The Mystery of Consciousness
John R. Searle, 1997

John Searle is Making Progress
I believe that John Searle's position on some issues has changed recently. I applaud that. Perhaps the
Chinese Room has been a learning experience. I do still have some criticisms of his views of the nature of computer software. More about that later. But first, to the issue of the basic nature of consciousness.

As stated in Chapter 1 , Searle's description of mind and awareness avoids turning, as others have turned, to ethereal or mysterious quantities as a "way out" of the philosophical dilemma of consciousness. He sees consciousness as arising from the workings of a material brain and so having characteristics similar to many of the abstractions which arise from the material, such as beauty, relatedness, and mathematical truths. Such abstractions do not, as Penrose has claimed in LSHM , require a reality separate from the material, even though they are not themselves strictly material objects. Consciousness seems to be like that.

In her chapter of Broadbent's recent book, Boden makes the point that the word "physicalist" should strictly be used in preference to the more widely accepted "materialist", since there are many well-known effects of physics which are based in energy rather than in mass. The term has never quite displaced the other, however, possibly because some like the extra emphasis it places on the material side of things.

Searle's Strong AI Position
Searle presents,
in point 4 , the distinction between Strong AI and computer use for simulation as if these were the only two possible ways to use computers in brain studies. He is quite careful in this section to be clear that the Strong AI view he debunks is only the narrow position of mind=software running in brain=hardware. This is not really a very strong position on the involvement of computing in brain functions. I really believe that very few people, especially of those with any significant involvement with computer software, subscribe now, if they ever did, to that narrow view.

As if to underscore this point (without having made it), the argument Searle puts forth to make this claim has power well beyond the requirements of debunking the narrow view. It is as if he is actually arguing against a much stronger type of "Strong AI", perhaps such as that described by Penrose and others.

As another perspective on the nature of the hardware/software dichotomy, we might look at Allen Newell's characterization , in the context of unified theories of cognition:

An architecture is a fixed set of mechanisms that enable the acquisition and use of content in a memory to guide behavior in the pursuit of goals. In effect, this is the hardware-software distinction: the architecture is the hardware that is supporting the software and the software is the collection of data structures that encode the content.
This is probably not quite what Searle thinks of when he starts his PC and loads Word to begin an editing session.

Syntactic Systems vs. Semantic Systems
I do not really understand Searle's concern with the distinction between syntactic and semantic processing systems. Let me begin with a quotation from Allen Newell, et al, who are convinced that symbolic systems (as Searle equates with syntactic processors) not only can implement intelligence, but have properties which are especially valuable for that application. From
Newell, Young & Polk pp 66,67 (I have rearranged the quotations slightly, while I think not changing the gist of the content), "It is easy to see why symbol systems are the foundation stone, both of computing and of human intelligence. ... Symbol systems permit the construction of new functions, i.e. new programs, because the programs can be the outputs of other programs (our learning). ... [T]he diversity of ways humans operate adaptively is immense beyond description. This implies that a symbol system forms the basic technology out of which human intelligence is built".

On page 11 of his introduction , Searle lists three basic points, based on the Chinese Room argument, intended to show that ...

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