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In What Is Thought? Eric Baum proposes a computational explanation of thought. Just as Erwin Schrodinger in his classic 1944 work What Is Life? argued ten years before the discovery of DNA that life must be explainable at a fundamental level by physics and chemistry, Baum contends that the present-day inability of computer science to explain thought and meaning is no reason to doubt there can be such an explanation. Baum argues that the complexity of mind is the outcome of evolution, which has built thought processes that act unlike the standard algorithms of computer science and that to understand the mind we need to understand these thought processes and the evolutionary process that produced them in computational terms.
Baum proposes that underlying mind is a complex but compact program that corresponds to the underlying structure of the world. He argues further that the mind is essentially programmed by DNA. We learn more rapidly than computer scientists have so far been able to explain because the DNA code has programmed the mind to deal only with meaningful possibilities. Thus the mind understands by exploiting semantics, or meaning, for the purposes of computation; constraints are built in so that although there are myriad possibilities, only a few make sense. Evolution discovered corresponding subroutines or shortcuts to speed up its processes and to construct creatures whose survival depends on making the right choice quickly. Baum argues that the structure and nature of thought, meaning, sensation, and consciousness therefore arise naturally from the evolution of programs that exploit the compact structure of the world.
muddled and long winded
| Customer Rating:   | | I'm really interested in machine learning and artificial intelligence, but the author's outright assumption that he is going to do for thought what Schrödinger's did for DNA (before its discover) is pretty arrogant. I don't know what it is (perhaps using mind as a proper noun inconsistently), but halfway through the book I couldn't take it anymore and ditched it in favor for Hawkins' "On Intelligence". Life is too short. |
Reviewing "What is Thought"
| Customer Rating: | In many respects Baum's book is orthodox cognitive science: "the
discussion in this book follows what I perceive to be folk wisdom
among computer scientists interested in cognition." (page 2) In
fact, it is probably the best such text that I've read in years.
I highly recommend this book to anyone studying cognitive systems.
Baum basically agrees with Werbos' definition of an intelligence:
"a system to handle all of the calculations from crude inputs
through to overt actions in an adaptive way so as to maximize
some measure of performance over time" (P. J. Werbos, IEEE Trans.
Systems, Man, and Cybernetics, 1987, pg 7). Or, in Baum's words:
"I am proposing to think about creatures...that are given a reward
function...learning and computing algorithms...The creatures then
apply these algorithms to maximize reward during life." (page 396)
Of course programs that do exactly that have been around for a long
time: "Adaptive systems using learning matrices" (K. Steinbuch and
E. Schmitt, Biocybernetics in Avionics, Gordon and Breach, 1967, pg
751).
In his book Baum frequently equates reward/fitness/utility, U, with
number of offspring a creature has, N. In fact, a more biologically
accurate model (for mammals) might be U=(N-2)/L where L is the
creature's lifespan.
But Baum is quite UNorthodox in that he believes in an extreme
dependence on innateness. He believes that via our DNA we receive
a large number of computational subroutines which contain a great
deal of knowledge about the world.
Baum believes that "semantics comes from compression...If one
compresses enough data into a small representation, the
representation captures real semantics, real meaning about the
world." (page 102) But, unfortunately, a number of DIFFERENT
models may fit the data. As Baum himself admits: "there are likely
many possible locally optimal solutions as good as the one evolution
has come up with that may differ considerably in detail." "There
may be many compact discriptions ...aliens might think of the world
using a substantially different description..." (page 212) So
something which has "meaning" for you, with your model of the world,
may have NO meaning for someone else (having some different world
view). Baum seems to admit as much on page 226: "...there is some
evidence for an evolved module for religious faith, which might well
exist whether or not there is in actuality an anthropomorphic god."
Unfortunately, then all meaning is purely RELATIVE and it makes no
sense for Baum to talk about some "concept really present in the
world." (page 162) Rather, concepts are defined (INVENTED) by
people in the course of their efforts to organize their observations
of the world. Our concepts need not really exist IN the world. They
are best regarded as mental fictions.
Although Baum frequently distinguishes animal intelligence from human
level intelligence he makes no room for the existance of an artificial
intelligence which is not isomorphic to human reasoning. In actual
fact there are many important applications waiting for an artificial
intelligence even IF it were not fully on a par with human reason.
Furthermore, with regard to human level AI Baum seems only to
recognize the ways in which humans outperform computers. Alongside
the list of things people do better than computers one should place a similar list of the many things that computers do better than humans:
computers have better memory, are better at logic, statistics, and
math, can be diskcopied, etc., etc. "What people can't do" (comp.ai,
21 May 1997, R. Jones) I would point out that my Asa H system
(Trans. Kansas Academy of Science, 2006, vol 109, no 3/4, pg 159)
has most of the functionality Baum requires of an intelligence.
