational level there may be differences between rat and human colour discrimination. But at the algorithmic level there is often a tacit assumption that human-like performance implies human-like strategy (or algorithm). This “same strategy assumption” is paralleled by deep learning: even if DNN units are different from biological neurons, if DNNs successfully recognise objects it seems natural to assume that they are using object shape like humans do. As a consequence, we need to distinguish between performance on a dataset and acquiring an ability, and exercise great care before attributing high-level abilities like “object recognition” or “language understanding” to machines, since there is often a much simpler explanation:Never attribute to high-level abilities that which can be adequately explained by shortcut learning.
The consequences of this behaviour are striking failures in generalization. Have a look at the figure below. On the left side there are a few directions in which humans would expect a model to generalize. A five is a five whether it is hand-drawn and black and white or a house number photographed in color. Similarly slight distortions or changes in pose, texture or background don’t influence our prediction about the main object in the image. In contrast a DNN can easily be fooled by all of them. Interestingly this does not mean that DNNs can’t generalize at all: In fact, they generalize perfectly well albeit in directions that hardly make sense to humans. The right side of the figure below shows some examples that range from the somewhat comprehensible - scrambling the image to keep only its texture - to the completely incomprehensible.
Deep learning systems used for applications such as autonomous driving are developed by training a machine learning model. Typically, the performance of the deep learning system is limited at least in part by the quality of the training set used to train the model.In many instances, significant resources are invested in collecting, curating, and annotating the training data. Traditionally, much of the effort to curate a training data set is done manually by reviewing potential training data and properly labeling the features associated with the data.The effort required to create a training set with accurate labels can be significant and is often tedious. Moreover, it is often difficult to collect and accurately label data that a machine learning model needs improvement on. Therefore, there exists a need to improve the process for generating training data with accurate labeled features.Tesla published patent 'Generating ground truth for machine learning from time series elements'Patent filing date: February 1, 2019Patent Publication Date: August 6, 2020The patent disclosed a machine learning training technique for generating highly accurate machine learning results. Using data captured by sensors on a vehicle a training data set is created. The sensor data may capture vehicle lane lines, vehicle lanes, other vehicle traffic, obstacles, traffic control signs, etc.
Currently, we produce ∼1021 digital bits of information annually on Earth. Assuming a 20% annual growth rate, we estimate that after ∼350 years from now, the number of bits produced will exceed the number of all atoms on Earth, ∼1050. After ∼300 years, the power required to sustain this digital production will exceed 18.5 × 1015 W, i.e., the total planetary power consumption today, and after ∼500 years from now, the digital content will account for more than half Earth’s mass, according to the mass-energy–information equivalence principle. Besides the existing global challenges such as climate, environment, population, food, health, energy, and security, our estimates point to another singular event for our planet, called information catastrophe.
In conclusion, we established that the incredible growth of digital information production would reach a singularity point when there are more digital bits created than atoms on the planet. At the same time, the digital information production alone will consume most of the planetary power capacity, leading to ethical and environmental concerns already recognized by Floridi who introduced the concept of “infosphere” and considered challenges posed by our digital information society.27 These issues are valid, regardless of the future developments in data storage technologies. In terms of digital data, the mass–energy–information equivalence principle formulated in 2019 has not yet been verified experimentally, but assuming this is correct, then in not the very distant future, most of the planet’s mass will be made up of bits of information. Applying the law of conservation in conjunction with the mass–energy–information equivalence principle, it means that the mass of the planet is unchanged over time. However, our technological progress inverts radically the distribution of the Earth’s matter from predominantly ordinary matter to the fifth form of digital information matter. In this context, assuming the planetary power limitations are solved, one could envisage a future world mostly computer simulated and dominated by digital bits and computer code.
When the neural network is being developed, called the training phase, GPT-3 is fed millions and millions of samples of text and it converts words into what are called vectors, numeric representations. That is a form of data compression. The program then tries to unpack this compressed text back into a valid sentence. The task of compressing and decompressing develops the program's accuracy in calculating the conditional probability of words.
