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
