Also, P=NP is about worst-case complexity, and what matters for AGI is
mainly average-case complexity calculated relative to the probability
distributions characteristic of the environments and problems the AI
system will actually need to deal with...
On Wed, Dec 5, 2018 at 7:50 AM 'Nil Geisweiller' via opencog
<[email protected]> wrote:
>
> On 12/5/18 5:23 PM, Sarah Connorh wrote:
> > Yeah but if P & P are negative ?
>
> Then I'll be back.
>
> Nil
>
> >
> > Le mer. 7 nov. 2018 à 14:19, 'Nil Geisweiller' via opencog
> > <[email protected] <mailto:[email protected]>> a écrit :
> >
> >     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.
> >      >
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