In July, Debuild cofounder and CEO Sharif Shameem tweeted about a project he created that allowed him to build a website simply by describing its design. In the text box, he typed, "the google logo, a search box, and 2 lightgrey buttons that say 'Search Google' and 'I'm Feeling Lucky." The program then generated a virtual copy of the Google homepage.This program uses GPT-3, a "natural language generation" tool from research lab OpenAI, which was cofounded by Elon Musk. GPT-3 was trained on massive swathes of data and can spit our results that mimic human writing. Developers have used it for creative writing, designing websites, writing business memos, and more. Now, Shameem is using GPT-3 for Debuild, a no-code tool for building web apps just by describing what they look like and how they work.With this program, the user just needs to type in and describe what the application will look like and how it will work, and the tool will create a website based on those descriptions.
San Francisco-based AI research laboratory OpenAI has added another member to its popular GPT (Generative Pre-trained Transformer) family. In a new paper, OpenAI researchers introduce GPT-f, an automated prover and proof assistant for the Metamath formalization language.While artificial neural networks have made considerable advances in computer vision, natural language processing, robotics and so on, OpenAI believes they also have potential in the relatively underexplored area of reasoning tasks. The new research explores this potential by applying a transformer language model to automated theorem proving.
The third edition of the Φ-week event, which is entirely virtual, focuses on how Earth observation can contribute to the concept of Digital Twin Earth – a dynamic, digital replica of our planet which accurately mimics Earth’s behavior. Constantly fed with Earth observation data, combined with in situ measurements and artificial intelligence, the Digital Twin Earth provides an accurate representation of the past, present, and future changes of our world.Digital Twin Earth will help visualize, monitor, and forecast natural and human activity on the planet. The model will be able to monitor the health of the planet, perform simulations of Earth’s interconnected system with human behavior, and support the field of sustainable development, therefore, reinforcing Europe’s efforts for a better environment in order to respond to the urgent challenges and targets addressed by the Green Deal.
It’s no coincidence that Transformer neural network architecture is gaining popularity across so many machine learning research fields. Best known for natural language processing (NLP) tasks, Transformers not only enabled OpenAI’s 175 billion parameter language model GPT-3 to deliver SOTA performance, the power- and potential-packed architecture also helped DeepMind’s AlphaStar bot defeat professional StarCraft players. Researchers have now introduced a way to make Transformers more compute-efficient, scalable and accessible.While previous learning approaches such as RNNs suffered from vanishing gradient problems, Transformers’ game-changing self-attention mechanism eliminated such issues. As explained in the paper introducing Transformers — Attention Is All You Need, the novel architecture is based on a trainable attention mechanism that identifies complex dependencies between input sequence elements.Transformers however scale quadratically when the number of tokens in an input sequence increases, making their use prohibitively expensive for large numbers of tokens. Even when fed with moderate token inputs, Transformers’ gluttonous appetite for computational resources can be difficult for many researchers to satisfy.A team from Google, University of Cambridge, DeepMind, and Alan Turing Institute have proposed a new type of Transformer dubbed Performer, based on a Fast Attention Via positive Orthogonal Random features (FAVOR+) backbone mechanism. The team designed Performer to be “capable of provably accurate and practical estimation of regular (softmax) full rank attention, but of only linear space and timely complexity and not relying on any priors such as sparsity or low-rankness.”
Are we trying to visualize something with lifeforms or without lifeforms ? I believe we can start off with one step at a time, first getting the solar system together then the galaxies and so on.
The tricky part comes next: yoking multiple abilities together. Deep learning is the most general approach we have, in that one deep-learning algorithm can be used to learn more than one task. AlphaZero used the same algorithm to learn Go, shogi (a chess-like game from Japan), and chess. DeepMind’s Atari57 system used the same algorithm to master every Atari video game. But the AIs can still learn only one thing at a time. Having mastered chess, AlphaZero has to wipe its memory and learn shogi from scratch.Legg refers to this type of generality as “one-algorithm,” versus the “one-brain” generality humans have. One-algorithm generality is very useful but not as interesting as the one-brain kind, he says: “You and I don’t need to switch brains; we don’t put our chess brains in to play a game of chess.”
