Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Stefan Kaitschick]

The evaluation is always at least as deep as leaves of the tree.
Still, you're right that the earlier in the game, the bigger the inherent
uncertainty.
One thing I don't understand: if the network does a thumbs up or down,
instead of answering with a probability,
what is the use of MSE? Why not just prediction rate?

On Thu, Feb 4, 2016 at 8:34 PM, Álvaro Begué  wrote:

> I am not sure how exactly they define MSE. If you look at the plot in
> figure 2b, the MSE at the very beginning of the game (where you can't
> possibly know anything about the result) is 0.50. That suggests it's
> something else than your [very sensible] interpretation.
>
> Álvaro.
>
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Petri Pitkanen]

Opent to intepretation if this method is brute force. I think it i. Uses
huge amounts of CPU power to run simulations and evaluate NN's. Even in
chess it was not just about tree search, it needs evaluationfunction ot
make sense of the search

2016-02-24 6:52 GMT+02:00 muupan :

> Congratulations, people at DeepMind! Your paper is very interesting to
> read.
>
> I have a question about the paper. On policy network training it says
>
> > On the first pass through the training pipeline, the baseline was set to
> zero; on the second pass we used the value network vθ(s) as a baseline;
>
> but I cannot find any other description about the "second pass". What is
> it? It uses vθ(s), so at least it is done after training vθ(s). Is it that
> after completing the whole training pipeline depicted in Fig. 1, only the
> RL policy network training part is repeated? Or training vθ(s) is also
> repeated? Is the second pass the last pass, or there are more passes? Sorry
> if I just missed the relevant part of the paper.
>
>
> 2016-02-13 12:21 GMT+09:00 John Tromp :
>
>> On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang  wrote:
>> > We are very excited to announce that our Go program, AlphaGo, has
>> beaten a
>> > professional player for the first time. AlphaGo beat the European
>> champion
>> > Fan Hui by 5 games to 0.
>>
>> It's interesting to go back nearly a decade and read this 2007 article:
>>
>> http://spectrum.ieee.org/computing/software/cracking-go
>>
>> where Feng-Hsiung Hsu, Deep Blue's lead developer, made this prediction:
>>
>> "Nevertheless, I believe that a world-champion-level Go machine can be
>> built within 10 years"
>>
>> Which now appears to be spot on. March 9 cannot come soon enough...
>> The remainder of his prediction rings less true though:
>>
>> ", based on the same method of intensive analysis—brute force,
>> basically—that Deep Blue employed for chess".
>>
>> regards,
>> -John
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>>
>
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[muupan]

Congratulations, people at DeepMind! Your paper is very interesting to read.

I have a question about the paper. On policy network training it says

> On the first pass through the training pipeline, the baseline was set to
zero; on the second pass we used the value network vθ(s) as a baseline;

but I cannot find any other description about the "second pass". What is
it? It uses vθ(s), so at least it is done after training vθ(s). Is it that
after completing the whole training pipeline depicted in Fig. 1, only the
RL policy network training part is repeated? Or training vθ(s) is also
repeated? Is the second pass the last pass, or there are more passes? Sorry
if I just missed the relevant part of the paper.


2016-02-13 12:21 GMT+09:00 John Tromp :

> On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang  wrote:
> > We are very excited to announce that our Go program, AlphaGo, has beaten
> a
> > professional player for the first time. AlphaGo beat the European
> champion
> > Fan Hui by 5 games to 0.
>
> It's interesting to go back nearly a decade and read this 2007 article:
>
> http://spectrum.ieee.org/computing/software/cracking-go
>
> where Feng-Hsiung Hsu, Deep Blue's lead developer, made this prediction:
>
> "Nevertheless, I believe that a world-champion-level Go machine can be
> built within 10 years"
>
> Which now appears to be spot on. March 9 cannot come soon enough...
> The remainder of his prediction rings less true though:
>
> ", based on the same method of intensive analysis—brute force,
> basically—that Deep Blue employed for chess".
>
> regards,
> -John
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[John Tromp]

On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang  wrote:
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0.

It's interesting to go back nearly a decade and read this 2007 article:

http://spectrum.ieee.org/computing/software/cracking-go

where Feng-Hsiung Hsu, Deep Blue's lead developer, made this prediction:

"Nevertheless, I believe that a world-champion-level Go machine can be
built within 10 years"

Which now appears to be spot on. March 9 cannot come soon enough...
The remainder of his prediction rings less true though:

", based on the same method of intensive analysis—brute force,
basically—that Deep Blue employed for chess".

regards,
-John
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

> One possibility is that 0=loss, 1=win, and the number they are
quoting is
> sqrt(average((prediction-outcome)^2)).


this makes perfectly sense for figure 2. even playouts seem reasonable.

But figure 2 is not consistent with the numbers in section 3 would be
0.234 (test set of the self-play data base. The figure looks more like
0.3 - 0.35 or even higher...



Am 04.02.2016 um 21:43 schrieb Álvaro Begué:
> I just want to see how to get 0.5 for the initial position on the
> board with some definition.
> 
> One possibility is that 0=loss, 1=win, and the number they are
> quoting is sqrt(average((prediction-outcome)^2)).
> 
> 
> On Thu, Feb 4, 2016 at 3:40 PM, Hideki Kato
>  wrote:
> 
>> I think the error is defined as the difference between the output
>> of the value network and the average output of the simulations
>> done by the policy network (RL) at each position.
>> 
>> Hideki
>> 
>> Michael Markefka:
>> 

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Hideki Kato]

Detlef Schmicker: <56b385ce.4080...@physik.de>: 
>-BEGIN PGP SIGNED MESSAGE-
>Hash: SHA1
>
>Hi,
>
>I try to reproduce numbers from section 3: training the value network
>
>On the test set of kgs games the MSE is 0.37. Is it correct, that the
>results are represented as +1 and -1?

Looks correct.

>This means, that in a typical board position you get a value of
>1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70% ?!

Since all positions of all games in the dataset are used, 
winrate should distributes from 0% to 100%, or -1 to 1, not 1.  
Then, the number 70% could be wrong.  MSE is 0.37 just means the 
average error is about 0.6, I think.

Hideki

>Is it really true, that a typical kgs 6d+ position is judeged with
>such a high win rate (even though it it is overfitted, so the test set
>number is to bad!), or do I misinterpret the MSE calculation?!
>
>Any help would be great,
>
>Detlef
>
>Am 27.01.2016 um 19:46 schrieb Aja Huang:
>> Hi all,
>> 
>> We are very excited to announce that our Go program, AlphaGo, has
>> beaten a professional player for the first time. AlphaGo beat the
>> European champion Fan Hui by 5 games to 0. We hope you enjoy our
>> paper, published in Nature today. The paper and all the games can
>> be found here:
>> 
>> http://www.deepmind.com/alpha-go.html
>> 
>> AlphaGo will be competing in a match against Lee Sedol in Seoul,
>> this March, to see whether we finally have a Go program that is
>> stronger than any human!
>> 
>> Aja
>> 
>> PS I am very busy preparing AlphaGo for the match, so apologies in
>> advance if I cannot respond to all questions about AlphaGo.
>> 
>> 
>> 
>> ___ Computer-go mailing
>> list Computer-go@computer-go.org 
>> http://computer-go.org/mailman/listinfo/computer-go
>> 
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

>> Since all positions of all games in the dataset are used, winrate
>> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
>> number 70% could be wrong.  MSE is 0.37 just means the average
>> error is about 0.6, I think.

0.6 in the range of -1 to 1,

which means -1 (eg lost by b) games -> typical value -0.4
and +1 games -> typical value +0.4 of the value network

if I rescale -1 to +1 to  0 - 100% (eg winrate for b) than I get about
30% for games lost by b and 70% for games won by B?

Detlef


Am 04.02.2016 um 20:10 schrieb Hideki Kato:
> Detlef Schmicker: <56b385ce.4080...@physik.de>: Hi,
> 
> I try to reproduce numbers from section 3: training the value
> network
> 
> On the test set of kgs games the MSE is 0.37. Is it correct, that
> the results are represented as +1 and -1?
> 
>> Looks correct.
> 
> This means, that in a typical board position you get a value of 
> 1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70%
> ?!
> 
>> Since all positions of all games in the dataset are used, winrate
>> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
>> number 70% could be wrong.  MSE is 0.37 just means the average
>> error is about 0.6, I think.
> 
>> Hideki
> 
> Is it really true, that a typical kgs 6d+ position is judeged with 
> such a high win rate (even though it it is overfitted, so the test
> set number is to bad!), or do I misinterpret the MSE calculation?!
> 
> Any help would be great,
> 
> Detlef
> 
> Am 27.01.2016 um 19:46 schrieb Aja Huang:
 Hi all,
 
 We are very excited to announce that our Go program, AlphaGo,
 has beaten a professional player for the first time. AlphaGo
 beat the European champion Fan Hui by 5 games to 0. We hope
 you enjoy our paper, published in Nature today. The paper and
 all the games can be found here:
 
 http://www.deepmind.com/alpha-go.html
 
 AlphaGo will be competing in a match against Lee Sedol in
 Seoul, this March, to see whether we finally have a Go
 program that is stronger than any human!
 
 Aja
 
 PS I am very busy preparing AlphaGo for the match, so
 apologies in advance if I cannot respond to all questions
 about AlphaGo.
 
 
 
 ___ Computer-go
 mailing list Computer-go@computer-go.org 
 http://computer-go.org/mailman/listinfo/computer-go
 
>> ___ Computer-go
>> mailing list Computer-go@computer-go.org 
>> http://computer-go.org/mailman/listinfo/computer-go
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Álvaro Begué]

I am not sure how exactly they define MSE. If you look at the plot in
figure 2b, the MSE at the very beginning of the game (where you can't
possibly know anything about the result) is 0.50. That suggests it's
something else than your [very sensible] interpretation.

Álvaro.



On Thu, Feb 4, 2016 at 2:24 PM, Detlef Schmicker  wrote:

> -BEGIN PGP SIGNED MESSAGE-
> Hash: SHA1
>
> >> Since all positions of all games in the dataset are used, winrate
> >> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
> >> number 70% could be wrong.  MSE is 0.37 just means the average
> >> error is about 0.6, I think.
>
> 0.6 in the range of -1 to 1,
>
> which means -1 (eg lost by b) games -> typical value -0.4
> and +1 games -> typical value +0.4 of the value network
>
> if I rescale -1 to +1 to  0 - 100% (eg winrate for b) than I get about
> 30% for games lost by b and 70% for games won by B?
>
> Detlef
>
>
> Am 04.02.2016 um 20:10 schrieb Hideki Kato:
> > Detlef Schmicker: <56b385ce.4080...@physik.de>: Hi,
> >
> > I try to reproduce numbers from section 3: training the value
> > network
> >
> > On the test set of kgs games the MSE is 0.37. Is it correct, that
> > the results are represented as +1 and -1?
> >
> >> Looks correct.
> >
> > This means, that in a typical board position you get a value of
> > 1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70%
> > ?!
> >
> >> Since all positions of all games in the dataset are used, winrate
> >> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
> >> number 70% could be wrong.  MSE is 0.37 just means the average
> >> error is about 0.6, I think.
> >
> >> Hideki
> >
> > Is it really true, that a typical kgs 6d+ position is judeged with
> > such a high win rate (even though it it is overfitted, so the test
> > set number is to bad!), or do I misinterpret the MSE calculation?!
> >
> > Any help would be great,
> >
> > Detlef
> >
> > Am 27.01.2016 um 19:46 schrieb Aja Huang:
>  Hi all,
> 
>  We are very excited to announce that our Go program, AlphaGo,
>  has beaten a professional player for the first time. AlphaGo
>  beat the European champion Fan Hui by 5 games to 0. We hope
>  you enjoy our paper, published in Nature today. The paper and
>  all the games can be found here:
> 
>  http://www.deepmind.com/alpha-go.html
> 
>  AlphaGo will be competing in a match against Lee Sedol in
>  Seoul, this March, to see whether we finally have a Go
>  program that is stronger than any human!
> 
>  Aja
> 
>  PS I am very busy preparing AlphaGo for the match, so
>  apologies in advance if I cannot respond to all questions
>  about AlphaGo.
> 
> 
> 
>  ___ Computer-go
>  mailing list Computer-go@computer-go.org
>  http://computer-go.org/mailman/listinfo/computer-go
> 
> >> ___ Computer-go
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> >> http://computer-go.org/mailman/listinfo/computer-go
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Álvaro Begué]

The positions they used are not from high-quality games. They actually
include one last move that is completely random.

