Hi Telmo,
On Sun, Oct 22, 2017 at 4:47 PM, Bruno Marchal <[email protected]>
wrote:
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.
Ok.
I suspect that transhumaning is our only hope of surviving our own
creations in the future.
In the "long run", I hope Earth will survived as a Museum of Carbon
Life, at least a virtual one, because the big challenge will be to
survive and remain connected when Andromeda and Milky Way will met,
assuming Magellan does not make to much mess meanwhile. It will be the
opportunity to find a younger sun, also.
To spread in the galaxy, we will come back to bacteria, but "modern
one" all connected in such a way that we keep our virtual human body,
and kids will have hard to learn that "we are bacteria spread in the
galaxy".
On the other hand, maybe it doesn't matter.
Perhaps we are too attached to being homo sapiens. Nothing ever stays
the same. If the future belongs to Jupiter-brain entities, I guess
they are people too...
We have partial control in the terrestrial plane, and can try to
reduce the harms, but this by itself can be risky.
I think that arithmetic emulates an infinite war between Security and
Liberty, at least in the normal dreams.
Democracy is a big progress in the harm reduction in that conflict,
and it is a bit of a splitting between []p & p and []p, like in my
comment to David.
It is interesting how we are worried about the existence of homo
sapiens in the far future, but we are not bothered by the fact that
they did not exist in the distant past. I find this reminiscent of
fear of "not-existing" after death, but no problem with not having
existed before birth. I think you will agree that it comes from a
misunderstanding of reality.
Or a misunderstanding of who you are. When I read Plotinus and its
critics, sometimes, I feel like nothing has really changed.
In arithmetic, infinitely many numbers asks themselves "why does that
shit happen to me, how could we fix this and that".
The universal machine are born "never completely satisfied". They
always feel, like in the song, that there is something more to say.
Death is useful memory-amnesia, in this plane.
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).
Agreed, I did not forget about Gödel :)
I love the challenge of reverse-engineering how our brain works. There
is some learning algorithm that it runs that we haven't cracked. There
are some clues:
Artificial neural networks use activation and inhibition (equivalent
to glutamate and GABA), but what about the other neurotransmitters?
For example dopamine, which appears to be strongly related to the
reward system -- and thus learning. In our current artificial models,
learning (mainly backpropagation) are still mostly "top-down". They
are something we impose on the network, as opposed to something that
emerges from local network behaviors.
Further, while glutamate and GABA appear to be mostly topological --
they propagate in cascades of neural activation across the network --
dopamine and others seem to work by diffusion. So the topology of the
brain networks is not the only thing that matters, it's spacial layout
also does. This is another layer of complexity. Let's not even get
into gene expression.
Which actually seems important, if only to regulate the populations of
neurotransmitters.
Yes, the descriptive complexity of the brain is huge, but it might be
the product of very simple ideas repeated since long. A deep Turing
Universal object: a "universe" all by itself.
Can we figure out how to design such a powerful learning algorithm? It
has been tried for so long with no success. Current AI-hype comes from
enormous computing power and big datasets. There is nothing new about
AlphaGo. Can this wave be ridden all the way to human-level
intelligence (or competence :) or is there something fundamental we
are still missing? I bet on the latter. But then, could it be that the
reason this algo has not be found yet is that its intrinsic complexity
is beyond the grasp human intelligence itself? If you agree with this
idea, do you feel it is related to the Gödelian limit?
We want them to learn fast, but today, we don't want them autonomous.
We behave with the machine a bit like with kids. We want them educated
enough, but not up to the point of saying that the boss is wrong, or
naked!
I don't know, Telmo, very hard question. I would be the Pope of an
Aristotelian Dogmatic religion, I would excommunicate all universal
numbers. Precaution principle!
My feeling is that the main algo has been found. It is the code of any
universal machine. Then we can tell her help yourself, multiply
yourself and just wait billions of years. But we want competent slaves
instead. If a machine get human-clever, we will fear it, and fight it,
alas.
But we will transform ourselves into machines before, and the
opposition between artificial and natural will eventually disappear,
or get equivalent to the difference between analytical (like The
Mandelbrot set) and non analytical (like the Burning ship)
https://www.youtube.com/watch?v=koqh0BOV_0k (Burning ship, use
absolute values; lost of Cauchy analyticity in the sense of complex
analysis)
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).
I feel this is consistent with what I find by introspection --
something that AI researchers perhaps do not do enough. Intuition is
crucial, but impossible to grasp. You cannot use it without also
introducing doubt.
You get it only through (sense) experience, and through hidden reason
related to surviving. We can't deny pain., but if you think about it,
there is a sort of lie or a nature"s argument per authority.
A machine which does not fear death or pain is a sort of dissident of
life. That is related to the theological trap, also.
With AlphaGo, it is curious that the heuristic half of the system is
also the one that becomes a black box.
When you have the time, you might elaborate on this.
Best!
Bruno
Best,
Telmo.
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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