Does this imply that Wisner-Goss is unmarried with no children? 
~PM

Date: Wed, 5 Mar 2014 16:01:24 +0100
Subject: Re: [agi] A new equation for intelligence?
From: [email protected]
To: [email protected]

Consider the exploration vs. exploitation dilemma:
A wise man will know that he knows very little of nature, so he knows that he 
has to continue exploring. At some point, he will realize that the nature is 
simply too big, so the best he can do is to avoid getting stuck in his 
ignorance, hence, he just tries to maximize the number of possible future 
options.

Something like that. :-)



On Wed, Mar 5, 2014 at 3:41 PM, Aaron Hosford <[email protected]> wrote:

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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