Artificial intelligence

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Artificial intelligence
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{{Redirect|AI|other uses|AI (disambiguation)|and|Artificial intelligence (disambiguation)}}{{pp-pc1}}{{short description|Intelligence demonstrated by machines}}{{Use dmy dates|date=January 2018}}{{Artificial intelligence}}In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. 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. 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".{{sfn|Russell|Norvig|2009|p=2}}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.{{Harvnb|McCorduck|2004|p=204}} A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."WEB,weblink Artificial Intelligence: An Introduction, p. 37, Maloof, Mark,, For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.MAGAZINE, Schank, Roger C., Where's the AI, AI magazine, 12, 4, 1991, 38, Modern machine capabilities generally classified as AI include successfully understanding human speech,{{sfn|Russell|Norvig|2009}} competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations.Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).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. General intelligence is among the field's long-term goals. 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".See the Dartmouth proposal, under Philosophy, below. This raises philosophical arguments about the nature of 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. Some people also consider AI to be a danger to humanity if it progresses unabated.WEB,weblink Stephen Hawking believes AI could be mankind's last accomplishment, 21 October 2016, BetaNews, live,weblink 28 August 2017, dmy-all, Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.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.{{toclimit|3}}


File:Didrachm Phaistos obverse CdM.jpg|thumb|Silver didrachma from Crete depicting Talos, an ancient mythical automatonautomatonThought-capable artificial beings appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley's Frankenstein or Karel ÄŒapek's R.U.R. (Rossum's Universal Robots). These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour".{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }} The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".{{sfn|Russell|Norvig|2009|p=16}}The field of AI research was born at a workshop at Dartmouth College in 1956, where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener.JOURNAL, McCarthy, John, John McCarthy (computer scientist), Review of The Question of Artificial Intelligence, Annals of the History of Computing, 10, 3, 1988, 224–229, , collected in BOOK, McCarthy, John, John McCarthy (computer scientist), Defending AI Research: A Collection of Essays and Reviews, CSLI, 1996, 10. Review of The Question of Artificial Intelligence, , p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. They and their students produced programs that the press described as "astonishing":{{sfn|Russell|Norvig|2003|p=18|quote=it was astonishing whenever a computer did anything kind of smartish}} computers were learning checkers strategies (c. 1954)Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA (and by 1959 were reportedly playing better than the average human),JOURNAL, Samuel, A. L., Some Studies in Machine Learning Using the Game of Checkers, IBM Journal of Research and Development, July 1959, 3, 3, 210–229, 10.1147/rd.33.0210,, solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI's founders were optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill{{sfn|Lighthill|1973}} and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. The success was due to increasing computational power (see Moore's law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.{{sfn|McCorduck|2004|pp=480–483}}In 2011, a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.{{sfn|Markoff|2011}} Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.WEB, Ask the AI experts: What's driving today's progress in AI?,weblink McKinsey & Company, 13 April 2018, en, The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI researchWEB,weblink Kinect's AI breakthrough explained, Administrator,, live,weblink" title="">weblink 1 February 2016, dmy-all, as do intelligent personal assistants in smartphones.WEB,weblink Virtual Personal Assistants & The Future Of Your Smartphone [Infographic], 15 January 2013, Rowinski, Dan, ReadWrite, live,weblink" title="">weblink 22 December 2015, dmy-all, In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.WEB,weblink AlphaGo – Google DeepMind, live,weblink 10 March 2016, dmy-all, NEWS, Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol,weblink 1 October 2016, BBC News, 12 March 2016, live,weblink" title="">weblink 26 August 2016, dmy-all, In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,JOURNAL,weblink After Win in China, AlphaGo's Designers Explore New AI, Wired, 27 May 2017, live,weblink 2 June 2017, dmy-all, who at the time continuously held the world No. 1 ranking for two years.WEB,weblink World's Go Player Ratings, May 2017, live,weblink 1 April 2017, dmy-all, WEB, 柯洁迎19岁生日 雄踞人类世界排名第一已两年,weblink Chinese, May 2017, live,weblink" title="">weblink 11 August 2017, dmy-all, This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is a relatively complex game, more so than Chess.According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks.WEB
, Why 2015 Was a Breakthrough Year in Artificial Intelligence
, Clark
, Jack
, Bloomberg News
, 8 December 2015
, 23 November 2016
, After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever.
, live
, 23 November 2016
, dmy-all
, He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people. In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes".WEB, Reshaping Business With Artificial Intelligence,weblink MIT Sloan Management Review, 2 May 2018, en, WEB, Lorica, Ben, The state of AI adoption,weblink O'Reilly Media, 2 May 2018, en, 18 December 2017, Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower".WEB,weblink Understanding China's AI Strategy, Allen, Gregory, February 6, 2019, Center for a New American Security, NEWS, Review {{!, How two AI superpowers – the U.S. and China – battle for supremacy in the field |url= |accessdate=4 November 2018 |work=Washington Post |date=2 November 2018 |language=en}} However, it has been acknowledged that reports regarding artificial intelligence have tended to be exaggerated.WEB,weblink Artificial Intelligence: You know it isn't real, yeah?, Alistair Dabbs 22 Feb 2019, at 10:11,, WEB,weblink Stop Calling it Artificial Intelligence, WEB,weblink AI isn't taking over the world – it doesn't exist yet, GBG Global website,


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. 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.”JOURNAL, Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence, Andreas, Kaplan, Michael, Haenlein, 1 January 2019, Business Horizons, 62, 1, 15–25, 10.1016/j.bushor.2018.08.004,


A typical AI analyzes its environment and takes actions that maximize its chance of success. An AI's intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Do mathematically similar actions to the ones 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.{{efn|The act of doling out rewards can itself be formalized or automated into a "reward function".}} Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food.{{sfn|Domingos|2015|loc=Chapter 5}} 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.{{sfn|Domingos|2015|loc=Chapter 7}} 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.Lindenbaum, M., Markovitch, S., & Rusakov, D. (2004). Selective sampling for nearest neighbor classifiers. Machine learning, 54(2), 125–152.AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.{{efn|Terminology varies; see algorithm characterizations.}} A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:{{sfn|Domingos|2015|loc=Chapter 1}}
  1. If someone has a "threat" (that is, two in a row), take the remaining square. Otherwise,
  2. if a move "forks" to create two threats at once, play that move. Otherwise,
  3. take the center square if it is free. Otherwise,
  4. if your opponent has played in a corner, take the opposite corner. Otherwise,
  5. take an empty corner if one exists. Otherwise,
  6. take any empty square.
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|date=June 2019}}. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial.{{sfn|Domingos|2015|loc=Chapter 2, Chapter 3}} For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding a pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.JOURNAL
, P. E.
, Hart
, Nilsson, N. J., Raphael, B.
, Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"
, SIGART Newsletter
, 37
, 28–29
, 1972
, 10.1145/1056777.1056779
The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have influenza". A second, more general, approach is Bayesian inference: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial "neurons" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.{{sfn|Domingos|2015|loc=Chapter 2, Chapter 4, Chapter 6}}NEWS, Can neural network computers learn from experience, and if so, could they ever become what we would call 'smart'?,weblink 24 March 2018, Scientific American, 2018, en, Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist". Learners also work on the basis of "Occam's razor": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.File:Overfitted Data.png|thumb|The blue line could be an example of overfittingoverfittingSettling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.{{sfn|Domingos|2015|loc=Chapter 6, Chapter 7}} Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.{{sfn|Domingos|2015|p=286}} A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.{{efn|Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.}}NEWS, Single pixel change fools AI programs,weblink 12 March 2018, BBC News, 3 November 2017, NEWS, AI Has a Hallucination Problem That's Proving Tough to Fix,weblink 12 March 2018, WIRED, 2018, Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint {{arXiv|1412.6572}} (2014).(File:Détection de personne - exemple 3.jpg|thumb|A self-driving car system may use a neural network to determine which parts of the picture seem to match previous training images of pedestrians, and then model those areas as slow-moving but somewhat unpredictable rectangular prisms that must be avoided.BOOK, Matti, D., Ekenel, H. K., Thiran, J. P., Combining LiDAR space clustering and convolutional neural networks for pedestrian detection, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017, 1–6, 10.1109/AVSS.2017.8078512, 978-1-5386-2939-0, 1710.06160, BOOK, Ferguson, Sarah, Luders, Brandon, Grande, Robert C., How, Jonathan P., Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions, Algorithmic Foundations of Robotics XI, 107, 2015, 161–177, 10.1007/978-3-319-16595-0_10, Springer, Cham, en, Springer Tracts in Advanced Robotics, 978-3-319-16594-3, 1405.5581, )Compared with humans, existing AI lacks several features of human "commonsense reasoning"; most notably, humans have powerful mechanisms for reasoning about "naïve physics" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "folk psychology" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.)NEWS, Cultivating Common Sense {{!,|url=|accessdate=24 March 2018|work=Discover Magazine|date=2017}}JOURNAL, Davis, Ernest, Marcus, Gary, Commonsense reasoning and commonsense knowledge in artificial intelligence, Communications of the ACM, 24 August 2015, 58, 9, 92–103, 10.1145/2701413,weblink JOURNAL, Winograd, Terry, Understanding natural language, Cognitive Psychology, January 1972, 3, 1, 1–191, 10.1016/0010-0285(72)90002-3, This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.NEWS, Don't worry: Autonomous cars aren't coming tomorrow (or next year),weblink 24 March 2018, Autoweek, 2016, NEWS, Knight, Will, Boston may be famous for bad drivers, but it's the testing ground for a smarter self-driving car,weblink 27 March 2018, MIT Technology Review, 2017, en, JOURNAL, Prakken, Henry, On the problem of making autonomous vehicles conform to traffic law, Artificial Intelligence and Law, 31 August 2017, 25, 3, 341–363, 10.1007/s10506-017-9210-0,

Challenges of AI

The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart.JOURNAL, Lieto, Antonio, May 2018, The knowledge level in cognitive architectures: Current limitations and possible developments, Cognitive Systems Research, 48, 39–55, 10.1016/j.cogsys.2017.05.001, 2318/1665207, The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.

Reasoning, problem solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.