It compresses what it learns, is guided by a value function module,
and is hierarchically (self)organized. Perhaps only the vast store
of innate categories is missing; waiting to be learned. |
fascinating but wrong
| Customer Rating: | Baum's book is always stimulating and in some ways admirable, especially in its instance that there is nothing magical in the brain. But he's wrong in several crucial ways, the same ways that Pinker get's wrong (for example, in "The Slate's Last Stand").
1. Despite his neural network background, Baum fatally underestimates the power of unsupervised learning. While he's right that complex networks cannot be explicitly trained without astronomically numerous examples, it's now clear that unsupervised learning (where the number of examples is quite literally astronomical) combined with the rather regular (albeit complex) structure of the world, can do most of the heavy lifting, with supervision filling in details. Explaining unsupervised learning to a lay audience is not easy (I know of no successful attempts) but cannot be shirked.
2. Because of his background, Baum fatally overestimates the power of Darwinian evolution. For example, he completely omits the Eigen error threshold problem, he does not take seriously the gap between the information content of genomes and brains, and he seems to think that adding one bit per generation (which is all evolution can do) is a powerful learning procedure.
3. He's hopelessly starry-eyed about the ability of Darwinian evolution to find "compressed descriptions" (though he's spot on in his emphasis on compression). Both evolution and learning are algorithms for adapting, and Baum completely overlooks the possibility that brains can implement the Darwinian algorithm in a different physical medium (synapses instead of nucleotides). To validly draw the conclusions he jumps to, he would have to prove that either the Darwinian algorithm cannot be implemented neurally, or that it would be far too slow (while the evidence suggests that the basic update can be done neurally a billion times faster neurally than genetically). As Dawkins has emphasised, Darwinism is the only way to get intelligence, but this does NOT mean that only DNA can do it.
In sum, a book for the beach, not for eternity. |
Interesting but replete with hasty argumentation
| Customer Rating: | The main thesis of this book, asserted repetitively, is that the mind is a computer program. Once this is borne in mind, pardon the alliteration, most of the book is reduced to an argument in its favour, rather than an investigation into its credibility. The book often reaches for blunt assertions to support its positions and only afterwards begins a slight retracing of steps. For example, we are told that inductive bias and learning algorithms are coded into the genome. It is obvious, bit of speculation on DNA, evolution and algorithms and out comes the result!
In his observance of Occam's Razor, the author confuses the appeal of the simplest explanatory hypothesis with the belief that he has found such. The discussion of neural networks leaves aside recurrent networks, which are probably more biologically plausible than competitors.
Likewise the idea that the brain essentially 'runs' compressed programs due to evolutionary endowments is unconvincing and philosophically leaky.
I don't want to be over critical of the book as it has brought together many interesting strands of work, but it just has not woven them into anything interesting. There is little new here, whether from modularity or evolutionary programming constraints on neural activity. A lot of it is speculative and several of the key themes are discordant due to under analysis of their assumptions.
Several of the elaborations verge on the frivolous. For example, there is a particularly woolly argument linking the learning of Scheme to "what goes on in constructing our understanding of the world" (p. 222). Likewsie in discussing awareness and consciousness, the author relies on the use of 'main' in C to metaphorically explain how information might come together in the brain (p. 413-415). All kinds of reification fallacies come to mind, leaving aside the thinnes of the argument.
The bottom line is that the book pursues a strong cognitivist program (the brain is a computer) without convincingly examining various sides of the argument. I was certainly no wiser off at the end of it. |
On the nature of thought
| Customer Rating: | In the introduction to this handsomely bound book, the author suggests that it is an appropriate time for an explanation of how the dynamics of a human brain can be accounted for by computer science. His title is motivated by Erwin Schrödinger's enormously influential "What is life?" which launched the field of evolutionary biology by inducing both Francis Crick and James Watson to successfully seek the molecular basis of biological evolution, but the analogy is strained for several reasons.
Schrödinger's book is less than 100 pages in a current edition, while Baum's is about five times as long. In the context of Schrödinger's lifelong interest in biological problems and based on a series of three public lectures that he presented to the Irish intelligentsia in 1943 (as one of his statutory duties as the founding director of the Dublin Institute of Advanced Studies), "What is Life?" is a classic example of his exceptional expository skill---in a second language, no less---whereas Baum's book would have profited from another round of copy-editing. But the most striking difference between these two titles lies in the cogency of their respective contents.
Although Max Delbrück and his colleagues had used measurements of mutation rates of fruit flies under X-radiation to show that their genes were necessarily of molecular dimensions in the mid-1930s, the implications of these data were unnoticed by the literate world of the mid-1940s. Thus Schrödinger's public lectures were newsworthy, being favorably noted by Time magazine in the spring of 1943, and his subsequent book---after some difficulties with an Irish publisher and the Roman Catholic Church over the religious implications of his ideas---went on to sell over 100,000 copies for Cambridge University Press, with translations into seven languages. Is there a similar communications gap in our current understanding of the nature of thought?
