Yes, Eric's treatment makes good sense to me. As a neuropsychologist,
I am of course interested in AI (or would be; actually, I do not really
follow this stuff) as a way of modeling some aspects of brain function
and testing some hypotheses about brain organization, or perhaps for
its potential heuristic value, suggesting questions to ask about real
brains, etc..
However, I can appreciate (from a distance) the beauty of devising ways
of performing cognitive or perceptual types of tasks that are different
from the ways that the brain does them.
IF Mentifex is actually achieving the latter, fine; he certainly is not
doing anything but caricature the former, and the labels he uses for
components of his "system" (?) suggest to me that he aspires to
modeling real brain function.
F. Frank LeFever, Ph.D.
New York Neuropsychology Group
In <37813C30.BC7A149E at gruver.net> eric at GRUVER.NET (eric) writes:
>>Sergio Navega wrote:
>> Today I see no point in doing anything related to AI without a
>> strong biological plausibility.
>>This strays a bit from the discussion, but let me point out
>that AI (Artificial Intelligence) and ANN (Artificial Neural
>Networks) were pretty much separated into two distinct
>fields by Marvin Minsky many years ago. From my point
>of view, there is little in AI today which has much
>biological plausibility. In ANN, there is both
>biological and non-biological approaches. In this
>newsgroup, the biological approach is more on-topic.
>>In the non-biological approach, a neural network,
>while originally a model for neural computation, is a
>mathematical tool that is useful for making predictions
>based on patterns of input. For these types of problems,
>the original biological basis is of little importance
>compared to the results gained. For example, using a
>neural net to analyse weather patterns, predict
>recurrence of breast cancer, or steer a driverless
>vehicle, the biological basis of the neural networks is
>really not important. In many cases, there are other
>approaches without any biological basis that may perform
>as well or better than the neural network approach.
>>A more biological approach using neural networks is to use
>the neural networks to model the organization of the brain.
>>> One line of argument is that we must
>> follow the path of the natural intelligences until we grasp what
>> are the core points of intelligence, because we *still* don't know
>> what they are. Only after that we will be able to "propose" new
>> methods and algorithms to enhance biological intelligence with
>> functionally equivalent (but better) processes.
>>>> This does not preclude experimentation: often we'll have to create
>> "strange" things, based on unlikely methods. What is important is
>> not to lose the goal of obtaining plausible results, comparing
>> our results with children's ways of learning and adult's methods
>> of tackling new problems.
>>But it is probably impossible for AI to mimic biological
>intelligence unless the architectures of the computer and
>the brain are quite similar. If nothing else, the very
>different architectures of the computer and the brain
>demand very different approaches to intelligence. You
>seem to be arguing that AI should work the same as biological
intelligence in
>spite of these architectural differences.
>>What is efficient for the brain is often very inefficient
>for the computer and vice versa. For example, in a game
>of chess, the human expert can quickly zero in on a limited
>number of moves and analyze them to a pretty good depth.
>In contrast, a computer will analyze a far greater number
>of moves but to a lesser depth. The use of heuristic rules
>to determine the more important moves can be used to
>reduce the number of different moves to consider and allow
>them to be analyzed to a greater depth, but the human
>expert will still consider a small fraction of the moves
>considered by any good computer program. In other words,
>based on the architecture, different approaches to the
>game are necessary.
>>> In summary, the road to intelligent systems is too elusive for us
>> to waste time with implausible and risky methods: we ought to follow
>> the steps of our brain, the best example of intelligence on Earth.
>>When we can build a computer based on biologically based neural
>nets so that the architectures of the computer and the brain
>are similar, then we might be able to have a more biologically
>based AI. Until then, I don't really see how it would be
>possible.
>>Eric Johnson