Knowledge representation

(File:GFO taxonomy tree.png|right|thumb|An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.)Knowledge representation and knowledge engineering are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.BOOK, Sikos, Leslie F., June 2017, Description Logics in Multimedia Reasoning,weblink Cham, Springer, 978-3-319-54066-5, 10.1007/978-3-319-54066-5, live,weblink 29 August 2017, dmy-all, The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,JOURNAL, Smoliar, Stephen W., Zhang, HongJiang, Content based video indexing and retrieval, IEEE Multimedia, 1994, 1, 2, 62–72, 10.1109/93.311653, scene interpretation,JOURNAL, Neumann, Bernd, Möller, Ralf, On scene interpretation with description logics, Image and Vision Computing, January 2008, 26, 1, 82–101, 10.1016/j.imavis.2007.08.013, clinical decision support,JOURNAL, Kuperman, G. J., Reichley, R. M., Bailey, T. C., Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations, Journal of the American Medical Informatics Association, 1 July 2006, 13, 4, 369–371, 10.1197/jamia.M2055, 16622160, 1513681, knowledge discovery (mining "interesting" and actionable inferences from large databases),JOURNAL, MCGARRY, KEN, A survey of interestingness measures for knowledge discovery, The Knowledge Engineering Review, 1 December 2005, 20, 1, 39, 10.1017/S0269888905000408, and other areas.CONFERENCE, Automatic annotation and semantic retrieval of video sequences using multimedia ontologies, Bertini, M, Del Bimbo, A, Torniai, C, 2006, ACM, MM '06 Proceedings of the 14th ACM international conference on Multimedia, 679–682, Santa Barbara, 14th ACM international conference on Multimedia, Among the most difficult problems in knowledge representation are:
Default reasoning and the qualification problem: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.
The breadth of commonsense knowledge: The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering—they must be built, by hand, one complicated concept at a time.
The subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"{{sfn|Dreyfus|Dreyfus|1986}} or an art critic can take one look at a statue and realize that it is a fake.{{sfn|Gladwell|2005}} These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge.


File:Hierarchical-control-system.svg|thumb| A hierarchical control system is a form of control systemcontrol systemIntelligent agents must be able to set goals and achieve them. They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or "value") of available choices.In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.


Machine learning (ML), a fundamental concept of AI research since the field's inception,Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".{{Harv|Turing|1950}} In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".{{Harv|Solomonoff|1956}} is the study of computer algorithms that improve automatically through experience.This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E."Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.JOURNAL, Jordan, M. I., Mitchell, T. M., Machine learning: Trends, perspectives, and prospects, Science, 16 July 2015, 349, 6245, 255–260, 10.1126/science.aaa8415, 26185243, 2015Sci...349..255J, In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing

File:ParseTree.svg|thumb| A parse tree represents the syntactic structure of a sentence according to some formal grammarformal grammarNatural language processing (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering"Versatile question answering systems: seeing in synthesis" {{webarchive|url= |date=1 February 2016 }}, Mittal et al., IJIIDS, 5(2), 119–142, 2011
and machine translation. Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.JOURNAL, Cambria, Erik, White, Bebo, Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article], IEEE Computational Intelligence Magazine, May 2014, 9, 2, 48–57, 10.1109/MCI.2014.2307227,


File:Ääretuvastuse näide.png|thumb|Feature detection (pictured: edge detectionedge detectionMachine perception is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.

Motion and manipulation

AI is heavily used in robotics. Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage. A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient's breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.{{sfn|Tecuci|2012}}JOURNAL, Cadena, Cesar, Carlone, Luca, Carrillo, Henry, Latif, Yasir, Scaramuzza, Davide, Neira, Jose, Reid, Ian, Leonard, John J., Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age, IEEE Transactions on Robotics, December 2016, 32, 6, 1309–1332, 10.1109/TRO.2016.2624754, 1606.05830, 2016arXiv160605830C, Moravec's paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".BOOK, Hans, Moravec, 1988, Mind Children, Harvard University Press, Hans Moravec, 15, NEWS, Chan, Szu Ping, This is what will happen when robots take over the world,weblink 23 April 2018, 15 November 2015, This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.NEWS, IKEA furniture and the limits of AI,weblink 24 April 2018, The Economist, 2018, en,

Social intelligence

File:Kismet robot at MIT Museum.jpg|thumb|KismetKismetMoravec's paradox can be extended to many forms of social intelligence.MAGAZINE, Thompson, Derek, What Jobs Will the Robots Take?,weblink 24 April 2018, The Atlantic, 2018, JOURNAL, Scassellati, Brian, Theory of mind for a humanoid robot, Autonomous Robots, 12, 1, 2002, 13–24, 10.1023/A:1013298507114, Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.JOURNAL, Cao, Yongcan, Yu, Wenwu, Ren, Wei, Chen, Guanrong, An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination, IEEE Transactions on Industrial Informatics, February 2013, 9, 1, 427–438, 10.1109/TII.2012.2219061, 1207.3231, Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects.{{sfn|Thro|1993}}{{sfn|Edelson|1991}}{{sfn|Tao|Tan|2005}} Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.JOURNAL, Poria, Soujanya, Cambria, Erik, Bajpai, Rajiv, Hussain, Amir, A review of affective computing: From unimodal analysis to multimodal fusion, Information Fusion, September 2017, 37, 98–125, 10.1016/j.inffus.2017.02.003, 1893/25490, In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.MAGAZINE, Waddell, Kaveh, Chatbots Have Entered the Uncanny Valley,weblink 24 April 2018, The Atlantic, 2018,

General intelligence

Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).BOOK, Pennachin, C., Goertzel, B., Contemporary Approaches to Artificial General Intelligence, Artificial General Intelligence. Cognitive Technologies, 2007, 10.1007/978-3-540-68677-4_1, Springer, Berlin, Heidelberg, Cognitive Technologies, 978-3-540-23733-4, Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.MAGAZINE, Roberts, Jacob, Thinking Machines: The Search for Artificial Intelligence, Distillations, 2016, 2, 2, 14–23,weblink 20 March 2018,weblink 19 August 2018, dead, Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at (Catastrophic interference#The Sequential Learning Problem: McCloskey and Cohen (1989)|sequential learning).NEWS, The superhero of artificial intelligence: can this genius keep it in check?,weblink 26 April 2018, the Guardian, 16 February 2016, en, JOURNAL, Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A., Veness, Joel, Bellemare, Marc G., Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K., Ostrovski, Georg, Petersen, Stig, Beattie, Charles, Sadik, Amir, Antonoglou, Ioannis, King, Helen, Kumaran, Dharshan, Wierstra, Daan, Legg, Shane, Hassabis, Demis, Human-level control through deep reinforcement learning, Nature, 26 February 2015, 518, 7540, 529–533, 10.1038/nature14236, 25719670, 2015Natur.518..529M, NEWS, Sample, Ian, Google's DeepMind makes AI program that can learn like a human,weblink 26 April 2018, the Guardian, 14 March 2017, en, Besides transfer learning,NEWS, From not working to neural networking,weblink 26 April 2018, The Economist, 2016, en, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web.{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.{{sfn|Domingos|2015|chapter=9. The Pieces of the Puzzle Fall into Place}} Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.JOURNAL, Goertzel, Ben, Lian, Ruiting, Arel, Itamar, de Garis, Hugo, Chen, Shuo, A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures, Neurocomputing, December 2010, 74, 1–3, 30–49, 10.1016/j.neucom.2010.08.012, Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). A problem like machine translation is considered "AI-complete", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.


There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about" {{Harv|Nilsson|1983|p=10}}. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?

Cybernetics and brain simulation

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England. By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.


When access to digital computers became possible in the mid 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI". During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt.Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Cognitive simulation

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.


Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.

Anti-logic or scruffy

Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.


When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.JOURNAL, Frederick, Hayes-Roth, William, Murray, Leonard, Adelman, Expert systems, en, 10.1036/1097-8542.248550, The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.


By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

Embodied intelligence

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).{{sfn|Weng|McClelland|Pentland|Sporns|2001}}{{sfn|Lungarella|Metta|Pfeifer|Sandini|2003}}{{sfn|Asada|Hosoda|Kuniyoshi|Ishiguro|2009}}{{sfn|Oudeyer|2010}}

Computational intelligence and soft computing

Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s. Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.

Statistical learning

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).{{efn|While such a "victory of the neats" may be a consequence of the field becoming more mature, (Artificial Intelligence: A Modern Approach|AIMA) states that in practice both neat and scruffy approaches continue to be necessary in AI research.}} Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.NEWS, Hutson, Matthew, Artificial intelligence faces reproducibility crisis,weblink 28 April 2018, Science Magazine, Science, 16 February 2018, 725–726, en, 10.1126/science.359.6377.725, 2018Sci...359..725H, Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language.{{sfn|Norvig|2012}} Critics note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.{{sfn|Langley|2011}}{{sfn|Katz|2012}}

Integrating the approaches

Intelligent agent paradigm: An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic artificial neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.
Agent architectures and cognitive architectures: Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. Some cognitive architectures are custom-built to solve a narrow problem; others, such as Soar, are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are hybrid intelligent systems that include both symbolic and sub-symbolic components.JOURNAL, Laird, John, Extending the Soar cognitive architecture, Frontiers in Artificial Intelligence and Applications, 2008, 171, 224,, JOURNAL, Lieto, Antonio, Lebiere, Christian, Oltramari, Alessandro, The knowledge level in cognitive architectures: Current limitations and possibile developments, Cognitive Systems Research, May 2018, 48, 39–55, 10.1016/j.cogsys.2017.05.001, 2318/1665207, JOURNAL, Lieto, Antonio, Bhatt, Mehul, Oltramari, Alessandro, Vernon, David, The role of cognitive architectures in general artificial intelligence, Cognitive Systems Research, May 2018, 48, 1–3, 10.1016/j.cogsys.2017.08.003, 2318/1665249,


AI has developed many tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Search and optimization

Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization.Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies. Heuristics limit the search for solutions into a smaller sample size.{{sfn|Tecuci|2012}}A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.File:ParticleSwarmArrowsAnimation.gif|thumb|A particle swarm seeking the global minimumglobal minimumEvolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).BOOK, Daniel Merkle, Martin Middendorf, Burke, Edmund K., Kendall, Graham, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2013, Springer Science & Business Media, 978-1-4614-6940-7, en, Swarm Intelligence,


Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning.Several different forms of logic are used in AI research. Propositional logic involves truth functions such as "or" and "not". First-order logic adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.{{efn|"There exist many different types of uncertainty, vagueness, and ignorance... [We] independently confirm the inadequacy of systems for reasoning about uncertainty that propagates numerical factors according to only to which connectives appear in assertions."JOURNAL, Elkin, Charles, The paradoxical success of fuzzy logic, IEEE Expert, 1994, 9, 4, 3–49, 10.1109/64.336150,, }}NEWS, What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?,weblink 5 May 2018, Scientific American, en, Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics; situation calculus, event calculus and fluent calculus (for representing events and time); causal calculus; belief calculus;"The Belief Calculus and Uncertain Reasoning", Yen-Teh Hsia and modal logics.Overall, qualitative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.{{sfn|Domingos|2015|loc=chapter 6}}JOURNAL, Logic and Probability,weblink Stanford Encyclopedia of Philosophy, 5 May 2018, 2013-03-07, Demey, Lorenz, Kooi, Barteld, Sack, Joshua,

Probabilistic methods for uncertain reasoning

File:EM Clustering of Old Faithful data.gif|right|frame|Expectation-maximization clustering of Old FaithfulOld FaithfulMany problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.Bayesian networks are a very general tool that can be used for various problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm),{{efn|Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown latent variables{{sfn|Domingos|2015|p=210}}}} planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other "loops" (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is{{citation needed|date=July 2019}}. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.{{sfn|Domingos|2015|loc=chapter 6}}A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.

