Marcus,
Let me clarify what I meant by saying that evolution is stochastic....
By "evolution", I do not mean genetic algorithms. Genetic algorithms
need not be, but can be, stochastic. Genetic algorithms are/adaptive;
/but they need not be/stochastically /adaptive. On the other hand,
biological evolution of life on earth is necessarily stochastically
adaptive - due to chance mutations.
As Jacques Monod points out in his book "Chance and Necessity", chance
mutations are the /only/ natural mechanism by which new species are
created. And it is completely subject to chance. Without this particular
stochasticicty, there would only ever have been one species on earth, if
that, and that species would now be long extinct because of its
inability to adapt.
On 8/8/17 6:43 PM, Marcus Daniels wrote:
Grant writes:
"On the other hand... evolution /is/ stochastic. (You actually did not
disagree with me on that. You only said that the reason I was right
was another one.) "
I think of logic programming systems as a traditional tool of AI
research (e.g. Prolog, now Curry, similar capabilities implemented in
Lisp) from the age before the AI winter. These systems provide a very
flexible way to pose constraint problems. But one problem is that
breadth-first and depth-first search are just fast ways to find
answers. Recent work seems to have shifted to SMT solvers and
specialized constraint solving algorithms, but these have somewhat
less expressiveness as programming languages. Meanwhile, machine
learning has come on the scene in a big way and tasks traditionally
associated with old-school AI, like natural language processing, are
now matched or even dominated using neural nets (LSTM). I find the
range of capabilities provided by groups like nlp.stanford.edu really
impressive -- there examples of both approaches (logic programming and
machine learning) and then don't need to be mutually exclusive.
Quantum annealing is one area where the two may increasingly come
together by using physical phenomena to accelerate the rate at which
high dimensional discrete systems can be solved, without relying on
fragile or domain-specific heuristics.
I often use evolutionary algorithms for hard optimization problems.
Genetic algorithms, for example, are robust to noise (or if you like
ambiguity) in fitness functions, and they are trivial to parallelize.
Marcus
------------------------------------------------------------------------
*From:* Friam <friam-boun...@redfish.com> on behalf of Grant Holland
<grant.holland...@gmail.com>
*Sent:* Tuesday, August 8, 2017 4:51:18 PM
*To:* The Friday Morning Applied Complexity Coffee Group; glen ☣
*Subject:* Re: [FRIAM] Future of humans and artificial intelligence
Thanks for throwing in on this one, Glen. Your thoughts are
ever-insightful. And ever-entertaining!
For example, I did not know that von Neumann put forth a set theory.
On the other hand... evolution /is/ stochastic. (You actually did not
disagree with me on that. You only said that the reason I was right
was another one.) A good book on the stochasticity of evolution is
"Chance and Necessity" by Jacques Monod. (I just finished rereading it
for the second time. And that proved quite fruitful.)
G.
On 8/8/17 12:44 PM, glen ☣ wrote:
I'm not sure how Asimov intended them. But the three laws is a trope that clearly shows
the inadequacy of deontological ethics. Rules are fine as far as they go. But they
don't go very far. We can see this even in the foundations of mathematics, the
unification of physics, and polyphenism/robustness in biology. Von Neumann (Burks) said
it best when he said: "But in the complicated parts of formal logic it is always one
order of magnitude harder to tell what an object can do than to produce the object."
Or, if you don't like that, you can see the same perspective in his iterative
construction of sets as an alternative to the classical conception.
The point being that reality, traditionally, has shown more expressiveness than
any of our rule sets.
There are ways to handle the mismatch in expressivity between reality versus
our rule sets. Stochasticity is the measure of the extent to which a rule set
matches a set of patterns. But Grant's right to qualify that with evolution,
not because of the way evolution is stochastic, but because evolution requires
a unit to regularly (or sporadically) sync with its environment.
An AI (or a rule-obsessed human) that sprouts fully formed from Zeus' head will
*always* fail. It's guaranteed to fail because syncing with the environment
isn't *built in*. The sync isn't part of the AI's onto- or phylo-geny.
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