Roughly in order of maturity, they are:Unsupervised or self-supervised learning. Labeling data sets (e.g., tagging all pictures of cats with “cat”) to tell AIs what they’re looking at during training is the key to what’s known as supervised learning. It’s still largely done by hand and is a major bottleneck. AI needs to be able to teach itself without human guidance—e.g., looking at pictures of cats and dogs and learning to tell them apart without help, or spotting anomalies in financial transactions without having previous examples flagged by a human. This, known as unsupervised learning, is now becoming more common.Transfer learning, including few-shot learning. Most deep-learning models today can be trained to do only one thing at a time. Transfer learning aims to let AIs transfer some parts of their training for one task, such as playing chess, to another, such as playing Go. This is how humans learn.Common sense and causal inference. It would be easier to transfer training between tasks if an AI had a bedrock of common sense to start from. And a key part of common sense is understanding cause and effect. Giving common sense to AIs is a hot research topic at the moment, with approaches ranging from encoding simple rules into a neural network to constraining the possible predictions that an AI can make. But work is still in its early stages. Learning optimizers. These are tools that can be used to shape the way AIs learn, guiding them to train more efficiently. Recent work shows that these tools can be trained themselves—in effect, meaning one AI is used to train others. This could be a tiny step toward self-improving AI, an AGI goal.
Geoffrey Hinton is an Engineering Fellow at Google where he manages the Brain Team Toronto, which is a new part of the Google Brain Team and is located at Google's Toronto office at 111 Richmond Street. Brain Team Toronto does basic research on ways to improve neural network learning techniques. He is also the Chief Scientific Adviser of the new Vector Institute and an Emeritus Professor at the University of Toronto. Recorded: December 4th, 2017
Andrej Karpathy@karpathy·Jul 19By posting GPT generated text we’re polluting the data for its future versions
Quote from: hamdani yusuf on 24/10/2020 13:11:30The dog's behavior is not entirely surprising either. Especially if you have some future version of neuralink implanted on its head, or you are a veterinarian.Here is the definition of intelligence accorsing to dictionary.Quote the ability to acquire and apply knowledge and skills. Usually, it represents problem solving or information processing capability, but doesn't take into account the ability to manipulate its environment nor self awareness. AlphaGo is considered intelligent since it can solve problem of playing go better then human champion. Alpha zero is even more intelligent since it can beat Alpha Go 100:0.Even though they don't have the ability to move any piece of go.On the other hand, consciousness covers more factors into account. For example, if you got paralyzed so you can't move your arms and legs, you are considered less conscious than your normal state, even though you can still think clearly.Traditionally, an agent is considered intelligent if it can solve problem, especially when it's better than expectation. A dog who can get you newspaper is considered intelligent.https://en.wikipedia.org/wiki/Artificial_intelligenceQuoteArtificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".[4]As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.[5] A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."[6] For instance, optical character recognition is frequently excluded from things considered to be AI,[7] having become a routine technology.[8] Modern machine capabilities generally classified as AI include successfully understanding human speech,[9] competing at the highest level in strategic game systems (such as chess and Go),[10] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.[11]QuoteComputer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] A more elaborate definition characterizes AI as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation."[70]A typical AI analyzes its environment and takes actions that maximize its chance of success.[3] An AI's intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Perform actions mathematically similar to ones that succeeded in the past"). Goals can be explicitly defined or induced. If the AI is programmed for "reinforcement learning", goals can be implicitly induced by rewarding some types of behavior or punishing others.[a] Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similar to how animals evolved to innately desire certain goals such as finding food.[71] Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data.[72] Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task.[73]https://en.wikipedia.org/wiki/AI_effectQuoteThe AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.[1]Author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'."