Álvaro.


On Thursday, February 4, 2016, Detlef Schmicker  wrote:

> -BEGIN PGP SIGNED MESSAGE-
> Hash: SHA1
>
> Hi,
>
> I try to reproduce numbers from section 3: training the value network
>
> On the test set of kgs games the MSE is 0.37. Is it correct, that the
> results are represented as +1 and -1?
>
> This means, that in a typical board position you get a value of
> 1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70% ?!
>
> Is it really true, that a typical kgs 6d+ position is judeged with
> such a high win rate (even though it it is overfitted, so the test set
> number is to bad!), or do I misinterpret the MSE calculation?!
>
> Any help would be great,
>
> Detlef
>
> Am 27.01.2016 um 19:46 schrieb Aja Huang:
> > Hi all,
> >
> > We are very excited to announce that our Go program, AlphaGo, has
> > beaten a professional player for the first time. AlphaGo beat the
> > European champion Fan Hui by 5 games to 0. We hope you enjoy our
> > paper, published in Nature today. The paper and all the games can
> > be found here:
> >
> > http://www.deepmind.com/alpha-go.html
> >
> > AlphaGo will be competing in a match against Lee Sedol in Seoul,
> > this March, to see whether we finally have a Go program that is
> > stronger than any human!
> >
> > Aja
> >
> > PS I am very busy preparing AlphaGo for the match, so apologies in
> > advance if I cannot respond to all questions about AlphaGo.
> >
> >
> >
> > ___ Computer-go mailing
> > list Computer-go@computer-go.org 
> > http://computer-go.org/mailman/listinfo/computer-go
> >
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

Hi,

I try to reproduce numbers from section 3: training the value network

On the test set of kgs games the MSE is 0.37. Is it correct, that the
results are represented as +1 and -1?

This means, that in a typical board position you get a value of
1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70% ?!

Is it really true, that a typical kgs 6d+ position is judeged with
such a high win rate (even though it it is overfitted, so the test set
number is to bad!), or do I misinterpret the MSE calculation?!

Any help would be great,

Detlef

Am 27.01.2016 um 19:46 schrieb Aja Huang:
> Hi all,
> 
> We are very excited to announce that our Go program, AlphaGo, has
> beaten a professional player for the first time. AlphaGo beat the
> European champion Fan Hui by 5 games to 0. We hope you enjoy our
> paper, published in Nature today. The paper and all the games can
> be found here:
> 
> http://www.deepmind.com/alpha-go.html
> 
> AlphaGo will be competing in a match against Lee Sedol in Seoul,
> this March, to see whether we finally have a Go program that is
> stronger than any human!
> 
> Aja
> 
> PS I am very busy preparing AlphaGo for the match, so apologies in
> advance if I cannot respond to all questions about AlphaGo.
> 
> 
> 
> ___ Computer-go mailing
> list Computer-go@computer-go.org 
> http://computer-go.org/mailman/listinfo/computer-go
> 
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

Thanks for the response, I do not refer to the finaly used data set:
in the referred chapter they state, they have used their kgs dataset
in a first try (which is in another part of the paper referred to
being a 6d+ data set).

Am 04.02.2016 um 18:11 schrieb Álvaro Begué:
> The positions they used are not from high-quality games. They
> actually include one last move that is completely random.
> 
> Álvaro.
> 
> 
> On Thursday, February 4, 2016, Detlef Schmicker 
> wrote:
> 
> Hi,
> 
> I try to reproduce numbers from section 3: training the value
> network
> 
> On the test set of kgs games the MSE is 0.37. Is it correct, that
> the results are represented as +1 and -1?
> 
> This means, that in a typical board position you get a value of 
> 1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70%
> ?!
> 
> Is it really true, that a typical kgs 6d+ position is judeged with 
> such a high win rate (even though it it is overfitted, so the test
> set number is to bad!), or do I misinterpret the MSE calculation?!
> 
> Any help would be great,
> 
> Detlef
> 
> Am 27.01.2016 um 19:46 schrieb Aja Huang:
 Hi all,
 
 We are very excited to announce that our Go program, AlphaGo,
 has beaten a professional player for the first time. AlphaGo
 beat the European champion Fan Hui by 5 games to 0. We hope
 you enjoy our paper, published in Nature today. The paper and
 all the games can be found here:
 
 http://www.deepmind.com/alpha-go.html
 
 AlphaGo will be competing in a match against Lee Sedol in
 Seoul, this March, to see whether we finally have a Go
 program that is stronger than any human!
 
 Aja
 
 PS I am very busy preparing AlphaGo for the match, so
 apologies in advance if I cannot respond to all questions
 about AlphaGo.
 
 
 
 ___ Computer-go
 mailing list Computer-go@computer-go.org  
 http://computer-go.org/mailman/listinfo/computer-go
 
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> 
> 
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Álvaro Begué]

I re-read the relevant section and I agree with you. Sorry for adding noise
to the conversation.

Álvaro.







On Thu, Feb 4, 2016 at 12:21 PM, Detlef Schmicker  wrote:

> -BEGIN PGP SIGNED MESSAGE-
> Hash: SHA1
>
> Thanks for the response, I do not refer to the finaly used data set:
> in the referred chapter they state, they have used their kgs dataset
> in a first try (which is in another part of the paper referred to
> being a 6d+ data set).
>
> Am 04.02.2016 um 18:11 schrieb Álvaro Begué:
> > The positions they used are not from high-quality games. They
> > actually include one last move that is completely random.
> >
> > Álvaro.
> >
> >
> > On Thursday, February 4, 2016, Detlef Schmicker 
> > wrote:
> >
> > Hi,
> >
> > I try to reproduce numbers from section 3: training the value
> > network
> >
> > On the test set of kgs games the MSE is 0.37. Is it correct, that
> > the results are represented as +1 and -1?
> >
> > This means, that in a typical board position you get a value of
> > 1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70%
> > ?!
> >
> > Is it really true, that a typical kgs 6d+ position is judeged with
> > such a high win rate (even though it it is overfitted, so the test
> > set number is to bad!), or do I misinterpret the MSE calculation?!
> >
> > Any help would be great,
> >
> > Detlef
> >
> > Am 27.01.2016 um 19:46 schrieb Aja Huang:
>  Hi all,
> 
>  We are very excited to announce that our Go program, AlphaGo,
>  has beaten a professional player for the first time. AlphaGo
>  beat the European champion Fan Hui by 5 games to 0. We hope
>  you enjoy our paper, published in Nature today. The paper and
>  all the games can be found here:
> 
>  http://www.deepmind.com/alpha-go.html
> 
>  AlphaGo will be competing in a match against Lee Sedol in
>  Seoul, this March, to see whether we finally have a Go
>  program that is stronger than any human!
> 
>  Aja
> 
>  PS I am very busy preparing AlphaGo for the match, so
>  apologies in advance if I cannot respond to all questions
>  about AlphaGo.
> 
> 
> 
>  ___ Computer-go
>  mailing list Computer-go@computer-go.org 
>  http://computer-go.org/mailman/listinfo/computer-go
> 
> >> ___ Computer-go
> >> mailing list Computer-go@computer-go.org 
> >> http://computer-go.org/mailman/listinfo/computer-go
> >
> >
> >
> > ___ Computer-go mailing
> > list Computer-go@computer-go.org
> > http://computer-go.org/mailman/listinfo/computer-go
> >
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Michael Markefka]

That sounds like it'd be the MSE as classification error of the eventual result.

I'm currently not able to look at the paper, but couldn't you use a
softmax output layer with two nodes and take the probability
distribution as winrate?

On Thu, Feb 4, 2016 at 8:34 PM, Álvaro Begué  wrote:
> I am not sure how exactly they define MSE. If you look at the plot in figure
> 2b, the MSE at the very beginning of the game (where you can't possibly know
> anything about the result) is 0.50. That suggests it's something else than
> your [very sensible] interpretation.
>
> Álvaro.
>
>
>
> On Thu, Feb 4, 2016 at 2:24 PM, Detlef Schmicker  wrote:
>>
>> -BEGIN PGP SIGNED MESSAGE-
>> Hash: SHA1
>>
>> >> Since all positions of all games in the dataset are used, winrate
>> >> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
>> >> number 70% could be wrong.  MSE is 0.37 just means the average
>> >> error is about 0.6, I think.
>>
>> 0.6 in the range of -1 to 1,
>>
>> which means -1 (eg lost by b) games -> typical value -0.4
>> and +1 games -> typical value +0.4 of the value network
>>
>> if I rescale -1 to +1 to  0 - 100% (eg winrate for b) than I get about
>> 30% for games lost by b and 70% for games won by B?
>>
>> Detlef
>>
>>
>> Am 04.02.2016 um 20:10 schrieb Hideki Kato:
>> > Detlef Schmicker: <56b385ce.4080...@physik.de>: Hi,
>> >
>> > I try to reproduce numbers from section 3: training the value
>> > network
>> >
>> > On the test set of kgs games the MSE is 0.37. Is it correct, that
>> > the results are represented as +1 and -1?
>> >
>> >> Looks correct.
>> >
>> > This means, that in a typical board position you get a value of
>> > 1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70%
>> > ?!
>> >
>> >> Since all positions of all games in the dataset are used, winrate
>> >> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
>> >> number 70% could be wrong.  MSE is 0.37 just means the average
>> >> error is about 0.6, I think.
>> >
>> >> Hideki
>> >
>> > Is it really true, that a typical kgs 6d+ position is judeged with
>> > such a high win rate (even though it it is overfitted, so the test
>> > set number is to bad!), or do I misinterpret the MSE calculation?!
>> >
>> > Any help would be great,
>> >
>> > Detlef
>> >
>> > Am 27.01.2016 um 19:46 schrieb Aja Huang:
>>  Hi all,
>> 
>>  We are very excited to announce that our Go program, AlphaGo,
>>  has beaten a professional player for the first time. AlphaGo
>>  beat the European champion Fan Hui by 5 games to 0. We hope
>>  you enjoy our paper, published in Nature today. The paper and
>>  all the games can be found here:
>> 
>>  http://www.deepmind.com/alpha-go.html
>> 
>>  AlphaGo will be competing in a match against Lee Sedol in
>>  Seoul, this March, to see whether we finally have a Go
>>  program that is stronger than any human!
>> 
>>  Aja
>> 
>>  PS I am very busy preparing AlphaGo for the match, so
>>  apologies in advance if I cannot respond to all questions
>>  about AlphaGo.
>> 
>> 
>> 
>>  ___ Computer-go
>>  mailing list Computer-go@computer-go.org
>>  http://computer-go.org/mailman/listinfo/computer-go
>> 
>> >> ___ Computer-go
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>> >> http://computer-go.org/mailman/listinfo/computer-go
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Hideki Kato]

I think the error is defined as the difference between the 
output of the value network and the average output of the 
simulations done by the policy network (RL) at each position.