Classifiers and statistical learning methods

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree is perhaps the most widely used machine learning algorithm.{{sfn|Domingos|2015|p=88}} Other widely used classifiers are the neural network,k-nearest neighbor algorithm,{{efn|The most widely used analogical AI until the mid-1990s{{sfn|Domingos|2015|p=187}}}}kernel methods such as the support vector machine (SVM),{{efn|SVM displaced k-nearest neighbor in the 1990s{{sfn|Domingos|2015|p=188}}}}Gaussian mixture model, and the extremely popular naive Bayes classifier.{{efn|Naive Bayes is reportedly the "most widely used learner" at Google, due in part to its scalability.{{sfn|Domingos|2015|p=152}}}} Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.{{sfn|Russell|Norvig|2009|loc=18.12: Learning from Examples: Summary}}

Artificial neural networks

File:Artificial neural network.svg|thumb|A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brainhuman brainNeural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared{{efn|Each individual neuron is likely to participate in more than one concept.}} neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car.{{efn|Steering for the 1995 "No Hands Across America" required "only a few human assists".}}{{sfn|Domingos|2015|loc=Chapter 4}} In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.NEWS, Why Deep Learning Is Suddenly Changing Your Life,weblink 12 March 2018, Fortune, 2016, NEWS, Google leads in the race to dominate artificial intelligence,weblink 12 March 2018, The Economist, 2017, en, The study of non-learning artificial neural networks began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others{{citation needed|date=July 2019}}.The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ("fire together, wire together"), GMDH or competitive learning.Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,Seppo Linnainmaa (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master's Thesis (in Finnish), Univ. Helsinki, 6–7.Griewank, Andreas (2012). Who Invented the Reverse Mode of Differentiation?. Optimization Stories, Documenta Matematica, Extra Volume ISMP (2012), 389–400. and was introduced to neural networks by Paul Werbos.Paul Werbos, "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences", PhD thesis, Harvard University, 1974.Paul Werbos (1982). Applications of advances in nonlinear sensitivity analysis. In System modeling and optimization (pp. 762–770). Springer Berlin Heidelberg. Online {{webarchive|url= |date=14 April 2016 }}Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches{{citation needed|date=July 2019}}. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".NEWS, Artificial intelligence can 'evolve' to solve problems,weblink 7 February 2018, Science {{!, AAAS|date=10 January 2018|language=en}}

Deep feedforward neural networks

Deep learning is any artificial neural network that can learn a long chain of causal links{{dubious|date=July 2019}}. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a "credit assignment path" (CAP) depth of seven{{citation needed|date=July 2019}}. Many deep learning systems need to be able to learn chains ten or more causal links in length. Deep learning has transformed many important subfields of artificial intelligence{{why|date=July 2019}}, including computer vision, speech recognition, natural language processing and others.Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press. Online {{webarchive|url= |date=16 April 2016 }}JOURNAL, Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., Kingsbury, B., 2012, Deep Neural Networks for Acoustic Modeling in Speech Recognition – The shared views of four research groups, IEEE Signal Processing Magazine, 29, 6, 82–97, 10.1109/msp.2012.2205597, JOURNAL, Schmidhuber, J., 2015, Deep Learning in Neural Networks: An Overview, Neural Networks, 61, 85–117, 1404.7828, 10.1016/j.neunet.2014.09.003, 25462637, According to one overview,JOURNAL, Schmidhuber, Jürgen, Jürgen Schmidhuber, 2015, Deep Learning, Scholarpedia, 10, 11, 32832, 10.4249/scholarpedia.32832, dmy-all, 2015SchpJ..1032832S, the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in 1986Rina Dechter (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.Online {{webarchive|url= |date=19 April 2016 }} and gained traction afterIgor Aizenberg and colleagues introduced it to artificial neural networks in 2000.Igor Aizenberg, Naum N. Aizenberg, Joos P.L. Vandewalle (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media. The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.BOOK, Cybernetic Predicting Devices, Ivakhnenko, Alexey, Naukova Dumka, 1965, Kiev, {{page needed|date=December 2016}} These networks are trained one layer at a time. Ivakhnenko's 1971 paperJOURNAL, 10.1109/TSMC.1971.4308320, Polynomial Theory of Complex Systems, IEEE Transactions on Systems, Man, and Cybernetics, 4, 364–378, 1971, Ivakhnenko, A. G., describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning.{{sfn|Hinton|2007}} Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.WEB, Research, AI, Deep Neural Networks for Acoustic Modeling in Speech Recognition,weblink, 23 October 2015, 23 October 2015, Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.JOURNAL, Fukushima, K., 1980, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, 36, 4, 193–202, 10.1007/bf00344251, 7370364, In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.Yann LeCun (2016). Slides on Deep Learning Online {{webarchive|url= |date=23 April 2016 }}Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's "AlphaGo Lee", the program that beat a top Go champion in 2016.JOURNAL, David, Silver, David Silver (programmer), Julian, Schrittwieser, Karen, Simonyan, Ioannis, Antonoglou, Aja, Huang, Aja Huang, Arthur, Guez, Thomas, Hubert, Lucas, Baker, Matthew, Lai, Adrian, Bolton, Yutian, Chen, Chen Yutian, Timothy, Lillicrap, Hui, Fan, Fan Hui, Laurent, Sifre, George van den, Driessche, Thore, Graepel, Demis, Hassabis, Demis Hassabis, Mastering the game of Go without human knowledge, Nature (journal), Nature, 0028-0836, 354–359, 550, 7676, 10.1038/nature24270, 29052630, 19 October 2017, AlphaGo Lee... 12 convolutional layers, 2017Natur.550..354S,weblink {{closed access}}

Deep recurrent neural networks

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs) which are in theory Turing completeJOURNAL, Hyötyniemi, Heikki, Turing machines are recurrent neural networks, Proceedings of STeP '96/Publications of the Finnish Artificial Intelligence Society, 13–24, 1996, and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning. RNNs can be trained by gradient descentP. J. Werbos. Generalization of backpropagation with application to a recurrent gas market model" Neural Networks 1, 1988.A. J. Robinson and F. Fallside. The utility driven dynamic error propagation network. Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department, 1987.R. J. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum, 1994. but suffer from the vanishing gradient problem.Sepp Hochreiter (1991), Untersuchungen zu dynamischen neuronalen Netzen {{webarchive|url= |date=6 March 2015 }}, Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber. In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.JOURNAL, Schmidhuber, J., 1992, Learning complex, extended sequences using the principle of history compression, Neural Computation, 4, 2, 234–242, 10.1162/neco.1992.4.2.234,, Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.Hochreiter, Sepp; and Schmidhuber, Jürgen; Long Short-Term Memory, Neural Computation, 9(8):1735–1780, 1997 LSTM is often trained by Connectionist Temporal Classification (CTC).Alex Graves, Santiago Fernandez, Faustino Gomez, and Jürgen Schmidhuber (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural nets. Proceedings of ICML'06, pp. 369–376. At Google, Microsoft and Baidu this approach has revolutionised speech recognition.ARXIV
, Hannun, Awni
, Case, Carl
, Casper, Jared
, Catanzaro, Bryan
, Diamos, Greg
, Elsen, Erich
, Prenger, Ryan
, Satheesh, Sanjeev
, Sengupta, Shubho
, Coates, Adam
, Ng, Andrew Y., Andrew Ng
, 2014
, Deep Speech: Scaling up end-to-end speech recognition
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, Hasim Sak and Andrew Senior and Francoise Beaufays (2014). Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of Interspeech 2014.ARXIV
, Li, Xiangang
, Wu, Xihong
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, Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition
, 1410.4281, cs.CL
, For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.Haşim Sak, Andrew Senior, Kanishka Rao, Françoise Beaufays and Johan Schalkwyk (September 2015): Google voice search: faster and more accurate. {{webarchive|url= |date=9 March 2016 }} Google also used LSTM to improve machine translation,ARXIV
, Sutskever, Ilya
, Vinyals, Oriol
, Le, Quoc V.
, 2014
, Sequence to Sequence Learning with Neural Networks
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, Language ModelingARXIV
, Jozefowicz, Rafal
, Vinyals, Oriol
, Schuster, Mike
, Shazeer, Noam
, Wu, Yonghui
, 2016
, Exploring the Limits of Language Modeling
, 1602.02410, cs.CL
, and Multilingual Language Processing.ARXIV
, Gillick, Dan
, Brunk, Cliff
, Vinyals, Oriol
, Subramanya, Amarnag
, 2015
, Multilingual Language Processing From Bytes
, 1512.00103, cs.CL
, LSTM combined with CNNs also improved automatic image captioningARXIV
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, Toshev, Alexander
, Bengio, Samy
, Erhan, Dumitru
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, Show and Tell: A Neural Image Caption Generator
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, and a plethora of other applications.

