Re: [Computer-go] Game 4: a rare insight

[Robert Jasiek]

On 14.03.2016 03:17, Horace Ho wrote:

According this analysis, move 78 is not a "miracle" move ...

http://card.weibo.com/article/h5/s#cid=23041853a2e03d0102w6rl;


I have not had time to verify the tactics by reading yet but suppose 
this webpage's sequences are right, move 78 and the preceding sequence 
is a well-timed, cute trick play and the Alphago teams needs to 
understand why the trick worked. I'd guess that it would have found a 
correct reply if the moyo defense had been a local, short term problem.


--
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Re: [Computer-go] Game 4: a rare insight

[Horace Ho]

According this analysis, move 78 is not a "miracle" move ...

http://card.weibo.com/article/h5/s#cid=23041853a2e03d0102w6rl;

On Mon, Mar 14, 2016 at 4:08 AM, Martin Mueller 
wrote:

> On Mar 13, 2016, at 6:00 AM, computer-go-requ...@computer-go.org wrote:
>
>
> So, what would be Lee's best effort to exploit this? Complicating
> and playing hopefully-unexpected-tesuji moves?
>
>
> Judging from this game, setting up multiple interrelated tactical fights,
> such that no subset of them works, but all together they work to capture or
> kill something.
>
> For tactical fights, I would expect the value network to be relatively
> weaker than for quiet territorial positions.
> So it comes down to solving the problem by search.
>
> Aja and me wrote a paper a few years back that showed that even on a 9x9
> board, having two safe but not entirely safe-in-playouts groups on the
> board confuses most Go programs and can push the “bad news” over the search
> horizon. Now imagine having 3, 4, 5 or more simultaneous tactics. The
> combinatorics of searching through all of those by brute force are
> enormous. But humans know exactly what they are looking for.
> Martin
>
> Reference:
> http://webdocs.cs.ualberta.ca/~mmueller/publications.html#2013
>
> *S.-C. Huang* and M. Müller. Investigating the Limits of Monte Carlo Tree
> Search Methods in Computer Go
> .
> Computers and Games 2013, p. 39-48.
> *Erratum
> *
>  for
> this paper - in test case 2 Black wins.
>
>
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Re: [Computer-go] *****SPAM***** Congratulations to AlphaGo

[David Doshay]

The SiliValley Go club is getting requests to join our email notifications at 
about 5 times the normal rate since the AlphaGo paper was published. So far 
everyone has had some prior knowledge of the game, and several have not played 
in a while. Some are beginners, but so far no people who do not yet know how to 
play.

Cheers,
David G Doshay

ddos...@mac.com





> On 13, Mar 2016, at 3:44 PM, David Fotland  wrote:
> 
> Smart-games.com  is getting a big increase in 
> traffic, so there is certainly more interest in the game now.  I hope it 
> holds up for the long term.
>  
> David
>  
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
> Dmitry Kamenetsky
> Sent: Saturday, March 12, 2016 2:18 PM
> To: computer-go@computer-go.org
> Subject: *SPAM* [Computer-go] Congratulations to AlphaGo
>  
> Congratulations to AlphaGo and its team! You have done what many of us could 
> only dream to do and in such short time I may add. This is a truly historical 
> moment and an amazing achievement for AI research!
>  
> I hope this is not the end of Go and only sparks more interest in this 
> beautiful game. What an exciting time we live in and I can't wait to see what 
> the future holds. 
>  
>  
> Regards,
> Dmitry Kamenetsky
> ___
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> http://computer-go.org/mailman/listinfo/computer-go

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Re: [Computer-go] *****SPAM***** Congratulations to AlphaGo

[David Fotland]

Smart-games.com is getting a big increase in traffic, so there is certainly 
more interest in the game now.  I hope it holds up for the long term.

 

David

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Dmitry Kamenetsky
Sent: Saturday, March 12, 2016 2:18 PM
To: computer-go@computer-go.org
Subject: *SPAM* [Computer-go] Congratulations to AlphaGo

 

Congratulations to AlphaGo and its team! You have done what many of us could 
only dream to do and in such short time I may add. This is a truly historical 
moment and an amazing achievement for AI research!

 

I hope this is not the end of Go and only sparks more interest in this 
beautiful game. What an exciting time we live in and I can't wait to see what 
the future holds. 

 

 

Regards,

Dmitry Kamenetsky

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Re: [Computer-go] Game 4: a rare insight

[Martin Mueller]

On Mar 13, 2016, at 6:00 AM, computer-go-requ...@computer-go.org wrote:
> 
>> So, what would be Lee's best effort to exploit this? Complicating
>> and playing hopefully-unexpected-tesuji moves?

Judging from this game, setting up multiple interrelated tactical fights, such 
that no subset of them works, but all together they work to capture or kill 
something.

For tactical fights, I would expect the value network to be relatively weaker 
than for quiet territorial positions.
So it comes down to solving the problem by search.

Aja and me wrote a paper a few years back that showed that even on a 9x9 board, 
having two safe but not entirely safe-in-playouts groups on the board confuses 
most Go programs and can push the “bad news” over the search horizon. Now 
imagine having 3, 4, 5 or more simultaneous tactics. The combinatorics of 
searching through all of those by brute force are enormous. But humans know 
exactly what they are looking for.
Martin

Reference:
http://webdocs.cs.ualberta.ca/~mmueller/publications.html#2013
S.-C. Huang and M. Müller. Investigating the Limits of Monte Carlo Tree Search 
Methods in Computer Go 
. 
Computers and Games 2013, p. 39-48. 
Erratum 

 for this paper - in test case 2 Black wins.


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Re: [Computer-go] AlphaGo & DCNN: Handling long-range dependency

[Brian Cloutier]

>  not because of a new better algorithm but because the Deep Blue's 11.38
GFLOP power is available on desktop from about 2006F

This isn't true, modern chess engines look at far fewer positions than Deep
Blue did.

>From wikipedia : "Chess
engines continue to improve. In 2009, chess engines running on slower
hardware have reached the grandmaster level. A mobile phone won a category
6 tournament with a performance rating 2898: chess engine Hiarcs 13 running
inside Pocket Fritz 4 on the mobile phone HTC Touch HD won the Copa
Mercosur tournament in Buenos Aires, Argentina with 9 wins and 1 draw on
August 4–14, 2009.[20] Pocket Fritz 4 searches fewer than 20,000 positions
per second.[21] This is in contrast to supercomputers such as Deep Blue
that searched 200 million positions per second."

>From Quora
:
" In such, it could calculate 200 million moves per second. This raw,
brute-force approach made Deep Blue such a challenge to Kasparov. Today,
the strength of engines like Deep Fritz, Houdini, and Rybka is rooted in
the software rather than the dedicated chess hardware. These engines are
far more efficient, using specialized heuristics to evaluate far less moves
than Deep Blue, but to a greater depth of variation. For comparison, a
decent personal computer running Rybka can evaluate up to 8 million moves
per second"

I agree that Google is likely to move on but AlphaGo is by no means the
final word in computer go. I'm excited to see the developments that will be
required to make a program with the same strength possible on your phone.

On Fri, Mar 11, 2016 at 4:17 AM Рождественский Дмитрий 
wrote:

> I think that a desktop computer's calculating power appear to develop to a
> necessary level sooner then the algorithm may be optimized to use the power
> nowdays available. For example, I belive that chess programs run on a
> desktop well not because of a new better algotrithm but because the Deep
> Blue's 11.38 GFLOP power is available on desktop from about 2006, in ten
> years only. So I think the speculation that Deep Mind will change the
> objective to a more advanced task is right :)
>
> Dmitry
>
> 11.03.2016, 14:28, "Darren Cook" :
> >>>  global, more long-term planning. A rumour so far suggests to have
> used the
> >>>  time for more learning, but I'd be surprised if this should have
> sufficed.
> >>
> >>  My personal hypothesis so far is that it might - the REINFORCE might
> >>  scale amazingly well and just continuous application of it...
> >
> > Agreed. What they have built is a training data generator, that can
> > churn out 9-dan level moves, 24 hours a day. Over the years I've had to
> > throw away so many promising ideas because they came down to needing a
> > 9-dan pro to, say, do the tedious job of ranking all legal moves in each
> > test position.
> >
> > What I'm hoping Deep Mind will do next is study how to maintain the same
> > level but using less hardware, until they can shrink it down to run on,
> > say, a high-end desktop computer. The knowledge gained obviously has a
> > clear financial benefit just in running costs, and computer-go is a nice
> > objective domain to measure progress. (But the cynic in me suspects
> > they'll just move to the next bright and shiny AI problem.)
> >
> > Darren
> >
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Re: [Computer-go] Game 4: a rare insight

[Sorin Gherman]

There is no way to not know that O10 was dead after white plays O9, since
AlphaGo handled much more complicated fights even in the games in October.

My only guess from looking at the sequence around O10, where black makes
its own big group bigger is that it was preparing for a ko-fight, and
wanted to have ONE huge ko-threat in that area, something like that - I
don't see any other reasonable explanation.

On Sun, Mar 13, 2016 at 7:55 AM, Olivier Teytaud 
wrote:

> Should we understand that AlphaGo had not understood that O10 was dead ?
> (sorry for Go beginner question :-) )
>
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Re: [Computer-go] Game 4: a rare insight

[Brian Sheppard]

I have the impression that the value network is used to initialize the score of 
a node to, say, 70% out of N trials. Then the MCTS is trial N+1, N+2, etc. 
Still asymptotically optimal, but if the value network is accurate then you 
have a big acceleration in accuracy because the scores start from a higher 
point instead of wobbling unstably for a while.

But then I didn't follow the back-up policy. That is, if you do a search, and 
the color to move loses, but the evaluation at the leaf node was winning by 
70%, then what update is made to this node?

In MCTS, you only use the W/L value. But if you are using a value network then 
it seems inconsistent not to use the 70% in some way.

So I also have to go back to read the paper again...

-Original Message-
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Darren Cook
Sent: Sunday, March 13, 2016 2:20 PM
To: computer-go@computer-go.org
Subject: Re: [Computer-go] Game 4: a rare insight

> You are right, but from fig 2 of the paper can see, that mc and value 
> network should give similar results:
> 
> 70% value network should be comparable to 60-65% MC winrate from this 
> paper, usually expected around move 140 in a "human expert game" (what 
> ever this means in this figure :)

Thanks, that makes sense.

>>> Assuming that is an MCTS estimate of winning probability, that 70% 
>>> sounds high (i.e. very confident);
> 
>> That tweet says 70% is from value net, not from MCTS estimate.

I guess I need to go back and read the AlphaGo papers again; I thought it was 
still an MCTS program at the top-level, and the value network was being used to 
influence the moves the tree explores. But from this, and some other comments 
I've seen, I have the feeling I've misunderstood.

Darren




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Re: [Computer-go] Game 4: a rare insight

[Darren Cook]

> You are right, but from fig 2 of the paper can see, that mc and value
> network should give similar results:
> 
> 70% value network should be comparable to 60-65% MC winrate from this
> paper, usually expected around move 140 in a "human expert game" (what
> ever this means in this figure :)

Thanks, that makes sense.

>>> Assuming that is an MCTS estimate of winning probability, that
>>> 70% sounds high (i.e. very confident);
> 
>> That tweet says 70% is from value net, not from MCTS estimate.

I guess I need to go back and read the AlphaGo papers again; I thought
it was still an MCTS program at the top-level, and the value network was
being used to influence the moves the tree explores. But from this, and
some other comments I've seen, I have the feeling I've misunderstood.

Darren





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Re: [Computer-go] Game 4: a rare insight

[Richard Lorentz]

And a related question from a fellow "beginner": At what point was that 
group actually dead?


On 03/13/2016 07:55 AM, Olivier Teytaud wrote:

Should we understand that AlphaGo had not understood that O10 was dead ?
(sorry for Go beginner question :-) )

On Sun, Mar 13, 2016 at 1:42 PM, Detlef Schmicker > wrote:


-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

You are right, but from fig 2 of the paper can see, that mc and value
network should give similar results:

70% value network should be comparable to 60-65% MC winrate from this
paper, usually expected around move 140 in a "human expert game" (what
ever this means in this figure :)

Am 13.03.2016 um 12:48 schrieb Seo Sanghyeon:
> 2016-03-13 17:54 GMT+09:00 Darren Cook >:
>> From Demis Hassabis: When I say 'thought' and 'realisation' I
>> just mean the output of #AlphaGo value net. It was around 70% at
>> move 79 and then dived on move 87
>>
>> https://twitter.com/demishassabis/status/708934687926804482


>>
>> Assuming that is an MCTS estimate of winning probability, that
>> 70% sounds high (i.e. very confident);
>
> That tweet says 70% is from value net, not from MCTS estimate.
>
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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] Game 4: a rare insight

[Olivier Teytaud]

Should we understand that AlphaGo had not understood that O10 was dead ?
(sorry for Go beginner question :-) )

On Sun, Mar 13, 2016 at 1:42 PM, Detlef Schmicker  wrote:

> -BEGIN PGP SIGNED MESSAGE-
> Hash: SHA1
>
> You are right, but from fig 2 of the paper can see, that mc and value
> network should give similar results:
>
> 70% value network should be comparable to 60-65% MC winrate from this
> paper, usually expected around move 140 in a "human expert game" (what
> ever this means in this figure :)
>
> Am 13.03.2016 um 12:48 schrieb Seo Sanghyeon:
> > 2016-03-13 17:54 GMT+09:00 Darren Cook :
> >> From Demis Hassabis: When I say 'thought' and 'realisation' I
> >> just mean the output of #AlphaGo value net. It was around 70% at
> >> move 79 and then dived on move 87
> >>
> >> https://twitter.com/demishassabis/status/708934687926804482
> >>
> >> Assuming that is an MCTS estimate of winning probability, that
> >> 70% sounds high (i.e. very confident);
> >
> > That tweet says 70% is from value net, not from MCTS estimate.
> >
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> ___
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=
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Paris-Sud),
bat 490 Univ. Paris-Sud F-91405 Orsay Cedex France
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[Computer-go] Verification Reading for Probabilities

[Robert Jasiek]

Suppose an MC/NN program suggests a move with the supposedly highest 
winning probability. Due to the imperfect information for suggesting 
this move, I suggest to apply my human player principle "verify by 
reading" by verifying the suggested move by reading. This can use the 
following method "verification reading for probabilities", which I 
suggest now:


We have the current position with a winning probability and the 
suggested first move in the current position. Do _reading_ to quiet 
leaves, for which we know or determine a winning probability. A decision 
in the tree is a success iff it results in _at least_ the same 
probability [alternative: a threshold probability] as that of the 
current position.


I.e., the verification reading shall ensure that the winning probability 
does not decrease as a consequence of starting with the suggested move.


Such should prevent the program from suddenly experiencing harshly 
dropping probabilities during a few successive moves / mistakes of the 
game, such as in game 4 of the AlphaGo - Lee Sedol match.


--
robert jasiek
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[Computer-go] Congratulations to AlphaGo

[Dmitry Kamenetsky]

Congratulations to AlphaGo and its team! You have done what many of us
could only dream to do and in such short time I may add. This is a truly
historical moment and an amazing achievement for AI research!

I hope this is not the end of Go and only sparks more interest in this
beautiful game. What an exciting time we live in and I can't wait to see
what the future holds.


Regards,
Dmitry Kamenetsky
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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] Game 4: a rare insight

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

You are right, but from fig 2 of the paper can see, that mc and value
network should give similar results:

70% value network should be comparable to 60-65% MC winrate from this
paper, usually expected around move 140 in a "human expert game" (what
ever this means in this figure :)

Am 13.03.2016 um 12:48 schrieb Seo Sanghyeon:
> 2016-03-13 17:54 GMT+09:00 Darren Cook :
>> From Demis Hassabis: When I say 'thought' and 'realisation' I
>> just mean the output of #AlphaGo value net. It was around 70% at
>> move 79 and then dived on move 87
>> 
>> https://twitter.com/demishassabis/status/708934687926804482
>> 
>> Assuming that is an MCTS estimate of winning probability, that
>> 70% sounds high (i.e. very confident);
> 
> That tweet says 70% is from value net, not from MCTS estimate.
> 
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Re: [Computer-go] Game 4: a rare insight

[Seo Sanghyeon]

2016-03-13 17:54 GMT+09:00 Darren Cook :
> From Demis Hassabis:
>   When I say 'thought' and 'realisation' I just mean the output of
>   #AlphaGo value net. It was around 70% at move 79 and then dived
>   on move 87
>
>   https://twitter.com/demishassabis/status/708934687926804482
>
> Assuming that is an MCTS estimate of winning probability, that 70%
> sounds high (i.e. very confident);

That tweet says 70% is from value net, not from MCTS estimate.

-- 
Seo Sanghyeon
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Re: [Computer-go] Game 4: a rare insight

[Brian Sheppard]

I would not place too much confidence in the observers. Even though they are 
pro players, they don't have the same degree of concentration as the game's 
participants, and they have an obligation to speak about the game on a regular 
basis which further deteriorates analytic skills. Figuring out where a player 
went wrong will take much longer than it takes to play the game.

If you really want good insight, better to use several programs to play out 
games based on moves that they collectively propose, with the additional 
ability to take moves from a human. Over a period of days you can get quite 
strong analysis, even if the players are not that strong. There was a great 
paper describing the method from a few years ago.

-Original Message-
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Marc Landgraf
Sent: Sunday, March 13, 2016 5:26 AM
To: computer-go@computer-go.org
Subject: Re: [Computer-go] Game 4: a rare insight

What is the most interesting part is, that at this point many pro commentators 
found a lot of aji, but did not find a "solution" for Lee Sedol that broke 
AlphaGos position. So the question remains: Did AlphaGo find a hole in it's own 
position and tried to dodge that? Was it too strong for its own good? Or was it 
a misevaluation due to the immense amounts of aji, which would not result in 
harm, if played properly?


2016-03-13 9:54 GMT+01:00 Darren Cook :
> From Demis Hassabis:
>   When I say 'thought' and 'realisation' I just mean the output of
>   #AlphaGo value net. It was around 70% at move 79 and then dived
>   on move 87
>
>   https://twitter.com/demishassabis/status/708934687926804482
>
> Assuming that is an MCTS estimate of winning probability, that 70% 
> sounds high (i.e. very confident); when I was doing the computer-human 
> team experiments, on 9x9, with three MCTS programs, I generally knew 
> I'd found a winning move when the percentages moved from the 48-52% 
> range to, say, 55%.
>
> I really hope they reveal the win estimates for each move of the 5 
> games. It will especially be interesting to then compare that to the 
> other leading MCTS programs.
>
> Darren
>
> ___
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Re: [Computer-go] Game 4: a rare insight

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1



Am 13.03.2016 um 11:28 schrieb Josef Moudrik:
> How well do you think the mcts-weakness we have witnessed today is
> hidden in AG? Or, how can one go about exploiting it
> systematically?
> 
> I think it might be well hidden by the value network being very
> strong and true most of the time - it is much harder to get AG to
> this state, than traditional mcts bots with much less truthful
> evaluations.
> 
> So, what would be Lee's best effort to exploit this? Complicating
> and playing hopefully-unexpected-tesuji moves?
> 
> Detlef: Demmis tweeted that the w78 caused b79 mistake, that only
> surfaced out some ten moves later. Ca you share the development of
> the value evals during these moves? Did your net fall down right
> after move 78?

My net is very unstable in the sequence, jumping around a lot. But I
am in a very early state of value network, just wanted to state:

This is probably more a problem of the value network, than of the MC
playouts, they are quite stable in the sequence, but don't put to much
into this, I am not really convinced of my net at the moment :(


> 
> What an interesting times we live in :-)
> 
> Regards, Josef
> 
> Dne ne 13. 3. 2016 10:33 uživatel Marc Landgraf
>  napsal:
> 
>> Oh, is it possible to provide those variants? Or is there a
>> recording of the broadcast, reading the board is probably enough
>> to roughly understand it.
>> 
>> 2016-03-13 10:32 GMT+01:00 Chun Sun :
>>> Hi Marc,
>>> 
>>> "but did not find a "solution" for Lee Sedol that broke
>>> AlphaGos
>> position"
>>> -- this is not true. Ke Jie and Gu Li both found more than one
>>> way to
>> break
>>> the position :)
>>> 
>>> On Sun, Mar 13, 2016 at 5:26 AM, Marc Landgraf
>>> 
>> wrote:
 
 What is the most interesting part is, that at this point many
 pro commentators found a lot of aji, but did not find a
 "solution" for Lee Sedol that broke AlphaGos position. So the
 question remains: Did AlphaGo find a hole in it's own
 position and tried to dodge that? Was it too strong for its
 own good? Or was it a misevaluation due to the immense
 amounts of aji, which would not result in harm, if played 
 properly?
 
 
 2016-03-13 9:54 GMT+01:00 Darren Cook :
> From Demis Hassabis: When I say 'thought' and 'realisation'
> I just mean the output of #AlphaGo value net. It was around
> 70% at move 79 and then dived on move 87
> 
> https://twitter.com/demishassabis/status/708934687926804482
>
>
> 
Assuming that is an MCTS estimate of winning probability, that 70%
> sounds high (i.e. very confident); when I was doing the
> computer-human team experiments, on 9x9, with three MCTS
> programs, I generally knew
>> I'd
> found a winning move when the percentages moved from the
> 48-52% range to, say, 55%.
> 
> I really hope they reveal the win estimates for each move
> of the 5 games. It will especially be interesting to then
> compare that to the other leading MCTS programs.
> 
> Darren
> 
> ___ Computer-go
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> 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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>>> mailing list Computer-go@computer-go.org 
>>> http://computer-go.org/mailman/listinfo/computer-go
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>> mailing list Computer-go@computer-go.org 
>> http://computer-go.org/mailman/listinfo/computer-go
> 
> 
> 
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> list Computer-go@computer-go.org 
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> 
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Re: [Computer-go] Game 4: a rare insight

[Josef Moudrik]

How well do you think the mcts-weakness we have witnessed today is hidden
in AG? Or, how can one go about exploiting it systematically?

I think it might be well hidden by the value network being very strong and
true most of the time - it is much harder to get AG to this state, than
traditional mcts bots with much less truthful evaluations.

So, what would be Lee's best effort to exploit this? Complicating and
playing hopefully-unexpected-tesuji moves?

Detlef: Demmis tweeted that the w78 caused b79 mistake, that only surfaced
out some ten moves later. Ca you share the development of the value evals
during these moves? Did your net fall down right after move 78?

What an interesting times we live in :-)

Regards,
Josef

Dne ne 13. 3. 2016 10:33 uživatel Marc Landgraf 
napsal:

> Oh, is it possible to provide those variants? Or is there a recording
> of the broadcast, reading the board is probably enough to roughly
> understand it.
>
> 2016-03-13 10:32 GMT+01:00 Chun Sun :
> > Hi Marc,
> >
> > "but did not find a "solution" for Lee Sedol that broke AlphaGos
> position"
> > -- this is not true. Ke Jie and Gu Li both found more than one way to
> break
> > the position :)
> >
> > On Sun, Mar 13, 2016 at 5:26 AM, Marc Landgraf 
> wrote:
> >>
> >> What is the most interesting part is, that at this point many pro
> >> commentators found a lot of aji, but did not find a "solution" for Lee
> >> Sedol that broke AlphaGos position. So the question remains: Did
> >> AlphaGo find a hole in it's own position and tried to dodge that? Was
> >> it too strong for its own good? Or was it a misevaluation due to the
> >> immense amounts of aji, which would not result in harm, if played
> >> properly?
> >>
> >>
> >> 2016-03-13 9:54 GMT+01:00 Darren Cook :
> >> > From Demis Hassabis:
> >> >   When I say 'thought' and 'realisation' I just mean the output of
> >> >   #AlphaGo value net. It was around 70% at move 79 and then dived
> >> >   on move 87
> >> >
> >> >   https://twitter.com/demishassabis/status/708934687926804482
> >> >
> >> > Assuming that is an MCTS estimate of winning probability, that 70%
> >> > sounds high (i.e. very confident); when I was doing the computer-human
> >> > team experiments, on 9x9, with three MCTS programs, I generally knew
> I'd
> >> > found a winning move when the percentages moved from the 48-52% range
> >> > to, say, 55%.
> >> >
> >> > I really hope they reveal the win estimates for each move of the 5
> >> > games. It will especially be interesting to then compare that to the
> >> > other leading MCTS programs.
> >> >
> >> > Darren
> >> >
> >> > ___
> >> > 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] Game 4: a rare insight

[Chun Sun]

Hi Marc,

"but did not find a "solution" for Lee Sedol that broke AlphaGos position"
-- this is not true. Ke Jie and Gu Li both found more than one way to break
the position :)

On Sun, Mar 13, 2016 at 5:26 AM, Marc Landgraf  wrote:

> What is the most interesting part is, that at this point many pro
> commentators found a lot of aji, but did not find a "solution" for Lee
> Sedol that broke AlphaGos position. So the question remains: Did
> AlphaGo find a hole in it's own position and tried to dodge that? Was
> it too strong for its own good? Or was it a misevaluation due to the
> immense amounts of aji, which would not result in harm, if played
> properly?
>
>
> 2016-03-13 9:54 GMT+01:00 Darren Cook :
> > From Demis Hassabis:
> >   When I say 'thought' and 'realisation' I just mean the output of
> >   #AlphaGo value net. It was around 70% at move 79 and then dived
> >   on move 87
> >
> >   https://twitter.com/demishassabis/status/708934687926804482
> >
> > Assuming that is an MCTS estimate of winning probability, that 70%
> > sounds high (i.e. very confident); when I was doing the computer-human
> > team experiments, on 9x9, with three MCTS programs, I generally knew I'd
> > found a winning move when the percentages moved from the 48-52% range
> > to, say, 55%.
> >
> > I really hope they reveal the win estimates for each move of the 5
> > games. It will especially be interesting to then compare that to the
> > other leading MCTS programs.
> >
> > Darren
> >
> > ___
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Re: [Computer-go] Game 4: a rare insight

[Marc Landgraf]

What is the most interesting part is, that at this point many pro
commentators found a lot of aji, but did not find a "solution" for Lee
Sedol that broke AlphaGos position. So the question remains: Did
AlphaGo find a hole in it's own position and tried to dodge that? Was
it too strong for its own good? Or was it a misevaluation due to the
immense amounts of aji, which would not result in harm, if played
properly?


2016-03-13 9:54 GMT+01:00 Darren Cook :
> From Demis Hassabis:
>   When I say 'thought' and 'realisation' I just mean the output of
>   #AlphaGo value net. It was around 70% at move 79 and then dived
>   on move 87
>
>   https://twitter.com/demishassabis/status/708934687926804482
>
> Assuming that is an MCTS estimate of winning probability, that 70%
> sounds high (i.e. very confident); when I was doing the computer-human
> team experiments, on 9x9, with three MCTS programs, I generally knew I'd
> found a winning move when the percentages moved from the 48-52% range
> to, say, 55%.
>
> I really hope they reveal the win estimates for each move of the 5
> games. It will especially be interesting to then compare that to the
> other leading MCTS programs.
>
> Darren
>
> ___
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Re: [Computer-go] Game 4: a rare insight

[Detlef Schmicker]

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

Interesting, my value net does the same, even it was trained totally
different from 7d+ games :)

Am 13.03.2016 um 09:54 schrieb Darren Cook:
> From Demis Hassabis: When I say 'thought' and 'realisation' I just
> mean the output of #AlphaGo value net. It was around 70% at move 79
> and then dived on move 87
> 
> https://twitter.com/demishassabis/status/708934687926804482
> 
> Assuming that is an MCTS estimate of winning probability, that 70% 
> sounds high (i.e. very confident); when I was doing the
> computer-human team experiments, on 9x9, with three MCTS programs,
> I generally knew I'd found a winning move when the percentages
> moved from the 48-52% range to, say, 55%.
> 
> I really hope they reveal the win estimates for each move of the 5 
> games. It will especially be interesting to then compare that to
> the other leading MCTS programs.
> 
> Darren
> 
> ___ Computer-go mailing
> list Computer-go@computer-go.org 
> http://computer-go.org/mailman/listinfo/computer-go
> 
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Re: [Computer-go] Sedol won 4th game!

[Lukas van de Wiel]

Congratz to Lee Sedol!

Actually from Black S 11 you knew things were going downhill for AlphaGo.

Cheers
Lukas

On Sun, Mar 13, 2016 at 9:55 PM, Hiroshi Yamashita  wrote:

> (;GM[1]SZ[19]
> PB[AlphaGo]
> PW[Lee Sedol]
> DT[2016-03-13]RE[W+R]KM[7.5]TM[7200]RU[Chinese]
> ;B[pd];W[dp];B[cd];W[qp];B[op];W[oq];B[nq];W[pq];B[cn];W[fq]
> ;B[mp];W[po];B[iq];W[ec];B[hd];W[cg];B[ed];W[cj];B[dc];W[bp]
> ;B[nc];W[qi];B[ep];W[eo];B[dk];W[fp];B[ck];W[dj];B[ej];W[ei]
> ;B[fi];W[eh];B[fh];W[bj];B[fk];W[fg];B[gg];W[ff];B[gf];W[mc]
> ;B[md];W[lc];B[nb];W[id];B[hc];W[jg];B[pj];W[pi];B[oj];W[oi]
> ;B[ni];W[nh];B[mh];W[ng];B[mg];W[mi];B[nj];W[mf];B[li];W[ne]
> ;B[nd];W[mj];B[lf];W[mk];B[me];W[nf];B[lh];W[qj];B[kk];W[ik]
> ;B[ji];W[gh];B[hj];W[ge];B[he];W[fd];B[fc];W[ki];B[jj];W[lj]
> ;B[kh];W[jh];B[ml];W[nk];B[ol];W[ok];B[pk];W[pl];B[qk];W[nl]
> ;B[kj];W[ii];B[rk];W[om];B[pg];W[ql];B[cp];W[co];B[oe];W[rl]
> ;B[sk];W[rj];B[hg];W[ij];B[km];W[gi];B[fj];W[jl];B[kl];W[gl]
> ;B[fl];W[gm];B[ch];W[ee];B[eb];W[bg];B[dg];W[eg];B[en];W[fo]
> ;B[df];W[dh];B[im];W[hk];B[bn];W[if];B[gd];W[fe];B[hf];W[ih]
> ;B[bh];W[ci];B[ho];W[go];B[or];W[rg];B[dn];W[cq];B[pr];W[qr]
> ;B[rf];W[qg];B[qf];W[jc];B[gr];W[sf];B[se];W[sg];B[rd];W[bl]
> ;B[bk];W[ak];B[cl];W[hn];B[in];W[hp];B[fr];W[er];B[es];W[ds]
> ;B[ah];W[ai];B[kd];W[ie];B[kc];W[kb];B[gk];W[ib];B[qh];W[rh]
> ;B[qs];W[rs];B[oh];W[sl];B[of];W[sj];B[ni];W[nj];B[oo];W[jp])
>
> Hiroshi Yamashita
>
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[Computer-go] Sedol won 4th game!

