Hi, A couple weeks ago, I finally got around to finishing Eric Baum's book "What Is Thought?"
I thought of writing a formal review, but I haven't found the time and suspect I won't, so for now I'll just write a long email instead. First of all, I think the book gives an excellent review of various aspects of modern computing and cognitive science. For the educated layperson who's willing to sweat a bit, it's a valuable resource just for this reason. Regarding the scientific thesis, it seems to me that the main achievement of the book is that it elaborates the theory of Solomonoff induction in the context of contemporary cognitive science. This is not a small achievement. [In fact, I think I covered a lot of the same ground in some of my books in the early and mid 90's, but I didn't do so as clearly or accessibly as Baum does here.] By Solomonoff induction I mean the theory that what the mind does is seek simple algorithmic models of reality (and itself). One peculiarity is that Ray Solomonoff and his work are not explicitly mentioned even though Baum's crucial point seems about the same as Solomonoff's. However, algorithmic information theory (AIT) and minimum description length theory are mentioned. AIT was independently developed by Solomonoff, Chaitin and Kolmogorov, but of those three inventors, Solomonoff was the only one who initially saw the theory's implications for AI and cognitive science (which are the implications Baum focuses on). Along with this omission, Baum also omits to mention recent work done in the Solomonoff-induction vein, including work by Marcus Hutter and Juergen Schmidhuber that has been discussed on this list. This is an odd omission, because Hutter's work is in many ways a more rigorous version of some of the ideas Baum puts forth in his book. However, Solomonoff, Hutter and Schmidhuber basically dwell in the realm of mathematical abstraction. Baum ties in the "Occam's Razor" approach with all sorts of other things, such as evolutionary theory, linguistics, and the psychology of heuristics. For this reason his book is a valuable and fascinating contribution. He analyzes DNA as encoding a powerful "inductive bias," which biases us to search for certain types of compact programs summarizing the data we perceive in the world and in ourselves. This is doubtless the case, and is an important point of view to get across. My personal scientific opinion, however, is that he overstates the case for this latter point. This is my main disagreement with Baum as a theorist. It's not exactly a critique of his *book*, however, as the book puts forth his own point of view very well. Baum basically believes that creating an AI isn't plausible in the near or medium term without recreating in fairly much detail the particular inductive biases that live in the human brain. My own belief is that this is only the case if one wants to create a very closely human-like AI. In essence, Baum seems to think that the human mind consists of: * Fairly simple algorithms for finding compact programs summarizing data * A lot of specific guidelines for how to find compact programs in specific domains of evolutionary value to humans I agree that the human mind contains both of these. However, I ascribe a greater role than he does to complex algorithms for finding compact programs summarizing data in broad classes of domains. I think that we can create such complex algorithms and embody them in digital computer programs and thus achieve human-level and greater intelligence -- without exactly copying the complex algorithms of this nature that exist in the human mind. Among other topics, Baum describes his own AI program Hayek, which is a very interesting approach to the credit assignment problem, but which fails to be an adequately flexible and effective "algorithm for finding compact programs summarizing data in a broad class of domains." Hayek, fascinating as it is, typifies the "toy systems" that are common in mainstream AI today. I don't think toy systems like this are going to lead anyone to AGI; I think people need to build integrative systems, and that the right kinds of algorithms exist only in diverse self-organizng networks that embody mixtures of agents at various levels of specialization and generality. But OK -- you already know my schtick -- this was supposed to be about Baum's book ;-) Well Eric -- thanks for a stimulating read! -- Ben G ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
