Yasemin Erden on BBC

AISB Committee member, and Philosophy Programme Director and Lecturer, Dr Yasemin J. Erden interviewed for the BBC on 29 October 2013. Speaking on the Today programme for BBC Radio 4, as well as the Business Report for BBC world N...


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Mark Bishop, Chair of the Study of Artificial Intelligence and the Simulation of Behaviour, appeared on Newsnight to discuss the ethics of ‘killer robots’. He was approached to give his view on a report raising questions on the et...


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The AISB has launched a YouTube channel: http://www.youtube.com/user/AISBTube (http://www.youtube.com/user/AISBTube). The channel currently holds a number of videos from the AISB 2010 Convention. Videos include the AISB round t...


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Lighthill Debates

The Lighthill debates from 1973 are now available on YouTube. You need to a flashplayer enabled browser to view this YouTube video  


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Notice

The Relationship of AI to Other Disciplines

 

About this Page

We have constructed this page in order to sketch how AI is linked to various other disciplines, in both the sciences and humanities. We have done this not only in the hope of helping students and others who are just starting to study AI but also of facilitating further interactions between the AI community and other communities.

The set of links is not exhaustive, nor is the explanation given for each individual link.

In particular, we do not include all disciplines that do or could use AI tools and/or contribute tools to AI. We concentrate rather on disciplines where there is profound interaction in terms of research ideas. In most cases the interaction is two-way.

We also omit discussion of the somewhat contentious topic of the relationship of AI to Computer Science, which may be treated in future in a separate document. (Most people would agree that the boundary between AI and CS is fuzzy and subjective. But some people think AI is part of CS, some people think the reverse is true, and some people think they merely overlap. Some people think AI is often too vague to be counted as computer "science", other people think that CS is scientifically deficient if it doesn't overlap or include AI.)

 

Psychology

AI is intrinsically one source of creative suggestions for Psychology. Much AI research is directed ultimately at proposing detailed models for explaining the cognitive, perceptual and emotional qualities of people and animals. AI is not just about creating useful artefacts that have abilities grossly comparable to those of people and animals, though this is itself of immense importance.

Conversely, Psychological research can help AI:

Psychology can suggest cognitive, perceptual, emotional, ... mechanisms for use in AI systems even when there is no intention for those systems to act as models for biological creatures.

AI systems interacting with people, especially via humanoid avatars (screen images) or robotic bodies, need often to at least look as though they have appropriate emotions, even if they don't actually have them. The psychology of how emotion is linked to thought, perception and body movements therefore comes into play for this practical reason.

More subtly:- Imagine an AI system that is meant to be able to communicate with ordinary people about everyday matters. The system needs to be able to know how people commonsensically reason about things (or needs to be able simulate such reasoning). This in order to be able to understand people's linguistic and other communications about those things and in order, conversely, to be able to communicate things that will be understandable to people. Thus, results from Psychology about how people think about everyday matters are useful. This is so even if the AI agent is not itself meant to be a model of a person.

 

(Neuro)Physiology and Biology more generally

These disciplines can help AI:

Results from the study of the nature and working of biological bodies and especially of brains are helpful in the development of computational infrastructures for AI (e.g.: artificial neural networks; algorithmic systems analogous to the immune system) and robotic hardware and body control systems.

The study of biological and cultural evolution has led to artefactual evolutionary computing, with applications within AI ranging from those at a fine scale (e.g., self-developing algorithms for doing some specific cognitive task) to those at a gross scale, as in evolution of populations of whole AI agents.

The complex computation effected by large swarms of relatively simple organisms (e.g., ants) has led to analogous forms of computation within AI, again both within an individual agent and in terms of large groups of whole agents.

AI can help those disciplines: Development of thinking agents and robots within AI, even if it has no specific intention to illuminate biology, can in fact do so, in terms of physiology, evolution, swarm activity, etc.

 

Philosophy

AI has much overlap with Philosophy, at both a grand scale and a more detailed technical scale.

