What is Artificial
(AI) is both the intelligence of machines and the branch of computer
science which aims to create it.
Major AI textbooks
define artificial intelligence as "the study and design of intelligent
agents," where an intelligent
agent is a system that perceives its environment and takes actions which
maximize its chances of success.
AI can be seen as a realization of an abstract intelligent agent (AIA)
which exhibits the functional essence of intelligence.
John McCarthy, who coined the term in 1956,
defines it as "the science and engineering of making intelligent machines."
Among the traits
that researchers hope machines will exhibit are reasoning, knowledge,
planning, learning, , perception and the ability to move and manipulate
intelligence (or "strong AI") has not yet been achieved and is a long-term
goal of AI research.
AI research uses
tools and insights from many fields, including computer science, psychology,
philosophy, neuroscience, cognitive science, linguistics, ontology,
operations research, economics, control theory, probability, optimization
and logic. AI research
also overlaps with tasks such as robotics, control systems, scheduling,
data mining, logistics, speech recognition, facial recognition and many
others. Other names
for the field have been proposed, such as computational intelligence,
or computational rationality.
of AI research
In the middle of
the 20th century, a handful of scientists began a new approach to building
intelligent machines, based on recent discoveries in neurology, a new
mathematical theory of information, an understanding of control and
stability called cybernetics, and above
all, by the invention of the digital computer, a machine based on the
abstract essence of mathematical reasoning.
The field of modern
AI research was founded at conference on the campus of Dartmouth College
in the summer of 1956.
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:
computers were solving word problems in algebra, proving logical theorems
and speaking English.
By the middle 60s their research was heavily funded by the U.S. Department
of Defense and they
were optimistic about the future of the new field:
- 1965, H. A. Simon:
"[M]achines will be capable, within twenty years, of doing any work
a man can do"
- 1967, Marvin
Minsky: "Within a generation ... the problem of creating 'artificial
intelligence' will substantially be solved."
and many like them, would not come true. They had failed to recognize
the difficulty of some of the problems they faced.
In 1974, in response to the criticism of England's Sir James Lighthill
and ongoing pressure from Congress to fund more productive projects,
the U.S. and British governments cut off all undirected, exploratory
research in AI. This was the first AI Winter.
In the early 80s,
AI research was revived by the commercial success of expert systems
(a form of AI program that simulated the knowledge and analytical skills
of one or more human experts) and by 1985 the market for AI had reached
more than a billion dollars.
Minsky and others warned the community that enthusiasm for AI had spiraled
out of control and that disappointment was sure to follow.
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.
In the 90s and early
21st century 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.
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.
Can the brain
be simulated by a digital computer? If it can, then would the simulation
have a mind in the same sense that people do?
In a classic 1950
paper, Alan Turing posed the question "Can Machines Think?" In the years
since, the philosophy of artificial intelligence has attempted to answer
- Turing's "polite
convention": If a machine acts as intelligently as a human being,
then it is as intelligent as a human being. Alan Turing realized
that, ultimately, we can only judge the intelligence of machine based
on its behavior. This insight 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 assertion was printed in the proposal for the Dartmouth
Conference of 1956, and represents the position of most working AI
- Newell and Simon's
physical symbol system hypothesis: A physical symbol system has
the necessary and sufficient means of general intelligent action.
This statement claims that the essence of intelligence is symbol manipulation.
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
- Godel's incompleteness
theorem: A physical symbol system can not prove all true statements.
Roger Penrose is among those who claim that Godel's theorem limits
what machines can do.
- Searle's "strong
AI position": A physical symbol system can have a mind and mental
states. 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.
- The artificial
brain argument: The brain can be simulated. 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. This argument
combines the idea that a suitably powerful machine can simulate any
process, with the materialist idea that the mind is the result of
a physical process in the brain.
Mackworth & Goebel 1998, p.
1 (who use the term "computational intelligence" as a synonym
for artificial intelligence). Other textbooks that define AI this
way include Nilsson (1998),
and Russell & Norvig
(2003) (who prefer the term "rational agent") and write "The
whole-agent view is now widely accepted in the field" (Russell
& Norvig 2003, p. 55)
definition, in terms of goals, actions, perception and environment,
is due to Russell &
Norvig (2003). Other definitions also include knowledge and
learning as additional components.
Intelligent Agents: Paradigms, Foundations and Conceptualization
Problems, A.M. Gadomski, J.M. Zytkow, in "Abstract Intelligent
Agent, 2". Printed by ENEA, Rome 1995, ISSN/1120-558X]
there is some controversy on this point (see Crevier
1993, p. 50), McCarthy
states unequivocally "I came up with the term" in a c|net interview.
Machines to Think Like Us.)
is Artificial Intelligence?
list of intelligent traits is based on the topics covered by the
major AI textbooks, including: Russell
& Norvig 2003, Luger
& Stubblefield 2004, Poole,
Mackworth & Goebel 1998 and Nilsson
General intelligence (strong
AI) is discussed by popular introductions to AI, such as: Kurzweil
1999, Kurzweil 2005,
Hawkins & Blakeslee
& Norvig 2003, pp. 5-16
Mackworth & Goebel 1998, p.
