Hello Linas, I agree, the constant y should be in reasonable bounds for a solution to be usable.
Hello Nil, > If P = NP it would mean that, given a problem reframed as reasoning > such that its solution is a proof p of size s, one could construct p > in a polynomial time of s, which sounds very doubtful. Thank you very much, that is exactly a kind of answer I was hoping for. It is very inspiring and induces many thoughts. That's in theory, in practice however I think we could make P = NP for > some class of inputs via clever use of meta-learning (such as > inference control meta-learning that we're experimenting within > opencog, see > > https://blog.singularitynet.io/introspective-reasoning-within-the-opencog-framework-1bc7e182827 > ). > > In fact I had this dream where we could have a sequence of NP problems > and progressively learn how to solve them in P. > Very interesting approach. I found myself many times thinking of AI as a black box that could solve anything we place before it. And after all this time, I still consider the final AI capable of things intellectually unimaginable by merely mortal humans. Although there are problem setups that are contradictory, and as such unsolvable, P vs NP problem sounds solid to me. My approach is to manually resolve some NP complete <https://en.wikipedia.org/wiki/NP-completeness> problems such are Boolean SAT or Traveling salesman in polynomial time. I have some indices that it is a possible task. (My apologies to Singularity.net crew due to mess that P = NP would do to AGI Coin. But there are so much important possibilities it would open for many science tasks - including protein folding - to leave the big question unresolved. I don't know what to hope for. P != NP makes no mess at all, but P = NP generates too many possibilities just to ignore it if P = NP is possible. Time will tell.) Obviously for a finite set of inputs, one can turn any complex > algorithm into a logarithmic one (think of a pre-calculated binary > decision tree, where each branch is a bit describing the input and > each leaf is the solution). But it should still be possible to learn > an actual algorithm rather than a finite giant decision tree, that > performs worse that log, is more compact, but performs better than NP > for a bunch of real-world problems. > I think of algorithms as a possible compressed forms of their output. sri, 7. stu 2018. u 14:19 'Nil Geisweiller' via opencog < [email protected]> napisao je: > Hello Ivan, > > If P = NP it would mean that, given a problem reframed as reasoning > such that its solution is a proof p of size s, one could construct p > in a polynomial time of s, which sounds very doubtful. > > That's in theory, in practice however I think we could make P = NP for > some class of inputs via clever use of meta-learning (such as > inference control meta-learning that we're experimenting within > opencog, see > > https://blog.singularitynet.io/introspective-reasoning-within-the-opencog-framework-1bc7e182827 > ). > > In fact I had this dream where we could have a sequence of NP problems > and progressively learn how to solve them in P. > > Obviously for a finite set of inputs, one can turn any complex > algorithm into a logarithmic one (think of a pre-calculated binary > decision tree, where each branch is a bit describing the input and > each leaf is the solution). But it should still be possible to learn > an actual algorithm rather than a finite giant decision tree, that > performs worse that log, is more compact, but performs better than NP > for a bunch of real-world problems. > > Nil > > On 11/6/18 7:00 PM, Ivan Vodišek wrote: > > Hello everyone :) > > > > I have a question regarding to my independent research relating to > > OpenCog. I read somewhere (I really don't remember where) that if P = NP > > <https://en.wikipedia.org/wiki/P_versus_NP_problem> then it would be > > beneficial to AI in general. > > > > There are science fields which would obviously benefit if P = NP. But my > > question is: how would specifically OpenCog benefit from that solution? > > Somehow, it should be a matter of reducing a large number of possible > > combinations, but I don't really see were would AI fit into this > > equation. Googling around didn't produce anything interesting, so I'm > > making a post to this OpenCog community in a hope for an answer. > > > > Thank you all for your time, > > Ivan V. > > > > -- > > You received this message because you are subscribed to the Google > > Groups "opencog" group. > > To unsubscribe from this group and stop receiving emails from it, send > > an email to [email protected] > > <mailto:[email protected]>. > > To post to this group, send email to [email protected] > > <mailto:[email protected]>. > > Visit this group at https://groups.google.com/group/opencog. > > To view this discussion on the web visit > > > https://groups.google.com/d/msgid/opencog/CAB5%3Dj6W5UhHawabD6NAyT7hEMfkJNCAhd%2BzJcXgFZpJu-MhoMg%40mail.gmail.com > > < > https://groups.google.com/d/msgid/opencog/CAB5%3Dj6W5UhHawabD6NAyT7hEMfkJNCAhd%2BzJcXgFZpJu-MhoMg%40mail.gmail.com?utm_medium=email&utm_source=footer > >. > > For more options, visit https://groups.google.com/d/optout. > > -- > You received this message because you are subscribed to the Google Groups > "opencog" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at https://groups.google.com/group/opencog. > To view this discussion on the web visit > https://groups.google.com/d/msgid/opencog/e1418e0e-8604-2421-0597-4846a7644d3c%40gmail.com > . > For more options, visit https://groups.google.com/d/optout. > -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/CAB5%3Dj6W6GfPkQpUM0i_g6LjwBERf0XM906p%2B7rTn-nvRvPEZ7Q%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
