- AI redirects here; for alternate uses, see Ai.
Artificial intelligence is defined as intelligence exhibited by anything manufactured (i.e. artificial) by humans or other sentient beings or systems (should such things
ever exist on Earth or elsewhere).
Overview
The question of what artificial intelligence is, even as defined above, can be reduced to two parts: "what is the nature of
artifice" and "what is intelligence"? The first question is fairly easy to answer, though it does point to the question of what
it is possible to manufacture (within the constraints of certain types of system, e.g. classical computational systems, of
available processes of manufacturing and of possible limits on human intellect, for instance).
The second is much harder, raising questions of consciousness and
self, mind (including the unconscious mind) and the question of what components are involved in the
only type of intelligence it is universally agreed we
have available to study: that of human beings. Study of animals and artificial systems that are not just models of what exists
already are widely considered very pertinent, too.
Several distinct types of artificial intelligence have been elucidated below. Also, the subject divisions, history, proponents
and opponents and applications of research in the subject are described. Finally, references to fictional and non-fictional
descriptions of AI are provided.
Strong AI and Weak AI
One popular and early definition of artificial intelligence research, put forth by John McCarthy at the Dartmouth
Conference in 1955 is "making a machine behave in ways that would be called intelligent
if a human were so behaving." However this definition seems to ignore the possibility of strong AI (see below). Another
definition of artificial intelligence is intelligence arising from an artificial device. Most definitions could be categorized as
concerning either systems that think like humans, systems that act like humans, systems that think
rationally or systems that act rationally.
Strong artificial intelligence
Strong artificial intelligence research deals with the creation of some form of computer-based artificial
intelligence that can truly reason and solve problems; a strong form of AI is said to be sentient, or self-aware. In theory, there are two types
of strong AI:
- Human-like AI, in which the computer program thinks and reasons much like a human mind.
- Non-human-like AI, in which the computer program develops a totally non-human sentience, and a non-human way of thinking and
reasoning.
Weak artificial intelligence
Weak artificial intelligence research deals with the creation of some form of computer-based artificial
intelligence that cannot truly reason and solve problems; such a machine would, in some ways, act as if it were
intelligent, but it would not possess true intelligence or sentience.
To date, much of the work in this field has been done with computer simulations
of intelligence based on predefined sets of rules. Very little progress has been made in strong AI. Depending on how one defines
one's goals, a moderate amount of progress has been made in weak AI.
Philosophical criticism and support of strong AI
Several philosophers, notably John Searle and Hubert Dreyfus, have argued on
philosophical grounds against the feasibility of building human-like consciousness or intelligence in a disembodied machine.
Searle is most known for his Chinese room argument,
which claims to demonstrate that even a machine that passed the Turing test
would not necessarily be conscious in the human sense. Dreyfus, in his book What Computers Still Can't Do: A Critique of
Artificial Reason, has argued that consciousness cannot be captured by rule- or logic-based systems or by systems that are not attached to a physical body, but leaves open the possibility that a
robotic system using neural networks or similar mechanisms might
achieve artificial intelligence.
Other philosophers hold opposing views. Many see no problem with Weak AI but there is much support for Strong AI too. Daniel C. Dennett argues in Consciousness Explained that if there is no magic spark or soul, then Man is just a
machine, and he asks why the Man-machine should have a privileged position over all other possible machines when it comes to
intelligence.
Some philosophers hold that if Weak AI is accepted as possible then so must Strong AI. The Weak AI position, that intelligence
might be apparent but would not be real, is debunked by many but one accessible example can be found in Simon Blackburn's introduction to philosophy, Think. Blackburn
points out that you might appear intelligent but there is no way of telling if that intelligence is real
(whatever that means in this context): We have to take it on trust.
Supporters of Strong AI claim that the anti-AI argument boils down in the end to arrogance (a privileged position is claimed,
a magic spark is introduced, perhaps by God) or to definition (by defining
intelligence as that of which machines are incapable) or to
both.
An argument supporting Strong AI which those who deny its possibility must
necessarily attack:
- Given that the mind is the software/hardware brain, and
- Given the Church-Turing thesis,
- The possibility of Strong AI must be accepted.
Some (including Roger Penrose) attack the Church-Turing thesis. Others
say the mind is not (completely) physical.
History
Development of AI theory
Much of the (original) focus of artificial intelligence research draws from an experimental approach to psychology, and emphasizes what may be called linguistic intelligence (best exemplified
in the Turing test).
Approaches to artificial intelligence that do not focus on linguistic intelligence include robotics and collective intelligence
approaches, which focus on active manipulation of an environment, or consensus decision making, and draw from biology and
political science when seeking models of how "intelligent"
behavior is organized.
Artificial intelligence theory also draws from animal studies, in particular with
insects, which are easier to emulate as robots (see artificial life),
as well as animals with more complex cognition, including apes, who resemble humans in many
ways but have less developed capacities for planning and cognition. AI researchers argue that animals, which are simpler than
humans, ought to be considerably easier to mimic. But satisfactory computational models for animal intelligence are not
available.
Seminal papers advancing the concept of machine intelligence include A Logical Calculus of the Ideas Immanent in Nervous
Activity (1943), by Warren
McCulloch and Walter Pitts, and On Computing Machinery and Intelligence (1950), by Alan Turing, and Man-Computer
Symbiosis by J.C.R. Licklider. See cybernetics and Turing test for further discussion.
There were also early papers which denied the possibility of machine intelligence on logical or philosophical grounds such as Minds, Machines and Gödel (1961) by John Lucas [1]
.
With the development of practical techniques based on AI research, advocates of AI have argued that opponents of AI have
repeatedly changed their position on tasks such as computer chess or
speech recognition that were previously regarded as
"intelligent" in order to deny the accomplishments of AI. They point out that this moving of the goalposts effectively defines
"intelligence" as "whatever humans can do that machines cannot".
John von Neumann (quoted by E.T. Jaynes) anticipated this in 1948 by saying, in response to a comment
at a lecture that it was impossible for a machine to think: "You insist that there is something a machine cannot do. If you will
tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that!". Von
Neumann was presumably alluding to the Church-Turing thesis
which states that any effective procedure can be simulated by a (generalized) computer.
In 1969 McCarthy and Hayes started the discussion about the frame problem with their essay, "Some Philosophical Problems from the Standpoint
of Artificial Intelligence".
Experimental AI research
Artificial intelligence began as an experimental field in the 1950s with such pioneers as Allen Newell and Herbert Simon, who founded the first
artificial intelligence laboratory at Carnegie-Mellon University, and McCarthy and Marvin Minsky, who founded the MIT AI Lab in 1959. They all
attended the aforementioned Dartmouth College summer AI
conference in 1956, which was organized by McCarthy, Minsky, Nathan Rochester of IBM and Claude Shannon.
Historically, there are two broad styles of AI research - the "neats" and "scruffies". "Neat", classical or symbolic AI research, in general, involves symbolic manipulation of abstract concepts,
and is the methodology used in most expert systems. Parallel to this are the "scruffy", or "connectionist", approaches, of which
neural networks are the best-known example, which try to "evolve"
intelligence through building systems and then improving them through some automatic process rather than systematically designing
something to complete the task. Both approaches appeared very early in AI history. Throughout the 1960s and 1970s scruffy
approaches were pushed to the background, but interest was regained in the 1980s when the limitations of the "neat" approaches of
the time became clearer. However, it has become clear that contemporary methods using both broad approaches have severe
limitations.
Artificial intelligence research was very heavily funded in the 1980s by the Defense Advanced Research
Projects Agency in the United States and by the Fifth Generation Computer project in Japan. The failure of the work funded at the time to produce immediate results, despite the
grandiose promises of some AI practitioners, led to correspondingly large cutbacks in funding by government agencies in the late
1980s, leading to a general downturn in activity in the field known as AI winter. Over the following decade, many AI researchers moved into related areas with more
modest goals such as machine learning, robotics, and computer vision, though research
in pure AI continued at reduced levels.
Practical applications of AI techniques
Whilst progress towards the ultimate goal of human-like intelligence has been slow, many spinoffs have come in the process.
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 McCarthy, Minsky, Seymour Papert (who developed Logo there), Terry Winograd (who
abandoned AI after developing SHRDLU).
Many other useful systems have been built using technologies that at least once were active areas of AI research. Some
examples include:
The vision of artificial intelligence replacing human professional judgment has arisen many times in the history of the field,
in science fiction and today in some specialized areas where
"expert systems" are used to augment or to replace professional judgment
in some areas of engineering and of medicine.