[2] AIS researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'"[3]Quote"The AI effect" tries to redefine AI to mean: AI is anything that has not been done yetA view taken by some people trying to promulgate the AI effect is: As soon as AI successfully solves a problem, the problem is no longer a part of AI.Pamela McCorduck calls it an "odd paradox" that "practical AI successes, computational programs that actually achieved intelligent behavior, were soon assimilated into whatever application domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the "failures", the tough nuts that couldn't yet be cracked."[4]When IBM's chess playing computer Deep Blue succeeded in defeating Garry Kasparov in 1997, people complained that it had only used "brute force methods" and it wasn't real intelligence.[5] Fred Reed writes:"A problem that proponents of AI regularly face is this: When we know how a machine does something 'intelligent,' it ceases to be regarded as intelligent. If I beat the world's chess champion, I'd be regarded as highly bright."[6]Douglas Hofstadter expresses the AI effect concisely by quoting Larry Tesler's Theorem:"AI is whatever hasn't been done yet."[7]When problems have not yet been formalised, they can still be characterised by a model of computation that includes human computation. The computational burden of a problem is split between a computer and a human: one part is solved by computer and the other part solved by a human. This formalisation is referred to as human-assisted Turing machine.[8]AI applications become mainstreamSoftware and algorithms developed by AI researchers are now integrated into many applications throughout the world, without really being called AI.Michael Swaine reports "AI advances are not trumpeted as artificial intelligence so much these days, but are often seen as advances in some other field". "AI has become more important as it has become less conspicuous", Patrick Winston says. "These days, it is hard to find a big system that does not work, in part, because of ideas developed or matured in the AI world."[9]According to Stottler Henke, "The great practical benefits of AI applications and even the existence of AI in many software products go largely unnoticed by many despite the already widespread use of AI techniques in software. This is the AI effect. Many marketing people don't use the term 'artificial intelligence' even when their company's products rely on some AI techniques. Why not?"[10]Marvin Minsky writes "This paradox resulted from the fact that whenever an AI research project made a useful new discovery, that product usually quickly spun off to form a new scientific or commercial specialty with its own distinctive name. These changes in name led outsiders to ask, Why do we see so little progress in the central field of artificial intelligence?"[11]Nick Bostrom observes that "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labelled AI anymore."[12]QuoteSaving a place for humanity at the top of the chain of beingMichael Kearns suggests that "people subconsciously are trying to preserve for themselves some special role in the universe".[14] By discounting artificial intelligence people can continue to feel unique and special. Kearns argues that the change in perception known as the AI effect can be traced to the mystery being removed from the system. In being able to trace the cause of events implies that it's a form of automation rather than intelligence.A related effect has been noted in the history of animal cognition and in consciousness studies, where every time a capacity formerly thought as uniquely human is discovered in animals, (e.g. the ability to make tools, or passing the mirror test), the overall importance of that capacity is deprecated.[citation needed]Herbert A. Simon, when asked about the lack of AI's press coverage at the time, said, "What made AI different was that the very idea of it arouses a real fear and hostility in some human breasts. So you are getting very strong emotional reactions. But that's okay. We'll live with that."[15]
The dog's behavior is not entirely surprising either. Especially if you have some future version of neuralink implanted on its head, or you are a veterinarian.Here is the definition of intelligence accorsing to dictionary.Quote the ability to acquire and apply knowledge and skills. Usually, it represents problem solving or information processing capability, but doesn't take into account the ability to manipulate its environment nor self awareness. AlphaGo is considered intelligent since it can solve problem of playing go better then human champion. Alpha zero is even more intelligent since it can beat Alpha Go 100:0.Even though they don't have the ability to move any piece of go.On the other hand, consciousness covers more factors into account. For example, if you got paralyzed so you can't move your arms and legs, you are considered less conscious than your normal state, even though you can still think clearly.
the ability to acquire and apply knowledge and skills.
Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".[4]As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.[5] A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."[6] For instance, optical character recognition is frequently excluded from things considered to be AI,[7] having become a routine technology.[8] Modern machine capabilities generally classified as AI include successfully understanding human speech,[9] competing at the highest level in strategic game systems (such as chess and Go),[10] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.[11]
Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] A more elaborate definition characterizes AI as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation."[70]A typical AI analyzes its environment and takes actions that maximize its chance of success.[3] An AI's intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Perform actions mathematically similar to ones that succeeded in the past"). Goals can be explicitly defined or induced. If the AI is programmed for "reinforcement learning", goals can be implicitly induced by rewarding some types of behavior or punishing others.[a] Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similar to how animals evolved to innately desire certain goals such as finding food.[71] Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data.[72] Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task.[73]
The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.[1]Author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'."[2] AIS researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'"[3]
"The AI effect" tries to redefine AI to mean: AI is anything that has not been done yetA view taken by some people trying to promulgate the AI effect is: As soon as AI successfully solves a problem, the problem is no longer a part of AI.Pamela McCorduck calls it an "odd paradox" that "practical AI successes, computational programs that actually achieved intelligent behavior, were soon assimilated into whatever application domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the "failures", the tough nuts that couldn't yet be cracked."[4]When IBM's chess playing computer Deep Blue succeeded in defeating Garry Kasparov in 1997, people complained that it had only used "brute force methods" and it wasn't real intelligence.[5] Fred Reed writes:"A problem that proponents of AI regularly face is this: When we know how a machine does something 'intelligent,' it ceases to be regarded as intelligent. If I beat the world's chess champion, I'd be regarded as highly bright."[6]Douglas Hofstadter expresses the AI effect concisely by quoting Larry Tesler's Theorem:"AI is whatever hasn't been done yet."[7]When problems have not yet been formalised, they can still be characterised by a model of computation that includes human computation. The computational burden of a problem is split between a computer and a human: one part is solved by computer and the other part solved by a human. This formalisation is referred to as human-assisted Turing machine.[8]AI applications become mainstreamSoftware and algorithms developed by AI researchers are now integrated into many applications throughout the world, without really being called AI.Michael Swaine reports "AI advances are not trumpeted as artificial intelligence so much these days, but are often seen as advances in some other field". "AI has become more important as it has become less conspicuous", Patrick Winston says. "These days, it is hard to find a big system that does not work, in part, because of ideas developed or matured in the AI world."[9]According to Stottler Henke, "The great practical benefits of AI applications and even the existence of AI in many software products go largely unnoticed by many despite the already widespread use of AI techniques in software. This is the AI effect. Many marketing people don't use the term 'artificial intelligence' even when their company's products rely on some AI techniques. Why not?"[10]Marvin Minsky writes "This paradox resulted from the fact that whenever an AI research project made a useful new discovery, that product usually quickly spun off to form a new scientific or commercial specialty with its own distinctive name. These changes in name led outsiders to ask, Why do we see so little progress in the central field of artificial intelligence?"[11]Nick Bostrom observes that "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labelled AI anymore."[12]
Saving a place for humanity at the top of the chain of beingMichael Kearns suggests that "people subconsciously are trying to preserve for themselves some special role in the universe".[14] By discounting artificial intelligence people can continue to feel unique and special. Kearns argues that the change in perception known as the AI effect can be traced to the mystery being removed from the system. In being able to trace the cause of events implies that it's a form of automation rather than intelligence.A related effect has been noted in the history of animal cognition and in consciousness studies, where every time a capacity formerly thought as uniquely human is discovered in animals, (e.g. the ability to make tools, or passing the mirror test), the overall importance of that capacity is deprecated.[citation needed]Herbert A. Simon, when asked about the lack of AI's press coverage at the time, said, "What made AI different was that the very idea of it arouses a real fear and hostility in some human breasts. So you are getting very strong emotional reactions. But that's okay. We'll live with that."[15]
Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".[4]As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.[5] A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."[6] For instance, optical character recognition is frequently excluded from things considered to be AI,[7] having become a routine technology.[8] Modern machine capabilities generally classified as AI include successfully understanding human speech,[9] competing at the highest level in strategic game systems (such as chess and Go),[10] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.[11]Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism,[12][13] followed by disappointment and the loss of funding (known as an "AI winter"),[14][15] followed by new approaches, success and renewed funding.[13][16] After AlphaGo successfully defeated a professional Go player in 2015, artificial intelligence once again attracted widespread global attention.[17] For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other.[18] These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"),[19] the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences.[22][23][24] Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).[18]The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[19] General intelligence is among the field's long-term goals.[25] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".[26] This raises philosophical arguments about the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity.[31] Some people also consider AI to be a danger to humanity if it progresses unabated.[32][33] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[34]In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[35][16]
Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] A more elaborate definition characterizes AI as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation."[71]
Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world.[citation needed] These learners could therefore derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is seldom possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering a broad range of possibilities unlikely to be beneficial.