Evaluating progress

{{Further|Progress in artificial intelligence|Competitions and prizes in artificial intelligence}}AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.NEWS, Brynjolfsson, Erik, Mitchell, Tom, What can machine learning do? Workforce implications,weblink 7 May 2018, Science, 22 December 2017, 1530–1534, en, 10.1126/science.aap8062, 2017Sci...358.1530B, While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.NEWS, Sample, Ian, 'It's able to create knowledge itself': Google unveils AI that learns on its own,weblink 7 May 2018, the Guardian, 18 October 2017, en, NEWS, The AI revolution in science,weblink 7 May 2018, Science {{!, AAAS|date=5 July 2017|language=en}} Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI."NEWS, Will your job still exist in 10 years when the robots arrive?,weblink 7 May 2018, South China Morning Post, 2017, en, Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.NEWS, Borowiec, Tracey Lien, Steven, AlphaGo beats human Go champ in milestone for artificial intelligence,weblink 7 May 2018,, 2016, NEWS, Brown, Noam, Sandholm, Tuomas, Superhuman AI for heads-up no-limit poker: Libratus beats top professionals,weblink 7 May 2018, Science, 26 January 2018, 418–424, en, 10.1126/science.aao1733, E-sports such as StarCraft continue to provide additional public benchmarks.JOURNAL, Ontanon, Santiago, Synnaeve, Gabriel, Uriarte, Alberto, Richoux, Florian, Churchill, David, Preuss, Mike, A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft, IEEE Transactions on Computational Intelligence and AI in Games, December 2013, 5, 4, 293–311, 10.1109/TCIAIG.2013.2286295,, NEWS, Facebook Quietly Enters StarCraft War for AI Bots, and Loses,weblink 7 May 2018, WIRED, 2017, There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.WEB,weblink ILSVRC2017,, en, 2018-11-06, The "imitation game" (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.JOURNAL, Schoenick, Carissa, Clark, Peter, Tafjord, Oyvind, Turney, Peter, Etzioni, Oren, Moving beyond the Turing Test with the Allen AI Science Challenge, Communications of the ACM, 23 August 2017, 60, 9, 60–64, 10.1145/3122814, 1604.04315, A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.{{sfn|O'Brien|Marakas|2011}}Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.JOURNAL, Hernández-Orallo, José, Dowe, David L., Hernández-Lloreda, M.Victoria, Universal psychometrics: Measuring cognitive abilities in the machine kingdom, Cognitive Systems Research, March 2014, 27, 50–74, 10.1016/j.cogsys.2013.06.001,


File:Automated online assistant.png|thumb|An automated online assistantautomated online assistantAI is relevant to any intellectual task.{{sfn|Russell|Norvig|2009|p=1}} Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.{{sfn|CNN|2006}}High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,Using AI to predict flight delays, prediction of judicial decisions,JOURNAL, N. Aletras, D. Tsarapatsanis, D. Preotiuc-Pietro, V. Lampos, Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective, PeerJ Computer Science, 2, e93, 2016, dmy-all, 10.7717/peerj-cs.93, targeting online advertisements, {{sfn|Russell|Norvig|2009|p=1}}NEWS, The Economist Explains: Why firms are piling into artificial intelligence,weblink 19 May 2016, The Economist, 31 March 2016, live,weblink" title="">weblink 8 May 2016, dmy-all, NEWS,weblink The Promise of Artificial Intelligence Unfolds in Small Steps, Lohr, Steve, The New York Times, 28 February 2016, 29 February 2016, live,weblink" title="">weblink 29 February 2016, dmy-all, and energy storageWEB,weblink A Californian business is using A.I. to change the way we think about energy storage, Frangoul, Anmar, 2019-06-14, CNBC, en, 2019-11-05, With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution,WEB,weblink Social media 'outstrips TV' as news source for young people, 15 June 2016, Wakefield, Jane, BBC News, live,weblink" title="">weblink 24 June 2016, dmy-all, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.WEB,weblink So you think you chose to read this article?, 22 July 2016, Smith, Mark, BBC News, live,weblink" title="">weblink 25 July 2016, dmy-all, AI can also produce Deepfakes, a content-altering technology. ZDNet reports, "It presents something that did not actually occur,” Though 88% of Americans believe Deepfakes can cause more harm than good, only 47% of them believe they can be targeted. The boom of election year also opens public discourse to threats of videos of falsified politician media. WEB,weblink Half of Americans do not believe deepfake news could target them online, Brown, Eileen, ZDNet, en, 2019-12-03,


File:Laproscopic Surgery Robot.jpg|thumb| A patient-side surgical arm of Da Vinci Surgical SystemDa Vinci Surgical SystemAI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high risk patients for population health. The breadth of applications is rapidly increasing.As an example, AI is being applied to the high cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.NEWS,weblink 10 Promising AI Applications in Health Care, 2018-05-10, Harvard Business Review, 2018-08-28,weblink 15 December 2018, dead, File:X-ray of a hand with automatic bone age calculation.jpg|thumb|X-ray of a hand, with automatic calculation of bone agebone ageArtificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.WEB, Dina Bass, Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments,weblink 20 September 2016, Bloomberg, live,weblink 11 May 2017, dmy-all, There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover"{{citation needed|date=July 2019}}. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.NEWS,weblink Artificial intelligence 'as good as cancer doctors', Gallagher, James, 26 January 2017, BBC News, en-GB, 26 January 2017, live,weblink" title="">weblink 26 January 2017, dmy-all, Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.{{Citation|title=Remote monitoring of high-risk patients using artificial intelligence|date=18 Oct 1994|url=|editor-last=Langen|editor2-last=Katz|editor3-last=Dempsey|editor-first=Pauline A.|editor2-first=Jeffrey S.|editor3-first=Gayle|issue=US5357427 A|accessdate=27 February 2017|url-status=live|archiveurl=|archivedate=28 February 2017|df=dmy-all}} One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.WEB,weblink Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning, Kermany, D, Goldbaum, M,, 2018-12-18, Zhang, Kang, According to CNN, a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.NEWS, Senthilingam, Meera, Are Autonomous Robots Your next Surgeons?, CNN, Cable News Network, 12 May 2016, 4 December 2016,weblink live,weblink" title="">weblink 3 December 2016, dmy-all, IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson has struggled to achieve success and adoption in healthcare.WEB,weblink Full Page Reload, IEEE Spectrum: Technology, Engineering, and Science News, en, 2019-09-03,


Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. {{as of|2016}}, there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple."33 Corporations Working On Autonomous Vehicles". CB Insights. N.p., 11 August 2016. 12 November 2016.Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.West, Darrell M. "Moving forward: Self-driving vehicles in China, Europe, Japan, Korea, and the United States". Center for Technology Innovation at Brookings. N.p., September 2016. 12 November 2016.Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.JOURNAL, Burgess, Matt, The UK is about to Start Testing Self-Driving Truck Platoons,weblink Wired UK, 20 September 2017, live,weblink" title="">weblink 22 September 2017, dmy-all, 2017-08-24, Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.JOURNAL, Davies, Alex, World's First Self-Driving Semi-Truck Hits the Road,weblink WIRED, 20 September 2017, live,weblink 28 October 2017, dmy-all, 2015-05-05, One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.McFarland, Matt. "Google's artificial intelligence breakthrough may have a huge impact on self-driving cars and much more". The Washington Post 25 February 2015. Infotrac Newsstand. 24 October 2016 Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions."Programming safety into self-driving cars". National Science Foundation. N.p., 2 February 2015. 24 October 2016.Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.ArXiv, E. T. (26 October 2015). Why Self-Driving Cars Must Be Programmed to Kill. Retrieved 17 November 2017, fromweblink{{Dead link|date=October 2019 |bot=InternetArchiveBot |fix-attempted=yes }} The programming of the car in these situations is crucial to a successful driver-less automobile.

Finance and economics

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards.WEB,weblink Impact of Artificial Intelligence on Banking, Christy, Charles A.,, 2019-09-10, Programs like Kasisto and Moneystream are using AI in financial services.Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.WEB,weblink Accounting, automation and AI, Eleanor, O'Neill,, English, 31 July 2016, 18 November 2016, live,weblink 18 November 2016, dmy-all, In August 2001, robots beat humans in a simulated financial trading competition.Robots Beat Humans in Trading Battle. {{webarchive|url= |date=9 September 2009 }} (8 August 2001) AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.NEWS,weblink CTO Corner: Artificial Intelligence Use in Financial Services – Financial Services Roundtable, 2 April 2015, Financial Services Roundtable, en-US, 18 November 2016, dead,weblink" title="">weblink 18 November 2016, dmy-all, WEB,weblink Artificial Intelligence Solutions, AI Solutions,, WEB,weblink Palantir once mocked the idea of salespeople. Now it's hiring them, Chapman, Lizette,, 2019-02-28, AI is also being used by corporations. Whereas AI CEO's are still 30 years away,WEB,weblink Jack Ma: In 30 years, the best CEO could be a robot, Sherisse, Pham, 24 April 2017, CNNMoney, WEB,weblink Can't find a perfect CEO? Create an AI one yourself, 22 October 2016, robotic process automation (RPA) is already being used today in corporate finance. RPA uses artificial intelligence to train and teach software robots to process transactions, monitor compliance and audit processes automatically.WEB,weblink The Robots Are Coming To Corporate Finance, Patrick, Taylor, Forbes, The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.BOOK, Marwala, Tshilidzi, Hurwitz, Evan, Artificial Intelligence and Economic Theory: Skynet in the Market, 2017, Springer Science+Business Media, Springer, London, 978-3-319-66104-9, For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades{{citation needed|date=July 2019}}. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient{{citation needed|date=July 2019}}. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking{{citation needed|date=July 2019}}.. In August 2019, the AICPA introduced AI training course for accounting professionals.WEB,weblink Miles Education {{!, Future Of Finance {{!}} Blockchain Fundamentals for F&A Professionals Certificate||access-date=2019-09-26}}


{{See also|Mass surveillance in China}}Artificial intelligence paired with facial recognition systems may be used for mass surveillance. This is already the case in some parts of China.NEWS,weblink How China Uses High-Tech Surveillance to Subdue Minorities, Chris, Buckley, Paul, Mozur, 22 May 2019, The New York Times, WEB,weblink Security lapse exposed a Chinese smart city surveillance system, An artificial intelligence has also competed in the Tama City mayoral elections in 2018.In 2019, the tech city of Bengaluru in India is set to deploy AI managed traffic signal systems across the 387 traffic signals in the city. This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets.WEB,weblink AI traffic signals to be installed in Bengaluru soon, 2019-09-24, NextBigWhat, en-US, 2019-10-01,

Higher Education

{{cleanup|reason=present linked references better|date=November 2019}}AI is being implemented in university settings to aid in student learning, particularly for students with learning disabilities. With more students learning remotely and universities seeking ways to further provide assistive technologies to students who need it. These students include the 20,000 deaf or hard of hearing students who attend post-secondary educational institutions each year. AI is being used to capture important data, provide transcription and captioning live in lectures, and more. The Americans with Disabilities Act (ADA), which came into law in 1990, pushed many universities to invest more in assistive technologies which are rooted in AI. Now, universities are moving from a reactionary approach to this law which requires them to provide tools, but to implement AI technologies to aid in the learning of all students and create personalized learning experiences for them through video recommendations and more data.

Video games

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).NEWS,weblink Why AI researchers like video games, The Economist, live,weblink 5 October 2017, dmy-all, Yannakakis, G. N. (2012, May). Game AI revisited. In Proceedings of the 9th conference on Computing Frontiers (pp. 285–292). ACM.


