One other thing Matt. We have talked about this before. Your interest
in compression should tell you intuitively that the P<>NP theory is
unlikely. Are there any other compression schemes - that use systems
of algorithms but don't use special non-general individual
substitutions - that are exponentially efficient (for the strings that
they compress) and which are not reducible to Bool SAT?

I would very interested in learning something about how something like
that works.
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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