1. Why were you initially drawn to computational and/or informational issues? My Oxford DPhil thesis was about whether Hume was right in saying all knowledge is either empirical or analytic (where the latter involves being true by definition and therefore trivial), or Kant was right in saying that there are kinds of knowledge that are non-empirical and synthetic (i.e. not based on or requiring empirical testing, and neither true by definition, nor lacking significant content). As someone with a first degree in mathematics, I knew that what Kant said about mathematical knowledge was closer to the truth. I tried to explain why in my thesis (which I believe will soon be digitised by the Oxford University Library and so become freely available) but did not get very far. I continued thinking about that problem and other problems while I held a lectureship in philosophy first at Hull university then Sussex University. In 1969 Max Clowes, a leading UK researcher in Artificial Intelligence came to Sussex, in the Experimental Psychology Lab in the School of Biological Sciences. Somehow we met and started talking regularly. I found his ideas and the general field of AI very interesting and decided to sit in on the lectures on AI and programming offered to students in experimental psychology. Eventually I became convinced that the best way to make progress in studying my philosophical problem and many other philosophical problem was to try to design a working mind or at least working fragments of minds, such as a mathematical mind. However, it soon became clear to me that the forms of representation used in AI, based on logic and other forms of representation that I called 'Fregean' because they all made use of syntax based on the application of functions to arguments, were inadequate for the task, and in particular inadequate for the kinds of geometrical reasoning done by mathematicians and others. Urged on by Max Clowes I submitted to the second international joint conference on AI (held at Imperial College in 1971) a paper on that topic, criticising the 1969 paper by McCarthy and Hayes ('Some philosophical problems from the standpoint of AI') because it proposed using only logic for all of AI. I suggested that for some purposes 'analogical representations' of which diagrams and maps were examples, could also be useful and that manipulations of those non-Fregean forms of representation could as much form patterns of valid reasoning as manipulations of Fregean structures. As a result I was invited to spend a year at Edinburgh university where I learnt a lot more about AI and AI programming. The more I learnt about AI and computing concepts the more convinced I became that philosophy needed to be redone in the framework of AI, and said so in a widely ignored book published in 1978 'The computer Revolution in Philosophy: Philosophy Science and Models of Mind'. (Since the online version was copied to the ASSC eprints web site, this seems to be changing.) One of the consequences of all that was that I started doing research in vision in an AI framework around 1975. I thought then and still think that understanding the forms of representation and computation required for vision was the key to understanding many aspects of mind. But I have also learnt that that is far beyond the current state of the art, and that most of the AI research that claims to be about vision is not about vision but about something else, e.g. statistics. 2. What example(s) from your work (or the work of others) best illustrates the fruitful use of a computational and/or informational approach for foundational researches and/or applications? Almost everything I have been doing for the last 35 years would have to be listed in answer to that question. I've started pulling the threads together on this web site: http://www.cs.bham.ac.uk/~axs/my-doings.html However the ideas are continuing to develop so quickly that I cannot keep that web page up to date. Recent examples include: a new view of vision as involving primarily the perception of processes rather than static structures, http://www.cs.bham.ac.uk/research/projects/cosy/papers/#pr0505 a new analysis of information as a rich and deep concept that cannot be defined except implicitly in the context of a theory of varities of information processing http://www.cs.bham.ac.uk/research/projects/cogaff/misc/whats-information.html a new view of compositional semantics in both human language and internal forms of representation as inherently context sensitive, with implications for Gricean theories of communication and the nature of language learning as a collaborative creative problem solving process rather than absorption of an external structure http://www.cs.bham.ac.uk/research/projects/cosy/papers/spatial-prepositions.html a view of causation which, instead of attempting to choose between a Humean (correlational, statistical) notion of causation and a Kantian (structure-based, deterministic) notion of causation accmmodates both, while much learning involves a move from Humean to Kantian understanding of particular types of causation http://www.cs.bham.ac.uk/research/projects/cosy/papers/#pr0506 a new view of conceptual analysis in philosophy, according to which underlying what Ryle referred to as 'logical geography' there is something deeper which I have labelled 'logical topography', the study of which involves theory construction (using abduction) in a way that brings philosophy much closer to science than most philosophers allow, or want to allow. http://www.cs.bham.ac.uk/research/projects/cogaff/misc/logical-geography.html a new view of evolution as involving tradeoffs that lead to design problems requiring a spectrum of solutions ranging between precocial (or genetically pre-configured) and altricial (genetically meta-configured, i.e. based on learning and development controlled by high level implicit assumptions about the nature of the environment). This work is being done with a biologist, and is illustrated by these two papers (one short, one long) http://www.cs.bham.ac.uk/research/projects/cosy/papers/jablonka-sloman-chappell.html http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0609 and many more. 3. What is the proper role of computer science and/or information science in relation to other disciplines? There is not just one proper role. 4. What do you consider the most neglected topics and/or contributions in late 20th century studies of computation and/or information? Two things. Study of the variety of functions of vision and the extraordinarily complex set of requirements for visual mechanisms. Study of the implications of the fact that running virtual machines which are not physical machines, but require physical machines for their implication and do things, i.e. cause events both in virtual machines and in other things. Most philosophers, psychologists and neuroscientists are totally ignorant about this even though they use several virtual machines in their daily life and work (operating systems, word-processors, spelling checkers, internet browsers, email systems, computer games, and many more.) Because current computing education mostly focuses on the USE of computers rather than understanding the principles on which they operate and how they can be designed and extended people studying information processing systems such as brains and minds lack the conceptual framework required to make real progress. A partial remedy is proposed here: a syllabus for teaching AI in schools: http://www.cs.bham.ac.uk/~axs/courses/alevel-ai.html 5. What are the most important open problems concerning computation and/or information and what are the prospects for progress? vision vision vision Progress will depend on deep new ideas that don't seem to be anywhere on the horizon as yet. But I have been working out some of the requirements for those ideas, and that work is making progress.