{{Further|Artificial intelligence arms race|Lethal autonomous weapon|Unmanned combat aerial vehicle}}The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability.WEB, Slyusar, Vadym, Artificial intelligence as the basis of future control networks., Preprint, 2019,weblink Artificial Intelligence technologies enables coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.NEWS, Getting to grips with military robotics,weblink 7 February 2018, The Economist, 25 January 2018, en, WEB, Autonomous Systems: Infographic,weblink, 7 February 2018, en, Military drones capable of autonomous action are widely considered a useful asset.WEB,weblink Understanding China's AI Strategy, Allen, Gregory, February 6, 2019,, Center for a New American Security,weblink March 17, 2019, March 17, 2019, Many artificial intelligence researchers seek to distance themselves from military applications of AI.NEWS, Metz, Cade, Pentagon Wants Silicon Valley's Help on A.I.,weblink 19 March 2018, The New York Times, 15 March 2018,


For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.JOURNAL, Chang, Hsihui, Kao, Yi-Ching, Mashruwala, Raj, Sorensen, Susan M., Technical Inefficiency, Allocative Inefficiency, and Audit Pricing, Journal of Accounting, Auditing & Finance, 33, 4, 10 April 2017, 580–600, 10.1177/0148558X17696760,


It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.Matz, S. C., et al. "Psychological targeting as an effective approach to digital mass persuasion." Proceedings of the National Academy of Sciences (2017): 201710966. A documented case reports that online gambling companies were using AI to improve customer targeting.WEB, Busby, Mattha, Revealed: how bookies use AI to keep gamblers hooked,weblink the Guardian, en, 30 April 2018, Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.Celli, Fabio, Pietro Zani Massani, and Bruno Lepri. "Profilio: Psychometric Profiling to Boost Social Media Advertising." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017 weblink


{{Further|Computer art}}Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMAWEB,weblink Thinking Machines: Art and Design in the Computer Age, 1959–1989, The Museum of Modern Art, en, 2019-07-23, provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the deepdream algorithmRetrieved July 29 and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017.WEB,weblink Unhuman: Art in the Age of AI – State Festival,, 2018-09-13, WEB,weblink It's Getting Hard to Tell If a Painting Was Made by a Computer or a Human, Chun, Rene, 2017-09-21, Artsy, en, 2019-07-23, In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts.Retrieved July 29 The Austrian Ars Electronica and Museum of Applied Arts, Vienna opened exhibitions on AI in 2019.WEB,weblink Retrieved September 2019, WEB,weblink Retrieved October 2019, The Ars Electronica's 2019 festival "Out of the box" extensively thematized the role of arts for a sustainable societal transformation with AI.WEB,weblink Retrieved September 2019,

Philosophy and ethics

There are three philosophical questions related to AI:
  1. Is artificial general intelligence possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish?
  2. Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically?
  3. Can a machine have a mind, consciousness and mental states in exactly the same sense that human beings do? Can a machine be sentient, and thus deserve certain rights? Can a machine intentionally cause harm?

The limits of artificial general intelligence

Can a machine be intelligent? Can it "think"?
Alan Turing's "polite convention": We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test.
The Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956.
Newell and Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols. Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus' critique of AI.)
Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules." {{Harv|Dreyfus|1992|p=156}}
Gödelian arguments: Gödel himself, John Lucas (in 1961) and Roger Penrose (in a more detailed argument from 1989 onwards) made highly technical arguments that human mathematicians can consistently see the truth of their own "Gödel statements" and therefore have computational abilities beyond that of mechanical Turing machines. However, some people do not agree with the "Gödelian arguments".WEB, Graham Oppy, Gödel's Incompleteness Theorems,weblink Stanford Encyclopedia of Philosophy, 27 April 2016, 20 January 2015, These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail., Graham Oppy, BOOK, Stuart J. Russell, Peter Norvig, Peter Norvig, Artificial Intelligence: A Modern Approach, 2010, Prentice Hall, Upper Saddle River, NJ, 978-0-13-604259-4, 3rd, 26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection, even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations., Artificial Intelligence: A Modern Approach, Stuart J. Russell, Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail."
The artificial brain argument: The brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original.
The AI effect: Machines are already intelligent, but observers have failed to recognize it. When Deep Blue beat Garry Kasparov in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn't been done yet."

Potential harm{{anchor|Potential_risks_and_moral_reasoning}}

Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute, among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.Russel, Stuart., Daniel Dewey, and Max Tegmark. Research Priorities for Robust and Beneficial Artificial Intelligence. AI Magazine 36:4 (2015). 8 December 2016.The potential negative effects of AI and automation are a major issue for Andrew Yang's presidential campaign.JOURNAL,weblink Andrew Yang's Presidential Bid Is So Very 21st Century, Wired, Matt, Simon, 1 April 2019,,

Existential risk

Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race".NEWS, Rawlinson, Kevin, Microsoft's Bill Gates insists AI is a threat,weblink BBC News, 30 January 2015, live,weblink" title="">weblink 29 January 2015, dmy-all, 2015-01-29, NEWS, Bill Gates on dangers of artificial intelligence: 'I don't understand why some people are not concerned',weblink The Washington Post, 28 January 2015, 30 October 2015, 0190-8286, Peter, Holley, live,weblink 30 October 2015, dmy-all, NEWS, Elon Musk: artificial intelligence is our biggest existential threat,weblink The Guardian, 30 October 2015, Samuel, Gibbs, live,weblink" title="">weblink 30 October 2015, dmy-all, 2014-10-27, }}In his book (Superintelligence: Paths, Dangers, Strategies|Superintelligence), Nick Bostrom provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal.Concern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including Peter Thiel, Amazon Web Services and Musk have committed $1billion to OpenAI, a nonprofit company aimed at championing responsible AI development.WEB,weblink Tech titans like Elon Musk are spending $1 billion to save you from terminators, Washington, Post, live,weblink" title="">weblink 7 June 2016, dmy-all, The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.JOURNAL
, Müller
, Vincent C.
, Bostrom
, Nick
, 2014
, Future Progress in Artificial Intelligence: A Poll Among Experts
, AI Matters
, 1
, 1
, 9–11
, 10.1145/2639475.2639478
, live
,weblink" title="">weblink
, 15 January 2016
, dmy-all
, Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. Oracle CEO Mark Hurd has stated that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems.WEB,weblink Oracle CEO Mark Hurd sees no reason to fear ERP AI, SearchERP, en, 2019-05-06, Facebook CEO Mark Zuckerberg believes AI will "unlock a huge amount of positive things," such as curing disease and increasing the safety of autonomous cars.WEB,weblink Mark Zuckerberg responds to Elon Musk's paranoia about AI: 'AI is going to... help keep our communities safe.', 25 May 2018, Business Insider, 2019-05-06, In January 2015, Elon Musk donated ten million dollars to the Future of Life Institute to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as Google DeepMind and Vicarious to "just keep an eye on what's going on with artificial intelligence.WEB, The mysterious artificial intelligence company Elon Musk invested in is developing game-changing smart computers,weblink Tech Insider, 30 October 2015, live,weblink" title="">weblink 30 October 2015, dmy-all, I think there is potentially a dangerous outcome there."WEB, Musk-Backed Group Probes Risks Behind Artificial Intelligence,weblink, 30 October 2015, Jack, Clark, live,weblink" title="">weblink 30 October 2015, dmy-all, WEB, Elon Musk Is Donating $10M Of His Own Money To Artificial Intelligence Research,weblink Fast Company, 30 October 2015, live,weblink" title="">weblink 30 October 2015, dmy-all, 2015-01-15, For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching.WEB, Is artificial intelligence really an existential threat to humanity?,weblink Bulletin of the Atomic Scientists, 30 October 2015, live,weblink" title="">weblink 30 October 2015, dmy-all, 2015-08-09, WEB, The case against killer robots, from a guy actually working on artificial intelligence,weblink, 31 January 2016, live,weblink" title="">weblink 4 February 2016, dmy-all, Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence.WEB, Will artificial intelligence destroy humanity? Here are 5 reasons not to worry.,weblink Vox, 30 October 2015, live,weblink" title="">weblink 30 October 2015, dmy-all, 2014-08-22,

Devaluation of humanity

Joseph Weizenbaum wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapyIn the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool. {{Harv|Crevier|1993|pp=132–144}} was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points suggest that AI research devalues human life.

Social justice

One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men.WEB,weblink Commentary: Bad news. Artificial intelligence is biased, CNA, Support for artificial intelligence is higher among men (with 47% approving) than women (35% approving).Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants.WEB,weblink How We Analyzed the COMPAS Recidivism Algorithm, Jeff Larson, Julia Angwin, 2016-05-23, ProPublica, en, 2019-07-23, COMPAS (an acronym for Correctional Offender Management Profiling for Alternative Sanctions) counts among the most widely utilized commercially available solutions. It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected.

Decrease in demand for human labor

{{Further|Technological unemployment#21st century}}The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects.E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".NEWS, Automation and anxiety,weblink 13 January 2018, The Economist, 9 May 2015, Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S. jobs as "high risk".NEWS, Lohr, Steve, Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says,weblink 13 January 2018, The New York Times, 2017, JOURNAL, 1 January 2017, The future of employment: How susceptible are jobs to computerisation?, Technological Forecasting and Social Change, 114, 254–280, 10.1016/j.techfore.2016.08.019, 0040-1625, Frey, Carl Benedikt, Osborne, Michael A,, Arntz, Melanie, Terry Gregory, and Ulrich Zierahn. "The risk of automation for jobs in OECD countries: A comparative analysis." OECD Social, Employment, and Migration Working Papers 189 (2016). p. 33. Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.NEWS, Mahdawi, Arwa, What jobs will still be around in 20 years? Read this to prepare your future,weblink 13 January 2018, The Guardian, 26 June 2017, Author Martin Ford and others go further and argue that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI.NEWS, Ford, Martin, Colvin, Geoff, Will robots create more jobs than they destroy?,weblink 13 January 2018, The Guardian, 6 September 2015,

Autonomous weapons

{{See also|Lethal autonomous weapon}}Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones.WEB, Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence,weblink Observer, 30 October 2015, live,weblink" title="">weblink 30 October 2015, dmy-all, 2015-08-19,

Ethical machines

Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use ethical reasoning to better choose their actions in the world. Research in this area includes machine ethics, artificial moral agents, friendly AI and discussion towards building a human rights framework is also in talks._WEB,weblink Ethical AI Learns Human Rights Framework, 10 November 2019, Voice of America,

Artificial moral agents

Wendell Wallach introduced the concept of artificial moral agents (AMA) in his book Moral MachinesWendell Wallach (2010). Moral Machines, Oxford University Press. For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions"Wallach, pp 37–54. and "Can (Ro)bots Really Be Moral".Wallach, pp 55–73. For Wallach the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs.Wallach, Introduction chapter.