Descartes has pointed out that the only self evident information a conscious agent can get is its own existence. Any other information requires corroborating evidences to support it. So in the end, the reliability of an information will be measured/valued by its ability to help preserving conscious agents.
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.[1]When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors.[1] After this learning step, a DBN can be further trained with supervision to perform classification.[2]DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs)[1] or autoencoders,[3] where each sub-network's hidden layer serves as the visible layer for the next. An RBM is an undirected, generative energy-based model with a "visible" input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the "lowest" pair of layers (the lowest visible layer is a training set).The observation[2] that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms.[4]:6 Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography,[5] drug discovery[6][7][8]).
This thread is another spinoff from my earlier thread called universal utopia. This time I try to attack the problem from another angle, which is information theory point of view.I have started another thread related to this subject asking about quantification of accuracy and precision. It is necessary for us to be able to make comparison among available methods to describe some aspect of objective reality, and choose the best option based on cost and benefit consideration. I thought it was already a common knowledge, but the course of discussion shows it wasn't the case. I guess I'll have to build a new theory for that. It's unfortunate that the thread has been removed, so new forum members can't explore how the discussion developed.In my professional job, I have to deal with process control and automation, engineering and maintenance of electrical and instrumentation systems. It's important for us to explore the leading technologies and use them for our advantage to survive in the fierce industrial competition during this industrial revolution 4.0. One of the technology which is closely related to this thread is digital twin.Just like my other spinoff discussing about universal morality, which can be reached by expanding the groups who develop their own subjective morality to the maximum extent permitted by logic, here I also try to expand the scope of the virtualization of real world objects like digital twin in industrial sector to cover other fields as well. Hopefully it will lead us to global governance, because all conscious beings known today share the same planet. In the future the scope needs to expand even further because the exploration of other planets and solar systems is already on the way.
What law says that our memories are stored in our minds ! How do we know that we are not just accessing a mainframe server and we are no more than confused bots .
The trial was described in a preprint paper written by a team led by Cerebras’s Michael James and NETL’s Dirk Van Essendelft and presented at the supercomputing conference SC20 this week. The team said the CS-1 completed a simulation of combustion in a power plant roughly 200 times faster than it took the Joule 2.0 supercomputer to do a similar task.The CS-1 was actually faster-than-real-time. As Cerebrus wrote in a blog post, “It can tell you what is going to happen in the future faster than the laws of physics produce the same result.”
Cut the CommuteComputer chips begin life on a big piece of silicon called a wafer. Multiple chips are etched onto the same wafer and then the wafer is cut into individual chips. While the WSE is also etched onto a silicon wafer, the wafer is left intact as a single, operating unit. This wafer-scale chip contains almost 400,000 processing cores. Each core is connected to its own dedicated memory and its four neighboring cores.Putting that many cores on a single chip and giving them their own memory is why the WSE is bigger; it’s also why, in this case, it’s better.Most large-scale computing tasks depend on massively parallel processing. Researchers distribute the task among hundreds or thousands of chips. The chips need to work in concert, so they’re in constant communication, shuttling information back and forth. A similar process takes place within each chip, as information moves between processor cores, which are doing the calculations, and shared memory to store the results.
Simulating the World as It UnfoldsIt’s worth noting the chip can only handle problems small enough to fit on the wafer. But such problems may have quite practical applications because of the machine’s ability to do high-fidelity simulation in real-time. The authors note, for example, the machine should in theory be able to accurately simulate the air flow around a helicopter trying to land on a flight deck and semi-automate the process—something not possible with traditional chips.