Noting his background in computer science, one mightclassify Eric Baum among those who believe that ``our souls are software'', but this is not quite fair. Although he states that ``the obvious inability of present-day computer science to account for [the brain's behavior] is no reason at all for doubting that they can be accounted for by computer science,'' the intellectual perspectives of "What is Thought?" are broader than this assertion seems to suggest. The book begins with several interesting chapters on the nature of computation (I particularly liked the presentation of the traveling-salesman problem), which include discussions of the importance of making decisions at the level of semantics, the Turing test, properties of neural nets, hill climbing in a fitness landscape, among several other relevant topics. These discussions lead into the author's central thesis that the mind, like all efficient computer programs, is necessarily modular. In other words, each aspect of the brain's dynamics comprises several subroutines, which presumably can be further broken down into hierarchical structures of nested activities, and he discusses several permutations of this important concept. Curiously, Baum's otherwise comprehensive list of references does not include Donald Hebb's seminal and classic work, in which the notion of ``cell assemblies'' (which are dynamically self-sufficient modules of neurons) was first suggested over a half-century ago. As a psychologist, Hebb aimed to ``bridge the long gap between the facts of psychology and those of neurology,'' and coming at about the same time as the development of the digital computer, his formulation has provided the basis for many numerical studies starting in the 1950s and continuing to the present day which are in accord with a growing body of electrophysiological data. Setting this quibble aside, Baum offers compelling psychological evidence for the modular structure of mind and provides his readers with an interesting and informative account of how the structure of our thinking may have developed over the course of biological evolution, with particular attention paid to computational constraints on the development of learning mechanisms. Importantly, his perspectives are broader than those of many of his colleagues, as he asserts that the ``whole program'' of a brain's dynamics includes the ``complex society'' in which it is embedded. Indeed, the author's evident humility in the face of awesome intricacy of mental activity is, to me, one of the more appealing aspects of "What is Thought?"
The often suggested possibilities for quantum computation are discussed in some detail, along with an analysis of the widely noted example of ``Schrödinger's cat'' which was originally proposed to emphasize the difficulties of applying ideas developed for atomic dynamics to complex macroscopic systems. Considering that a quantum computer---if it is at all possible to construct one---must be carefully isolated from structural irregularities and operated near absolute zero of temperature, Baum joins the majority of physical scientists in concluding that it is ``highly unlikely that quantum computation is relevant to the mind.''
Eric Baum has a dog, and---like most of us dog owners---he is convinced that his pet is conscious, but he goes on to assert that ``we do not need to posit new qualitative modes of thinking to explain human advance over animals. To my mind, the difference between human intelligence and animal intelligence is straightforwardly explainable by cumulative progress once there is the ability to communicate programs.'' Here, again, Baum could profit from reading Hebb's book, which contains but a single mathematical expression, namely A/S. This parameter represents the ratio of the associative area (A) of a mammalian neocortex to its sensory area (S), and it becomes greater as one progresses from rats through dogs to humans. A related physiological parameter---with profound significance for the ease and rate at which modules (or cell assemblies) can switch on and off---is the percentage of inhibitory intercortical neurons, varying as follows: rabbit (31%), cat (35%), monkey (45%), human (75%) [6]. Of course, these relative differences may be examples of the ``cumulative progress'' to which Baum refers.
In a penultimate section, Baum discusses the question of free will, noting that ``our decisions look, from any reasonable perspective short of knowing the exact state of our brains and simulating them in detail, like they are introducing genuinely new information.'' In reaching this conclusion, he may be confused by the continuing tendency of many scientists to overlook a phenomenon called ``sensitive dependence on initial conditions'' first studied by the eminent French mathematician Henri Poincaré and widely observed nowadays by those who study nonlinear dynamic phenomena (chaos theory). As Poincare` famously put it over a century ago:
"If we knew exactly the laws of nature and the situation of the universe at the initial moment, we could predict exactly the situation of that same universe at a succeeding moment, but even if it were the case that the natural laws had no longer any secret for us, we could still only know the initial situation approximately. If that enabled us to predict the succeeding situation with the same approximation, that is all we require, and we should say that the phenomenon had been predicted, that it is governed by laws. But it is not always so; it may happen that small differences in the initial conditions produce very great ones in the final phenomena. A small error in the former will produce an enormous error in the latter. Prediction becomes impossible, and we have the fortuitous phenomenon."
For an author who bases many of his conclusions on close mathematical reasoning and offers a theory that purports to be ``capable of explaining everything,'' the implications of these ``fortuitious phenomena'' should be carefully digested.
Alwyn Scott
http://personal.riverusers.com/~rover/ |
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