Machine ethics

The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics."WEB,weblink Machine Ethics,, dead,weblink" title="">weblink 29 November 2014, Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics" that stems from the AAAI Fall 2005 Symposium on Machine Ethics.

Malevolent and friendly AI

Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent.JOURNAL, Rubin, Charles, Charles T. Rubin, Spring 2003, Artificial Intelligence and Human Nature |'The New Atlantis, 1, 88–100,weblink dead,weblink" title="">weblink 11 June 2012, dmy, He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.One proposal to deal with this is to ensure that the first generally intelligent AI is 'Friendly AI' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place.Leading AI researcher Rodney Brooks writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI, and the enormity and complexity of building sentient volitional intelligence."WEB, Brooks, Rodney, artificial intelligence is a tool, not a threat, 10 November 2014,weblink dead,weblink" title="">weblink 12 November 2014, dmy-all,

Machine consciousness, sentience and mind

If an AI system replicates all key aspects of human intelligence, will that system also be sentient—will it have a mind which has conscious experiences? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the hard problem of consciousness.


David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.JOURNAL,weblink Facing up to the problem of consciousness, Chalmers, David, David Chalmers, Journal of Consciousness Studies, 2, 3, 1995, 200–219, See also this link
The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all. Human information processing is easy to explain, however human subjective experience is difficult to explain.
For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know what red looks like. (Consider that a person born blind can know that something is red without knowing what red looks like.){{efn|This is based on Mary's Room, a thought experiment first proposed by Frank Jackson in 1982}} Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain.

Computationalism and functionalism

Computationalism is the position in the philosophy of mind that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing.Horst, Steven, (2005) "The Computational Theory of Mind" in The Stanford Encyclopedia of Philosophy Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind-body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.

Strong AI hypothesis

The philosophical position that John Searle has named "strong AI" states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be.

Robot rights

If a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature. Some critics of transhumanism argue that any hypothetical robot rights would lie on a spectrum with animal rights and human rights. JOURNAL, Evans, Woody, Woody Evans, Posthuman Rights: Dimensions of Transhuman Worlds, Teknokultura, 12, 2, 2015, dmy-all, 10.5209/rev_TK.2015.v12.n2.49072, The subject is profoundly discussed in the 2010 documentary film Plug & Pray,WEB,weblink Content: Plug & Pray Film – Artificial Intelligence – Robots -, maschafilm,, live,weblink" title="">weblink 12 February 2016, dmy-all, and many sci fi media such as Star Trek Next Generation, with the character of Commander Data, who fought being disassembled for research, and wanted to "become human", and the robotic holograms in Voyager.


Are there limits to how intelligent machines—or human-machine hybrids—can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. Superintelligence may also refer to the form or degree of intelligence possessed by such an agent.

Technological singularity

If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario "singularity". Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.


Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998.


The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit, if productivity gains are redistributed.WEB,weblink Robots and Artificial Intelligence,, 2019-07-03,

In fiction

File:Capek play.jpg|thumb|The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal RobotsRossum's Universal RobotsThought-capable artificial beings appeared as storytelling devices since antiquity,and have been a persistent theme in science fiction.A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as (2001: A Space Odyssey (novel)|Arthur C. Clarke's) and (2001: A Space Odyssey (film)|Stanley Kubrick's) (2001: A Space Odyssey) (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.JOURNAL, Buttazzo, G., Artificial consciousness: Utopia or real possibility?, Computer (magazine), Computer, July 2001, 34, 7, 24–30, 10.1109/2.933500, dmy-all, Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;Anderson, Susan Leigh. "Asimov's "three laws of robotics" and machine metaethics." AI & Society 22.4 (2008): 477–493. while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.JOURNAL, McCauley, Lee, 2007, AI armageddon and the three laws of robotics, Ethics and Information Technology, 9, 2, 153–164, 10.1007/s10676-007-9138-2,, Transhumanism (the merging of humans and machines) is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.JOURNAL, Galvan, Jill, 1 January 1997, Entering the Posthuman Collective in Philip K. Dick's "Do Androids Dream of Electric Sheep?", Science Fiction Studies, 24, 3, 413–429, 4240644, {{div col end}}

