Artificial Intelligence

Information about Artificial Intelligence

“AI” redirects here. For other uses of "AI" and "Artificial intelligence", see AI (disambiguation).

Garry Kasparov playing against Deep Blue, the first machine to win a chess match against a reigning world champion.

The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.^[1] John McCarthy, who coined the term in 1956,^[2] defines it as "the science and engineering of making intelligent machines."^[3] Other names for the field have been proposed, such as computational intelligence,^[4] synthetic intelligence^[4]^[5] or computational rationality.^[6] The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates.

AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic.^[7] AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.

History

Main articles: History of artificial intelligence and Timeline of artificial intelligence

See also:

The field was born at a conference on the campus of Dartmouth College in the summer of 1956.^[8] Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing:^[9] computers were solving word problems in algebra, proving logical theorems and speaking English.^[10] By the middle 60s their research was heavily funded by DARPA^[11] and they would make extraordinary predictions about their work:

1965, H. A. Simon: "machines will be capable, within twenty years, of doing any work a man can do"^[12]
1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."^[13]

These predictions, and many like them, would not come true. They had failed to anticipate the difficulty of some of the problems they faced: the lack of raw computer power,^[14] the intractable combinatorial explosion of their algorithms,^[15] the difficulty of representing commonsense knowledge and doing commonsense reasoning,^[16] the incredible difficulty of perception and motion^[17] and the failings of logic.^[18] In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from congress to fund more productive projects, DARPA cut off all undirected, exploratory research in AI. This was the first AI Winter.^[19]

In the early 80s, the field was revived by the commercial success of expert systems and by 1985 the market for AI had reached more than a billion dollars.^[20] Minsky and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow.^[21] Minsky was right. Beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.^[22]

In the 90s AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas.^[23] The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.^[24]

Mechanisms

Expert systems were one of the earliest types of AI system. They are built around automated inference engines including forward reasoning and backwards reasoning. Based on certain conditions ("if") the system infers certain consequences ("then").

In terms of consequences, 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 most AI systems.

Classifiers make use of pattern recognition for condition matching. In many cases this does not imply absolute, but rather the closest match. Techniques to achieve this divide roughly into two schools of thought: Conventional AI and Computational intelligence (CI).

Conventional AI research focuses on attempts to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. This approach limits the situations to which conventional AI can be applied. Lotfi Zadeh stated that "we are also in possession of computational tools which are far more effective in the conception and design of intelligent systems than the predicate-logic-based methods which form the core of traditional AI." These techniques, which include fuzzy logic, have become known as soft computing. These often biologically inspired methods stand in contrast to conventional AI and compensate for the shortcomings of symbolicism.^[25] These two methodologies have also been labeled as neats vs. scruffies, with neats emphasizing the use of logic and formal representation of knowledge while scruffies take an application-oriented heuristic bottom-up approach.^[26]

Classifiers

Classifiers are functions that 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 mainly statistical and machine learning approaches.

A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science.

The most widely used classifiers are the neural network, support vector machine, k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision tree.

Conventional AI

Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI). (Also see semantics.) Methods include:

Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.
Case based reasoning: stores a set of problems and answers in an organized data structure called cases. A case based reasoning system upon being presented with a problem finds a case in its knowledge base that is most closely related to the new problem and presents its solutions as an output with suitable modifications.^[27]
Bayesian networks
Behavior based AI: a modular method of building AI systems by hand.

Computational intelligence

Computational intelligence involves iterative development or learning (e.g., parameter tuning in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing. Subjects in computational intelligence as defined by IEEE Computational Intelligence Society mainly include:

Neural networks: trainable systems with very strong pattern recognition capabilities.
Fuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems; capable of working with concepts such as 'hot', 'cold', 'warm' and 'boiling'.
Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g., genetic algorithms) and swarm intelligence (e.g., ant algorithms).

With hybrid intelligent systems, attempts are made to combine these two groups. Expert inference rules can be generated through neural network or production rules from statistical learning such as in ACT-R or CLARION (see References below). It is thought that the human brain uses multiple techniques to both formulate and cross-check results. Thus, systems integration is seen as promising and perhaps necessary for true AI, especially the integration of symbolic and connectionist models (e.g., as advocated by Ron Sun).

AI programming languages and styles

AI research has led to many advances in programming languages including the first list processing language by Allen Newell et al., Lisp dialects, Planner, Actors, the Scientific Community Metaphor, production systems, and rule-based languages.

GOFAI TEST research is often done in programming languages such as Prolog or Lisp. Matlab and Lush (a numerical dialect of Lisp) include many specialist probabilistic libraries for Bayesian systems. AI research often emphasises rapid development and prototyping, using such interpreted languages to empower rapid command-line testing and experimentation. Real-time systems are however likely to require dedicated optimized software.