Hideki

Michael Markefka: 

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

[Álvaro Begué]

I just want to see how to get 0.5 for the initial position on the board
with some definition.

One possibility is that 0=loss, 1=win, and the number they are quoting is
sqrt(average((prediction-outcome)^2)).


On Thu, Feb 4, 2016 at 3:40 PM, Hideki Kato  wrote:

> I think the error is defined as the difference between the
> output of the value network and the average output of the
> simulations done by the policy network (RL) at each position.
>
> Hideki
>
> Michael Markefka: 

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Oliver Lewis]

Is the paper still available for download? The direct link appears to be
broken.

Thanks

Oliver


On Wed, Feb 3, 2016 at 2:06 AM, Igor Polyakov 
wrote:

> I think it would be an awesome commercial product for strong Go players.
> Maybe even if the AI shows the continuations and the score estimates
> between different lines, it will give the player enough reasoning to
> understand why one move is better than the other.
>
>
> On 2016-02-02 8:29, Jim O'Flaherty wrote:
>
> And to meta this awesome short story...
>
> AI Software Engineers: Robert, please stop asking our AI for explanations.
> We don't want to distract it with limited human understanding. And we don't
> want the Herculean task of coding up that extremely frail and error prone
> bridge.
> On Feb 1, 2016 3:03 PM, "Rainer Rosenthal"  wrote:
>
>> ~~
>> Robert: "Hey, AI, you should provide explanations!"
>> AI: "Why?"
>> ~~
>>
>> Cheers,
>> Rainer
>>
>>> Date: Mon, 1 Feb 2016 08:15:12 -0600
>>> From: "Jim O'Flaherty" 
>>> To: computer-go@computer-go.org
>>> Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural
>>> Networks and Tree Search
>>> Message-ID:
>>> <
>>> cakx5gkjc7j0uq_pmxyumyfre7r+7ydltigbna5oo7kvnzq7...@mail.gmail.com>
>>> Content-Type: text/plain; charset="utf-8"
>>>
>>> Robert,
>>>
>>> I'm not seeing the ROI in attempting to map human idiosyncratic
>>> linguistic
>>> systems to/into a Go engine.
>>>
>>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Álvaro Begué]

I searched for the file name on the web and found this copy:
http://airesearch.com/wp-content/uploads/2016/01/deepmind-mastering-go.pdf

Álvaro.



On Wed, Feb 3, 2016 at 4:37 AM, Oliver Lewis  wrote:

> Is the paper still available for download? The direct link appears to be
> broken.
>
> Thanks
>
> Oliver
>
>
> On Wed, Feb 3, 2016 at 2:06 AM, Igor Polyakov 
> wrote:
>
>> I think it would be an awesome commercial product for strong Go players.
>> Maybe even if the AI shows the continuations and the score estimates
>> between different lines, it will give the player enough reasoning to
>> understand why one move is better than the other.
>>
>>
>> On 2016-02-02 8:29, Jim O'Flaherty wrote:
>>
>> And to meta this awesome short story...
>>
>> AI Software Engineers: Robert, please stop asking our AI for
>> explanations. We don't want to distract it with limited human
>> understanding. And we don't want the Herculean task of coding up that
>> extremely frail and error prone bridge.
>> On Feb 1, 2016 3:03 PM, "Rainer Rosenthal"  wrote:
>>
>>> ~~
>>> Robert: "Hey, AI, you should provide explanations!"
>>> AI: "Why?"
>>> ~~
>>>
>>> Cheers,
>>> Rainer
>>>
 Date: Mon, 1 Feb 2016 08:15:12 -0600
 From: "Jim O'Flaherty" 
 To: computer-go@computer-go.org
 Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural
 Networks and Tree Search
 Message-ID:
 <
 cakx5gkjc7j0uq_pmxyumyfre7r+7ydltigbna5oo7kvnzq7...@mail.gmail.com>
 Content-Type: text/plain; charset="utf-8"

 Robert,

 I'm not seeing the ROI in attempting to map human idiosyncratic
 linguistic
 systems to/into a Go engine.

>>>
>>> ___
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>>
>>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 02.02.2016 17:29, Jim O'Flaherty wrote:

AI Software Engineers: Robert, please stop asking our AI for explanations.
We don't want to distract it with limited human understanding. And we don't
want the Herculean task of coding up that extremely frail and error prone
bridge.


Currently I do not ask a specific AI engine explanations. If an AI 
program only has the goal of playing strong, then - while it is playing 
or preparing play - it should not be disturbed with extra tasks.


Explanations can come from AI programs, their programmers, researchers 
providing the theory applied in those programs, researchers analysing 
the program codes, data structures or outputs.


I do not expect everybody to be interested in explanations, but I ask 
those interested. It must be possible to study theory for playing 
programs, their data structures or outputs and find connections to 
explanatory theory - as much as it must be possible to use explanatory 
theory to improve "brute force" programs.


Herculean task? Likely. The research in explanatory theory is, too.

Error prone? I disagree. Errors are not created due to volume of a task 
but due to carelessness or missing study of semantic conflicts.


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Olivier Teytaud]

>
> If AlphaGo had lost at least one game, I'd understand how people can have
>> an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
>> have an upper bound on his level. After all, AlphaGo might just have played
>> well enough for crushing Fan Hui, and a weak move while the position is
>> still in favor of AlphaGo is not really a weak move (at least in a
>> game-theoretic point of view...).
>>
>
> I just want to point that according to Myungwan Kim 9p (video referenced
> in this thread) on the first game, Alpha Go did some mistake early in the
> game and was behind during nearly the whole game so some of his moves
> should be weak in game-theoric point of view.
>

Thanks, this point is interesting - that's really an argument limiting the
strength of AlphaGo.

On the other hand, they have super strong people in the team (at the pro
level, maybe ? if Aja has pro level...),
and one of the guys said he is "quietly confident", which suggests they
have strong reasons for believing they have a big chance :-)

Good luck AlphaGo :-) I'm grateful because since this happened many more
doors are opened for people
working with these tools, even if they don't touch games, and this is
really useful for the world :-)



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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Xavier Combelle]

2016-02-01 12:24 GMT+01:00 Olivier Teytaud :

> If AlphaGo had lost at least one game, I'd understand how people can have
> an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
> have an upper bound on his level. After all, AlphaGo might just have played
> well enough for crushing Fan Hui, and a weak move while the position is
> still in favor of AlphaGo is not really a weak move (at least in a
> game-theoretic point of view...).
>

I just want to point that according to Myungwan Kim 9p (video referenced in
this thread) on the first game, Alpha Go did some mistake early in the game
and was behind during nearly the whole game so some of his moves should be
weak in game-theoric point of view.
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Marc Landgraf]

What? You have mixed up things.

http://www.europeangodatabase.eu/EGD/Player_Card.php?=17374016

2016-02-02 20:21 GMT+01:00 Olivier Teytaud :
>>> If AlphaGo had lost at least one game, I'd understand how people can have
>>> an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
>>> have an upper bound on his level. After all, AlphaGo might just have played
>>> well enough for crushing Fan Hui, and a weak move while the position is
>>> still in favor of AlphaGo is not really a weak move (at least in a
>>> game-theoretic point of view...).
>>
>>
>> I just want to point that according to Myungwan Kim 9p (video referenced
>> in this thread) on the first game, Alpha Go did some mistake early in the
>> game and was behind during nearly the whole game so some of his moves should
>> be weak in game-theoric point of view.
>
>
> Thanks, this point is interesting - that's really an argument limiting the
> strength of AlphaGo.
>
> On the other hand, they have super strong people in the team (at the pro
> level, maybe ? if Aja has pro level...),
> and one of the guys said he is "quietly confident", which suggests they have
> strong reasons for believing they have a big chance :-)
>
> Good luck AlphaGo :-) I'm grateful because since this happened many more
> doors are opened for people
> working with these tools, even if they don't touch games, and this is really
> useful for the world :-)
>
>
>
> --
> =
> "I will never sign a document with logos in black & white." A. Einstein
> Olivier Teytaud, olivier.teyt...@inria.fr, http://www.slideshare.net/teytaud
>
>
>
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 02.02.2016 20:21, Olivier Teytaud wrote:

On the other hand, they have super strong people in the team (at the pro
level, maybe ? if Aja has pro level...)


Ca. 5d amateur in the team is enough, regardless of whether Myongwan Kim 
thinks that only 9p can understand. Not so. Kim's above 5d amateur 
comments were related to reading or by heart knowledge of the latest 
nadare variations (before the post-joseki aji mistakes, which can be 
detected by 5d, or even below), but reading / joseki is not AlphaGo's 
weakness.


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Igor Polyakov]

I think it would be an awesome commercial product for strong Go players. 
Maybe even if the AI shows the continuations and the score estimates 
between different lines, it will give the player enough reasoning to 
understand why one move is better than the other.


On 2016-02-02 8:29, Jim O'Flaherty wrote:


And to meta this awesome short story...

AI Software Engineers: Robert, please stop asking our AI for 
explanations. We don't want to distract it with limited human 
understanding. And we don't want the Herculean task of coding up that 
extremely frail and error prone bridge.


On Feb 1, 2016 3:03 PM, "Rainer Rosenthal" > wrote:


~~
Robert: "Hey, AI, you should provide explanations!"
AI: "Why?"
~~

Cheers,
Rainer

Date: Mon, 1 Feb 2016 08:15:12 -0600
From: "Jim O'Flaherty" >
To: computer-go@computer-go.org

Subject: Re: [Computer-go] Mastering the Game of Go with Deep
Neural
Networks and Tree Search
Message-ID:
   
>
Content-Type: text/plain; charset="utf-8"

Robert,

I'm not seeing the ROI in attempting to map human
idiosyncratic linguistic
systems to/into a Go engine.


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 02.02.2016 19:07, David Fotland wrote:

consider some of this as the difference between math and engineering.  Math 
desires rigor.
Engineering desires working solutions.  When an engineering solution is being 
described,
you shouldn't expect the same level of rigor as in a mathematical proof.  Often 
all we can
say is something like, "I tried a bunch of things, and this one worked best".  
Both have value.


Of course. This is perfectly fine. - I have criticised something else: 
the hiding of ambiguity of things portrayed as maths when statements of 
the kind "this is a heuristic / engineering / first guess" are easily 
possible. Research papers should be honest. (They may hide secret 
details, but this is another topic.)


