[Computer-go] AlphaGo Zero

Hideki Kato hideki_katoh at ybb.ne.jp
Sat Oct 21 17:29:17 PDT 2017

The games look like previously published ones.  Just 


mic: <31fa8de6-c157-5de6-78fb-a66e6957a357 at gmx.de>:
>There are several AlphaGo instances playing against each other on Tygem 

>at this moment.



>On 21.10.2017 14:21, David Ongaro wrote:

>> Am 10/21/2017 um 03:12 AM schrieb uurtamo .:


>>> This sounds like a nice idea that is a misguided project.


>>> [...]

>>> Just accept that something awesome happened and that studying those 

>>> things that make it work well are more interesting than translating 

>>> coefficients into a bad understanding for people.


>>> I'm sorry that this NN can't teach anyone how to be a better player 

>>> through anything other than kicking their ass, but it wasn't built for 

>>> that.


>> Roberts approach might be misguided, but I don't agree that having the 

>> raw network data couldn't teach us something. E.g. have a look at this 

>> guy who was able to identify the neurons responsible for generating URLs 

>> in a wikipedia text generating RNN: 



>> E.g. it might be possible to find the network Part of AlphaGo Zero which 

>> is responsible for L&D problems and use it to dream up new Problems! The 

>> possibilities could be endless. This kind of research might have been 

>> easier with the "classic" AlphaGo with separated policy and value 

>> networks, but should be possible anyways.


>> Also lets not forget DeepMinds own substantial research in this area: 

>> https://deepmind.com/blog/cognitive-psychology/.


>> I understand that DeepMind might be unable to release the source code of 

>> AlphaGo due to policy or licensing reasons, but it would be great (and 

>> probably much more valuable) if they could release the fully trained 

>> network. As Gian-Carlo Pascutto has pointed out, replicating this would 

>> not only incur high hardware costs but also take a long time.


>> David O.



>>> On Fri, Oct 20, 2017 at 8:24 AM, Robert Jasiek <jasiek at snafu.de 

>>> <mailto:jasiek at snafu.de>> wrote:


>>>     On 20.10.2017 15:07, Adrian.B.Robert at gmail.com

>>>     <mailto:Adrian.B.Robert at gmail.com> wrote:


>>>             1) Where is the semantic translation of the neural net to

>>>             human theory

>>>             knowledge?


>>>         As far as (1), if we could do it, it would mean we could

>>>         relate the

>>>         structures embedded in the net's weight patterns to some other

>>>         domain --



>>>     The other domain can be "human go theory". It has various forms,

>>>     from informal via textbook to mathematically proven. Sure, it is

>>>     also incomplete but it can cope with additions.


>>>     The neural net's weights and whatnot are given. This raw data can

>>>     be deciphered in principle. By humans, algorithms or a combination.


>>>     You do not know where to start? Why, that is easy: test! Modify

>>>     ONE weight and study its effect on ONE aspect of human go theory,

>>>     such as the occurrance (frequency) of independent life. No effect?

>>>     Increase the modification, test a different weight, test a subset

>>>     of adjacent weights etc. It has been possible to study semantics

>>>     of parts of DNA, e.g., from differences related to illnesses.

>>>     Modifications on the weights is like creating causes for illnesses

>>>     (or improved health).


>>>     There is no "we cannot do it", but maybe there is too much

>>>     required effort for it to be financially worthwhile for the "too

>>>     specialised" case of Go? As I say, a mathematical proof of a

>>>     complete solution of Go will occur before AI playing perfectly;)


>>>         So far neural

>>>         nets have been trained and applied within single domains, and 

>>>         "generalization" means within that domain.



>>>     Yes.


>>>     -- 

>>>     robert jasiek


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