Many expert systems are organized collections of if-then such statements, called productions. These can include stochastic elements, producing intrinsic variation, or rely on variation produced in response to a dynamic environment.

Research challenges

A legged league game from RoboCup 2004 in Lisbon, Portugal.

The 800 million-Euro EUREKA Prometheus Project on driverless cars (1987-1995) showed that fast autonomous vehicles, notably those of Ernst Dickmanns and his team, can drive long distances (over 100 miles) in traffic, automatically recognizing and tracking other cars through computer vision, passing slower cars in the left lane. But the challenge of safe door-to-door autonomous driving in arbitrary environments will require additional research.

The DARPA Grand Challenge was a race for a $2 million prize where cars had to drive themselves over a hundred miles of challenging desert terrain without any communication with humans, using GPS, computers and a sophisticated array of sensors. In 2005, the winning vehicles completed all 132 miles of the course in just under seven hours. This was the first in a series of challenges aimed at a congressional mandate stating that by 2015 one-third of the operational ground combat vehicles of the US Armed Forces should be unmanned.^[28] For November 2007, DARPA introduced the DARPA Urban Challenge. The course will involve a sixty-mile urban area course. Darpa has secured the prize money for the challenge as $2 million for first place, $1 million for second and $500,000 for third.

A popular challenge amongst AI research groups is the RoboCup and FIRA annual international robot soccer competitions. Hiroaki Kitano has formulated the International RoboCup Federation challenge: "In 2050 a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup."^[29]

In the post-dot-com boom era, some search engine websites use a simple form of AI to provide answers to questions entered by the visitor. Questions such as What is the tallest building? can be entered into the search engine's input form, and a list of answers will be returned.

AI in other disciplines

AI is not only seen in computer science and engineering. It is studied and applied in various different sectors.

Philosophy

Main articles: Philosophy of artificial intelligence and Ethics of artificial intelligence

The strong AI vs. weak AI debate ("can a man-made artifact be conscious?") is still a hot topic amongst AI philosophers. This involves philosophy of mind and the mind-body problem. Most notably Roger Penrose in his book The Emperor's New Mind and John Searle with his "Chinese room" thought experiment argue that true consciousness cannot be achieved by formal logic systems, while Douglas Hofstadter in Gödel, Escher, Bach and Daniel Dennett in Consciousness Explained argue in favour of functionalism. In many strong AI supporters' opinions, artificial consciousness is considered the holy grail of artificial intelligence. Edsger Dijkstra famously opined that the debate had little importance: "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim."

Epistemology, the study of knowledge, also makes contact with AI, as engineers find themselves debating similar questions to philosophers about how best to represent and use knowledge and information (e.g., semantic networks).

Neuro-psychology

Main article: Cognitive science

Techniques and technologies in AI which have been directly derived from neuroscience include neural networks, Hebbian learning and the relatively new field of Hierarchical Temporal Memory which simulates the architecture of the neocortex.

Computer Science

Notable examples include the languages LISP and Prolog, which were invented for AI research but are now used for non-AI tasks. Hacker culture first sprang from AI laboratories, in particular the MIT AI Lab, home at various times to such luminaries as John McCarthy, Marvin Minsky, Seymour Papert (who developed Logo there) and Terry Winograd (who abandoned AI after developing SHRDLU).

Business

Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition (BBC News, 2001).^[30] A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information. Many practical applications are dependent on artificial neural networks, networks that pattern their organization in mimicry of a brain's neurons, which have been found to excel in pattern recognition. Financial institutions have long used such systems to detect charges or claims outside of the norm, flagging these for human investigation. Neural networks are also being widely deployed in homeland security, speech and text recognition, medical diagnosis (such as in Concept Processing technology in EMR software), data mining, and e-mail spam filtering.

Robots have become common in many industries. They are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading. General Motors uses around 16,000 robots for tasks such as painting, welding, and assembly. Japan is the leader in using and producing robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan.^[31]

Fiction

Main article: Artificial intelligence in fiction

In science fiction AI is often portrayed as an upcoming power trying to overthrow human authority, usually in the form of futuristic humanoid robots. Best known examples include the films The Terminator and The Matrix, as well as TV shows such as the re-imagined Battlestar Galactica series.

Another common theme is the suspicion and hatred by humanity for AIs and the AIs attempt to gain human acceptance. Films include Bicentennial Man, and The Iron Giant. This concept is also explored in the Uncanny Valley hypothesis.