Hypothetical consequences of AI
Some observers foresee the development of systems that are far more intelligent and complex than anything currently known. One
name for these hypothetical systems is artilects. With the introduction of artificially intelligent
non-deterministic systems, many ethical issues will arise. Many of these issues have
never been encountered by humanity.
Over time, debates have tended to focus less and less on "possibility" and more on "desirability", as emphasized in the
"Cosmist" (versus "Terran") debates
initiated by Hugo de Garis and Kevin Warwick. A Cosmist, according to de Garis, is actually seeking to build more intelligent successors to
the human species. The emergence of this debate suggests that desirability questions may also have influenced some of the early
thinkers "against".
Some issues that bring up interesting ethical questions are:
- Determining the sentience of a system we create.
- Can AI be defined in a graded sense?
- Freedoms and rights for these systems
- Can AIs be "smarter" than humans in the same way that we are "smarter" than other animals?
- Designing systems that are far more intelligent than any one human
- Deciding how much safe-guards to design into these systems
- Seeing how much learning capability a system needs to replicate human thought, or how well it could do tasks without it (eg
expert system)
- The Singularity
- Affect on careers and jobs. The problems may resemble problems seen under free
trade.
Famous Figures
Machines displaying some degree of "intelligence"
There are many examples of programs displaying some degree of intelligence. Some of these are:
- The Start
Project - a web-based system which answers
questions on English.
- Cyc, a knowledge base with vast collection of facts about the real world and logical
reasoning ability.
- ALICE, a chatterbot
- Alan , another chatterbot
- ELIZA, a program which pretends to be a psychoterapist, developed circa 1970.
- PAM (Plan Applier Mechanism) - a story understanding system developed by John Wilensky in 1978.
- SAM (Script applier mechanism) - a story understanding system, dveloped in 1975.
- SHRDLU - an early natural language understanding computer program developed in
1968-1970.
- Creatures, a computer game with breeding, evolving creatures coded from the
genetic level upwards using a sophistcated biochemistry and neural network brains.
- BBC news story on the creator of Creatures latest creation.
Steve Grand's Lucy.
- EURISKO - a language for solving
problems which consists of heuristics, including heuristics describing how to use and change its heuristics. Developed in 1978 by
Douglas Lenat.
- X-Ray Vision for Surgeons - a group in MIT which researches medical
vision.
- Neural networks-based progams for
backgammon and go .
AI Researchers
There are many thousands of AI researchers around the world at hundreds of research institutions and companies. Among the many
who have made significant contributions are:
To some computer scientists, the phrase artificial intelligence has acquired somewhat of a bad name due to the large
discrepancy between what has been achieved so far in the field and some more usual notions of intelligence. This problem has been
aggrevated by various irresponsible popular science writers and media personalities such as Kevin Warwick whose work has raised the expectations of AI research way above its current capabilities. For
this reason, some researchers working on topics related to artificial intelligence say they work in cognitive science, informatics, statistical inference or
information engineering. However, progress has in
fact been made, and AI is today routinely employed in thousands of industrial systems around the world. See Raj Reddy's AAAI paper for a huge review of real-world AI systems in deployment
today.
Resources
Further Reading
Non-Fiction
Fiction
See also List of fictional
robots and androids
AI related organizations
Sources
- John McCarthy: Proposal for the Dartmouth Summer Research Project On Artificial Intelligence. [2]
See also
Important publications in artificial intelligence.
Sub-fields of AI research
Logic programming was sometimes considered a field of
artificial intelligence, but this is no longer the case.
Philosophy
Logic
Science
Applications
Uncategorised
- Collective intelligence - the idea that a
relatively large number of people co-operating in one process can lead to reliable action.
- Quantum mind - the idea that large-scale quantum coherence is necessary
to understand the brain.
- the Singularity - a time at which technological progress
accelerates beyond the ability of current-day human beings to understand it, or the point in time of the emergence of
smarter-than-human intelligence.
- Mindpixel - A project to collect simple true / false assertions and
collaboratively validate them with the aim of using them as a body of human common sense knowledge that can be utilised by a
machine.
- Game programming AI
- artificial consciousness
External links
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