"Genetic algorithms need not be, but can be, stochastic. Genetic algorithms are adaptive; but they need not be stochastically adaptive"
[..] "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." If an algorithm can result in there being one species it is not adaptive. I meant to imply a GA has a non-zero mutation rate (not just selection) and that mutation is random, without specifying particular distributional properties or distinguishing between pseudo-random and `truly' random. Marcus ________________________________ From: Grant Holland <grant.holland...@gmail.com> Sent: Wednesday, August 9, 2017 12:40:48 AM To: The Friday Morning Applied Complexity Coffee Group; Marcus Daniels; glen ☣ Subject: Re: [FRIAM] Future of humans and artificial intelligence 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><mailto:friam-boun...@redfish.com> on behalf of Grant Holland <grant.holland...@gmail.com><mailto: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. ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove
============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove
