12.1 Design for a Thinking Machine

   A theory of mind and a theory of brain are two very different things. I have sketched an
abstract Platonic structure, the master network, and claimed that the structure of every intelligent
entity must contain a component approximating this structure. But it would be folly to deny that
different entities may approximate this structure in very different ways.
   A general study of the emergence of minds from physical systems would require a general
theory of networks of programs. But of course no such theory presently exists (see Appendix 2
for a summary of certain preliminary results in the field). Thus we cannot give a comprehensive
answer to the question: what sorts of machines, if constructed, would be able to think? In talking
about thinking machines, we will have to be contented with very specialized considerations,
improvising on the themes of computer science and neuroscience.
   Most of the workings of the brain are still rather obscure. We have an excellent understanding
of the workings of individual brain cells (Hille, 1984); and we have long known which regions of
the brain concentrate on which functions. What is lacking, however, is a plausible theory of the
intermediate scale. The study of the visual cortex, reviewed above, has brought us a great deal
closer to this goal. But even here there is no plausible theory relating thoughts, feelings and
"mind’s-eye" pictures to the microscopic details of the brain.
   In Chapters 6 and 10, lacking a truly effective theory of intermediate-level brain structure, we
have made use of what I consider to be the next best thing: Edelman’s "Neural Darwinism," a
slightly speculative but impressively detailed model of low-to-intermediate-scale brain structure.
I suspect that Neural Darwinism is incapable of explaining the higher levels of cognition and
memory; but, be that as it may, the theory is nonetheless essential. As suggested in Chapters 6
and 10, it indicates how one might go about establishing a nontrivial connection between brain
and mind. And furthermore, it leads to several interesting ideas as to how, given sufficient
technology, one might go about constructing an intelligent machine. In closing, let us sketch one
of these ideas.
OMPs, AMPs and nAMPs
   In what follows I will speculate as to what global neural structure might conceivably look
like. This should not be considered a theory of the brain but a design for a brain, or rather a
sketch of such a design — an indication of how one might draw blueprints for a thinking
machine, based loosely on both the idea of the master network and the theory of Neural
Darwinism. The "zero level" of this design consists of relatively sophisticated
"optimization/memory processors" or OMPS, each of which stores one function or a fairly small
set of related functions, and each of which has the capacity to solve optimization problems over
the space of discrete functions — e.g. to search for patterns in an input — using the functions
which it stores as initial guesses or "models". For instance, the multi-leveled "Neural Darwinist"
network of maps described at the end of Chapter 10 could serve as an OMP. It is biologically
plausible that the brain is composed of a network of such networks, interconnected in a highly
structured manner.
   Next, define an "analogy-memory processor," an AMP, as a processor which searches for
patterns in its input by selecting the most appropriate — by induction/analogy/deduction — from
among an assigned pool of OMPs and setting them to work on it. Each AMP is associated with a
certain specific subset of OMPs; and each AMP must contain within it procedures for general
deductive, inductive and analogical reasoning, or reasonable approximations thereof. Also, each
AMP must be given the power to reorganize its assigned pool of OMPs, so as to form a
structurally associative memory. There should be a large amount of duplication among the OMP
pools of various AMPs.
   And similarly, define a "second-level analogy-memory processor," a 2AMP, as a processor
which assigns to a given input the AMP which it determines — by induction/analogy/deduction —
will be most effective at recognizing patterns in it. Define a 3AMP, 4AMP, etc., analogously.
Assume that each nAMP (n>1) refers to and has the power to reorganize into rough structural
associativity a certain pool of (n-1)AMPS.
   Assume also that each nAMP, n=2,…, can cause the (n-1)AMPs which ituses frequently to be
"replicated" somehow, so that it can use them as often as necessary. And assume that each AMP
can do the same with OMPs. Physically speaking, perhaps the required (n-1)AMPs or OMPs
could be put in the place of other (n-1)AMPs or OMPs which are almost never used.
   A high-level nAMP, then, is a sort of fractal network of networks of networks of networks…
of networks. It is, essentially, an additional control structure imposed upon the Neural Darwinist
network of maps. I suspect that the Neural Darwinist network of maps, though basically an
accurate model, is inadequately structured — and that, in order to be truly effective, it needs to be
"steered" by external processes.
   I will venture the hypothesis that, if one built a nAMP with, say, 15 levels and roughly the size
and connectivity of the human brain — and equipped it with programs embodying a small subset
of those special techniques that are already standard in AI — it would be able to learn in roughly
the same way as a human infant. All the most important aspects of the master network are
implicitly or explicitly present in the nAMP: induction, pattern recognition, analogy, deduction
structurally associative memory, and the perception and motor control hierarchies.
   In conclusion: the nAMP, whatever its shortcomings, is an example of a design for an
intelligent machine which is neither AI-style nor neural-network-style. It is neither an ordinary
program nor an unstructured assemblage of programs; nor a self-organizing network of neurons
or neural clusters without coherent global structure. It is a program, and a network of physical
entities — but more importantly it is a network of networks of networks … of networks of
programs; a network of networks of networks… of networks of neural clusters. In this context it
seems appropriate to repeat Hebb’s words, quoted above: "it is… on a class of theory that I
recommend you to put your money, rather than any specific formulation that now exists." The
details of the nAMP are not essential. The point is that, somehow, the dynamics of neurons and
synapses must intersect with the abstract logic of pattern. And the place to look for this
intersection is in the behavior of extremely complex networks of interacting programs.
Kaynak: A New Mathematical Model of Mind
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