Isaac Asimov wrote stories where engineers understood these potential problems and designed their robots accordingly. Positive examples of AIs include Robby from Forbidden Planet, R2D2, C3PO and Data (Star Trek)

The inevitability of the integration of AI into human society is also argued by some science/futurist writers such as Kevin Warwick and Hans Moravec and the manga Ghost in the Shell

Toys and games

The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of AI, specifically in the form of Tamagotchis and Giga Pets, the Internet (example: basic search engine interfaces are one simple form), and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.

List of applications

Typical problems to which AI methods are applied:

Other fields in which AI methods are implemented:

Lists of researchers, projects & publications

List of AI projects
List of important AI publications

Notes

1. ^ Textbooks that define AI this way include and (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field"
2. ^ Although there is some controversy on this point (see ), McCarthy states unequivocally "I came up with the term" in a c|net interview. (See Getting Machines to Think Like Us.)
3. ^ See WHAT IS ARTIFICIAL INTELLIGENCE? by John McCarthy
4. ^
5. ^
6. ^
7. ^
8. ^ and
9. ^ Russell and Norvig write "it was astonishing whenever a computer did anything remotely clever."
10. ^ , and . The programs described are Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU.
11. ^
12. ^ quoted in
13. ^ quoted in
14. ^ , ,
15. ^
16. ^ , , (Introduction) and
17. ^ and see Moravec's paradox
18. ^ , and see the frame problem, qualification problem and ramification problem.
19. ^ , , under "Shift to Applied Research Increases Investment." and also see Howe, J. "Artificial Intelligence at Edinburgh University : a Perspective"
20. ^ and and
21. ^
22. ^
23. ^ , under "Artificial Intelligence in the 90s"
24. ^
25. ^ J.-S. R. Jang, C.-T. Sun, E. Mizutani, (foreword L. Zadeh) "Neuro-Fuzzy and Soft Computing," Prentice Hall, 1997
26. ^ G.F. Luger, W.A. Stubblefield "Artificial Intelligence and the Design of Expert Systems"
27. ^ Hammond J, Kristian. Case-based planning: viewing planning as a memory task. Academic Press Perspectives In Artificial Intelligence; Vol 1. Pages: 277. 1989. ISBN 0-12-322060-2
28. ^ Congressional Mandate DARPA
29. ^ The RoboCup2003 Presents: Humanoid Robots playing Soccer PRESS RELEASE: 2 June 2003
30. ^ Robots beat humans in trading battle. BBC News, Business. The British Broadcasting Corporation (August 8 2001). Retrieved on 2006-11-02.
31. ^ "Robot," Microsoft® Encarta® Online Encyclopedia 2006

References

id="CITEREFBrooks1990">Brooks, Rodney (1990), "Elephants Don't Play Chess", Robotics and Autonomous Systems 6: 3-15, <[1] (retrieved on 30 August 2007)

id="CITEREFBuchanan2005">Buchanan, Bruce G. (2005), "A (Very) Brief History of Artificial Intelligence", AI Magazine: 53-60, <[2] (retrieved on 30 August 2007)

id="CITEREFLenat1989">Lenat, Douglas (1989), Building Large Knowledge-Based Systems, Addison-Wesley

id="CITEREFLaw1994">Law, Diane (1994), Searle, Subsymbolic Functionalism and Synthetic Intelligence, <[3]

id="CITEREFLighthill1973">Lighthill, Professor Sir James (1973), "Artificial Intelligence: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council

id="CITEREFMcCarthyHayes1969">McCarthy, John & P. J. Hayes (1969), "Some philosophical problems from the standpoint of artificial intelligence", Machine Intelligence 4: 463-502, <[4]

id="CITEREFMinsky1967">Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall

id="CITEREFMoravec1976">Moravec, Hans (1976), The Role of Raw Power in Intelligence, <[5]

id="CITEREFMoravec1988">Moravec, Hans (1988), Mind Children, Harvard University Press

id="CITEREFNRC1999">NRC (1999), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press

id="CITEREFNewellSimon1963">Newell, Allen & H. A. Simon (1963), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E.A. & J. Feldman, Computers and Thought, McGraw-Hill

id="CITEREFPooleMackworthGoebel1998">Poole, David; Alan Mackworth & Randy Goebel (1998), Computational Intelligence: A Logical Approach, Oxford University Press, <[6]

Samuel, Arthur L. (July 1959). "Some studies in machine learning using the game of checkers". IBM Journal of Research and Development 3 (3): 210-219. ISSN 0018-8646. Retrieved on 2007-08-20.

id="CITEREFSearle1980">Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences 3 (3): 417-457, <[7]

id="CITEREFSimon1965">Simon, H. A. (1965), The Shape of Automation for Men and Management, New York: Harper & Row

Weizenbaum, Joseph (1976). Computer Power and Human Reason. San Francisco: W.H. Freeman & Company. ISBN 0716704641.

