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 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 

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.


From: Friam <> on behalf of Grant Holland 
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.)


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.

FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
to unsubscribe
FRIAM-COMIC by Dr. Strangelove

Reply via email to