[Computer-go] Knowledge Details

[Robert Jasiek]

The current fashion favours general AI approaches forgoing knowledge 
details. Given enough calculation power applied to well chosen AI 
techiques, many knowledge details are redundant because they are 
generated automatically: AlphaGo does play (at least some) ko fights 
with ko threats, tesujis, test moves, (at least some) life and death or 
semeai problems etc. At the same time, AI calculation power is still not 
large enough to generate all human knowledge details. Aji with long-term 
impact and maintaining the life status "independently alive" instead of 
unnecessarily transforming it to "(ko|independently alive)" (aka 
"unsettled") are prime examples. Programs also play for the win 
regardless of whether moves are suboptimal for the score difference - 
human players tend to avoid such (programs would also profit from 
avoiding such to prevent losing when making a later mistake due to a 
knowledge gap related to insufficient error handling). There is another 
great threat related to knowledge details, which is not immediately 
apparent and will be even much less apparent when programs will exceed 
top human playing strength: A program can run into a situation where an 
infrequent knowledge detail becomes relevant. And a program can run into 
ordinary software or hardware bugs, something that must be detected and 
correct on the AI level.


My conclusion is: human expert knowledge on details of go theory matters.

There have been 9p players committing self-atari when filling a dame, so 
you might argue that programs may infrequently make similar blunders. 
When I issued a million dollar prize, I'd prefer human expert knowledge 
implemented at least as an additional layer of error handling.


(Other fun includes internet connection trouble, server bugs of 
distributed computers, hardware bugs of the local interface computers or 
interrupted power supply.)


--
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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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>
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Re: [Computer-go] 57%

[Petr Baudis]

  Hi!

On Wed, Feb 03, 2016 at 10:24:51AM +0100, Robert Jasiek wrote:
> AlphaGo is said to predict 57% of professionals' moves. How is this number
> measured and from which sample?
> 
> At some turns, there is only one correct move - at other turns, strong go
> players would say that there are several valid supposedly correct moves.
> This is one of the reasons why 100% cannot be the optimum but a smaller
> percentage must be the best.
> 
> Pro players, or players of the database sample (incl. real world 3d players
> being 9d on KGS), make mistakes. A neural net learns from a sample and
> therefore also learns the mistakes. This is the most important reason why
> 100% cannot be the optimum but a smaller percentage must be the best.
> 
> (Roughly) which percentage is optimal? Why? Is the optimum greater or
> smaller than 57%?

  You are right about these questions.  This is from the KGS dataset
http://u-go.net/gamerecords/ that contains 7d+ games.  Yes, optimum is
very far below 100%; there was some discussion on this mailing list in
the past about checking strong human performance on this dataset, but
AFAIK nothing came out of that yet...

  My impression is that current neural networks seem to converge to
about 60%.  It would be interesting if humans can still do better.
Another idea would be considering accuracy as a top N measure among
selected moves, whether it has better discernive power for currently
used models.

-- 
Petr Baudis
If you have good ideas, good data and fast computers,
you can do almost anything. -- Geoffrey Hinton
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[Computer-go] 57%

[Robert Jasiek]

AlphaGo is said to predict 57% of professionals' moves. How is this 
number measured and from which sample?


At some turns, there is only one correct move - at other turns, strong 
go players would say that there are several valid supposedly correct 
moves. This is one of the reasons why 100% cannot be the optimum but a 
smaller percentage must be the best.


Pro players, or players of the database sample (incl. real world 3d 
players being 9d on KGS), make mistakes. A neural net learns from a 
sample and therefore also learns the mistakes. This is the most 
important reason why 100% cannot be the optimum but a smaller percentage 
must be the best.


(Roughly) which percentage is optimal? Why? Is the optimum greater or 
smaller than 57%?


--
robert jasiek
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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.

>>>
>>> ___
>>> 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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>> http://computer-go.org/mailman/listinfo/computer-go
>>
>
>
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Re: [Computer-go] Knowledge Details

[Robert Jasiek]

On 03.02.2016 15:34, Jim O'Flaherty wrote:

BTW, I have my own personal aspirations which have been thwarted by this
development. I have several thousand hours of doing my own research and
development [...] although I
will likely drift further away from Go as the focal point of motivation.


Maybe I should know but I have not always closely followed the relations 
between persons and computer project / research names. Therefore please 
let me ask: Which have been your personal aspirations, motivations and 
research investments?



Best of luck finding your way through your meaning and value (emotional)
reintegration of this newest reality update.


Nothing has changed (or will change when "brute force" surpasses top 
human play) for me because my main research goals are the strong 
solution of go under every go ruleset and the explanation of go theory 
to human players (incl. myself).


