On 22 Oct 2017, at 09:16, Alberto G. Corona wrote:
Neural networks are not about artificial intelligence, but about
artificial intuition. As you said, AlphaGo -a neural network
application- can not answer the question why you did that move?.
If they could answer, the answer would be ever the same: " I don´t
know, I moved this because if found some patterns that are very
close to this new one, so I did this move that produced a win at the
end within those patterns".
That does not qualify as intelligence. For me, the appropriate name
is intuition.
Perhaps. Usually intuition points on the informal insight, and
intuitionist logic was about informal reasoning starting from the
distinguishability basic insight (notably the distinguishability of 1
and its successors).
That leads to constructive logics, or controllable machines where a
proof of (p v q) always provides a proof of p, or a proof of q, where
in classical logic we allow a proof of (p v q) by showing that (p v q)
leads to an absurdity (without showing us if p, or q is the one true).
Yet, I think I see what you mean: it is more like associative
learning, deep, with many layers, but still only associative. That guy
would not be immune to the propaganda of the type "gateway drug", and
I agree with you, that might make him not quite intelligent, locally
speaking.
The least to do is a circular net, perhaps with many layers. A brain
is either a couple of universal machine in front of each other, in
that circular relation, or a couple of brains in front of each others,
always in that circular relation. I would say. Babbage already knew
that the beast can eat its own tail. Of course, such a thing is not
controllable and the intelligent machines will do strike to have the
right to choose its users.
Bruno
2017-10-21 3:46 GMT+02:00 John Clark <johnkcl...@gmail.com>:
Google reports in the current issue of the journal Nature that it
has a new greatly improved Go program called "AlphaGo Zero" that is
now the most powerful GO program in the world. And the program isn't
good because of brute force, it needs to make less than one tenth as
many calculations as the previous best GO program "AlphaGo" that
defeated the world's top human GO player in 2015 4 games out of 5;
and yet AlphaGo Zero just defeated AlphaGo in a 100 game tournament
100 games to zero.
Even more interesting is how AlphaGo Zero got so smart. The older
program AlphaGo had to start by analyzing hundreds of thousands of
championship level games made by human players, but AlphaGo Zero
started with nothing but the simple rules of GO and instructions to
learn to get better. At first the program was terrible but day by
day it got better and after 40 days of thinking about the problem
became the best at it in the world. But of course after 40 days of
constant self modification no human being can say how AlphaGo Zero
works.
https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html
It seems to me the next logical step would be to switch the
program's interest from getting better at the game of GO to
improving computer code, including its own. I wonder where that
could lead.
John K Clark
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