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][2][3] John McCarthy, who coined the term in 1956,
[4] defines it as "the science and engineering of making intelligent machines."
[5] Other names for the field have been proposed, such as
computational intelligence,
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.
[6] AI research overlaps with tasks such as
robotics,
control systems,
scheduling,
data mining,
logistics,
speech recognition,
facial recognition and many others.
[9]
History
The field was born at a
conference on the campus of
Dartmouth College in the summer of 1956. Those who attended, especially
John McCarthy,
Marvin Minsky,
Allen Newell and
Herbert Simon, would be become the leaders of AI research for many decades.
[10] Within a few years, they founded laboratories at
MIT,
CMU and
Stanford that were heavily funded by
DARPA.
[6] They and their students wrote programs that were, to most people, simply astonishing:
[12] computers were solving word problems in algebra, proving logical theorems and speaking English.
[13] 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"
[14]
★ 1967,
Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
[14]
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,
[16] the
intractable combinatorial explosion of their algorithms,
[6] the difficulty of representing
commonsense knowledge and doing
commonsense reasoning,
[18] the incredible difficulty of perception and motion
[19] and the failings of logic.
[20] In 1974, in response to the criticism of England's
Sir James Lighthill and ongoing pressure from congress to fund more productive research,
DARPA cut off all undirected, exploratory research in AI. This was the first
AI Winter.
[21]
In the early 80s, the field would be revived by the commercial success of
expert systems and by 1985 the market for AI had reached more than a billion dollars.
[22] Minsky and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow.
[6] 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.
[6]
In the 90s AI achieved it's a 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.
[25] 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.
[6]
Mechanisms
Generally speaking AI systems are built around automated
inference engines including forward reasoning and
backwards reasoning. Based on certain conditions ("if") the system infers certain consequences ("then"). AI applications are generally divided into two types, in terms of consequences: 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.
[27] 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.
[28]
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.
[29]
★
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.
[30] 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 thousand 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 [sic] with the official rule [sic] of the FIFA, against the winner of the most recent World Cup."
[31]
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
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 articles: 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).
[32] 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.
[33]
Fiction
Main articles: 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. Alternative plots depict civilizations which chose to be managed by AI or to ban AI completely. Best known examples include films such as
The Matrix and
The inevitability of world domination by AI is also argued by some science/futurist writers such as
Kevin Warwick,
Hans Moravec and
Isaac Asimov. This concept is also explored in the
Uncanny Valley hypothesis.
Toys and games
The 1990s saw some of the first attempts to massproduce 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 A.I, specifically in the form of
Tamogatchis and
Giga Pets, the
Internet(ex. 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:
★
Pattern recognition
★
★
Optical character recognition
★
★
Handwriting recognition
★
★
Speech recognition
★
★
Face recognition
★
Artificial Creativity
★
Computer vision,
Virtual reality and
Image processing
★
Diagnosis (artificial intelligence)
★
Game theory and
Strategic planning
★
Game artificial intelligence and
Computer game bot
★
Natural language processing,
Translation and
Chatterbots
★
Non-linear control and
Robotics
;Other fields in which AI methods are implemented:
★
Artificial life
★
Automated reasoning
★
Automation
★
Biologically-inspired computing
★
Colloquis
★
Concept mining
★
Data mining
★
Knowledge representation
★
Semantic Web
★
E-mail spam filtering
★
Robotics
★
★
Behavior-based robotics
★
★
Cognitive robotics
★
★
Cybernetics
★
★
Developmental robotics
★
★
Epigenetic robotics
★
★
Evolutionary robotics
★
Hybrid intelligent system
★
Intelligent agent
★
Intelligent control
★
Litigation
;Lists of researchers, projects & publications
★
★
List of AI projects
★
List of important AI publications
See also
: ''Main list:
List of basic artificial intelligence topics''
★
History of artificial intelligence
★
AI effect
★
AI winter
★
Artificial intelligence systems integration
★
Association for the Advancement of Artificial Intelligence
★
Autonomous foraging
★
Cognitive science
★
Fifth generation computer
★
Generative systems
★
German Research Centre for Artificial Intelligence
★
Intelligent system
★
International Joint Conference on Artificial Intelligence
★
Loebner prize
★
Nanotechnology
★
Neuromancer
★
Nouvelle AI
★
PEAS
★
Personhood
★
Predictive analytics
★
Robot
★
Singularitarianism
★
Three Laws of Robotics
★
Transhuman
Notes
1. (who prefer the term "rational agent")
2.
3. "The whole-agent view is now widely accepted in the field"
4. 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.)
5. See WHAT IS ARTIFICIAL INTELLIGENCE? by John McCarthy
6.
7.
8.
9. Citation needed
10. and
11.
12. Russell and Norvig write "it was astonishing whenever a computer did anything remotely clever."
13. , and . The programs described are Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU.
14. quoted in
15. quoted in
16. , ,
17.
18. , , (Introduction) and
19. and see Moravec's paradox
20. , and see the frame problem, qualification problem and ramification problem.
21. , , under "Shift to Applied Research Increases Investment." and also see Howe, J. ''"Artificial Intelligence at Edinburgh University : a Perspective"''
22. and and
23.
24.
25. , under "Artificial Intelligence in the 90s"
26.
27. J.-S. R. Jang, C.-T. Sun, E. Mizutani, (foreword L. Zadeh) "Neuro-Fuzzy and Soft Computing," Prentice Hall, 1997
28. G.F. Luger, W.A. Stubblefield "Artificial Intelligence and the Design of Expert Systems"
29. 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
30. Congressional Mandate DARPA
31. The RoboCup2003 Presents: Humaniod Robots playing Soccer PRESS RELEASE: 2 June 2003
32. Robots beat humans in trading battle
33. "Robot," Microsoft® Encarta® Online Encyclopedia 2006
References
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
Further reading
★ R. Sun & L. Bookman, (eds.), Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.
External links
★
★
AI-Tools, the Open Source AI community homepage
★
Artificial Intelligence Directory, a directory of Web resources related to artificial intelligence
★
The Association for the Advancement of Artificial Intelligence
★
Freeview Video 'Machines with Minds' by the Vega Science Trust and the BBC/OU
★
Heuristics and artificial intelligence in finance and investment
★
John McCarthy's frequently asked questions about AI
★
Jonathan Edwards looks at AI (BBC audio)
★
Generation5 - Large artificial intelligence portal with articles and news.
★
Mindmakers.org, an online organization for people building large scale A.I. systems
★
Ray Kurzweil's website dedicated to AI including prediction of future development in AI
★
AI articles on the Accelerating Future blog
★
Tel Aviv University Makes First Steps In Creating Cyborg
★
AI Genealogy Project
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