--
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Re: [Computer-go] Knowledge Details

[Jim O'Flaherty]

Robert,

How have these things emerged in the chess AI world following Deep Blue and
Kasperov's loss over a decade ago? To what degree does "human expert
details of chess theory matters" (where the term "matters" is pretty
squishy). From what I can see, that is not what happened and while I am not
privy to every detail of every motivation in the chess AI world, I'm
certainly not seeing this assertion or the supporting values arising to any
level of relevance, much less primacy.

And for those who were working in the Chess knowledge world, how was their
work, business, grants and funding affected by the Deep Blue/Kasperov
results and then the rapid improvement of more than one chess engine to
beyond the highest skilled humans? What happened to prize tournaments? To
what degree is it reasonable to predict a similar pattern will occur in and
about Go and those who are working in the Go knowledge world?

BTW, I have my own personal aspirations which have been thwarted by this
development. I have several thousand hours of doing my own research and
development (of my personal spare time outside my day job, over many years)
which has been rendered considerably less valuable (other than my own
personal development in the non-Go related parts). And I'm finding it
difficult to embrace this "change" as I had no idea just how much
motivation it created in the present having the Go AI goal as an inspiring
future. The loss of that motivation has created anxiety and uncertainty.

And even in spite of the loss and the grief I am experiencing in that loss,
I am still very enthusiastic about Aja and his team's achievements. And I
will be following all the teams who continue to work in this area. For
myself, I will now look for other ways to apply my knowledge, although I
will likely drift further away from Go as the focal point of motivation.

Best of luck finding your way through your meaning and value (emotional)
reintegration of this newest reality update.


Namaste,

Jim


On Wed, Feb 3, 2016 at 3:51 AM, Robert Jasiek  wrote:

> The current fashion favours general AI approaches forgoing knowledge
> details. Given enough calculation power applied to well chosen AI
> techiques, many knowledge details are redundant because they are generated
> automatically: AlphaGo does play (at least some) ko fights with ko threats,
> tesujis, test moves, (at least some) life and death or semeai problems etc.
> At the same time, AI calculation power is still not large enough to
> generate all human knowledge details. Aji with long-term impact and
> maintaining the life status "independently alive" instead of unnecessarily
> transforming it to "(ko|independently alive)" (aka "unsettled") are prime
> examples. Programs also play for the win regardless of whether moves are
> suboptimal for the score difference - human players tend to avoid such
> (programs would also profit from avoiding such to prevent losing when
> making a later mistake due to a knowledge gap related to insufficient error
> handling). There is another great threat related to knowledge details,
> which is not immediately apparent and will be even much less apparent when
> programs will exceed top human playing strength: A program can run into a
> situation where an infrequent knowledge detail becomes relevant. And a
> program can run into ordinary software or hardware bugs, something that
> must be detected and correct on the AI level.
>
> My conclusion is: human expert knowledge on details of go theory matters.
>
> There have been 9p players committing self-atari when filling a dame, so
> you might argue that programs may infrequently make similar blunders. When
> I issued a million dollar prize, I'd prefer human expert knowledge
> implemented at least as an additional layer of error handling.
>
> (Other fun includes internet connection trouble, server bugs of
> distributed computers, hardware bugs of the local interface computers or
> interrupted power supply.)
>
> --
> robert jasiek
> ___
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[Computer-go] Forecasting Lee Sedol vs. AlphaGo

[Andrés Román]

Hello !

I would like to share with this list the possibility to participate
forecasting on the forecoming game,

"Will Google's AlphaGo beat world champion Lee Sedol in the five game Go
match planned for March 2016?"

That is posted in the Good Judgement Project open forecasting site.

https://www.gjopen.com/questions/133-will-google-s-alphago-beat-world-champion-lee-sedol-in-the-five-game-go-match-planned-for-march-2016#

It will be great to read the forecasts and opinions of members of this list.

Andres
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Re: [Computer-go] Knowledge Details

[uurtamo .]

Just as an aside,

One nice thing about having "expert" chess players is the ability to easily
discover cheating and to estimate the "player rank" of any move. Because
the computer is effectively an oracle for that game, it gives incidental
feedback about strength of any given move.

steve
On Feb 3, 2016 6:34 AM, "Jim O'Flaherty"  wrote:

> Robert,
>
> How have these things emerged in the chess AI world following Deep Blue
> and Kasperov's loss over a decade ago? To what degree does "human expert
> details of chess theory matters" (where the term "matters" is pretty
> squishy). From what I can see, that is not what happened and while I am not
> privy to every detail of every motivation in the chess AI world, I'm
> certainly not seeing this assertion or the supporting values arising to any
> level of relevance, much less primacy.
>
> And for those who were working in the Chess knowledge world, how was their
> work, business, grants and funding affected by the Deep Blue/Kasperov
> results and then the rapid improvement of more than one chess engine to
> beyond the highest skilled humans? What happened to prize tournaments? To
> what degree is it reasonable to predict a similar pattern will occur in and
> about Go and those who are working in the Go knowledge world?
>
> BTW, I have my own personal aspirations which have been thwarted by this
> development. I have several thousand hours of doing my own research and
> development (of my personal spare time outside my day job, over many years)
> which has been rendered considerably less valuable (other than my own
> personal development in the non-Go related parts). And I'm finding it
> difficult to embrace this "change" as I had no idea just how much
> motivation it created in the present having the Go AI goal as an inspiring
> future. The loss of that motivation has created anxiety and uncertainty.
>
> And even in spite of the loss and the grief I am experiencing in that
> loss, I am still very enthusiastic about Aja and his team's achievements.
> And I will be following all the teams who continue to work in this area.
> For myself, I will now look for other ways to apply my knowledge, although
> I will likely drift further away from Go as the focal point of motivation.
>
> Best of luck finding your way through your meaning and value (emotional)
> reintegration of this newest reality update.
>
>
> Namaste,
>
> Jim
>
>
> On Wed, Feb 3, 2016 at 3:51 AM, Robert Jasiek  wrote:
>
>> The current fashion favours general AI approaches forgoing knowledge
>> details. Given enough calculation power applied to well chosen AI
>> techiques, many knowledge details are redundant because they are generated
>> automatically: AlphaGo does play (at least some) ko fights with ko threats,
>> tesujis, test moves, (at least some) life and death or semeai problems etc.
>> At the same time, AI calculation power is still not large enough to
>> generate all human knowledge details. Aji with long-term impact and
>> maintaining the life status "independently alive" instead of unnecessarily
>> transforming it to "(ko|independently alive)" (aka "unsettled") are prime
>> examples. Programs also play for the win regardless of whether moves are
>> suboptimal for the score difference - human players tend to avoid such
>> (programs would also profit from avoiding such to prevent losing when
>> making a later mistake due to a knowledge gap related to insufficient error
>> handling). There is another great threat related to knowledge details,
>> which is not immediately apparent and will be even much less apparent when
>> programs will exceed top human playing strength: A program can run into a
>> situation where an infrequent knowledge detail becomes relevant. And a
>> program can run into ordinary software or hardware bugs, something that
>> must be detected and correct on the AI level.
>>
>> My conclusion is: human expert knowledge on details of go theory matters.
>>
>> There have been 9p players committing self-atari when filling a dame, so
>> you might argue that programs may infrequently make similar blunders. When
>> I issued a million dollar prize, I'd prefer human expert knowledge
>> implemented at least as an additional layer of error handling.
>>
>> (Other fun includes internet connection trouble, server bugs of
>> distributed computers, hardware bugs of the local interface computers or
>> interrupted power supply.)
>>
>> --
>> robert jasiek
>> ___
>> Computer-go mailing list
>> Computer-go@computer-go.org
>> http://computer-go.org/mailman/listinfo/computer-go
>
>
>
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Re: [Computer-go] Knowledge Details

[David Ongaro]

On 03 Feb 2016, at 06:58, Robert Jasiek  wrote:
> 
> On 03.02.2016 15:34, Jim O'Flaherty wrote:
>> Best of luck finding your way through your meaning and value (emotional)
>> reintegration of this newest reality update.
> 
> Nothing has changed (or will change when "brute force" surpasses top human 
> play) for me because my main research goals are the strong solution of go 
> under every go ruleset and the explanation of go theory to human players 
> (incl. myself).

I admire your high aspirations. At the same time I've to point out that you 
seem to plan to get very old. I also doubt that a meagre million $ will get you 
far on that endeavour. So much more resources are needed…

In the meantime the Go community will move on and follow approaches which 
provide a much better and faster return on investment. And to state the 
obvious: fb and Google are not really interested to provide knowledge systems 
for Go in order to improve Go understanding for humans. Their motivation is to 
use Go as a testbed for a general AI which can be applied to a wide range of 
applications.

But even as a Go-Community that should fill us with satisfaction: don't ask 
what your country can do for you, ask what you can do for your country. If Go 
can help progressing mankind that also reflects positively on the game itself.

Whereas you and me are probably mostly interested in Go itself and just follow 
computer Go and AI because of that, I guess it's the other way around for most 
people on this mailing list. But we all only can win. I just hope that Lee 
Sedol will win the March match so that Go will win more admires.

David O.

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Re: [Computer-go] Knowledge Details

[Robert Jasiek]

On 04.02.2016 02:52, David Ongaro wrote:

At the same time I've to point out that you seem to plan to get very old.


I will not see the solution, which needs at least another 400 years 
unless computers learn to research.


--
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