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 01.02.2016 23:01, Brian Cloutier wrote:> I had to search a lot of 
papers on MCTS which

> mentioned "terminal states" before finding one which defined them.
> [...] they defined it as a position where there are no more legal
> moves.

On 01.02.2016 23:15, Brian Sheppard wrote:

You play until neither player wishes to make a move. The players

> are willing to move on any point that is not self-atari, and they

are willing to make self-atari plays if capture would result in a
Nakade (http://senseis.xmp.net/?Nakade)


Defining "terminal state" as no more legal moves is probably 
inappropriate. The phrase "willing to move" is undefined, unless they 
exactly define it as "to make self-atari plays iff capture would result 
in a Nakade". This requires a proof that this is the only exception. 
Where is that proof? It also requires a definition of nakade. Where is 
that definition?


In my book Capturing Races 1, I have outlined a definition of 
"[semeai-]eye" and, in Life and Death Problems 1, of "nakade". Such are 
more complicated by far than naive descriptions online suggest. In 
particular, such outlined definitions depend on the still undefined 
"essential [string]", "seki" [sic, undefined as a strategic object 
because the Japanese 2003 Rules' definition does not distinguish good 
from bad strategy!] and "lake" [connected part of the potential 
eyespace..., which in turn is still undefined as a strategic object]. 
They also depend on "ko", but at least this I have defined: 
http://home.snafu.de/jasiek/ko.pdf Needless to say, determining the 
objects that are essential, seki, lake, ko is a hard task in itself.


So where is the mathematically strict "definition" of nakade? Has 
anybody proceeded beyond my definition attempts? I suspect the standard 
problem of research again: definition by reference to a different paper 
with an ambiguous description. If ambiguous terms are presumed for 
pragmatic reasons, this must be stated! My mentioned terms are ambiguous 
but less so than every other attempt - or where are the better attempts?


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Ingo Althöfer]

Hi George,

welcome, and thanks for your valuable hint on the Google-whitepaper.

Do/did you have/see any cross-relations between your research and
computer Go?
 
Cheers, Ingo.
 

Gesendet: Dienstag, 02. Februar 2016 um 05:14 Uhr
Von: "George Dahl" 
An: computer-go 
Betreff: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks 
and Tree Search

If anything, the other great DCNN applications predate the application of these 
methods to Go. Deep neural nets (convnets and other types) have been 
successfully applied in computer vision, robotics, speech recognition, machine 
translation, natural language processing, and hosts of other areas. The first 
paragraph of the TensorFlow whitepaper 
(http://download.tensorflow.org/paper/whitepaper2015.pdf) even mentions dozens 
at Alphabet specifically.
 
Of course the future will hold even more exciting applications, but these 
techniques have been proven in many important problems long before they had 
success in Go and they are used by many different companies and research 
groups. Many example applications from the literature or at various companies 
used models trained on a single machine with GPUs.
 
On Mon, Feb 1, 2016 at 12:00 PM, Hideki Kato 
 wrote:Ingo Althofer: 
:
>Hi Hideki,
>
>first of all congrats to the nice performance of Zen over the weekend!
>
>> Ingo and all,
>> Why you care AlphaGo and DCNN so much?
>
>I can speak only for myself. DCNNs may be not only applied to
>achieve better playing strength. One may use them to create
>playing styles, or bots for go variants.
>
>One of my favorites is robot frisbee go.
>http://www.althofer.de/robot-play/frisbee-robot-go.jpg[http://www.althofer.de/robot-play/frisbee-robot-go.jpg]
>Perhaps one can teach robots with DCNN to throw the disks better.
>
>And my expectation is: During 2016 we will see many more fantastic
>applications of DCNN, not only in Go. (Olivier had made a similar
>remark already.)

Agree but one criticism.  If such great DCNN applications all
need huge machine power like AlphaGo (upon execution, not
training), then the technology is hard to apply to many areas,
autos and robots, for examples.  Are DCNN chips the only way to
reduce computational cost?  I don't forecast other possibilities.
Much more economical methods should be developed anyway.
#Our brain consumes less than 100 watt.

Hideki

>Ingo.
>
>PS. Dietmar Wolz, my partner in space trajectory design, just told me
>that in his company they started woth deep learning...
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Hideki Kato 

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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Jim O'Flaherty]

And to meta this awesome short story...

AI Software Engineers: Robert, please stop asking our AI for explanations.
We don't want to distract it with limited human understanding. And we don't
want the Herculean task of coding up that extremely frail and error prone
bridge.
On Feb 1, 2016 3:03 PM, "Rainer Rosenthal"  wrote:

> ~~
> Robert: "Hey, AI, you should provide explanations!"
> AI: "Why?"
> ~~
>
> Cheers,
> Rainer
>
>> Date: Mon, 1 Feb 2016 08:15:12 -0600
>> From: "Jim O'Flaherty" 
>> To: computer-go@computer-go.org
>> Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural
>> Networks and Tree Search
>> Message-ID:
>> <
>> cakx5gkjc7j0uq_pmxyumyfre7r+7ydltigbna5oo7kvnzq7...@mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Robert,
>>
>> I'm not seeing the ROI in attempting to map human idiosyncratic linguistic
>> systems to/into a Go engine.
>>
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Olivier Teytaud]

> Without clarity, progress is delayed. Every professor at university will
> confirm this to you.
>

IMHO, Petr contributed enough to academic research
for not needing a discussion with a professor at university
for learning how to do/clarify research :-)






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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 02.02.2016 11:49, Petr Baudis wrote:

you seem to come off as perhaps a little too
aggressive in your recent few emails...


If I were not aggressively critical about inappropriate ambiguity, it 
would continue for further decades. Papers containing mathematical 
contents must clarify when something whose use or annotation looks 
mathematical is not a definition / well-defined term but intentionally 
ambiguous. This clarity is a fundamental of mathematical, informatical 
or scientific research. Without clarity, progress is delayed. Every 
professor at university will confirm this to you.



   The question was about the practical implementation of an MC
simulation, which does *not* require formal definitions of all concepts
used in the description, or any proofs.  It's just a heuristic, and it
can be arbitrarily complicated, making a tradeoff between speed and
accuracy.


Fine, provided it is clearly stated that it is an ambiguous heuristic 
and not an [unambiguous] definition / term. References / links (possibly 
iterative) hiding ambiguity without declaring it are inappropriate.


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Hideki Kato]

Ingo Althofer: 
: 
>Hi Hideki,
>
>first of all congrats to the nice performance of Zen over the weekend!
>
>> Ingo and all,
>> Why you care AlphaGo and DCNN so much?  
>
>I can speak only for myself. DCNNs may be not only applied to
>achieve better playing strength. One may use them to create
>playing styles, or bots for go variants.
>
>One of my favorites is robot frisbee go. 
>http://www.althofer.de/robot-play/frisbee-robot-go.jpg
>Perhaps one can teach robots with DCNN to throw the disks better.
>
>And my expectation is: During 2016 we will see many more fantastic
>applications of DCNN, not only in Go. (Olivier had made a similar
>remark already.)

Agree but one criticism.  If such great DCNN applications all 
need huge machine power like AlphaGo (upon execution, not 
training), then the technology is hard to apply to many areas, 
autos and robots, for examples.  Are DCNN chips the only way to 
reduce computational cost?  I don't forecast other possibilities.  
Much more economical methods should be developed anyway.
#Our brain consumes less than 100 watt.

Hideki

>Ingo.
>
>PS. Dietmar Wolz, my partner in space trajectory design, just told me
>that in his company they started woth deep learning...
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Rainer Rosenthal]

~~
Robert: "Hey, AI, you should provide explanations!"
AI: "Why?"
~~

Cheers,
Rainer

Date: Mon, 1 Feb 2016 08:15:12 -0600
From: "Jim O'Flaherty" 
To: computer-go@computer-go.org
Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural
Networks and Tree Search
Message-ID:

Content-Type: text/plain; charset="utf-8"

Robert,

I'm not seeing the ROI in attempting to map human idiosyncratic linguistic
systems to/into a Go engine.


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Ingo Althöfer]

Hi Hideki,

you put it wonderfully into two lines:

**
**
******
***  Much more economical methods should be developed anyway.  ***
*** #Our brain consumes less than 100 watt.***
******
**
**


Hopefully the box remains formatted nicely ;-)

Ingo.
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Álvaro Begué]

Aja,

I read the paper with great interest. [Insert appropriate praises here.]

I am trying to understand the part where you use reinforcement learning to
improve upon the CNN trained by imitating humans. One thing that is not
explained is how to determine that a game is over, particularly when a
player is simply a CNN that has a probability distribution as its output.
Do you play until every point is either a suicide or looks like an eye? Do
you do anything to make sure you don't play in a seki?

I am sure you are a busy man these days, so please answer only when you
have time.

Thanks!
Álvaro.



On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang  wrote:

> Hi all,
>
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
> today. The paper and all the games can be found here:
>
> http://www.deepmind.com/alpha-go.html
>
> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
> March, to see whether we finally have a Go program that is stronger than
> any human!
>
> Aja
>
> PS I am very busy preparing AlphaGo for the match, so apologies in advance
> if I cannot respond to all questions about AlphaGo.
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Brian Sheppard]

You play until neither player wishes to make a move. The players are willing to 
move on any point that is not self-atari, and they are willing to make 
self-atari plays if capture would result in a Nakade 
(http://senseis.xmp.net/?Nakade)

 

This correctly plays seki. 

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Brian Cloutier
Sent: Monday, February 1, 2016 5:02 PM
To: computer-go 
Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks 
and Tree Search

 

> One thing that is not explained is how to determine that a game is over

You'll find that very little of the literature explicitly covers this. When I 
asked this question I had to search a lot of papers on MCTS which mentioned 
"terminal states" before finding one which defined them.

Let me see if I can find the actual paper, but they defined it as a position 
where there are no more legal moves. You're right though, that ignores sekis, 
which makes me think I'm remembering wrong.

On Mon, Feb 1, 2016, 13:45 Álvaro Begué  > wrote:

 

Aja,

 

I read the paper with great interest. [Insert appropriate praises here.]

 

I am trying to understand the part where you use reinforcement learning to 
improve upon the CNN trained by imitating humans. One thing that is not 
explained is how to determine that a game is over, particularly when a player 
is simply a CNN that has a probability distribution as its output. Do you play 
until every point is either a suicide or looks like an eye? Do you do anything 
to make sure you don't play in a seki?

 

I am sure you are a busy man these days, so please answer only when you have 
time.

 

Thanks!

Álvaro.

 

 

 

On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang  > wrote:

Hi all,

 

We are very excited to announce that our Go program, AlphaGo, has beaten a 
professional player for the first time. AlphaGo beat the European champion Fan 
Hui by 5 games to 0. We hope you enjoy our paper, published in Nature today. 
The paper and all the games can be found here: 

 

http://www.deepmind.com/alpha-go.html

 

AlphaGo will be competing in a match against Lee Sedol in Seoul, this March, to 
see whether we finally have a Go program that is stronger than any human! 

 

Aja

 

PS I am very busy preparing AlphaGo for the match, so apologies in advance if I 
cannot respond to all questions about AlphaGo.


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Darren Cook]

> someone cracked Go right before that started. Then I'd have plenty of
> time to pick a new research topic." It looks like AlphaGo has 
> provided.

It seems [1] the smart money might be on Lee Sedol:

1. Ke Jie (world champ) – limited strength…but still amazing… Less than
5% chance against Lee Sedol now. But as it can go stronger, who knows
its future…
2. Mi Yuting (world champ) – appears to be a ‘chong-duan-shao-nian (kids
on the path to pros)’, ~high-level amateur.
3, Li Jie (former national team player) – appears to be pro-level. one
of the games is almost perfect (for AlphaGo)


On the other hand, AlphaGo got its jump in level very quickly (*), so it
is hard to know if they just got lucky (i.e. with ideas things working
first time) or if there is still some significant tweaking possible in
these 5 months of extra development (October 2015 to March 2016).

Have the informal game SGFs been uploaded anywhere? I noticed (Extended
Data Table 1) they were played *after* the official game each day, so
the poor pro should have been tired, but instead he won 2 of the 5 (day
1 and day 5). Was this just due to the short time limits, or did Fan Hui
play a different style (e.g. more aggressively)?


Darren



[1]: Comment by xli199 at
http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/

[2]: When did DeepMind start working on go? I suspect it might only
after have been after the video games project started to wound down,
which would've Feb 2015? If so, that is only 6-8 months (albeit with a
fairly large team).
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Michael Markefka]

On Mon, Feb 1, 2016 at 10:19 AM, Darren Cook  wrote:
> It seems [1] the smart money might be on Lee Sedol:

In the DeepMind press conferences (
https://www.youtube.com/watch?v=yR017hmUSC4 -
https://www.youtube.com/watch?v=_r3yF4lV0wk ) Demis Hassabis stated,
that he was quietly confident.

I assume that means they've got a version up and running that at least
matches Lee Sedol's Elo rating, perhaps even slightly exceeding it.
They might be wary of the engine displaying some idiosyncracy they
haven't picked up on yet, which Sedol might notice and then exploit.
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Thomas Wolf]

The next type of event could be a new 'Pair Go'
Where a human and a program make up a pair, like Mark Zuckerberg and his 
facebook
program against a Google VP and alphaGo. :-)

Thomas

On Mon, 1 Feb 2016, John Tromp wrote:


For those of you who missed it, chess grandmaster Hikaru Nakamura,
rated 2787, recently played a match against the world's top chess program
Komodo, rated 3368. Each of the 4 games used a different kind of handicap:

Pawn and Move Odds
Pawn Odds
Exchange Odds
4-Move Odds

As you can see, handicaps in chess are no easy matter:-(
When AlphaGo surpasses the top human professionals we may see such
handicap challenges in the future. One may wonder if we'll ever see a
computer giving 4 handicap to a professional...

So how did Nakamura fare? See for yourself at

https://www.chess.com/news/komodo-beats-nakamura-in-final-battle-1331

regards,
-John
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Jim O'Flaherty]

Robert,

I'm not seeing the ROI in attempting to map human idiosyncratic linguistic
systems to/into a Go engine. Which language would be the one to use;
English, Chinese, Japanese, etc? As abstraction goes deeper, the nuance of
each human language diverges from the others (due to the way the human
brain is just a fractal based analogy making engine). The scare resource is
human mind power producing advances on the main goal making a superior AI
to what already exists. As the linguistic pathway hasn't emerged in Chess
in the last decade, then I find it considerably less likely it will end up
emerging for Go...unless you are, of course, suggesting that is something
you are taking up. :)

The AI world is changing to make explaining computation cognition to humans
less necessary, or even desirable. Why bound the solution space to only
what cognitively linguistically limited humans can imagine and/or consider?
And given even one AI team is thinking this way, the nature of competition
will drive other competing teams to similar motivation(s). Welcome to
"memetic evolution in action". Kind of makes those of us in the nearby
human cognitive domains just a wee bit more nervous about what is rapidly
approaching as human cognition automateable. For example, books about
josekis could be rendered far less valuable if/when AlphaGo and some other
AI competitor more strongly influenced by josekis pushes AlphaGo into new
spaces which involve much longer resolution horizons than humans used for
those that exist now.

No matter what, the future sure does sound very exciting now that Alpha Go
has broken the Go AI ceiling. I cannot WAIT to see the results of the event
against Lee Sedol.

Congratulations, Alpha Go team and Aja!


Jim


On Mon, Feb 1, 2016 at 12:50 AM, Robert Jasiek  wrote:

> On 01.02.2016 07:30, Petri Pitkanen wrote:
>
>> Explaining why the move is good in human terms is useless goal. Good chess
>> programs cannot do it nor it is meaningful. As the humans and computers
>> have vastly different approach to selecting a move then  by the definition
>> have reasons for moves. As an example your second item 'long-term aji',
>> For
>> human an important short cut but computer a mere result for seeing far
>> enough in the future or combining several features of postion into
>> non-linear/linear computation.
>>
>
> Such is not "useless" but requires additional research or implementation.
>
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Petr Baudis]

  Hi!

On Mon, Feb 01, 2016 at 01:38:28PM +, Aja Huang wrote:
> On Mon, Feb 1, 2016 at 11:38 AM, Petr Baudis  wrote:
> >
> > That's right, but unless I've overlooked something, I didn't see Fan Hui
> > create any complicated fight, there wasn't any semeai or complex
> > life (besides the by-the-book oonadare).  This, coupled with the
> > fact that there is no new mechanism to deal with these (unless the value
> > network has truly astonishing generalization capacity, but it just
> > remembering common tsumego and joseki shapes is imho a simpler
> > explanation), leads me to believe that it remains a weakness.
> >
> 
> If you check Myungwan Kim 9p's comments in the video, in the 4th game there
> was a semeai that AlphaGo read out at top side. See the game at
> 
> http://britgo.org/deepmind2016/summary

  (It's at ~1:33:00+ https://www.youtube.com/watch?v=NHRHUHW6HQE)

  Well, there was a potential semeai, but did AlphaGo read it out?
I don't know, you probably do. :-)

> Unfortunately before the Lee match I'm not allowed to answer some of the
> interesting questions raised in this thread, or mention how strong is
> AlphaGo now. But for now what I can say is that in the nature paper (about
> 5 months ago) AlphaGo reached nearly 100% win rate against the latest
> commercial versions of Crazy Stone and Zen, and AlphaGo still did well even
> on 4 handicap stones, suggesting AlphaGo may do much better in tactical
> situations than Crazy Stone and Zen.

  But CrazyStone and Zen are also pretty bad at semeai and tsumego, it's
a bit of a self-play problem; when playing against MCTS programs, some
mistakes aren't revealed.

  (I guess that you probably played tens of games against AlphaGo
yourself, so you'll have a pretty good idea about its capabilities.
I just can't imagine how will the value network count and pick liberties
or tsumego sequence combinations; it might just have more memory
capacity than we'd imagine.)

> I understand you bet on Lee but I hope you will enjoy watching the match. :)

  I certainly will!  And in my heart, maybe I root for AlphaGo too :)

-- 
Petr Baudis
If you have good ideas, good data and fast computers,
you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Petr Baudis]

On Mon, Feb 01, 2016 at 12:24:21PM +0100, Olivier Teytaud wrote:
> If AlphaGo had lost at least one game, I'd understand how people can have
> an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
> have an upper bound on his level. After all, AlphaGo might just have played
> well enough for crushing Fan Hui, and a weak move while the position is
> still in favor of AlphaGo is not really a weak move (at least in a
> game-theoretic point of view...).

That's right, but unless I've overlooked something, I didn't see Fan Hui
create any complicated fight, there wasn't any semeai or complex
life (besides the by-the-book oonadare).  This, coupled with the
fact that there is no new mechanism to deal with these (unless the value
network has truly astonishing generalization capacity, but it just
remembering common tsumego and joseki shapes is imho a simpler
explanation), leads me to believe that it remains a weakness.

Of course there are other possibilities, like AlphaGo always steering
the game in a calmer direction due to some emergent property.  But
sometimes, you just have to go for the fight, don't you?

Petr Baudis
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Hideki Kato]

Olivier Teytaud: 

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Petr Baudis]

  Hi!

On Mon, Feb 01, 2016 at 09:19:56AM +, Darren Cook wrote:
> > someone cracked Go right before that started. Then I'd have plenty of
> > time to pick a new research topic." It looks like AlphaGo has 
> > provided.
> 
> It seems [1] the smart money might be on Lee Sedol:
> 
> 1. Ke Jie (world champ) – limited strength…but still amazing… Less than
> 5% chance against Lee Sedol now. But as it can go stronger, who knows
> its future…
> 2. Mi Yuting (world champ) – appears to be a ‘chong-duan-shao-nian (kids
> on the path to pros)’, ~high-level amateur.
> 3, Li Jie (former national team player) – appears to be pro-level. one
> of the games is almost perfect (for AlphaGo)
> 
> 
> On the other hand, AlphaGo got its jump in level very quickly (*), so it
> is hard to know if they just got lucky (i.e. with ideas things working
> first time) or if there is still some significant tweaking possible in
> these 5 months of extra development (October 2015 to March 2016).

  AlphaGo's achievement is impressive, but I'll bet on Lee Sedol
any time if he gets some people to explain the weaknesses of computers
and does some serious research.

  AlphaGo didn't seem to solve the fundamental reading problems of
MCTS, just compensated with great intuition that can also remember
things like corner life shapes.  But if Lee Sedol gets the game to
a confusing fight with a long semeai or multiple unusual life
shapes, I'd say based on what I know on AlphaGo that it'll collapse just
as current programs would.  And, well, Lee Sedol is rather famous for
his fighting style.  :)

  Unless of course AlphaGo did achieve yet another fundamental
breakthrough since October, but I suspect it'll be a long process yet.
For the same reason, I think strong players that'd play against AlphaGo
would "learn to beat it" just as you see with weaker players+bots on
KGS.

  I wonder how AlphaGo would react to an unexpected deviation from a
joseki that involves a corner semeai.

> [1]: Comment by xli199 at
> http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/
> 
> [2]: When did DeepMind start working on go? I suspect it might only
> after have been after the video games project started to wound down,
> which would've Feb 2015? If so, that is only 6-8 months (albeit with a
> fairly large team).

  Remember the two first authors of the paper:

  * David Silver - his most cited paper is "Combining online and offline
knowledge in UCT", the 2007 paper that introduced RAVE

  * Aja Huang - the author of Erica, among many other things

  So this isn't a blue sky research at all, and I think they had Go in
crosshairs for most of the company's existence.  I don't know the
details of how DeepMind operates, but I'd imagine the company works
on multiple things at once. :-)

-- 
Petr Baudis
If you have good ideas, good data and fast computers,
you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Olivier Teytaud]

If AlphaGo had lost at least one game, I'd understand how people can have
an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
have an upper bound on his level. After all, AlphaGo might just have played
well enough for crushing Fan Hui, and a weak move while the position is
still in favor of AlphaGo is not really a weak move (at least in a
game-theoretic point of view...).



On Mon, Feb 1, 2016 at 12:12 PM, Petr Baudis  wrote:

>   Hi!
>
> On Mon, Feb 01, 2016 at 09:19:56AM +, Darren Cook wrote:
> > > someone cracked Go right before that started. Then I'd have plenty of
> > > time to pick a new research topic." It looks like AlphaGo has
> > > provided.
> >
> > It seems [1] the smart money might be on Lee Sedol:
> >
> > 1. Ke Jie (world champ) – limited strength…but still amazing… Less than
> > 5% chance against Lee Sedol now. But as it can go stronger, who knows
> > its future…
> > 2. Mi Yuting (world champ) – appears to be a ‘chong-duan-shao-nian (kids
> > on the path to pros)’, ~high-level amateur.
> > 3, Li Jie (former national team player) – appears to be pro-level. one
> > of the games is almost perfect (for AlphaGo)
> >
> >
> > On the other hand, AlphaGo got its jump in level very quickly (*), so it
> > is hard to know if they just got lucky (i.e. with ideas things working
> > first time) or if there is still some significant tweaking possible in
> > these 5 months of extra development (October 2015 to March 2016).
>
>   AlphaGo's achievement is impressive, but I'll bet on Lee Sedol
> any time if he gets some people to explain the weaknesses of computers
> and does some serious research.
>
>   AlphaGo didn't seem to solve the fundamental reading problems of
> MCTS, just compensated with great intuition that can also remember
> things like corner life shapes.  But if Lee Sedol gets the game to
> a confusing fight with a long semeai or multiple unusual life
> shapes, I'd say based on what I know on AlphaGo that it'll collapse just
> as current programs would.  And, well, Lee Sedol is rather famous for
> his fighting style.  :)
>
>   Unless of course AlphaGo did achieve yet another fundamental
> breakthrough since October, but I suspect it'll be a long process yet.
> For the same reason, I think strong players that'd play against AlphaGo
> would "learn to beat it" just as you see with weaker players+bots on
> KGS.
>
>   I wonder how AlphaGo would react to an unexpected deviation from a
> joseki that involves a corner semeai.
>
> > [1]: Comment by xli199 at
> >
> http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/
> >
> > [2]: When did DeepMind start working on go? I suspect it might only
> > after have been after the video games project started to wound down,
> > which would've Feb 2015? If so, that is only 6-8 months (albeit with a
> > fairly large team).
>
>   Remember the two first authors of the paper:
>
>   * David Silver - his most cited paper is "Combining online and offline
> knowledge in UCT", the 2007 paper that introduced RAVE
>
>   * Aja Huang - the author of Erica, among many other things
>
>   So this isn't a blue sky research at all, and I think they had Go in
> crosshairs for most of the company's existence.  I don't know the
> details of how DeepMind operates, but I'd imagine the company works
> on multiple things at once. :-)
>
> --
> Petr Baudis
> If you have good ideas, good data and fast computers,
> you can do almost anything. -- Geoffrey Hinton
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-- 
=
Olivier Teytaud, olivier.teyt...@inria.fr, TAO, LRI, UMR 8623(CNRS - Univ.
Paris-Sud),
bat 490 Univ. Paris-Sud F-91405 Orsay Cedex France
http://www.slideshare.net/teytaud
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Olivier Teytaud]

Ok, it's not blitz according to http://senseis.xmp.net/?BlitzGames
(limit at 10s/move for Blitz). But really shorter time settings.

I've seen (as you all) many posts guessing that AlphaGo will lose, but I
find
that hard to know. If Fan Hui had won one game, I would say that AlphaGo is
not ready for Lee Sedol, but with 5-0...

(incidentally, there is one great piece of news for machine learning
people: people in industry are much more interested than before for letting
us try our deep learning algorithms on their data and that's good for the
world :-) )
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[John Tromp]

For those of you who missed it, chess grandmaster Hikaru Nakamura,
rated 2787, recently played a match against the world's top chess program
Komodo, rated 3368. Each of the 4 games used a different kind of handicap:

Pawn and Move Odds
Pawn Odds
Exchange Odds
4-Move Odds

As you can see, handicaps in chess are no easy matter:-(
When AlphaGo surpasses the top human professionals we may see such
handicap challenges in the future. One may wonder if we'll ever see a
computer giving 4 handicap to a professional...

So how did Nakamura fare? See for yourself at

https://www.chess.com/news/komodo-beats-nakamura-in-final-battle-1331

regards,
-John
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[George Dahl]

If anything, the other great DCNN applications predate the application of
these methods to Go. Deep neural nets (convnets and other types) have been
successfully applied in computer vision, robotics, speech recognition,
machine translation, natural language processing, and hosts of other areas.
The first paragraph of the TensorFlow whitepaper (
http://download.tensorflow.org/paper/whitepaper2015.pdf) even mentions
dozens at Alphabet specifically.

Of course the future will hold even more exciting applications, but these
techniques have been proven in many important problems long before they had
success in Go and they are used by many different companies and research
groups. Many example applications from the literature or at various
companies used models trained on a single machine with GPUs.

On Mon, Feb 1, 2016 at 12:00 PM, Hideki Kato  wrote:

> Ingo Althofer:
>  >:
> >Hi Hideki,
> >
> >first of all congrats to the nice performance of Zen over the weekend!
> >
> >> Ingo and all,
> >> Why you care AlphaGo and DCNN so much?
> >
> >I can speak only for myself. DCNNs may be not only applied to
> >achieve better playing strength. One may use them to create
> >playing styles, or bots for go variants.
> >
> >One of my favorites is robot frisbee go.
> >http://www.althofer.de/robot-play/frisbee-robot-go.jpg
> >Perhaps one can teach robots with DCNN to throw the disks better.
> >
> >And my expectation is: During 2016 we will see many more fantastic
> >applications of DCNN, not only in Go. (Olivier had made a similar
> >remark already.)
>
> Agree but one criticism.  If such great DCNN applications all
> need huge machine power like AlphaGo (upon execution, not
> training), then the technology is hard to apply to many areas,
> autos and robots, for examples.  Are DCNN chips the only way to
> reduce computational cost?  I don't forecast other possibilities.
> Much more economical methods should be developed anyway.
> #Our brain consumes less than 100 watt.
>
> Hideki
>
> >Ingo.
> >
> >PS. Dietmar Wolz, my partner in space trajectory design, just told me
> >that in his company they started woth deep learning...
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 01.02.2016 15:15, Jim O'Flaherty wrote:

I'm not seeing the ROI in attempting to map human idiosyncratic linguistic
systems to/into a Go engine. Which language would be the one to use;
English, Chinese, Japanese, etc? As abstraction goes deeper, the nuance of
each human language diverges from the others (due to the way the human
brain is just a fractal based analogy making engine). [...]

> unless you are, of course, suggesting that is something

you are taking up. :)


The human language for interaction with / translation to programming 
language includes


- well-defined terms / concepts
- rules / principles with stated presuppositions
- methods / procedures / informal algorithms
- proofs / strong evidence for the aforementioned being correct / 
successful (always or to some extent)


Of course, I am an example of a person having been doing this for many 
years. In fact, I might be the leading generalist for go theory expert 
knowledge stated in writing.



The AI world is changing to make explaining computation cognition to humans
less necessary, or even desirable.


I disagree strongly.

Almost all the AI world has done is creating strong programs. Explaining 
human thinking and explaining program thinking in terms of human 
thinking is as important as it has always been.



Why bound the solution space to only
what cognitively linguistically limited humans can imagine and/or consider?


Indeed. I prefer to exceed limitations by creating new terms, 
definitions for undefined terms, principles, methods etc. Human beings 
can better learn if they know what to learn because the contents is 
described clearly.



about what is rapidly
approaching as human cognition automateable.


Eh? Besides GoTools, there has been very little, AFAIK.

--
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Ingo Althöfer]

Hi Hideki,

first of all congrats to the nice performance of Zen over the weekend!

> Ingo and all,
> Why you care AlphaGo and DCNN so much?  

I can speak only for myself. DCNNs may be not only applied to
achieve better playing strength. One may use them to create
playing styles, or bots for go variants.

One of my favorites is robot frisbee go. 
http://www.althofer.de/robot-play/frisbee-robot-go.jpg
Perhaps one can teach robots with DCNN to throw the disks better.

And my expectation is: During 2016 we will see many more fantastic
applications of DCNN, not only in Go. (Olivier had made a similar
remark already.)

Ingo.

PS. Dietmar Wolz, my partner in space trajectory design, just told me
that in his company they started woth deep learning...
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 01.02.2016 14:38, Aja Huang wrote:

AlphaGo may do much better in tactical
situations than Crazy Stone and Zen.


Judging very quickly from the Fan Hui games, AlphaGo's group-local 
"reading" is very deep and accurate but I'd need to read for myself 
equally deeply and carefully before I would want to confirm Myongwan 
Kim's related opinion.


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Peter Drake]

Let me add my congratulations to the chorus. Well done!

I'm due for a sabbatical next year. I had been joking, "It sure would be
good timing if someone cracked Go right before that started. Then I'd have
plenty of time to pick a new research topic." It looks like AlphaGo has
provided.

On Wed, Jan 27, 2016 at 10:46 AM, Aja Huang  wrote:

> Hi all,
>
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
> today. The paper and all the games can be found here:
>
> http://www.deepmind.com/alpha-go.html
>
> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
> March, to see whether we finally have a Go program that is stronger than
> any human!
>
> Aja
>
> PS I am very busy preparing AlphaGo for the match, so apologies in advance
> if I cannot respond to all questions about AlphaGo.
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Hideki Kato]

Ingo and all,

Why you care AlphaGo and DCNN so much?  Surely DeepMind team did 
a big leap but the big problems, such as detecting double-ko and 
solving complex positions are left unchanged.  Also it's well 
known that to attack these weakpoint of MCTS bots, the 
opponents have to be strong enough.  On 9x9, this was shown in 
fall 2012.  Now this can be applied 19x19 as well.

Hideki

Ingo Althofer: 
: 
>Hi Peter,

> 

> 

>> I'm due for a sabbatical next year. I had been joking, "It sure would be 
>> good 

>> timing if someone cracked Go right before that started. Then I'd have plenty 

>> of time to pick a new research topic." It looks like AlphaGo has provided.

> 

>you are not the only one in such a situation or a similar one...

>

>Probably you know, that the ICGA has its next Computer Olympiad end of

>June 2916 in Leiden (NL), together with a 3-day conference "Computers and 
>Games".

>It is an exciting question how the whole event will be. In which moods

>the programmers are, to which other games CNNs will be applied, which

>new directions of research will be started, which bots will start in the

>computer go competition ...

>

>I think the questions may become more urgent when certain 

>things will happen in March.

>

>Cheers, Ingo.

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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Petri Pitkanen]

Explaining why the move is good in human terms is useless goal. Good chess
programs cannot do it nor it is meaningful. As the humans and computers
have vastly different approach to selecting a move then  by the definition
have reasons for moves. As an example your second item 'long-term aji', For
human an important short cut but computer a mere result for seeing far
enough in the future or combining several features of postion into
non-linear/linear computation.

Petri

2016-02-01 2:36 GMT+02:00 Robert Jasiek :

> On 31.01.2016 20:28, Peter Drake wrote:
>
>> pick a new research topic.
>>
>
> - explain by the program to human players why MC / DNN play is good in
> terms of human understanding of the game
> - incorporate the difficult parts, such as long-term aji
> - solve the game: prove the correct score, prove a weak solution, prove a
> strong solution [These mathematics keep us busy for at least 400 years
> unless bot research occurs earlier.]
> - create computers that act as mathematicians incl. creativity, invention
> of propositions and their proving [so that the bot researchers can solve
> the game faster]
> - teach the computer expert knowledge so that a) MC / DNN bots become even
> stronger and b) programs can teach with explanation and reasoning
> understood by human pupils
> - apply computer go research to other fields while ensuring that the
> humans cannot be the victims of bugs and ambiguous responsibilty towards
> law and ethics [medicin or cars: who goes to jail if AI kills people, how
> to prevent AI from ruling the world]
> - Play "Conway / Jasiek": modify the rules, invent new games, apply
> computers.
>
> Enough for research for centuries if not millenia, I'd say.
>
> "Game over / intelligence solved" - never heard greater nonsense before.
>
> --
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

On 31.01.2016 20:28, Peter Drake wrote:

pick a new research topic.


- explain by the program to human players why MC / DNN play is good in 
terms of human understanding of the game

- incorporate the difficult parts, such as long-term aji
- solve the game: prove the correct score, prove a weak solution, prove 
a strong solution [These mathematics keep us busy for at least 400 years 
unless bot research occurs earlier.]
- create computers that act as mathematicians incl. creativity, 
invention of propositions and their proving [so that the bot researchers 
can solve the game faster]
- teach the computer expert knowledge so that a) MC / DNN bots become 
even stronger and b) programs can teach with explanation and reasoning 
understood by human pupils
- apply computer go research to other fields while ensuring that the 
humans cannot be the victims of bugs and ambiguous responsibilty towards 
law and ethics [medicin or cars: who goes to jail if AI kills people, 
how to prevent AI from ruling the world]
- Play "Conway / Jasiek": modify the rules, invent new games, apply 
computers.


Enough for research for centuries if not millenia, I'd say.

"Game over / intelligence solved" - never heard greater nonsense before.

--
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

Hi Ingo,

I think you are not alone: When I started computer go 4 years ago I
ask a good friend of mine, who did his PhD in Neural Networks back in
the 90s, if I have any chance to use them instead of pattern matching
and he said, they will probably not generalize in a good way :)

I think the big size of the nets make a qualitative difference,
therefore our intuition is misleading...


Congrats to the AlphaGo team,

Detlef

Am 29.01.2016 um 02:14 schrieb "Ingo Althöfer":
> Hi Simon,
> 
> do your remember my silly remarks in an email discussion almost a
> year ago?
> 
> You had written:
>>> So, yes, with all the exciting work in DCNN, it is very
>>> tempting to also do DCNN. But I am not sure if we should do
>>> so.
> 
> And my silly reply had been:
>> I think that DCNN is somehow in a dreamdancing appartment. My
>> opinion: We might mention it in our proposal, but not as a
>> central topic.
> 
> 
> In my mathematical life I have been wrong with my intuition only a
> few times. This DCNN topic was the worst case so far...
> 
> Greetings from the bottom, Ingo.
> 
> 
> 
> Gesendet: Donnerstag, 28. Januar 2016 um 16:41 Uhr Von: "Lucas,
> Simon M"  An: "computer-go@computer-go.org"
>  Betreff: Re: [Computer-go] Mastering
> the Game of Go with Deep Neural Networks and Tree Search
> 
> Indeed – Congratulations to Google DeepMind!
> 
> It’s truly an immense achievement.  I’m struggling to think of
> other examples of reasonably mature and strongly contested AI
> challenges where a new system has made such a huge improvement
> over existing systems – and I’m still struggling …
> 
> Simon Lucas ___ 
> Computer-go mailing list Computer-go@computer-go.org 
> http://computer-go.org/mailman/listinfo/computer-go
> 
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Michael Markefka]

On Thu, Jan 28, 2016 at 3:14 PM, Stefan Kaitschick
 wrote:

> That "value network" is just amazing to me.
> It does what computer go failed at for over 20 years, and what MCTS was
> designed to sidestep.

Thought it worth a mention: Detlef posted about trying to train a CNN
on win rate as well in February. So it seems he was onto something
there.
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Stefan Kaitschick]

I always thought the same. But I don't think they tackled the decomposition
problem directly.
Achieving good(non-terminal) board evaluations must have reduced the
problem.
If you don't do full playouts, you get much less thrashing between
independent problems.
It also implies a useful static L evaluation.
That "value network" is just amazing to me.
It does what computer go failed at for over 20 years, and what MCTS was
designed to sidestep.
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Michael Alford]

On 1/27/16 12:08 PM, Aja Huang wrote:


2016-01-27 18:46 GMT+00:00 Aja Huang >:

Hi all,

We are very excited to announce that our Go program, AlphaGo, has
beaten a professional player for the first time. AlphaGo beat the
European champion Fan Hui by 5 games to 0. We hope you enjoy our
paper, published in Nature today. The paper and all the games can be
found here:

http://www.deepmind.com/alpha-go.html


The paper is freely available to download at the bottom of the page.
https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf

Aja

AlphaGo will be competing in a match against Lee Sedol in Seoul,
this March, to see whether we finally have a Go program that is
stronger than any human!

Aja

PS I am very busy preparing AlphaGo for the match, so apologies in
advance if I cannot respond to all questions about AlphaGo.



Congratulations on your achievement. While scanning the web articles 
yesterday, I came across this one:


http://www.bloomberg.com/news/articles/2016-01-27/google-computers-defeat-human-players-at-2-500-year-old-board-game

It states that the winner of the March match gets $1mil. This is the 
only reference to any prize I have found. Is it correct?


Thank you,
Michael



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--

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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Ingo Althöfer]

Hi Simon,

do your remember my silly remarks in an email discussion almost a year ago?

You had written:
>> So, yes, with all the exciting work in DCNN, it is very tempting 
>> to also do DCNN. But I am not sure if we should do so.

And my silly reply had been:
> I think that DCNN is somehow in a dreamdancing appartment.
> My opinion: We might mention it in our proposal, but not as a central topic.
 

In my mathematical life I have been wrong with my intuition only a few times.
This DCNN topic was the worst case so far...

Greetings from the bottom,
Ingo.

 

Gesendet: Donnerstag, 28. Januar 2016 um 16:41 Uhr
Von: "Lucas, Simon M" 
An: "computer-go@computer-go.org" 
Betreff: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks 
and Tree Search

Indeed – Congratulations to Google DeepMind!
 
It’s truly an immense achievement.  I’m struggling
to think of other examples of reasonably mature
and strongly contested AI challenges where a new
system has made such a huge improvement over
existing systems – and I’m still struggling …
 
Simon Lucas
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[valkyria]

Congratulations!

What I find most impressive is the engineering effort, combining so many 
different parts, which even standalone would be a strong program.


I think the design philosophy of using 3 different sources of "go 
playing" strength is great in it self (and if you read the paper there 
are a lot of old school computer go programming expetise used as well). 
I think we oft get stuck trying to perfect one module when perhaps what 
we need is a new module that improves search effectively on a different  
scale. I have not time and resources to do neural networks learning, but 
for my new program I would like to experimentwith using patterns on many 
levels, and this is inspiring.


Magnus Persson

On 2016-01-27 19:46, Aja Huang wrote:

Hi all,

We are very excited to announce that our Go program, AlphaGo, has
beaten a professional player for the first time. AlphaGo beat the
European champion Fan Hui by 5 games to 0. We hope you enjoy our
paper, published in Nature today. The paper and all the games can be
found here:

http://www.deepmind.com/alpha-go.html [1]

AlphaGo will be competing in a match against Lee Sedol in Seoul, this
March, to see whether we finally have a Go program that is stronger
than any human!

Aja

PS I am very busy preparing AlphaGo for the match, so apologies in
advance if I cannot respond to all questions about AlphaGo.

Links:
--
[1] http://www.deepmind.com/alpha-go.html

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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Michael Markefka]

I think many amateurs would already benefit from a simple blunder
check and a short list of viable alternatives and short continuations
for every move.

If I could leave my PC running over night for a 30s/move analysis at
9d level and then walk through my game with that quality of analysis,
I'd be more than satisfied.


On Thu, Jan 28, 2016 at 7:42 AM, Robert Jasiek  wrote:
> Congratulations to the researchers!
>
> On 27.01.2016 21:10, Michael Markefka wrote:
>>
>> I really do hope that this also turns into a good analysis and
>> teaching tool for human player. That would be a fantastic benefit from
>> this advancement in computer Go.
>
>
> The programs successful as computer players mostly rely on computation power
> for learning and decision-making. This can be used for teaching tools that
> do not need to provide text explanations and other reasoning to the human
> pupils: computer game opponent, life and death playing opponent, empirical
> winning percentages of patterns etc.
>
> Currently such programs do not provide sophisticated explanations and
> reasoning about tactical decision-making, strategy and positional judgement
> fitting human players' / pupils' conceptual thinking.
>
> If always correct teaching is not the aim (but if a computer teacher may err
> as much as a human teacher errs), in principle it should be possible to
> combine the successful means of using computation power with the reasonably
> accurate human descriptions of sophisticated explanations and reasoning.
> This requires implementation of expert system knowledge adapted from the
> best (the least ambiguous, the most often correct / applicable) descriptions
> of human-understandable go theory and further research in the latter.
>
> --
> robert jasiek
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Petri Pitkanen]

I think such analysis might  not bee too usefull. At least chess players
think it is not very usefull. Usually for learning you need "wake-up" your
brains so computer analysis without reasons probabaly on marginally useful.
But very entertaining

2016-01-28 13:27 GMT+02:00 Michael Markefka :

> I think many amateurs would already benefit from a simple blunder
> check and a short list of viable alternatives and short continuations
> for every move.
>
> If I could leave my PC running over night for a 30s/move analysis at
> 9d level and then walk through my game with that quality of analysis,
> I'd be more than satisfied.
>
>
> On Thu, Jan 28, 2016 at 7:42 AM, Robert Jasiek  wrote:
> > Congratulations to the researchers!
> >
> > On 27.01.2016 21:10, Michael Markefka wrote:
> >>
> >> I really do hope that this also turns into a good analysis and
> >> teaching tool for human player. That would be a fantastic benefit from
> >> this advancement in computer Go.
> >
> >
> > The programs successful as computer players mostly rely on computation
> power
> > for learning and decision-making. This can be used for teaching tools
> that
> > do not need to provide text explanations and other reasoning to the human
> > pupils: computer game opponent, life and death playing opponent,
> empirical
> > winning percentages of patterns etc.
> >
> > Currently such programs do not provide sophisticated explanations and
> > reasoning about tactical decision-making, strategy and positional
> judgement
> > fitting human players' / pupils' conceptual thinking.
> >
> > If always correct teaching is not the aim (but if a computer teacher may
> err
> > as much as a human teacher errs), in principle it should be possible to
> > combine the successful means of using computation power with the
> reasonably
> > accurate human descriptions of sophisticated explanations and reasoning.
> > This requires implementation of expert system knowledge adapted from the
> > best (the least ambiguous, the most often correct / applicable)
> descriptions
> > of human-understandable go theory and further research in the latter.
> >
> > --
> > robert jasiek
> >
> > ___
> > Computer-go mailing list
> > Computer-go@computer-go.org
> > http://computer-go.org/mailman/listinfo/computer-go
> ___
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>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Lucas, Simon M]

Indeed – Congratulations to Google DeepMind!

It’s truly an immense achievement.  I’m struggling
to think of other examples of reasonably mature
and strongly contested AI challenges where a new
system has made such a huge improvement over
existing systems – and I’m still struggling …

Simon Lucas



From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Olivier Teytaud
Sent: 27 January 2016 20:27
To: computer-go 
Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks 
and Tree Search

Congratulations people at DeepMind :-)

I like the fact that alphaGo uses many forms of learning (as humans do!):
- imitation learning (on expert games, learning an actor policy);
- learning by playing (self play, policy gradient), incidentally generating 
games;
- use of those games for teaching a second deep network (supervised learning);
- real time learning with Monte Carlo simulations (including Rave ?).
==> just beautiful :-)




2016-01-27 21:18 GMT+01:00 Yamato 
>:
Congratulations Aja.

Do you have a plan to run AlphaGo on KGS?

It must be a 9d!

Yamato
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--
=
Olivier Teytaud, olivier.teyt...@inria.fr, 
TAO, LRI, UMR 8623(CNRS - Univ. Paris-Sud),
bat 490 Univ. Paris-Sud F-91405 Orsay Cedex France 
http://www.slideshare.net/teytaud
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Ingo Althöfer]

Sorry for a typo. I meant

> Hello Aja,
>  
> congratulations to the success of you and the other team memberS!

So, not singular, but plural.

Ingo.
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Aja Huang]

2016-01-27 18:46 GMT+00:00 Aja Huang :

> Hi all,
>
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
> today. The paper and all the games can be found here:
>
> http://www.deepmind.com/alpha-go.html
>

The paper is freely available to download at the bottom of the page.
https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf

Aja


> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
> March, to see whether we finally have a Go program that is stronger than
> any human!
>
> Aja
>
> PS I am very busy preparing AlphaGo for the match, so apologies in advance
> if I cannot respond to all questions about AlphaGo.
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Jason Li]

Congratulations to Aja!

A question to the community. Is anyone going to replicate the experimental
results?

https://www.quora.com/Is-anyone-replicating-the-experimental-results-of-the-human-level-Go-player-published-by-Google-Deepmind-in-Nature-in-January-2016
?

Jason

On Thu, Jan 28, 2016 at 9:26 AM, Erik van der Werf  wrote:

> Wow, excellent results, congratulations Aja & team!
>
> I'm surprised to see nothing explicitly on decomposing into subgames (e.g.
> for semeai). I always thought some kind of adaptive decomposition would be
> needed to reach pro-strength... I guess you must have looked into this;
> does this mean that the networks have learnt to do it by themselves? Or
> perhaps they play in a way that simply avoids their weaknesses?
>
> Would be interesting to see a demonstration that the networks have learned
> the semeai rules through reinforcement learning / self-play :-)
>
> Best,
> Erik
>
>
> On Wed, Jan 27, 2016 at 7:46 PM, Aja Huang  wrote:
>
>> Hi all,
>>
>> We are very excited to announce that our Go program, AlphaGo, has beaten
>> a professional player for the first time. AlphaGo beat the European
>> champion Fan Hui by 5 games to 0. We hope you enjoy our paper, published in
>> Nature today. The paper and all the games can be found here:
>>
>> http://www.deepmind.com/alpha-go.html
>>
>> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
>> March, to see whether we finally have a Go program that is stronger than
>> any human!
>>
>> Aja
>>
>> PS I am very busy preparing AlphaGo for the match, so apologies in
>> advance if I cannot respond to all questions about AlphaGo.
>>
>> ___
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>> http://computer-go.org/mailman/listinfo/computer-go
>>
>
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Erik van der Werf]

Wow, excellent results, congratulations Aja & team!

I'm surprised to see nothing explicitly on decomposing into subgames (e.g.
for semeai). I always thought some kind of adaptive decomposition would be
needed to reach pro-strength... I guess you must have looked into this;
does this mean that the networks have learnt to do it by themselves? Or
perhaps they play in a way that simply avoids their weaknesses?

Would be interesting to see a demonstration that the networks have learned
the semeai rules through reinforcement learning / self-play :-)

Best,
Erik


On Wed, Jan 27, 2016 at 7:46 PM, Aja Huang  wrote:

> Hi all,
>
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
> today. The paper and all the games can be found here:
>
> http://www.deepmind.com/alpha-go.html
>
> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
> March, to see whether we finally have a Go program that is stronger than
> any human!
>
> Aja
>
> PS I am very busy preparing AlphaGo for the match, so apologies in advance
> if I cannot respond to all questions about AlphaGo.
>
> ___
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> http://computer-go.org/mailman/listinfo/computer-go
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Robert Jasiek]

Congratulations to the researchers!

On 27.01.2016 21:10, Michael Markefka wrote:

I really do hope that this also turns into a good analysis and
teaching tool for human player. That would be a fantastic benefit from
this advancement in computer Go.


The programs successful as computer players mostly rely on computation 
power for learning and decision-making. This can be used for teaching 
tools that do not need to provide text explanations and other reasoning 
to the human pupils: computer game opponent, life and death playing 
opponent, empirical winning percentages of patterns etc.


Currently such programs do not provide sophisticated explanations and 
reasoning about tactical decision-making, strategy and positional 
judgement fitting human players' / pupils' conceptual thinking.


If always correct teaching is not the aim (but if a computer teacher may 
err as much as a human teacher errs), in principle it should be possible 
to combine the successful means of using computation power with the 
reasonably accurate human descriptions of sophisticated explanations and 
reasoning. This requires implementation of expert system knowledge 
adapted from the best (the least ambiguous, the most often correct / 
applicable) descriptions of human-understandable go theory and further 
research in the latter.


--
robert jasiek
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Yamato]

Congratulations Aja.

Do you have a plan to run AlphaGo on KGS?

It must be a 9d!

Yamato
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[David Fotland]

Google’s breakthrough is just as impactful as the invention of MCTS.  
Congratulations to the team.  It’s a huge leap for computer go, but more 
importantly it shows that DNN can be applied to many other difficult problems.

 

I just added an answer.  I don’t think anyone will try to exactly replicate it, 
but a year from now there should be several strong programs using very similar 
techniques, with similar strength.

 

An interesting question is, who has integrated or is integrating a DNN into 
their go program?  I’m working on it.  I know there are several others.

 

David

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Jason Li
Sent: Wednesday, January 27, 2016 3:14 PM
To: computer-go@computer-go.org
Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks 
and Tree Search

 

Congratulations to Aja!

 

A question to the community. Is anyone going to replicate the experimental 
results?

 

https://www.quora.com/Is-anyone-replicating-the-experimental-results-of-the-human-level-Go-player-published-by-Google-Deepmind-in-Nature-in-January-2016?

 

Jason

 

On Thu, Jan 28, 2016 at 9:26 AM, Erik van der Werf  
wrote:

Wow, excellent results, congratulations Aja & team!

 

I'm surprised to see nothing explicitly on decomposing into subgames (e.g. for 
semeai). I always thought some kind of adaptive decomposition would be needed 
to reach pro-strength... I guess you must have looked into this; does this mean 
that the networks have learnt to do it by themselves? Or perhaps they play in a 
way that simply avoids their weaknesses? 

 

Would be interesting to see a demonstration that the networks have learned the 
semeai rules through reinforcement learning / self-play :-) 

 

Best,

Erik

 

 

On Wed, Jan 27, 2016 at 7:46 PM, Aja Huang  wrote:

Hi all,

 

We are very excited to announce that our Go program, AlphaGo, has beaten a 
professional player for the first time. AlphaGo beat the European champion Fan 
Hui by 5 games to 0. We hope you enjoy our paper, published in Nature today. 
The paper and all the games can be found here: 

 

http://www.deepmind.com/alpha-go.html

 

AlphaGo will be competing in a match against Lee Sedol in Seoul, this March, to 
see whether we finally have a Go program that is stronger than any human! 

 

Aja

 

PS I am very busy preparing AlphaGo for the match, so apologies in advance if I 
cannot respond to all questions about AlphaGo.

 

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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Julian Schrittwieser]

Congratulations Aja, well done :)

On Wed, Jan 27, 2016 at 6:46 PM, Aja Huang  wrote:

> Hi all,
>
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
> today. The paper and all the games can be found here:
>
> http://www.deepmind.com/alpha-go.html
>
> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
> March, to see whether we finally have a Go program that is stronger than
> any human!
>
> Aja
>
> PS I am very busy preparing AlphaGo for the match, so apologies in advance
> if I cannot respond to all questions about AlphaGo.
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Michael Markefka]

I really do hope that this also turns into a good analysis and
teaching tool for human player. That would be a fantastic benefit from
this advancement in computer Go.

On Wed, Jan 27, 2016 at 9:08 PM, Aja Huang  wrote:
> 2016-01-27 18:46 GMT+00:00 Aja Huang :
>>
>> Hi all,
>>
>> We are very excited to announce that our Go program, AlphaGo, has beaten a
>> professional player for the first time. AlphaGo beat the European champion
>> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
>> today. The paper and all the games can be found here:
>>
>> http://www.deepmind.com/alpha-go.html
>
>
> The paper is freely available to download at the bottom of the page.
> https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf
>
> Aja
>
>>
>> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
>> March, to see whether we finally have a Go program that is stronger than any
>> human!
>>
>> Aja
>>
>> PS I am very busy preparing AlphaGo for the match, so apologies in advance
>> if I cannot respond to all questions about AlphaGo.
>>
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>
>
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Ingo Althöfer]

Hello Aja,
 
congratulations to the success of you and the other team member!
 
To the others: Should we call the game "Goo" in the future,
to honour Goo-gles progress?

CHeers, Ingo.


Gesendet: Mittwoch, 27. Januar 2016 um 19:46 Uhr
Von: "Aja Huang" 
An: computer-go@computer-go.org
Betreff: [Computer-go] Mastering the Game of Go with Deep Neural Networks and 
Tree Search

Hi all,
 
We are very excited to announce that our Go program, AlphaGo, has beaten a 
professional player for the first time. AlphaGo beat the European champion Fan 
Hui by 5 games to 0. We hope you enjoy our paper, published in Nature today. 
The paper and all the games can be found here: 
 
http://www.deepmind.com/alpha-go.html
 
AlphaGo will be competing in a match against Lee Sedol in Seoul, this March, to 
see whether we finally have a Go program that is stronger than any human! 
 
Aja
 
PS I am very busy preparing AlphaGo for the match, so apologies in advance if I 
cannot respond to all questions about 
AlphaGo.___ Computer-go mailing 
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Hideki Kato]

Congratulations Aja and David!

What an interesting idea to train the value network and surprising power 
of the cloud!

Then, when you will get +400 Elo? :)

Hideki

Aja Huang: 
: 
>Hi all,
>
>We are very excited to announce that our Go program, AlphaGo, has beaten a
>professional player for the first time. AlphaGo beat the European champion
>Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
>today. The paper and all the games can be found here:
>
>http://www.deepmind.com/alpha-go.html
>
>AlphaGo will be competing in a match against Lee Sedol in Seoul, this
>March, to see whether we finally have a Go program that is stronger than
>any human!
>
>Aja
>
>PS I am very busy preparing AlphaGo for the match, so apologies in advance
>if I cannot respond to all questions about AlphaGo.
> inline file
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-- 
Hideki Kato 
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