Ben, We seem to be thinking along similar lines in most aspects here...
The way the human brain seems to work is: * some of its architecture is oriented towards increasing the sum over n of int(S,n,rS,rT), where rS is given by brain capacity (enhanced by tools like writing?) and rT is different for different problems, but is a "humanly meaningful time scale." * some of its architecture is oriented towards increasing problem-solving ability for n that are so large that int(S,n,rS,rT) is totally miserable for realistic (rS, rT)
Right, for example my brain doesn't solve visual processing problems using completely general learning structures.
That is: any real-world-useful general intelligence is going to have a mix of "truly general intelligence methods" focused on boosting int(S,n,rS,rT) for small n, and "specialized intelligence methods" that are different from narrow-AI methods in that they specifically leverage a combination of * specialized heuristics * the small-n general intelligence methods
Yes, though we will have to be careful because an AI is likely to be a lot more dynamic than our brains. I'm not even talking about fancy stuff like having the AI recode itself, just the fact that an AI would be able to solve some problems using very general intelligence and then adapt that solution into a very efficient and quite specialized ability with relative easy. That ability could then be a stepping stone towards other problems and the development of further more complex specialized abilities as required.
Now, we're asking not just whether S can solve simple problems, we're asking whether S can solve problems that are simple against the background of its environment and its own mind at a given point in time. In this case, I think the same conclusions as above will hold, but more weakly. I.e. the general intelligence capability will hold robustly for somewhat larger n. But still there will be the dichotomy between small-n general intelligence, and large-n specialized-building-on-general intelligence. Because only some problems relating to self and environment are useful for achieving system goals, and the system S will be specialized for solving *these* problems.
Which I think is more or less what I also said above. Here the training of the AI becomes very important as this is what develops the abilities of the system. Or put another way; it's often the case that knowing how to solve a simpler or related problem to the problem you are currently facing is very useful. We don't just solve all problems from scratch, we draw on our experiences with similar problems from the past.
Now, moving on, I'll make the following claim: ** achieving "small-n general intelligence" is a basically simple math problem **
As you always like to say: given infinite resources... ;)
I think that the hard problem of AGI is actually the other part: BUILDING A SYSTEM CAPABLE OF SUPPORTING SPECIALIZED INTELLIGENCES THAT COMBINE NARROW-AI HEURISTICS WITH SMALL-N GENERAL INTELLIGENCE
Yes this is part of the problem, the other thing you don't mention is the difficulty of trying to solve small-n problems efficiently.
I would say that there is something very important here that you haven'tI agree, Shane, that algorithmic information theory is useful for the "small-n general intelligence" part. But it's just providing a complicated, sometimes elegant mathematical formalism for what is actually one of the *easier* parts of the practical AGI task.
mentioned: The value in having a precisely mathematically defined and
provably strong definition of what general intelligence actually is.
Clearly I accept that this is possible and I think you do too. However
let's not forget that almost NOBODY else in the field of AI (or psych
or anywhere else) accepts this or has even heard of the idea before!
Even if they had heard of it I'm sure there would be an enormous
amount of resistance to the idea.
So yeah, in terms of the "practical AGI task" is might not turn out
to be all that huge a deal, I'm really not sure. But in terms of
actually getting a sizable number of people all agreeing on what the
hell the goal at least in theory is, it could be very significant.
In the words of Charles Kettering,
"A problem well stated is a problem half solved."
The work of Marcus Hutter is, I believe, currently the most
significant piece of work in this direction to date.
Cheers
Shane
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