Yes we could do a 4x4 tic tac toe game like this in a PC. The training sets
can be generated simply by playing the agents against each other using
random moves and letting the agents know if it passed or failed as a
feedback mechanism.

Cheers,
Deepak

On Wed, Jul 21, 2010 at 9:02 AM, Matt Mahoney <matmaho...@yahoo.com> wrote:

> Mike, I think we all agree that we should not have to tell an AGI the steps
> to solving problems. It should learn and figure it out, like the way that
> people figure it out.
>
> The question is how to do that. We know that it is possible. For example, I
> could write a chess program that I could not win against. I could write the
> program in such a way that it learns to improve its game by playing against
> itself or other opponents. I could write it in such a way that initially
> does not know the rules for chess, but instead learns the rules by being
> given examples of legal and illegal moves.
>
> What we have not yet been able to do is scale this type of learning and
> problem solving up to general, human level intelligence. I believe it is
> possible, but it will require lots of training data and lots of computing
> power. It is not something you could do on a PC, and it won't be cheap.
>
>
> -- Matt Mahoney, matmaho...@yahoo.com
>
>
> ------------------------------
> *From:* Mike Tintner <tint...@blueyonder.co.uk>
> *To:* agi <agi@v2.listbox.com>
> *Sent:* Mon, July 19, 2010 9:07:53 PM
>
> *Subject:* Re: [agi] Of definitions and tests of AGI
>
> The issue isn't what a computer can do. The issue is how you structure the
> computer's or any agent's thinking about a problem. Programs/Turing machines
> are only one way of structuring thinking/problemsolving - by, among other
> things, giving the computer a method/process of solution. There is an
> alternative way of structuring a computer's thinking, which incl., among
> other things, not giving it a method/ process of solution, but making it
> rather than a human programmer do the real problemsolving.  More of that
> another time.
>
>  *From:* Matt Mahoney <matmaho...@yahoo.com>
> *Sent:* Tuesday, July 20, 2010 1:38 AM
> *To:* agi <agi@v2.listbox.com>
> *Subject:* Re: [agi] Of definitions and tests of AGI
>
>  Creativity is the good feeling you get when you discover a clever
> solution to a hard problem without knowing the process you used to discover
> it.
>
> I think a computer could do that.
>
>
> -- Matt Mahoney, matmaho...@yahoo.com
>
>
>  ------------------------------
> *From:* Mike Tintner <tint...@blueyonder.co.uk>
> *To:* agi <agi@v2.listbox.com>
> *Sent:* Mon, July 19, 2010 2:08:28 PM
> *Subject:* Re: [agi] Of definitions and tests of AGI
>
> Yes that's what people do, but it's not what programmed computers do.
>
> The useful formulation that emerges here is:
>
> narrow AI (and in fact all rational) problems  have *a method of solution*
> (to be equated with "general" method)   - and are programmable (a program is
> a method of solution)
>
> AGI  (and in fact all creative) problems do NOT have *a method of solution*
> (in the general sense)  -  rather a one.off *way of solving the problem* has
> to be improvised each time.
>
> AGI/creative problems do not in fact have a method of solution, period.
> There is no (general) method of solving either the toy box or the
> build-a-rock-wall problem - one essential feature which makes them AGI.
>
> You can learn, as you indicate, from *parts* of any given AGI/creative
> solution, and apply the lessons to future problems - and indeed with
> practice, should improve at solving any given kind of AGI/creative problem.
> But you can never apply a *whole* solution/way to further problems.
>
> P.S. One should add that in terms of computers, we are talking here of
> *complete, step-by-step* methods of solution.
>
>
>  *From:* rob levy <r.p.l...@gmail.com>
> *Sent:* Monday, July 19, 2010 5:09 PM
> *To:* agi <agi@v2.listbox.com>
> *Subject:* Re: [agi] Of definitions and tests of AGI
>
>
>
>>  And are you happy with:
>>
>> AGI is about devising *one-off* methods of problemsolving (that only apply
>> to the individual problem, and cannot be re-used - at
>>
>  least not in their totality)
>>
>>
>
> Yes exactly, isn't that what people do?  Also, I think that being able to
> recognize where past solutions can be generalized and where past solutions
> can be varied and reused is a detail of how intelligence works that is
> likely to be universal.
>
>
>
>>  vs
>>
>> narrow AI is about applying pre-existing *general* methods of
>> problemsolving  (applicable to whole classes of problems)?
>>
>>
>>
>>  *From:* rob levy <r.p.l...@gmail.com>
>> *Sent:* Monday, July 19, 2010 4:45 PM
>>  *To:* agi <agi@v2.listbox.com>
>> *Subject:* Re: [agi] Of definitions and tests of AGI
>>
>> Well, solving ANY problem is a little too strong.  This is AGI, not AGH
>> (artificial godhead), though AGH could be an unintended consequence ;).  So
>> I would rephrase "solving any problem" as being able to come up with
>> reasonable approaches and strategies to any problem (just as humans are able
>> to do).
>>
>> On Mon, Jul 19, 2010 at 11:32 AM, Mike Tintner 
>> <tint...@blueyonder.co.uk>wrote:
>>
>>>  Whaddya mean by "solve the problem of how to solve problems"? Develop a
>>> universal approach to solving any problem? Or find a method of solving a
>>> class of problems? Or what?
>>>
>>>  *From:* rob levy <r.p.l...@gmail.com>
>>> *Sent:* Monday, July 19, 2010 1:26 PM
>>>  *To:* agi <agi@v2.listbox.com>
>>> *Subject:* Re: [agi] Of definitions and tests of AGI
>>>
>>>
>>>>  However, I see that there are no valid definitions of AGI that explain
>>>> what AGI is generally , and why these tests are indeed AGI. Google - there
>>>> are v. few defs. of AGI or Strong AI, period.
>>>>
>>>
>>>
>>> I like Fogel's idea that intelligence is the ability to "solve the
>>> problem of how to solve problems" in new and changing environments.  I don't
>>> think Fogel's method accomplishes this, but the goal he expresses seems to
>>> be the goal of AGI as I understand it.
>>>
>>> Rob
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-- 
cheers,
Deepak



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agi
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