Jim: I have been hoping that Sergio would give up on the endless sales pitch
and explain the kernel of his idea,
Boris: sorry to interject, but think the reason for his "endless sales pitch"
is that there isn't much of a kernel. All his talk about physics, causality,
emergence, & so on, is a delusion. The real question is "what do you do with
the data?", & about the only thing he does is exhaustive "permutations" within
a matrix, plus some basic matrix scope adjustment. That's a brute-force search,
which is dumber than evolution & won't discover anything interesting in a
trillion years.
From: Jim Bromer
Sent: Saturday, August 18, 2012 8:01 AM
To: AGI
Subject: Re: [agi] Uncertainty, causality, entropy, self-organization, and
Schroedinger's cat.
I really am not trying to be disruptive. I think the conversation about
Sergio's theory is interesting. However, I don't see hubris as the avenue of
science.
Right now there are good models of simple neural connections but there aren't
any that explain how intelligence actually works.
I have been hoping that Sergio would give up on the endless sales pitch and
explain the kernel of his idea, but I guess I will have to study posets and try
to figure it out for myself.
The problem with the simplistic solutions is that they fail to deal with the
complications. So, ok, information theory might be used to analyze signals and
it might be used effectively in neural science, but it doesn't explain general
intelligence and it is not adequate for every kind of measurement you might
want to make in neural science. This should be so obvious that it should not
need to be said.
Similarly, Friston's ideas may be interesting but it hasn't been used
effectively to explain general intelligence. The problem is that, like most of
the other conjectures made so far, one can use the theory to model simple
problems (or to imagine simple problems being so modeled) but once you try to
turn that into a model of general intelligence the program will fail.
You can reduce the complications and complexity of the problem by any number of
methods but most of them won't work. There may be something similar to a
just-in-time method in AI that might be called when-its-needed, but so far, no
one has demonstrated how anything like that could work. A when-its-needed
computation or projection won't be based on global or a priori general entropy
reduction because, assuming that the rapidity of the development of thought and
of habit is dependent on the richness of the detail available and the extent of
hierarchical cross indexing available, I would say that general massive entropy
reduction would be an obstacle to insightful guessing, projection and learning.
Jim Bromer
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