[Computer-go] Number of Go positions is itself a Go position

[David Ongaro]

To quote from: http://tromp.github.io/go/legal.html

> It should come as no surprise that L19, viewed as a position, is
> itself illegal.

In this absolute form this statement got disproved in my German Go Forum
article at
http://www.dgob.de/yabbse/index.php?topic=5935.msg216064#msg216064

Basically it's using a more natural Hilbert-based curve instead of an
arbitrary row-wise mapping which doesn't take the topology of the
Go-grid into account. Let me now if you need any of the details in
German translated.

David O.


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Re: [Computer-go] sgf file for recent handicap games of pros vs programs

[David Ongaro]

And as Hideko Kato already mentioned yesterday, you've to do some
sanitation after extraction. E.g. with

sed -i 's/KM\[\(.\)50/CA[utf-8]KM[\1.50/' *.sgf

(assuming GNU sed here, but should work similarly with BSD sed in Mac OS)


Am 01/23/2018 um 01:58 PM schrieb David Ongaro:
> It's a zip file:
>
> david@belle:~/Downloads$ file FineArt_A-2hcp.sgf
> FineArt_A-2hcp.sgf: Zip archive data, at least v2.0 to extract
>
>
> Am 01/23/2018 um 12:52 PM schrieb Ray Tayek:
>> is anyone collecting the sgf file for these games?
>>
>> i get the stuff below when i try to download.
>>
>> thanks
>>
>>
>> C:\Users\ray\Downloads>od -c FineArt_A-2hcp.sgf | head
>> 000   P   K 003 004 024  \0  \0  \0  \0  \0   < 005   5 L  \0  \0
>> 020  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0 017  \0  \0  \0 F   i
>> 040   n   e   A   r   t   _   A   -   2   h   c   p   /   P K 003
>> 060 004 024  \0  \0  \b  \b  \0   t 004   5   L   L   9   ] 233  \t
>> 100 002  \0  \0 227 004  \0  \0   A  \0  \0  \0   F   i   n e   A
>> 120   r   t   _   A   -   2   h   c   p   /   [   8   8   8 8   8
>> 140   8   8   8   ]   v   s   [ 347 273 235 350 211 272 346 214 207
>> 160 345 257 274   A   ]   1   5   1   6   0   7   2   2   0 8   0
>> 200   1   0   0   0   1   1   6   2   .   s   g   f   E 222 315 216
>> 220 323   0 024 205   _ 245   K 020 032 224 314   @ 325 241 253 026
>>
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Re: [Computer-go] sgf file for recent handicap games of pros vs programs

[David Ongaro]

It's a zip file:

david@belle:~/Downloads$ file FineArt_A-2hcp.sgf
FineArt_A-2hcp.sgf: Zip archive data, at least v2.0 to extract


Am 01/23/2018 um 12:52 PM schrieb Ray Tayek:
> is anyone collecting the sgf file for these games?
>
> i get the stuff below when i try to download.
>
> thanks
>
>
> C:\Users\ray\Downloads>od -c FineArt_A-2hcp.sgf | head
> 000   P   K 003 004 024  \0  \0  \0  \0  \0   < 005   5 L  \0  \0
> 020  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0 017  \0  \0  \0 F   i
> 040   n   e   A   r   t   _   A   -   2   h   c   p   /   P K 003
> 060 004 024  \0  \0  \b  \b  \0   t 004   5   L   L   9   ] 233  \t
> 100 002  \0  \0 227 004  \0  \0   A  \0  \0  \0   F   i   n e   A
> 120   r   t   _   A   -   2   h   c   p   /   [   8   8   8 8   8
> 140   8   8   8   ]   v   s   [ 347 273 235 350 211 272 346 214 207
> 160 345 257 274   A   ]   1   5   1   6   0   7   2   2   0 8   0
> 200   1   0   0   0   1   1   6   2   .   s   g   f   E 222 315 216
> 220 323   0 024 205   _ 245   K 020 032 224 314   @ 325 241 253 026
>

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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[david . ongaro]

On 2017-10-23 at 23:56, Thomas Rohde  wrote:
> On 2017-10-23 at 19:15, Xavier Combelle  wrote:
>
> > Hi Robert Jasiek,
> > 
> > you might have a delusional way to see the game of go and life,
> 
> this is quite an insult
Do you consider Robert's style of discussion "kind"? I for my part do
not.

I'm not saying that Robert's research in the area of Go corner cases
doesn't have any value, it certainly has. One probably needs a certain
kind of dedication to do it. But trying to bend every topic into this
area is more often than not uncalled for.

I don't know what it is. Maybe it's a certain kind of arrogance,
resulting from the fact of knowing more than anybody else about a
certain area in Go. But in the end it doesn't matter what it is, we
all have our faults. What matters is that I very seldom saw a
discussion with Robert lead to anything.

David

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Re: [Computer-go] AlphaGo Zero

[David Ongaro]

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


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

[...]
Just accept that something awesome happened and that studying those 
things that make it work well are more interesting than translating 
coefficients into a bad understanding for people.


I'm sorry that this NN can't teach anyone how to be a better player 
through anything other than kicking their ass, but it wasn't built for 
that.


Roberts approach might be misguided, but I don't agree that having the 
raw network data couldn't teach us something. E.g. have a look at this 
guy who was able to identify the neurons responsible for generating URLs 
in a wikipedia text generating RNN: 
http://karpathy.github.io/2015/05/21/rnn-effectiveness/#visualizing-the-predictions-and-the-neuron-firings-in-the-rnn.


E.g. it might be possible to find the network Part of AlphaGo Zero which 
is responsible for L problems and use it to dream up new Problems! The 
possibilities could be endless. This kind of research might have been 
easier with the "classic" AlphaGo with separated policy and value 
networks, but should be possible anyways.