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This disambiguation page covers alternative uses of the terms "Ai", "AI", and "A.I."

Ai (as a word, proper noun and set of initials) can refer to many things.

Technology

does not include companies or organizations

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intelligent agent (IA) is a software agent that assists users and will act on their behalf, in performing non-repetitive computer-related tasks. An agent in the sense of the word is like an insurance agent or travel agent.
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John McCarthy

John McCarthy at a summit in 2006
Born September 4 1927 (age 80)
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Computational intelligence (CI) is a successor of artificial intelligence. As an alternative to GOFAI it rather relies on heuristic algorithms such as in Fuzzy systems, Neural networks and Evolutionary computation.
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Strong AI is a term used by futurists, science fiction writers and forward looking researchers to describe artificial intelligence that matches or exceeds human intelligence.
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Intelligence is a property of mind that encompasses many related abilities, such as the capacities to reason, to plan, to solve problems, to think abstractly, to comprehend ideas, to use language, and to learn. There are several ways to define intelligence.
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Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems.
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Psychology (from Greek: Literally "talk about the soul" (from logos)) is both an academic and applied discipline involving the scientific study of mental processes and behavior.
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Philosophy is the discipline concerned with questions of how one should live (ethics); what sorts of things exist and what are their essential natures (metaphysics); what counts as genuine knowledge (epistemology); and what are the correct principles of reasoning (logic).
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Neuroscience is a field that is devoted to the scientific study of the nervous system. Such studies may include the structure, function, evolutionary history, development, genetics, biochemistry, physiology, pharmacology, and pathology of the nervous system.
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Cognitive science is most simply defined as the scientific study either of mind or of intelligence (e.g. Luger 1994). It is an interdisciplinary study drawing from relevant fields including psychology, philosophy, neuroscience, linguistics, anthropology, computer science,
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Computational linguistics is an interdisciplinary field dealing with the statistical and/or rule-based modeling of natural language from a computational perspective. This modeling is not limited to any particular field of linguistics.
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Operations Research or Operational Research (OR) is an interdisciplinary branch of mathematics which uses methods like mathematical modeling, statistics, and algorithms to arrive at optimal or good decisions in complex problems which are concerned with optimizing the maxima
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Computational economics explores the intersection of economics and computation. Areas encompassed under computational economics include agent-based computational modeling, computational econometrics and statistics, computational finance, computational modeling of dynamic
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For control theory in psychology and sociology, see .

Control theory is an interdisciplinary branch of engineering and mathematics, that deals with the behavior of dynamical systems. The desired output of a system is called the reference.
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Probability is the likelihood that something is the case or will happen. Probability theory is used extensively in areas such as statistics, mathematics, science and philosophy to draw conclusions about the likelihood of potential events and the underlying mechanics of
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In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set.
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Logic (from Classical Greek λόγος logos; meaning word, thought, idea, argument, account, reason, or principle) is the study of the principles and criteria of valid inference and demonstration.
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Robotics is the science and technology of robots, their design, manufacture, and application.^[1] Robotics requires a working knowledge of electronics, mechanics, and software, and is usually accompanied by a large working knowledge of many subjects.
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In military aviation, a Control system is frequently used in place of a Ground Control Station when describing an Unmanned Aircraft System control element which may be located anywhere, not just on the ground.
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Automated planning and scheduling is a branch of artificial intelligence that concerns the realisation of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.
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Data mining can be defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data".^[1] Data mining may also be defined as "the science of extracting useful information from large data sets or databases".
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Logistics is the art and science of managing and controlling the flow of goods, energy, information and other resources like products, services and people from the source of production to the marketplace.
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Speech recognition (in many contexts also known as automatic speech recognition, computer speech recognition or erroneously as voice recognition) is the process of converting a speech signal to a sequence of words in the form of digital data, by means of an
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A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.
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Limited computer power: There was not enough memory or processing speed to accomplish anything truly useful. For example, Ross Quillian's successful work on natural language was demonstrated with a vocabulary of only twenty
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See also: History of artificial intelligence

To 1900

Date Development
Antiquity Greek myths of Hephaestus and Pygmalion incorporate the idea of intelligent robots.
384-322 BCE Aristotle invented syllogistic logic, the first formal deductive reasoning system.
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The Dartmouth Summer Research Conference on Artificial Intelligence was the name of a conference now considered the seminal event for artificial intelligence as a field. The conference occurred in 1956.
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Dartmouth College is a private, coeducational university located in Hanover, New Hampshire, USA. Incorporated as "Trustees of Dartmouth College,"^[6]^[7]
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