The name of the journal Intelligent
& Norvig 2003, p. 17
2004, p. 5, Russell
& Norvig 2003, p. 939
Egyptian statue of Amun is
discussed by Crevier (1993,
p. 1). McCorduck (2004,
pp. 6-9) discusses Greek statues. Hermes
Trismegistus expressed the common belief that with these statues,
craftsman had reproduced "the true nature of the gods", their sensus
and spiritus. McCorduck makes the connection between sacred
automatons and Mosaic
law (developed around the same time), which expressly forbids
the worship of robots.
2004, p. 13-14 (Paracelsus)
1986, p. 53
2004, p. 6
Thirteenth Century Programmable Robot
2004, p. 17
2004, p. xviii
(2004, p. 190-25) discusses Frankenstein
and identifies the key ethical issues as scientific hubris and the
suffering of the monster, e.g. robot
could demand legal rights
the Times Online, Human
rights for robots? We’re getting carried away
rights: Russell Norvig,
& Norvig (2003, p. 960-961)
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. Weizenbaum: Crevier
1993, pp. 132−144, McCorduck
2004, pp. 356-373, Russell
& Norvig 2003, p. 961 and Weizenbaum
Kurzweil 2005, Russell
& Norvig 2003, p. 963
Quoted in McCorduck (2004,
the researchers who laid the foundations of the theory
of computation, cybernetics,
theory and neural
networks were Claude
Turing and John
Von Neumann. McCorduck
2004, pp. 51-107, Crevier
1993, pp. 27-32, Russell
& Norvig 2003, pp. 15,940, Moravec
1988, p. 3.
1993, pp. 47-49, Russell
& Norvig 2003, p. 17
and Norvig write "it was astonishing whenever a computer did anything
kind of smartish." Russell
& Norvig 2003, p. 18
1993, pp. 52-107, Moravec
1988, p. 9 and Russell
& Norvig 2003, p. 18-21. The programs described are
1993, pp. 64-65
1965, p. 96 quoted in Crevier
1993, p. 109
1967, p. 2 quoted in Crevier
1993, p. 109
of artificial intelligence — the problems.
Crevier 1993, pp. 115-117,
Russell & Norvig
2003, p. 22, NRC 1999
under "Shift to Applied Research Increases Investment." and also
see Howe, J. "Artificial
Intelligence at Edinburgh University : a Perspective"
1993, pp. 161-162,197-203 and and Russell
& Norvig 2003, p. 24
1993, p. 203
1993, pp. 209-210
Russell Norvig, p. 28,NRC
1999 under "Artificial Intelligence in the 90s"
Norvig, pp. 25-26
All of these positions are mentioned in standard discussions of
the subject, such as Russell
& Norvig 2003, pp. 947-960 and Fearn
2007, pp. 38-55
Turing 1950, Haugeland
1985, pp. 6-9, Crevier
1993, p. 24, Russell
& Norvig 2003, pp. 2-3 and 948
et al. 1955 See also Crevier
1993, p. 28
Newell & Simon 1963
and Russell & Norvig
2003, p. 18
Dreyfus criticized a version of the physical
symbol system hypothesis that he called the "psychological assumption":
"The mind can be viewed as a device operating on bits of information
according to formal rules". Dreyfus
1992, p. 156. See also Dreyfus
& Dreyfus 1986, Russell
& Norvig 2003, pp. 950-952, Crevier
& 1993 120-132 and Hearn
2007, pp. 50-51
This is a paraphrase of the most important implication of Godel's
theorems, according Hofstadter
(1979). See also Russell
& Norvig 2003, p. 949, Godel
1931, Church 1936,
Kleene 1935, Turing
1937, Turing 1950
under o(2) The Mathematical Objection”
Searle 1980. See also
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," although Searle's
arguments, such as the Chinese
Room, apply only to physical
symbol systems, not to machines in general (he would consider
the brain a machine). Also, notice that the positions as Searle
states them don't make any commitment to how much intelligence
the system has: it is one thing to say a machine can act intelligently,
it is another to say it can act as intelligently as a human being.
Moravec 1988 and Kurzweil
2005, p. 262. Also see Russell
Norvig, p. 957 and Crevier
1993, pp. 271 and 279. The most extreme form of this argument
(the brain replacement scenario) was put forward by Clark
Glymour in the mid-70s and was touched on by Zenon
Pylyshyn and John
Searle in 1980.
George & Stubblefield, William (2004), Artificial
Intelligence: Structures and Strategies for Complex Problem Solving
(5th ed.), The Benjamin/Cummings Publishing Company, Inc., pp. 720,
Nils (1998), Artificial Intelligence: A New Synthesis, Morgan
Stuart J. & Norvig, Peter (2003), Artificial
Intelligence: A Modern Approach (2nd ed.), Upper Saddle River,
NJ: Prentice Hall, <http://aima.cs.berkeley.edu/>
David; Mackworth, Alan & Goebel, Randy (1998), Computational
Intelligence: A Logical Approach, Oxford University Press,