and then there is the constant of relative visual perception of general reality?

> Date: Thu, 5 Mar 2015 22:01:21 -0500
> Subject: Re: [agi] SAT and Dynamic Programs of Models
> From: [email protected]
> To: [email protected]
> 
>  Matt said:
> Vision is a pattern recognition problem. You input a picture of a cat
> and output a label like "cat". It is not NP-complete because (1)
> experimentally, the problem scales polynomially with input size and
> (2) the time to verify that a label like "cat" is correct is about the
> same as the time it takes to label the image. Thus, the problem is in
> P and would not benefit even if P = NP.
> -------------------------------------------------
> This is a truly insipid response. You have taken one narrow situation
> and used it in an over-generalization of a kind of AGI problem. "The
> problem scales polynomially with input size? The point that I made is
> that the general analysis of imagery is presumably as bad or worse
> than NP (in the lexicon of the day). What I mean is that there is
> sufficient evidence that AGI is, in the worse case, at least
> exponentially difficult and that makes it worthwhile to examine why
> that may be. One reason, the reason I gave, is that the easiest
> methods to make a methodical and thorough analysis of the relations
> between associated pixels would be those that are (literally) in NP.
> The implied case of scaling a particular picture and arguing that it
> would scale polynomially with input size is analogous to saying that
> converting an unrestricted Boolean Satisfiability problem to 3-SAT
> scales polynomially (and that somehow proves that unrestricted SAT
> scales polynomially). It is pretty obvious that you have little
> experience with visual data.
> 
> This is an example of a blatant overgeneralization being declared as
> if  it were a factual statement. I can't casually explain why visual
> analysis is at least exponentially difficult because I am not enough
> of an expert to be that familiar with all the problems. However, I am
> confident that there is no overwhelming evidence to suggest that, in
> general, it is less difficult.
> Jim Bromer
> 
> 
> On Thu, Mar 5, 2015 at 1:04 PM, Matt Mahoney via AGI <[email protected]> wrote:
> > On Wed, Mar 4, 2015 at 2:51 AM, Jim Bromer <[email protected]> wrote:
> >>  On Tue, Feb 17, 2015 at 11:52 PM, Matt Mahoney via AGI <[email protected]> 
> >> wrote:
> >>> On Tue, Feb 17, 2015 at 10:26 PM, Jim Bromer via AGI <[email protected]> 
> >>> wrote:
> >>>> I started wondering about how a good Satisfiability model might be
> >>>> used with AGI.
> >>>
> >>> It wouldn't because the hard problems in AI like vision and language
> >>> are not NP-hard. The more useful application would be breaking nearly
> >>> all forms of cryptography. (One time pad would still be secure).
> >>> -- Matt Mahoney
> >>
> >> I seriously doubt the premise that the hard problems like vision and
> >> language in AI are not NP-hard.
> >
> > NP-hard means NP-complete or harder. NP-complete means that a solution
> > would solve any problem in NP. NP is the class of problems whose
> > answers can be verified in time that is a polynomial function of the
> > input size. P is the class of problems that can be solved in
> > polynomial time. It is widely believed by everyone except Jim Bromer
> > that P != NP. This belief is not because of any proof, but because
> > thousands of other people like Jim Bromer who believed P = NP failed
> > to find polynomial time solutions to any NP-complete problems after
> > years of effort until they were convinced they would be better off if
> > they gave up. The time it takes to give up is inversely proportional
> > to the person's efforts into studying the math and researching the
> > work of others instead of repeating their mistakes.
> >
> >> My (admittedly limited) experience
> >> with visual AI ran up against NP-Hard solutions that I thought would
> >> work.
> >
> > Vision is a pattern recognition problem. You input a picture of a cat
> > and output a label like "cat". It is not NP-complete because (1)
> > experimentally, the problem scales polynomially with input size and
> > (2) the time to verify that a label like "cat" is correct is about the
> > same as the time it takes to label the image. Thus, the problem is in
> > P and would not benefit even if P = NP.
> >
> >> And since language could be considered to be a form of
> >> cryptography then your conjunction of cases (not language but
> >> cryptography) does not look really strong.
> >
> > No, language is also a pattern recognition problem.
> >
> >> (Visual processing also
> >> might be considered to be a form of cryptography and indeed it is used
> >> as such in captchas.)
> >
> > Cryptography depends on the existence of one-way functions: given
> > function f and output f(x), you can't find input x any faster than
> > trying all possible values and comparing the outputs. If P = NP, then
> > one-way functions would not exist. You could build a circuit that
> > computes f and compares the output. Then set the bits of x one at a
> > time and ask your polynomial SAT solver if a solution exists. If not,
> > flip the bit before going to the next bit.
> >
> > You could argue that a captcha is a one way function. It is easy to
> > convert text to an image, but hard to convert it back. But it is
> > polynomially hard, not exponentially hard. Adding one bit to the image
> > doesn't double the solution time, like adding one bit to an encryption
> > key would.
> >
> > --
> > -- Matt Mahoney, [email protected]
> >
> >
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