Hi Telmo,
On 22 Oct 2017, at 09:58, Telmo Menezes wrote:
Hola Alberto,
On Sun, Oct 22, 2017 at 9:16 AM, Alberto G. Corona <[email protected]
> 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".
The neural network is used to prune the minimax search tree. AlphaGo
can tell you exactly what it hopes to achieve with a given move. What
it cannot tell you precisely is why it decided that a certain branch
of the tree could be ignored.
Under your terminology, one could say that the minimax search tree is
the intelligence part, while the neural network plays the role of
intuition. This is not so different from what a human player does.
(I am basing myself on the original AlphaGo paper, and assuming that
nothing fundamental changed in this incarnation)
That does not qualify as intelligence. For me, the appropriate name
is
intuition.
You are a bit on the side of Chomsky on this -- something that you
might not exactly like :)
In any case, I can see value in yours and Chomsky's position. Neural
networks and statistical learning in general are great, but we should
not lose sight of understanding intelligence. However, I would not be
surprised if a given mind cannot fully understand the mechanisms
underlying itself. Maybe there's a threshold of complexity that must
be somewhat below the complexity of the mind itself.
The machine can bet correctly on its syntactical, mechanical level,
and reason correctly with respect to that bet, made in practice with
respect to some bet on some universal environment. That amkes
transhumanism consistent.
But no machine can name or circumscribe its own semantic, that is what
incompleteness is about.
Any semantic requires some act of faith on the par of the machine
(probably in large part instinctive for the animals).
On the other hand: historically, what you call "intuition" has been
the hard part...
Yes, it is the soul, the knower, the feeler, the "hard" part of the
mind-body problem, fogetting that the "matter" is as much hard.
But the canonical theology, when understanding that incompletenees
make provability into a type of rational belief, is clear enough, I
think.
p is the truth
[]p can be used as the mind, the ideas, (Plato's Noùs). [] is sigma_1
complete, so we get all programs and all halting computations, and all
initial segment of non halting computations.
[]p & p gives the non nameable soul. You cannot define it by a
predicate like bew('p') & True('p') because of Tarski theorem. Scott-
Montague) generalization shows the same for []p & p. It exists, obeys
to S4Grz, but the machine cannot know that as such, but can prove it
for correct machine's in general. That gives an arithmetical
interpretation of intuitionist logics (by result due to Grzegorczyk,
Boolos, Goldblatt).
Best,
Bruno
Telmo.
2017-10-21 3:46 GMT+02:00 John Clark <[email protected]>:
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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