[Hiroshi Yamashita]

(;GM[1]SZ[19]
PB[AlphaGo]
PW[Lee Sedol]
DT[2016-03-13]RE[W+R]KM[7.5]TM[7200]RU[Chinese]
;B[pd];W[dp];B[cd];W[qp];B[op];W[oq];B[nq];W[pq];B[cn];W[fq]
;B[mp];W[po];B[iq];W[ec];B[hd];W[cg];B[ed];W[cj];B[dc];W[bp]
;B[nc];W[qi];B[ep];W[eo];B[dk];W[fp];B[ck];W[dj];B[ej];W[ei]
;B[fi];W[eh];B[fh];W[bj];B[fk];W[fg];B[gg];W[ff];B[gf];W[mc]
;B[md];W[lc];B[nb];W[id];B[hc];W[jg];B[pj];W[pi];B[oj];W[oi]
;B[ni];W[nh];B[mh];W[ng];B[mg];W[mi];B[nj];W[mf];B[li];W[ne]
;B[nd];W[mj];B[lf];W[mk];B[me];W[nf];B[lh];W[qj];B[kk];W[ik]
;B[ji];W[gh];B[hj];W[ge];B[he];W[fd];B[fc];W[ki];B[jj];W[lj]
;B[kh];W[jh];B[ml];W[nk];B[ol];W[ok];B[pk];W[pl];B[qk];W[nl]
;B[kj];W[ii];B[rk];W[om];B[pg];W[ql];B[cp];W[co];B[oe];W[rl]
;B[sk];W[rj];B[hg];W[ij];B[km];W[gi];B[fj];W[jl];B[kl];W[gl]
;B[fl];W[gm];B[ch];W[ee];B[eb];W[bg];B[dg];W[eg];B[en];W[fo]
;B[df];W[dh];B[im];W[hk];B[bn];W[if];B[gd];W[fe];B[hf];W[ih]
;B[bh];W[ci];B[ho];W[go];B[or];W[rg];B[dn];W[cq];B[pr];W[qr]
;B[rf];W[qg];B[qf];W[jc];B[gr];W[sf];B[se];W[sg];B[rd];W[bl]
;B[bk];W[ak];B[cl];W[hn];B[in];W[hp];B[fr];W[er];B[es];W[ds]
;B[ah];W[ai];B[kd];W[ie];B[kc];W[kb];B[gk];W[ib];B[qh];W[rh]
;B[qs];W[rs];B[oh];W[sl];B[of];W[sj];B[ni];W[nj];B[oo];W[jp])

Hiroshi Yamashita

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[Computer-go] Game 4: a rare insight

[Darren Cook]

From Demis Hassabis:
  When I say 'thought' and 'realisation' I just mean the output of
  #AlphaGo value net. It was around 70% at move 79 and then dived
  on move 87

  https://twitter.com/demishassabis/status/708934687926804482

Assuming that is an MCTS estimate of winning probability, that 70%
sounds high (i.e. very confident); when I was doing the computer-human
team experiments, on 9x9, with three MCTS programs, I generally knew I'd
found a winning move when the percentages moved from the 48-52% range
to, say, 55%.

I really hope they reveal the win estimates for each move of the 5
games. It will especially be interesting to then compare that to the
other leading MCTS programs.

Darren

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