Mind As Machine: A History of Cognitive Science Two-Volume Set Review

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Mind As Machine: A History of Cognitive Science Two-Volume Set ReviewReview of Volume 2 (August 25, 2007):
With few exceptions, the second volume of "Mind As Machine" respects the high quality of the first, and can be read and understood by anyone with a general background in artificial intelligence, neuroscience, or psychology. At times the author seems intimidated by some of the mathematical developments that have taken place in cognitive science, such as the use of the theory of dynamical systems, but in general she confronts issues with confidence and keen insight.
The second volume begins with a very provocative question, one that has plagued the field of artificial intelligence since its beginnings in the 1950's. The author asks, "when is a program not a program", and this is definitely a question whose typical answer is responsible for much of the lack of confidence in progress in artificial intelligence. The general prejudice in the cognitive science community is that a thought process or reasoning pattern cannot be viewed as a "program" because the latter is only to be taken as a collection of "instructions" that is to be run on a computer and will give the same answer when acting on the same information presented to it. The problem was then to distinguish between a "program" and an "intelligent" program, even though the designation of "intelligent" was (and still is to a great extent) extremely vague. The lack of a precise definition of intelligence would naturally lead to controversy regarding the identity of the first "intelligent" program, and the author discusses some of this controversy. The `Logic Theorist' program and the `Selfride-Dinneen' program are discussed as early candidates for being the first "AI programs" but the author points to some of the early skepticism as to their status as being intelligent. One of these continues to this day, namely that an "intelligent" program has to be highly complex, or "sexy" to use the author's terminology. But complexity, if viewed from the standpoint of the history of artificial intelligence, is in the eye of the beholder, and programs once deemed intelligent, like the ones behind checkers and chess, are now viewed as mere "programs." Such trivialization of intelligent "programs" could be avoided if they were designated, in full recognition of their status in cognitive science as "reasoning patterns" or "thought processes" rather than programs or "algorithms", with the latter names being more appropriate for a computer science context.
Many more of these peculiarities in the history of AI/cognitive science are discussed in Volume 2, such as the deliberate "underselling" of an intelligent technology so as not to instill fear into prospective customers who feel threatened by intelligent machines. As someone who has worked in the trenches of "technological AI" (as the author calls it in the book), this reviewer can report many such stories by vendors who do not want to "frighten" potential customers away by designating their product as "intelligent". Instead they usually play it down, just as author reports the salesman of the IBM 704 did back in 1979. Even highly educated customers, very familiar with modern technology, can view intelligent machines as disquieting, or even threatening, and are frequently hesitant to deploy them in a production environment. The Hollywood Skynet meme has diffused quickly and effectively throughout the world, stymieing the practical application of artificial intelligence. And the bar keeps getting raised for judging whether a machine is indeed intelligent. Chess used to be the holy grail, but now chess "programs" highly competitive with human players can be purchased cheaply at stores found in most neighborhoods throughout the country. Even the developers and researchers themselves, including the author, have dismissed progress as being either "trivial" or part of "technological AI", and predict "real" intelligent machines to be a few hundred years away.
The author also discusses in some detail the petty squabbles between research groups in cognitive science and artificial intelligence. Some readers may feel that this kind of discussion should be left out of the book. To omit it though would be a mistake, since the book is an historical account and readers should have an understanding of the degree to which even individuals deemed to be highly intelligent can engage in conduct that borders on triviality or blatant irrationality. Such dialog and behavior is sometimes intermixed with brilliant developments, proving indeed that good work can be done even if one is embedded in a contentious, degrading atmosphere.
A particularly valuable part of Volume 2 is the author's discussions on research into systems for the representation of semantic information. Sometimes called `semantic networks' at the present time, the goal is to be able to represent semantic content for many different domains or subject areas. Semantic networks would allow a machine to reason across these domains without any external intervention or tuning. The author discusses the work of M.R. Quillian in the early 1960's on semantic representations, which used what was called `localist connectionism' at the time. Work on the `semantic Web' is a good example of current research into semantic representations. The ability of a machine to deal with many knowledge domains is also of great interest to those researchers who are currently attempting to design machines with artificial "general" intelligence (GAI). A modest (but impressive) hint of how this could be done is given by the HACKER "program" which the author discusses in this volume. HACKER could engage in "self-criticism", and this ability is taken to be a sign of what the author has labeled as `Piagetian error-led constructive learning'. Enthusiasts for GAI have pointed to the need for this type of learning. The current enthusiasm for GAI was also taking place in the 1960's, as the author reports in this volume, but under pressure from "experts" was abandoned in the 1970's.
There are some annoying parts of this volume, but these are few. One is the author's continued reference to the "general public", apparently to distinguish them from members of academia or research labs, the latter two groups being the only ones qualified it seems to assess progress in cognitive science. Another is the inclusion of philosophical debate on artificial intelligence, with an entire chapter devoted to it. Certainly such an inclusion would be deemed appropriate since this volume is an historical overview, but such debate has only slowed the progress of artificial intelligence. It is the opinion of this reviewer that all who are involved in this kind of research should declare a moratorium on philosophical debate and get on with the design and construction of intelligent machines. The philosophers should be left alone to construct the gigantic, rhetorical conceptual spaces they usually get lost in. And lastly, the author seems to restrain any enthusiasm she has for the subject, with the belief that such enthusiasm comes from only those who want to advertise themselves or who do not have the appropriate background to understand the subject. Certainly the press has exaggerated some of the claims of progress in artificial intelligence, but on the other hand real progress has been made, and great enthusiasm should be expressed for this progress. Sadly, many academics seem to be too guarded and self-restrained to participate in such joyous emotions, judging it to be "unprofessional" to do so. There are exceptions to this though in the book, such as the author's refreshingly unbridled enthusiasm for the SHRDLU "program" of T. Winograd.
As the author details in this volume, and as can be gathered from conversations with specialists, AI has been subjected to harsh criticism, some of this justified but most frequently not. One of these criticisms was leveled by Drew McDermott, and is outlined by the author in this volume. McDermott's criticism goes at the heart of many of the problems in the acceptance of machines as exhibiting intelligence. The issue is the words that are used to describe processes that are occurring in machines. McDermott charges AI researchers with "self-deception" when they use "wishful mnemonics" to describe what he says are just "procedures" or "data structures." The author gives a few examples, one being when a procedure is called GOAL instead of something like G0034, the former name leading one to believe that a `real' goal has been achieved. McDermott does not want to think outside of the computer science paradigm, and as long as AI researchers listen to his admonitions and stay within this paradigm, they will never accept machines as being intelligent, no matter what the capabilities of these machines. Every process occurring in these machines will always be viewed as a procedure, and every knowledge or semantic representation will be viewed as a data structure. Machines will be thought of as entities that run programs, with these programs mere manipulations of data structures, even if the machine can beat every human at chess or backgammon, even if it can produce and prove original theorems in pure mathematics, or even if it can self-navigate on Mars and evaluate its surroundings with scientific curiosity.
But the views of McDermott are narrow and myopic, and can easily be stood on their head. One could for example speak of "accurate mnemonics" to describe what is going on in machines when they engage for example in learning or discovery. There is no reason why AI researchers should not call a "program" intelligent if it is indeed the case that it is. The issue is what kind of processes in a machine we should label as intelligent, and when we decide to do so it will be based on an understanding of learning and intelligence, and not on a rigid and unproductive adherence to...Read more›Mind As Machine: A History of Cognitive Science Two-Volume Set Overview

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