AI is intrinsically concerned with grand Philosophical issues such as the fundamental nature of thought, consciousness, mind/body relationship, morality, free will, aesthetic qualities, value, time, etc., and more specialized philosophical issues such as the fundamental nature of language, representation, information, logic, computation, rule-following, symbols and causation.

Many detailed technical problems studied in Philosophy are also technical challenges for AI. One example is the thorny representational and cognitive issues raised by propositional attitude reports (roughly: sentences reporting mental states of agents) and more generally by the need for AI systems to represent and reason about mental states of agents. Other examples include vague language and information, counterfactual hypotheses, obligations, and the nature of information about past and future.

AI raises new problems for Philosophy or intensifies the urgency of old problems. E.g., the fluidity of intelligent software agents on the web, where we can imagine agents merging, splitting or cloning themselves, or not having clear boundaries in the first place, raises/intensifies Philosophical issues about personhood and the notion of a self.

AI can provide working models embodying Philosophical theories and thus serve as a testbed for them (testing for their consistency, completeness, economy of structure, etc.)

On the other hand, Philosophical research on issues important to AI can have a level of refined subtlety and sophistication, and can have a breadth or degree of ambitiousness, that most AI researchers do not have the resources or opportunity to achieve.

 

Logic

Note: Logic could be deemed covered by Philosophy above, and is not so often counted as a separate discipline; but it has an especially salient role for AI. Also of interest for AI is the Psychological issue of what sorts of logical processing people do or don't do, get right or mess up, but this is covered more by the interaction of AI with Psychology.

AI has much overlap and interaction with Logic. Developments in Logic fundamentally affect not only research into explicitly logic-based processing in AI but also much other work on forms of representation, depending on the ways in which those forms translate (or fail to translate) into logic. But also the needs of AI to deal with taxonomies (and "ontologies"), uncertainty, inconsistent information, vagueness, time, mental states, obligations, etc. lead to new or revised logics being invented and explored that are of interest in the field of Logic itself. One type of case is variations on modal logic for mental states where the usual provisions are augmented with considerations of mental resource limitations; another is the development of description logic (to couch taxonomies, ontologies, etc.).

Results such as Goedel's about the limitations of formal logic have a general importance for computational theory, and of course bear in particular upon the theoretical capabilities of intelligent agents based on logic. However, a more special point that affects AI is the use of those limitations in arguments that human beings are fundamentally superior to any conceivable (machine-like) artefact. Such arguments attack the very idea of "strong" AI (that genuine thought, consciousness, etc. can be artefactually realized). There is of course much contention over the validity of the arguments.

 

Linguistics (incl. study of sign language and gesture)

Note: The fields of Pragmatics and Rhetoric are considered to be covered in this section.

One of the main needs of an AI agent interacting with people is to be able to communicate with people in natural language (i.e., human language ... and the more "natural" in the ordinary sense the better!) and/or to be able to understand natural language communications between people. Linguistics is therefore an important source of knowledge and ideas in the development of such agents.

Conversely, AI can contribute ideas to Linguistics:

Practical approaches taken in the engineering of AI artefacts that have some capability in natural language processing may stimulate ideas and fruitful controversies about the nature of human language itself. E.g., statistical approaches to machine translation of languages can act as a foil to traditional theorizing about the nature of the human language faculty.

The practical imperative in AI to deal with some of the most abstruse problems in language such as discourse structure, point of view expression, metaphor and emotional expression, even when only mundane conversation or text is at issue, leads to theoretical models that can be of use to Linguistics in its own studies of these matters.

Study of the automatic development of language within populations of AI agents contributes to fundamental understanding of language itself.

Communication in sign language and through gesture (especially as linked to spoken language) has in recent years exploded in importance both within Linguistics and within AI and Computer Science.

Studies of gesture have suggested the nature of some of the cognitive processes underlying spoken language itself. This is useful for AI.

