His equation might show some general property of intelligence. We are aware
of the equivalences of information theory with thermodynamics, digital
physics, etc..

 

I like my idea proposed a while ago on this list of deriving a unit of
intelligence from classical physics of energy and thermodynamics and giving
it the name Goertzel Unit. So 1 Goertzel or 1 Gtzl would be some derivation
of power, watt's, input with intelligence output. Does intelligence =
efficiency and/or doesn't have units? But for example x Watts would be able
to produce a maximum theoretical amount of intelligence or Gtzls. Gtzls per
Watt or Gtzl/Watt operating efficiency. 

 

Somebody help me out here J

 

john 

 

From: Aaron Hosford [mailto:[email protected]] 
Sent: Wednesday, March 5, 2014 9:41 AM
To: AGI
Subject: Re: [agi] A new equation for intelligence?

 

What is the difference between maximizing one's own future options and
seeking power? And what use is power, but the ability to accomplish your own
ends in your own time?

 

Go and chess playing are not, in fact, about "keeping your options
open". They are all about winning the game.  If that involves
eliminating future options and bringing the game to an end, so be it.

 

This is exactly what has been bothering me about this equation since I first
heard about it. I wrote a Reversi (a.k.a. Othello) game play engine -- back
before I heard about Wissner-Gross or his ideas -- which initially operated
on the principal of maximizing future options, and later on the principal of
maximizing its own future options while closing off the opponent's. It
worked quite nicely for the first half of the game, dominating the board,
but failed to close in on the win. I had to modify the value function to
successively migrate from keeping options open to closing options favorably
as game play continued. It is no use keeping options open if you aren't
going to take advantage of them when the time comes. And knowing when to do
that is a whole different dimension of intelligence.

 

On Tue, Mar 4, 2014 at 7:09 PM, Robert Levy <[email protected]> wrote:

There's an interesting and maybe humorous quasi-paradox in the idea of
settling on AWG's equation as the defining principle of intelligence. If you
disagree or find it unlikely that it is the kernel of intelligence but yet
find it useful, you are applying it recursively: you keep it around as a
potentially useful element to consider in various contexts but keep options
open, looking for other powerful/elegant principles. On the other hand
someone who is convinced very strongly it is the ultimate principle should
reflect on the possibility that this is not a very intelligent commitment to
make as it could introduce harmful path dependencies that could block the
discovery of more compelling insights into computational intelligence. The
other extreme is never committing to any leads, which is unintelligent in a
different way, because it is counter to any kind of useful curiosity (to
never pursue an interest to the exclusion of others), and counter to
pragmatic sensibilities of knowing when and where to apply effort to
worthwhile pursuits.

 

On Fri, Feb 21, 2014 at 5:56 PM, Ben Goertzel <[email protected]> wrote:

BTW, Wisner-Gross will be giving one of the keynotes at AGI-14 in
Quebec City in early August... I encourage y'all to come argue with
him in person !!!

I don't think he's found the holy grail of AGI, but I do think his
observations are interesting... I think causal path entropy (or
something like it) would sensibly be included as one of the high-level
goals of an AGI system...

ben


On Sat, Feb 22, 2014 at 4:20 AM, Bill Hibbard <[email protected]> wrote:
> Yes, the paper at:
> http://www.alexwg.org/publications/PhysRevLett_110-168702.pdf
> is more detailed and quite interesting.
>
> An interesting project would be to investigate
> the relation between this paper and AIXI. The
> paper includes probabilities of future histories,
> for a system interacting with an environment, in
> a new definition of entropy, called causal path
> entropy.
>
> Probabilities of future histories for a system
> interacting with an environment play a major role
> in the definition of intelligence in AIXI. It
> would be interesting to see how close the relation
> is between causal path entropy and AIXI.
>
> Bill
>
>
> On Fri, 21 Feb 2014, Matt Mahoney wrote:
>
>>>> From: Tim Tyler [mailto:[email protected]]
>>>>
>>>> "Alex Wissner-Gross: A new equation for intelligence"
>>>>
>>>>  - https://www.youtube.com/watch?v=ue2ZEmTJ_Xo
>>
>>
>> On Mon, Feb 10, 2014 at 6:46 PM, Piaget Modeler
>> <[email protected]> wrote:
>>>
>>>
>>> I found it too vague.
>>
>>
>> I did too, and the Entropica website wasn't any help. It just has the
>> same video clip you saw on TED. However, I did find a more detailed
>> explanation at
>> http://www.alexwg.org/publications/PhysRevLett_110-168702.pdf
>>
>> Unfortunately, if you were looking for the holy grail of AI, you can
>> keep looking. It doesn't shortcut the uncomputability of intelligence
>> proven by Hutter's AIXI model. In the entropic model, the idea is that
>> the optimal action of an intelligent agent is the one that maximizes
>> future entropy. Of course entropy in the information theoretic sense
>> is not computable because it depends on Kolmogorov complexity.
>>
>> However it might still be a useful principle, in the same way that
>> Occam's Razor is useful to machine learning. We do know that
>> computation requires energy. In particular, writing a bit of memory
>> decreases the information theoretic entropy of a computer's state by
>> up to 1 bit, and therefore requires a corresponding increase in
>> entropy of the environment of kT ln 2 where T is the temperature and k
>> is Boltzmann's constant. So it looks to me like the principle is to
>> choose the action that maximizes expected future computation.
>>
>> --
>> -- Matt Mahoney, [email protected]
>>
>>
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--

Ben Goertzel, PhD
http://goertzel.org

"In an insane world, the sane man must appear to be insane". -- Capt.
James T. Kirk



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