See also

{{col div|colwidth=20em}} {{colend}}

Explanatory notes



{{reflist|30em|refs=Definition of AI as the study of intelligent agents:
  • {{Harvnb|Poole|Mackworth|Goebel|1998|loc=p. 1}}, which provides the version that is used in this article. Note that they use the term "computational intelligence" as a synonym for artificial intelligence.
  • {{Harvtxt|Russell|Norvig|2003}} (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" {{Harv|Russell|Norvig|2003|p=55}}.
  • {{Harvnb|Nilsson|1998}}
  • {{Harvnb|Legg|Hutter|2007}}.
This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition." {{Harv|McCorduck|2004|p=34}} "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." {{Harv|McCorduck|2004|p=xviii}} "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction." {{Harv|McCorduck|2004|p=3}} She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods." {{Harv|McCorduck|2004|pp=340–400}}Pamela {{Harvtxt|McCorduck|2004|pp=424}} writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other."This list of intelligent traits is based on the topics covered by the major AI textbooks, including:
  • {{Harvnb|Russell|Norvig|2003}}
  • {{Harvnb|Luger|Stubblefield|2004}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998}}
  • {{Harvnb|Nilsson|1998}}
General intelligence (strong AI) is discussed in popular introductions to AI:
  • {{Harvnb|Kurzweil|1999}} and {{Harvnb|Kurzweil|2005}}
AI in myth:
  • {{Harvnb|McCorduck|2004|pp=4–5}}
  • {{Harvnb|Russell|Norvig|2003|p=939}}
AI in early science fiction.
  • {{Harvnb|McCorduck|2004|pp=17–25}}
Formal reasoning:
  • BOOK, David, Berlinski, 2000, The Advent of the Algorithm, Harcourt Books, David Berlinski, 978-0-15-601391-8, 46890682,weblink
{{page needed|date=December 2016}}AI's immediate precursors:
  • {{Harvnb|McCorduck|2004|pp=51–107}}
  • {{Harvnb|Crevier|1993|pp=27–32}}
  • {{Harvnb|Russell|Norvig|2003|pp=15, 940}}
  • {{Harvnb|Moravec|1988|p=3}}
See also {{slink|History of artificial intelligence|Cybernetics and early neural networks}}. Among the researchers who laid the foundations of AI were Alan Turing, John von Neumann, Norbert Wiener, Claude Shannon, Warren McCullough, Walter Pitts and Donald Hebb.Dartmouth conference:
  • {{Harvnb|McCorduck|2004|pp=111–136}}
  • {{Harvnb|Crevier|1993|pp=47–49}}, who writes "the conference is generally recognized as the official birthdate of the new science."
  • {{Harvnb|Russell|Norvig|2003|p=17}}, who call the conference "the birth of artificial intelligence."
  • {{Harvnb|NRC|1999|pp=200–201}}
Hegemony of the Dartmouth conference attendees:
  • {{Harvnb|Russell|Norvig|2003|p=17}}, who write "for the next 20 years the field would be dominated by these people and their students."
  • {{Harvnb|McCorduck|2004|pp=129–130}}
"Golden years" of AI (successful symbolic reasoning programs 1956–1973):
  • {{Harvnb|McCorduck|2004|pp=243–252}}
  • {{Harvnb|Crevier|1993|pp=52–107}}
  • {{Harvnb|Moravec|1988|p=9}}
  • {{Harvnb|Russell|Norvig|2003|pp=18–21}}
The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU.DARPA pours money into undirected pure research into AI during the 1960s:
  • {{Harvnb|McCorduck|2004|pp=131}}
  • {{Harvnb|Crevier|1993|pp=51, 64–65}}
  • {{Harvnb|NRC|1999|pp=204–205}}
AI in England:
  • {{Harvnb|Howe|1994}}
Optimism of early AI:
  • Herbert Simon quote: {{Harvnb|Simon|1965|p=96}} quoted in {{Harvnb|Crevier|1993|p=109}}.
  • Marvin Minsky quote: {{Harvnb|Minsky|1967|p=2}} quoted in {{Harvnb|Crevier|1993|p=109}}.
First AI Winter, Mansfield Amendment, Lighthill report
  • {{Harvnb|Crevier|1993|pp=115–117}}
  • {{Harvnb|Russell|Norvig|2003|p=22}}
  • {{Harvnb|NRC|1999|pp=212–213}}
  • {{Harvnb|Howe|1994}}
  • {{Harvnb|Newquist|1994|pp=189–201}}
Expert systems:
  • {{Harvnb|ACM|1998|loc=I.2.1}}
  • {{Harvnb|Russell|Norvig|2003|pp=22–24}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=227–331}}
  • {{Harvnb|Nilsson|1998|loc=chpt. 17.4}}
  • {{Harvnb|McCorduck|2004|pp=327–335, 434–435}}
  • {{Harvnb|Crevier|1993|pp=145–62, 197–203}}
  • {{Harvnb|Newquist|1994|pp=155–183}}
Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI:
  • {{Harvnb|McCorduck|2004|pp=426–441}}
  • {{Harvnb|Crevier|1993|pp=161–162,197–203, 211, 240}}
  • {{Harvnb|Russell|Norvig|2003|p=24}}
  • {{Harvnb|NRC|1999|pp=210–211}}
  • {{Harvnb|Newquist|1994|pp=235–248}}
Second AI winter:
  • {{Harvnb|McCorduck|2004|pp=430–435}}
  • {{Harvnb|Crevier|1993|pp=209–210}}
  • {{Harvnb|NRC|1999|pp=214–216}}
  • {{Harvnb|Newquist|1994|pp=301–318}}
Formal methods are now preferred ("Victory of the neats"):
  • {{Harvnb|Russell|Norvig|2003|pp=25–26}}
  • {{Harvnb|McCorduck|2004|pp=486–487}}
AI applications widely used behind the scenes:
  • {{Harvnb|Russell|Norvig|2003|p=28}}
  • {{Harvnb|Kurzweil|2005|p=265}}
  • {{Harvnb|NRC|1999|pp=216–222}}
  • {{Harvnb|Newquist|1994|pp=189–201}}
AI becomes hugely successful in the early 21st century
  • {{Harvnb|Clark|2015}}
Problem solving, puzzle solving, game playing and deduction:
  • {{Harvnb|Russell|Norvig|2003|loc=chpt. 3–9}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|loc=chpt. 2,3,7,9}},
  • {{Harvnb|Luger|Stubblefield|2004|loc=chpt. 3,4,6,8}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 7–12}}
Uncertain reasoning:
  • {{Harvnb|Russell|Norvig|2003|pp=452–644}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=345–395}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=333–381}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 19}}
Intractability and efficiency and the combinatorial explosion:
  • {{Harvnb|Russell|Norvig|2003|pp=9, 21–22}}
Psychological evidence of sub-symbolic reasoning:
  • {{Harvtxt|Wason|Shapiro|1966}} showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task)
  • {{Harvtxt|Kahneman|Slovic|Tversky|1982}} have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples).
  • {{Harvtxt|Lakoff|Núñez|2000}} have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From)
Knowledge representation:
  • {{Harvnb|ACM|1998|loc=I.2.4}},
  • {{Harvnb|Russell|Norvig|2003|pp=320–363}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=23–46, 69–81, 169–196, 235–277, 281–298, 319–345}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=227–243}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 18}}
Knowledge engineering:
  • {{Harvnb|Russell|Norvig|2003|pp=260–266}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=199–233}},
  • {{Harvnb|Nilsson|1998|loc=chpt. ≈17.1–17.4}}
Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts):
  • {{Harvnb|Russell|Norvig|2003|pp=349–354}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=174–177}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=248–258}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 18.3}}
Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem):
  • {{Harvnb|Russell|Norvig|2003|pp=328–341}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281–298}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 18.2}}
Causal calculus:
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=335–337}}
Representing knowledge about knowledge: Belief calculus, modal logics:
  • {{Harvnb|Russell|Norvig|2003|pp=341–344}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=275–277}}
  • {{Harvnb|Russell|Norvig|2003|pp=320–328}}
Qualification problem:
  • {{Harvnb|McCarthy|Hayes|1969}}
  • {{Harvnb|Russell|Norvig|2003}}{{Page needed|date=February 2011}}
While McCarthy was primarily concerned with issues in the logical representation of actions, {{Harvnb|Russell|Norvig|2003}} apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge.Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"):
  • {{Harvnb|Russell|Norvig|2003|pp=354–360}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=248–256, 323–335}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=335–363}},
  • {{Harvnb|Nilsson|1998|loc=~18.3.3}}
Breadth of commonsense knowledge:
  • {{Harvnb|Russell|Norvig|2003|p=21}},
  • {{Harvnb|Crevier|1993|pp=113–114}},
  • {{Harvnb|Moravec|1988|p=13}},
  • {{Harvnb|Lenat|Guha|1989}} (Introduction)
Expert knowledge as embodied intuition:
  • {{Harvnb|Dreyfus|Dreyfus|1986}} (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI)
  • {{Harvnb|Gladwell|2005}} (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.)
  • {{Harvnb|Hawkins|Blakeslee|2005}} (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.)
  • {{Harvnb|ACM|1998|loc=~I.2.8}},
  • {{Harvnb|Russell|Norvig|2003|pp= 375–459}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281–316}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=314–329}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 10.1–2, 22}}
Information value theory:
  • {{Harvnb|Russell|Norvig|2003|pp=600–604}}
Classical planning:
  • {{Harvnb|Russell|Norvig|2003|pp=375–430}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281–315}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=314–329}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 10.1–2, 22}}
Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning:
  • {{Harvnb|Russell|Norvig|2003|pp=430–449}}
Multi-agent planning and emergent behavior:
  • {{Harvnb|Russell|Norvig|2003|pp=449–455}}
  • {{Harvnb|ACM|1998|loc=I.2.6}},
  • {{Harvnb|Russell|Norvig|2003|pp=649–788}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=397–438}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=385–542}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 3.3, 10.3, 17.5, 20}}
Reinforcement learning:
  • {{Harvnb|Russell|Norvig|2003|pp=763–788}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=442–449}}
Natural language processing:
  • {{Harvnb|ACM|1998|loc=I.2.7}}
  • {{Harvnb|Russell|Norvig|2003|pp=790–831}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=91–104}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=591–632}}
Applications of natural language processing, including information retrieval (i.e. text mining) and machine translation:
  • {{Harvnb|Russell|Norvig|2003|pp=840–857}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=623–630}}
  • {{Harvnb|ACM|1998|loc=I.2.9}},
  • {{Harvnb|Russell|Norvig|2003|pp=901–942}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=443–460}}
Moving and configuration space:
  • {{Harvnb|Russell|Norvig|2003|pp=916–932}}
Robotic mapping (localization, etc):
  • {{Harvnb|Russell|Norvig|2003|pp=908–915}}
Machine perception:
  • {{Harvnb|Russell|Norvig|2003|pp=537–581, 863–898}}
  • {{Harvnb|Nilsson|1998|loc=~chpt. 6}}
Computer vision:
  • {{Harvnb|ACM|1998|loc=I.2.10}}
  • {{Harvnb|Russell|Norvig|2003|pp=863–898}}
  • {{Harvnb|Nilsson|1998|loc=chpt. 6}}
Speech recognition:
  • {{Harvnb|ACM|1998|loc=~I.2.7}}
  • {{Harvnb|Russell|Norvig|2003|pp=568–578}}
Object recognition:
  • {{Harvnb|Russell|Norvig|2003|pp=885–892}}
Emotion and affective computing:
  • {{Harvnb|Minsky|2006}}
Artificial brain arguments: AI requires a simulation of the operation of the human brain
  • {{Harvnb|Russell|Norvig|2003|p=957}}
  • {{Harvnb|Crevier|1993|pp=271 and 279}}
A few of the people who make some form of the argument:
  • {{Harvnb|Moravec|1988}}
  • {{Harvnb|Kurzweil|2005|p=262}}
  • {{Harvnb|Hawkins|Blakeslee|2005}}
The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980.Biological intelligence vs. intelligence in general:
  • {{Harvnb|Russell|Norvig|2003|pp=2–3}}, who make the analogy with aeronautical engineering.
  • {{Harvnb|McCorduck|2004|pp=100–101}}, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones."
  • {{Harvnb|Kolata|1982}}, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real"WEB,weblink Science, August 1982, . McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence" {{Harv|Maker|2006}}.
Neats vs. scruffies:
  • {{Harvnb|McCorduck|2004|pp=421–424, 486–489}}
  • {{Harvnb|Crevier|1993|pp=168}}
  • {{Harvnb|Nilsson|1983|pp=10–11}}
Symbolic vs. sub-symbolic AI:
  • {{Harvtxt|Nilsson|1998|p=7}}, who uses the term "sub-symbolic".
{{Harvnb|Haugeland|1985|pp=112–117}}Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech):
  • {{Harvnb|McCorduck|2004|pp=139–179, 245–250, 322–323 (EPAM)}}
  • {{Harvnb|Crevier|1993|pp=145–149}}
Soar (history):
  • {{Harvnb|McCorduck|2004|pp=450–451}}
  • {{Harvnb|Crevier|1993|pp=258–263}}
McCarthy and AI research at SAIL and SRI International:
  • {{Harvnb|McCorduck|2004|pp=251–259}}
  • {{Harvnb|Crevier|1993}}
AI research at Edinburgh and in France, birth of Prolog:
  • {{Harvnb|Crevier|1993|pp=193–196}}
  • {{Harvnb|Howe|1994}}
AI at MIT under Marvin Minsky in the 1960s :
  • {{Harvnb|McCorduck|2004|pp=259–305}}
  • {{Harvnb|Crevier|1993|pp=83–102, 163–176}}
  • {{Harvnb|Russell|Norvig|2003|p=19}}
  • {{Harvnb|McCorduck|2004|p=489}}, who calls it "a determinedly scruffy enterprise"
  • {{Harvnb|Crevier|1993|pp=239–243}}
  • {{Harvnb|Russell|Norvig|2003|p=363−365}}
  • {{Harvnb|Lenat|Guha|1989}}
Knowledge revolution:
  • {{Harvnb|McCorduck|2004|pp=266–276, 298–300, 314, 421}}
  • {{Harvnb|Russell|Norvig|2003|pp=22–23}}
Embodied approaches to AI:
  • {{Harvnb|McCorduck|2004|pp=454–462}}
  • {{Harvnb|Brooks|1990}}
  • {{Harvnb|Moravec|1988}}
Revival of connectionism:
  • {{Harvnb|Crevier|1993|pp=214–215}}
  • {{Harvnb|Russell|Norvig|2003|p=25}}
Computational intelligence The intelligent agent paradigm:
  • {{Harvnb|Russell|Norvig|2003|pp=27, 32–58, 968–972}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=7–21}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=235–240}}
  • {{Harvnb|Hutter|2005|pp=125–126}}
The definition used in this article, in terms of goals, actions, perception and environment, is due to {{Harvtxt|Russell|Norvig|2003}}. Other definitions also include knowledge and learning as additional criteria.Agent architectures, hybrid intelligent systems:
  • {{Harvtxt|Russell|Norvig|2003|pp=27, 932, 970–972}}
  • {{Harvtxt|Nilsson|1998|loc=chpt. 25}}
Hierarchical control system:
  • {{Harvnb|Albus|2002}}
Search algorithms:
  • {{Harvnb|Russell|Norvig|2003|pp=59–189}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=113–163}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=79–164, 193–219}}
  • {{Harvnb|Nilsson|1998|loc=chpt. 7–12}}
Forward chaining, backward chaining, Horn clauses, and logical deduction as search:
  • {{Harvnb|Russell|Norvig|2003|pp=217–225, 280–294}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=~46–52}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=62–73}}
  • {{Harvnb|Nilsson|1998|loc=chpt. 4.2, 7.2}}
State space search and planning:
  • {{Harvnb|Russell|Norvig|2003|pp=382–387}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=298–305}}
  • {{Harvnb|Nilsson|1998|loc=chpt. 10.1–2}}
Uninformed searches (breadth first search, depth first search and general state space search):
  • {{Harvnb|Russell|Norvig|2003|pp=59–93}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=113–132}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=79–121}}
  • {{Harvnb|Nilsson|1998|loc=chpt. 8}}
Heuristic or informed searches (e.g., greedy best first and A*):
  • {{Harvnb|Russell|Norvig|2003|pp= 94–109}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=pp. 132–147}},
  • {{Harvnb|Luger|Stubblefield|2004|pp= 133–150}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 9}},
  • {{Harvnb|Poole|Mackworth|2017|loc=Section 3.6}}
Optimization searches:
  • {{Harvnb|Russell|Norvig|2003|pp=110–116,120–129}}
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=56–163}}
  • {{Harvnb|Luger|Stubblefield|2004|pp= 127–133}}
Artificial life and society based learning:
  • {{Harvnb|Luger|Stubblefield|2004|pp=530–541}}
Genetic programming and genetic algorithms:
  • {{Harvnb|Luger|Stubblefield|2004|pp=509–530}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 4.2}},
  • {{Harvnb|Holland|1975}},
  • {{Harvnb|Koza|1992}},
  • {{Harvnb|Poli|Langdon|McPhee|2008}}.
  • {{Harvnb|ACM|1998|loc=~I.2.3}},
  • {{Harvnb|Russell|Norvig|2003|pp=194–310}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=35–77}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 13–16}}
  • {{Harvnb|Russell|Norvig|2003|pp=402–407}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=300–301}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 21}}
Explanation based learning, relevance based learning, inductive logic programming, case based reasoning:
  • {{Harvnb|Russell|Norvig|2003|pp=678–710}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=414–416}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=~422–442}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 10.3, 17.5}}
Propositional logic:
  • {{Harvnb|Russell|Norvig|2003|pp=204–233}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=45–50}}
  • {{Harvnb|Nilsson|1998|loc=chpt. 13}}
First-order logic and features such as equality:
  • {{Harvnb|ACM|1998|loc=~I.2.4}},
  • {{Harvnb|Russell|Norvig|2003|pp=240–310}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=268–275}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=50–62}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 15}}
Fuzzy logic:
  • {{Harvnb|Russell|Norvig|2003|pp=526–527}}
Stochastic methods for uncertain reasoning:
  • {{Harvnb|ACM|1998|loc=~I.2.3}},
  • {{Harvnb|Russell|Norvig|2003|pp=462–644}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=345–395}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=165–191, 333–381}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 19}}
Bayesian networks:
  • {{Harvnb|Russell|Norvig|2003|pp=492–523}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=361–381}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=~182–190, ≈363–379}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 19.3–4}}
Bayesian inference algorithm:
  • {{Harvnb|Russell|Norvig|2003|pp=504–519}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=361–381}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=~363–379}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 19.4 & 7}}
Bayesian learning and the expectation-maximization algorithm:
  • {{Harvnb|Russell|Norvig|2003|pp=712–724}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=424–433}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 20}}
Bayesian decision theory and Bayesian decision networks:
  • {{Harvnb|Russell|Norvig|2003|pp=597–600}}
Stochastic temporal models:
  • {{Harvnb|Russell|Norvig|2003|pp=537–581}}
Dynamic Bayesian networks:
  • {{Harvnb|Russell|Norvig|2003|pp=551–557}}
Hidden Markov model:
  • {{Harv|Russell|Norvig|2003|pp=549–551}}
Kalman filters:
  • {{Harvnb|Russell|Norvig|2003|pp=551–557}}
decision theory and decision analysis:
  • {{Harvnb|Russell|Norvig|2003|pp=584–597}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=381–394}}
Markov decision processes and dynamic decision networks:
  • {{Harvnb|Russell|Norvig|2003|pp=613–631}}
Game theory and mechanism design:
  • {{Harvnb|Russell|Norvig|2003|pp=631–643}}
Statistical learning methods and classifiers:
  • {{Harvnb|Russell|Norvig|2003|pp=712–754}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=453–541}}
kernel methods such as the support vector machine:
  • {{Harvnb|Russell|Norvig|2003|pp=749–752}}
K-nearest neighbor algorithm:
  • {{Harvnb|Russell|Norvig|2003|pp=733–736}}
Gaussian mixture model:
  • {{Harvnb|Russell|Norvig|2003|pp=725–727}}
Naive Bayes classifier:
  • {{Harvnb|Russell|Norvig|2003|pp=718}}
Decision tree:
  • {{Harvnb|Russell|Norvig|2003|pp=653–664}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=403–408}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=408–417}}
Classifier performance:
  • {{Harvnb|van der Walt|Bernard|2006}}
Neural networks and connectionism:
  • {{Harvnb|Russell|Norvig|2003|pp=736–748}},
  • {{Harvnb|Poole|Mackworth|Goebel|1998|pp=408–414}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=453–505}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 3}}
  • {{Harvnb|Russell|Norvig|2003|pp=744–748}},
  • {{Harvnb|Luger|Stubblefield|2004|pp=467–474}},
  • {{Harvnb|Nilsson|1998|loc=chpt. 3.3}}
Feedforward neural networks, perceptrons and radial basis networks:
  • {{Harvnb|Russell|Norvig|2003|pp=739–748, 758}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=458–467}}
Recurrent neural networks, Hopfield nets:
  • {{Harvnb|Russell|Norvig|2003|p=758}}
  • {{Harvnb|Luger|Stubblefield|2004|pp=474–505}}
Competitive learning, Hebbian coincidence learning, Hopfield networks and attractor networks:
  • {{Harvnb|Luger|Stubblefield|2004|pp=474–505}}
Hierarchical temporal memory:
  • {{Harvnb|Hawkins|Blakeslee|2005}}
The Turing test:Turing's original publication:
  • {{Harvnb|Turing|1950}}
Historical influence and philosophical implications:
  • {{Harvnb|Haugeland|1985|pp=6–9}}
  • {{Harvnb|Crevier|1993|p=24}}
  • {{Harvnb|McCorduck|2004|pp=70–71}}
  • {{Harvnb|Russell|Norvig|2003|pp=2–3 and 948}}
Mathematical definitions of intelligence:
  • {{harvnb|Hernandez-Orallo|2000}}
  • {{harvnb|Dowe|Hajek|1997}}
  • {{harvnb|Hernandez-Orallo|Dowe|2010}}
Dartmouth proposal:
  • {{Harvnb|McCarthy|Minsky|Rochester|Shannon|1955}} (the original proposal)
  • {{Harvnb|Crevier|1993|p=49}} (historical significance)
The physical symbol systems hypothesis:
  • {{Harvnb|Newell|Simon|1976|p=116}}
  • {{Harvnb|McCorduck|2004|p=153}}
  • {{Harvnb|Russell|Norvig|2003|p=18}}
Dreyfus' critique of artificial intelligence:
  • {{Harvnb|Dreyfus|1972}}, {{Harvnb|Dreyfus|Dreyfus|1986}}
  • {{Harvnb|Crevier|1993|pp=120–132}}
  • {{Harvnb|McCorduck|2004|pp=211–239}}
  • {{Harvnb|Russell|Norvig|2003|pp=950–952}},
{{Harvnb|Gödel|1951}}: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact".The Mathematical Objection:
  • {{Harvnb|Russell|Norvig|2003|p=949}}
  • {{Harvnb|McCorduck|2004|pp=448–449}}
Making the Mathematical Objection:
  • {{Harvnb|Lucas|1961}}
  • {{Harvnb|Penrose|1989}}
Refuting Mathematical Objection:
  • {{Harvnb|Turing|1950}} under "(2) The Mathematical Objection"
  • {{Harvnb|Hofstadter|1979}}
  • {{Harvnb|Ref=none|Gödel|1931}}, {{Harvnb|Ref=none|Church|1936}}, {{Harvnb|Ref=none|Kleene|1935}}, {{Harvnb|Ref=none|Turing|1937}}
This version is from {{Harvtxt|Searle|1999}}, and is also quoted in {{Harvnb|Dennett|1991|p=435}}. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." {{Harv|Searle|1980|p=1}}. Strong AI is defined similarly by {{Harvtxt|Russell|Norvig|2003|p=947}}: "The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis."Searle's Chinese room argument:
  • {{Harvnb|Searle|1980}}. Searle's original presentation of the thought experiment.
  • {{Harvnb|Searle|1999}}.
  • {{Harvnb|Russell|Norvig|2003|pp=958–960}}
  • {{Harvnb|McCorduck|2004|pp=443–445}}
  • {{Harvnb|Crevier|1993|pp=269–271}}
Robot rights:
  • {{Harvnb|Russell|Norvig|2003|p=964}}
  • {{Harvnb|BBC News|2006}}
Prematurity of:
  • {{Harvnb|Henderson|2007}}
In fiction:
  • {{Harvtxt|McCorduck|2004|pp=190–25}} discusses Frankenstein and identifies the key ethical issues as scientific hubris and the suffering of the monster, i.e. robot rights.
Joseph Weizenbaum's critique of AI:
  • {{Harvnb|Weizenbaum|1976}}
  • {{Harvnb|Crevier|1993|pp=132–144}}
  • {{Harvnb|McCorduck|2004|pp=356–373}}
  • {{Harvnb|Russell|Norvig|2003|p=961}}
Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life.Technological singularity:
  • {{Harvnb|Vinge|1993}}
  • {{Harvnb|Kurzweil|2005}}
  • {{Harvnb|Russell|Norvig|2003|p=963}}
CONFERENCE, Omohundro, Steve, Steve Omohundro, 2008, The Nature of Self-Improving Artificial Intelligence, presented and distributed at the 2007 Singularity Summit, San Francisco, CA., Transhumanism:
  • {{Harvnb|Moravec|1988}}
  • {{Harvnb|Kurzweil|2005}}
  • {{Harvnb|Russell|Norvig|2003|p=963}}
AI as evolution:
  • Edward Fredkin is quoted in {{Harvtxt|McCorduck|2004|p=401}}.
  • {{Harvnb|Butler|1863}}
  • {{Harvnb|Dyson|1998}}

