[Computer-go] AlphaGo Zero

mic michael.sue at gmx.de
Sat Oct 21 11:22:27 PDT 2017

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: 
> http://karpathy.github.io/2015/05/21/rnn-effectiveness/#visualizing-the-predictions-and-the-neuron-firings-in-the-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 any
>>         "generalization" means within that domain.
>>     Yes.
>>     -- 
>>     robert jasiek
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