Keith Douglas' Web Page

About me Find out who I am and what I do.
My resumé A copy of my resumé and other documentation about my education and work experience for employers and the curious.
Reviews, theses, articles, presentations A collection of papers from my work, categorized and annotated.
Current research projects What I am currently working on, including some non-research material.
Interesting people People professionally "connected" to me in some way.
Interesting organizations Organizations I am "connected" to. (Some rather loosely.)
Intellectual/professional influences Influences on my work, including an organization chart. Here you can also buy many good books on philosophy and other subjects via I have included brief reviews of hundreds of books.
Professional resources Research sources, associates programs, etc.
What is the philosophy of computing? A brief introduction to my primary professional interest.
My intellectual heroes A partial list of important people. Limited to the dead.
My educational philosophy As a sometime teacher I've developed one. Includes book resources.

Book Influences: Philosophy of Science - Computing

Computationalism: New Directions Scheutz (ed.) Primarily about foundations of AI and the "computational theory of mind" (which are often uncritically run together), this collection is a stimulating one in general. However, it is the paper on computation in general, by B. C. Smith which absolutely knocked my socks off - he has written what I have sketched and alluded it to in my work for quite some time now but never got around to quite putting all together. Gist: the theory of computation is part of an as yet unforeseen new metaphysics (though he doesn't use that word) which will be useful for technology and science. The exception to the general high quality is the mixed bag that is Copeland's on hypercomputing and related topics: his exegesis of some positions is a bit sloppy. For example, he takes the Churchlands to task for using the Church-Turing thesis in support of the idea that a digital computer can display any systematic response to the environment whatever. Copeland has (apparently) read Chaitin; he thus must realize that there's a key word of systematic in there. An oracle machine is, at least at first glance, random, or otherwise indistinguishable in finite time from a non-oracle (as D. Scott and others have reminded us). Copeland also says that an oracle machine uses a finite input. Strictly speaking this is true, but as with many models of computation, one trades off input for internal state, which in an o-machine is infinite. Copeland's historical remarks about the history of computability also come as a mixed bag. Nevertheless, these details aside, the collection is well worth reading for those who are skeptical of current "computational theories of mind" and related notions but still think the computational ideas have merit. Ironically for the Bunges (and Penroses ...) of the world, the consensus is moving towards the idea that the brain is (like everything else) sub-Turing, not super-Turing. This is where another interesting case of understanding idealizations takes place, but that's another story for another time.
Defending AI Research: a collection of essays McCarthy As far as is possible with short essays, the reviews and other items in this book are to the point. I suspect, however, that his targets will largely misunderstand his criticism. (Particularly those critics of AI from a phenomenological/existentialist background, though McCarthy is right: they and other critics rarely cite any papers in AI beyond Turing's speculative piece from 1950 and some of the excesses of Simon and other earlier pioneers - but do not analyze the computing systems themselves at all.)
Extending Ourselves: Computational Science, Empiricism and Scientific Method Humphreys Humphreys argues that computational methods in factual science ought to affect our understanding of realism, empiricism, instrumentalism, etc. Along the way, we encounter discussions of idealizations, abstractions, the use of factual hypotheses to solve equations for use in factual contexts, comparisons of simulations and measuring instruments, etc. A slim but provocative (in a good way) book with much to think about. I especially enjoyed the section on factual hypotheses as constraints on acceptable solutions to equations.

Finished with this section? Go back to the list of book subjects here.

Some philosophy of computing books are under philosophy of technology ...