AI textbooks

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    first=Nils year=1998, Artificial Intelligence: A New Synthesis,weblink registration, Morgan Kaufmann, 978-1-55860-467-4,
    • {{Russell Norvig 2003}}.
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    last1=Poole, David Poole (researcher)last2=Mackworth, Alan Mackworth, 2017, Artificial Intelligence: Foundations of Computational Agentsedition=2nd, 978-1-107-19539-4,weblink

    History of AI

    • {{Crevier 1993}}.
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    Further reading

    • DH Author, ‘Why Are There Still So Many Jobs? The History and Future of Workplace Automation’ (2015) 29(3) Journal of Economic Perspectives 3.
    • Boden, Margaret, Mind As Machine, Oxford University Press, 2006.
    • Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192–98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.)
    • Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93.
    • Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65.
    • Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press
    • Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.)
    • E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3).
    • George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63.
    • Myers, Courtney Boyd ed. (2009). "The AI Report". Forbes June 2009
    • BOOK, Raphael, Bertram, Bertram Raphael, 1976, The Thinking Computer, W.H.Freeman and Company, 978-0-7167-0723-3,weblink
    • Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.)
    • JOURNAL, Serenko, Alexander, 2010, The development of an AI journal ranking based on the revealed preference approach,weblink Journal of Informetrics, 4, 4, 447–459, 10.1016/j.joi.2010.04.001,
    • JOURNAL, Serenko, Alexander, Michael Dohan, 2011, Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence,weblink Journal of Informetrics, 5, 4, 629–649, 10.1016/j.joi.2011.06.002,
    • Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.
    • WEB,weblink 2014 in Computing: Breakthroughs in Artificial Intelligence, Tom Simonite, 29 December 2014, MIT Technology Review,
    • Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)

    External links

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