Right. Then you use gradient ascent. But what if you are scheduling a job shop for throughput when there are thousands of variables most of which have discrete values?
Frank Frank Wimberly Phone (505) 670-9918 On Aug 8, 2017 10:41 PM, "Marcus Daniels" <mar...@snoutfarm.com> wrote: > Frank writes: > > > "My point was that depth-first and breadth-first can probably serve only > as a straw-man (straw-men?)." > > > Unless there is a robust meta-rule (not heuristic) or single deterministic > search algorithm to rule them all, then wouldn't those other suggestions > also be straw-men too? If I knew that there were no noise and the domain > was continuous and convex, then I wouldn't use a stochastic approach. > > > Marcus > ------------------------------ > *From:* Friam <friam-boun...@redfish.com> on behalf of Frank Wimberly < > wimber...@gmail.com> > *Sent:* Tuesday, August 8, 2017 10:15:05 PM > *To:* The Friday Morning Applied Complexity Coffee Group > *Subject:* Re: [FRIAM] Future of humans and artificial intelligence > > 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 >> > > ============================================================ > 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
