I was just working on my latest p=np? idea and I hit up against method
that is either in np or is otherwise extremely inefficient. So I have
to come to the conclusion that the human mind is not capable of SAT in
p.

So then how do we figure how to deal with so many complicated
situations? Of course I still don't know because so many situations
seem similar to a SAT problem. The mind must be able to detect many
different things that are going on at once or which might be useful to
recall from memory to deal with a situation. But still, there is
nothing in my own introspective analysis of my thinking which looks
anything like a p=np process.

So what is wrong with AI? One thing that AI has been consistently
lacking is the ability to learn through conversation. My feeling is
that this is not just a problem with communication but a learning
problem as well. In other words AI is not able to truly learn except
in a few special cases. Most of those special cases are examples of
narrow AI but there are others where the learning that takes place
isn't necessarily like other narrow AI but where the domain of
learning is so restricted that it is narrow in the sense that the
applicability of the method is limited.

Then I started thinking of an artificial language which can refer to
situations or objects in the IO data environment and which can be used
to instruct a program as it is running. I think this is an unusual
idea.

One of the characteristics about programming methods that seem to
catch on with programmers is that they can be used in a very simple
manner and in more complicated programming. I think an artificial
language which could be used to instruct a computer to notice objects
in the IO data environment and which could also be used to refine
those instructions using this artificial language with the references
that it had previously established has a lot of potential. And it can
help us become more clear about what is needed to make better AGI
programs.
Jim Bromer


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AGI
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