My point was that depth-first and breadth-first can probably serve only as a straw-man (straw-men?).
Frank Wimberly Phone (505) 670-9918 On Aug 8, 2017 10:11 PM, "Marcus Daniels" <mar...@snoutfarm.com> wrote: > Frank writes: > > > "Then there's best-first search, B*, C*, constraint-directed search, > etc. And these are just classical search methods." > > > Connecting this back to evolutionary / stochastic techniques, genetic > programming is one way to get the best of both approaches, at least in > principle. One can expose these human-designed algorithms as predefined > library functions. Typically in genetic programming the vocabulary > consists of simple routines (e.g. arithmetic), conditionals, and recursion. > > > In practice, this kind of seeding of the solution space can collapse > diversity. It is a drag to see tons of compute time spent on a million > little refinements around an already good solution. (Yes, I know that > solution!) More fun to see a set of clumsy solutions turn into to > decent-performing but weird solutions. I find my attention is drawn to > properties of sub-populations and how I can keep the historically good > performers _out_. Not a pure GA, but a GA where communities also have > fitness functions matching my heavy hand of justice.. (If I prove that > conservatism just doesn't work, I'll be sure to pass it along.) > > > Marcus > > > ------------------------------ > *From:* Friam <friam-boun...@redfish.com> on behalf of Frank Wimberly < > wimber...@gmail.com> > *Sent:* Tuesday, August 8, 2017 7:57:06 PM > *To:* The Friday Morning Applied Complexity Coffee Group > *Subject:* Re: [FRIAM] Future of humans and artificial intelligence > > Then there's best-first search, B*, C*, constraint-directed search, etc. > And these are just classical search methods. > > Feank > > Frank Wimberly > Phone (505) 670-9918 > > On Aug 8, 2017 7:20 PM, "Marcus Daniels" <mar...@snoutfarm.com> wrote: > >> "But one problem is that breadth-first and depth-first search are just >> fast ways to find answers." >> >> >> Just _not_ -- general but not efficient. [My dog was demanding >> attention! ] >> ------------------------------ >> *From:* Friam <friam-boun...@redfish.com> on behalf of Marcus Daniels < >> mar...@snoutfarm.com> >> *Sent:* Tuesday, August 8, 2017 6:43:40 PM >> *To:* The Friday Morning Applied Complexity Coffee Group; glen ☣ >> *Subject:* Re: [FRIAM] Future of humans and artificial intelligence >> >> >> 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. >> >> >> >> >> ============================================================ >> 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 >
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