 

Art, Music, Literature ... and Creativity

("Art" below includes cinema, music, dance, literature, etc. as well visual art in the usual sense. Also the field of Aesthetics is intended to be covered by this section.)

Insofar as AI is intended to emulate people and illuminate their nature it must attend not just to "dry" forms of cognition such as everyday reasoning and expert problem solving but also to the human artistic capabilities. Indeed, insofar as artistic endeavour appears central to the nature of people it would be dangerous within AI (and Psychology, Philosophy, etc.) to neglect it. Also, aesthetic sensibility is important even in science, given the tendency to prefer "elegant" theories and models.

More generally, the study of what creativity is and how to achieve it artefactually is important for AI, and goes beyond art into scientific theorizing, expert problem solving and commonsense reasoning. One technical topic shared between artistic creativity and non-artistic forms of it, and that has seen much detailed attention in AI, Psychology and elsewhere is the use of analogy.

Art is centrally concerned with human emotion, a topic that is of increasing importance within AI (and Computer Science). See also the comments on Psychology above.

Issues of representation and point of view that arise in artistic forms of expression present cognitively revealing problems beyond the more classical issues arising in language, logic, etc.

These problems also link to non-art issues in AI. For instance, the way point of view and mode of consciousness are expressed in language is much studied in literary theory and is important also for ordinary language processing. The way paintings (whether photographically realistic or abstract) express and represent things is linked to the way diagrams do; and the study of diagrammatic representation/reasoning is an important topic for AI.

AI intensifies and refines biological, evolutionary and philosophical questions of whether the arts and creativity are uniquely human qualities or, going in one direction, are shared with sub-human creatures or by constrast, in the other direction, require something mystical beyond physical processes.

 

Economics, Politics, Sociology, Business Management, etc.

AI is much concerned with the study of communities of agents, not just with single agents. Thus, many issues of cooperation, conflict, trust, social norms, governance, resource usage, organizational dynamics, etc. are shared with disciplines such as economics, politics, sociology and business management.

AI agents operating within real markets (e.g., doing automated stock trading) are becoming a crucial part of economic reality and may have fundamental effects on economic theory insofar as this theory needs to be concerned with timescales of and motivations for decisions.

The ability of AI agents to help in Medicine, Law, Policing, etc. raises important issues of ethics, responsibility, accountability and statutory regulation.

Virtual worlds not only have their own internal economics, politics, etc. but also interact with or overlap with the real world. This is a trend that is set to rise rapidly. The use of AI within virtual worlds will increasingly intensify the problems for humankind that this trend raises.

 

Medicine / Psychiatry / Healthcare / ...

The development of AI agents capable of performing or helping with medical diagnosis, treatment design, psychiatric therapy, more informal lifestyle advising, elder care, nursing, clinical trial analysis etc. has been an interest since the early days of AI and in some respects is currently burgeoning. Aside from possible practical AI contributions are the ethical, regulatory, etc. issues raised for theoreticians and practitioners in Medicine, etc., as mentioned above.

There is no real converse in the direction from Medicine etc. to AI (aside from the point that any application area of AI can of course stimulate the development of important new ideas in AI). One day, though, AI agents will need some analogue of human healthcare!

 

Law

AI and Law have a strong shared interest in the nature of commonsense reasoning, expert reasoning, evidence, persuasion, deception, exploration (investigation), intention, etc. Law is also an important application area for AI expert systems.

The use of examples within case-based reasoning in AI (a form of analogy-based reasoning, and similar also to "example-based reasoning" is Psychology) is highly analogous to the use of prior legal cases in Law. The relationship of case-based reasoning to "rule-based" reasoning in AI is analogous to the relationship between reasoning via cases and reasoning via statutes in Law.

The increase over time of the level of intelligence and degree of embeddedness in society of AI agents will lead to new challenges for Law, on matters such as responsibility, intention, evidence, ownership (notably of intellectual property, of web-crawling software agents, especially if they can collaborate to create other such agents), punishment, and the fundamental nature of legal processes themselves.

 

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