Access to the full content is only available to members of institutions that have purchased access. If you belong to such an institution, please log in or find out more about how to order.



Artificial intelligence

DOI: 10.4324/9780415249126-W001-1
Version: v1,  Published online: 1998
Retrieved January 20, 2018, from

Article Summary

Artificial intelligence (AI) tries to make computer systems (of various kinds) do what minds can do: interpreting a photograph as depicting a face; offering medical diagnoses; using and translating language; learning to do better next time.

AI has two main aims. One is technological: to build useful tools, which can help humans in activities of various kinds, or perform the activities for them. The other is psychological: to help us understand human (and animal) minds, or even intelligence in general.

Computational psychology uses AI concepts and AI methods in formulating and testing its theories. Mental structures and processes are described in computational terms. Usually, the theories are clarified, and their predictions tested, by running them on a computer program. Whether people perform the equivalent task in the same way is another question, which psychological experiments may help to answer. AI has shown that the human mind is more complex than psychologists had previously assumed, and that introspectively ‘simple’ achievements – many shared with animals – are even more difficult to mimic artificially than are ’higher’ functions such as logic and mathematics.

There are deep theoretical disputes within AI about how best to model intelligence. Classical (symbolic) AI programs consist of formal rules for manipulating formal symbols; these are carried out sequentially, one after the other. Connectionist systems, also called neural networks, perform many simple processes in parallel (simultaneously); most work in a way described not by lists of rules, but by differential equations. Hybrid systems combine aspects of classical and connectionist AI. More recent approaches seek to construct adaptive autonomous agents, whose behaviour is self-directed rather than imposed from outside and which adjust to environmental conditions. Situated robotics builds robots that react directly to environmental cues, instead of following complex internal plans as classical robots do. The programs, neural networks and robots of evolutionary AI are produced not by detailed human design, but by automatic evolution (variation and selection). Artificial life studies the emergence of order and adaptive behaviour in general and is closely related to AI.

Philosophical problems central to AI include the following. Can classical or connectionist AI explain conceptualization and thinking? Can meaning be explained by AI? What sorts of mental representations are there (if any)? Can computers, or non-linguistic animals, have beliefs and desires? Could AI explain consciousness? Might intelligence be better explained by less intellectualistic approaches, based on the model of skills and know-how rather than explicit representation?

Citing this article:
Boden, Margaret A.. Artificial intelligence, 1998, doi:10.4324/9780415249126-W001-1. Routledge Encyclopedia of Philosophy, Taylor and Francis,
Copyright © 1998-2018 Routledge.

Related Articles