Also lets not forget DeepMinds own substantial research in this area: 
https://deepmind.com/blog/cognitive-psychology/.


I understand that DeepMind might be unable to release the source code of 
AlphaGo due to policy or licensing reasons, but it would be great (and 
probably much more valuable) if they could release the fully trained 
network. As Gian-Carlo Pascutto has pointed out, replicating this would 
not only incur high hardware costs but also take a long time.


David O.


On Fri, Oct 20, 2017 at 8:24 AM, Robert Jasiek > wrote:


On 20.10.2017 15:07, adrian.b.rob...@gmail.com
 wrote:

1) Where is the semantic translation of the neural net to
human theory
knowledge?

As far as (1), if we could do it, it would mean we could
relate the
structures embedded in the net's weight patterns to some other
domain --


The other domain can be "human go theory". It has various forms,
from informal via textbook to mathematically proven. Sure, it is
also incomplete but it can cope with additions.

The neural net's weights and whatnot are given. This raw data can
be deciphered in principle. By humans, algorithms or a combination.

You do not know where to start? Why, that is easy: test! Modify
ONE weight and study its effect on ONE aspect of human go theory,
such as the occurrance (frequency) of independent life. No effect?
Increase the modification, test a different weight, test a subset
of adjacent weights etc. It has been possible to study semantics
of parts of DNA, e.g., from differences related to illnesses.
Modifications on the weights is like creating causes for illnesses
(or improved health).

There is no "we cannot do it", but maybe there is too much
required effort for it to be financially worthwhile for the "too
specialised" case of Go? As I say, a mathematical proof of a
complete solution of Go will occur before AI playing perfectly;)

So far neural
nets have been trained and applied within single domains, and any
"generalization" means within that domain.


Yes.

-- 
robert jasiek


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[Computer-go] Human Bias for 50 game selection? 0.5 Komi?

[David Ongaro]

Dear Aya,

I hope you still read this list and will answer a question once in a while even 
though AlphaGo has retired. On fb you answered Cho Seok-bins question "I heard 
that alphaGo master is 3stone stronger than sedol version.
Is it only rating deference? or did they have played with 3stone hadicap game?” 
with:

They played 3-stone handicap games with komi 0.5.

So I wonder how manage you overcome hardwired 7.5 Komi of the value network?

Another question floating around is how big the pool of games was from were the 
published 50 AlphaGo vs AlphaGo were selected from? And what was the selection 
criteria? Demis Hassabis mentioned that these were played with long time 
settings, so these don’t seem to be normal training games. Can you reveal what 
the actual time settings where? Were dedicated machines used for each AlphaGo?

Even if you can’t answer all question every answer is appreciated.

Thanks so much

David O.

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Re: [Computer-go] Yoon Young Sun (9p) on codecentric game

[David Ongaro]

Lukas didn’t play DeepZen, just “Zen” (or Zen19X on KGS to be specific, also 
see https://blog.codecentric.de/en/2016/08/codecentric-go-challenge-2016/ 
). As 
the organizer of the event you’re ought to know that?


> On Jan 18, 2017, at 4:54 AM, Ingo Althöfer <3-hirn-ver...@gmx.de> wrote:
> 
> Hello in the round,
> 
> Yoon Young Sun (9p; living in Hamburg) has commented 
> round 3 of the codecentric Challenge 2016 (between
> DeepZen (Black) and Lukas Kraemer (White)) in
> Germany's Go magazine (DGoZ, issue 06/2016, pp.42-45).
> 
> At the end sYoon makes some general comments on DeepZen
> (translated to English by me):
>> Zen is playing already very strong and is making 
>> reasonable moves. In my impression it plays more
>> human-like than AlphaGo who's moves partly need 
>> very much of getting used to.
>> In principle, in the whole game only move 89 [by Zen]
>> was really strange. All other of its moves I like.
>> 
>> Black has been leading already in the opening,
>> in particular by help of the exchange 28 for 29.
>> After the anxious move 58, 59 cemented the lead.
>> After this, White could only try to reach a narrow
>> outcome; a win [for White] was no longer possible.
> 
> For your memories: DeepZen had been winning the 2016
> codecentric Challenge by 3-1.
> 
> Ingo.

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Re: [Computer-go] Are the AlphaGols coming?

[David Ongaro]

> On Jan 9, 2017, at 6:51 AM, Robert Jasiek <jas...@snafu.de> wrote:
> 
> On 09.01.2017 07:19, David Ongaro wrote:
> >> accurate positional judgement
>> you also rely on “feelings” otherwise you wouldn’t be able to survive.
> 
> In my go decision-making, feelings / subconscious thinking (other than usage 
> of prior sample knowledge, such as status knowledge for particular shapes) 
> have an only marginal impact. For me, they serve as a preselection filter 
> besides my used methodical preselection filters. In blitz, the impact is 
> larger when time is insufficient for always using the methodical ones.

It is understandable that you believe that. That seems to be one of these 
strong illusions wich are helping survival. But tests have shown that decisions 
are normally made subconsciously seconds before we get aware of them (and 
therefore seconds before we consciously rationalize them). Among others 
John-Dylan Haynes did a lot of interesting related experiments for that. E.g 
see a short summary for two of them at 
https://www.youtube.com/watch?v=CT43MogXAjI=youtu.be=8m3s. Don’t be 
distracted by the fact that these where relatively simple experiments, with not 
much reasoning for making a choice involved. E.g. split brain experiments have 
shown that people can rationalize their action with one half of their brain 
while the other half actually did the decision and action for a different 
reason. The scary part is that they are convinced that the rationalization was 
actually the reason for their action. (If needed I can look up references for 
this, but I guess you already heard about these experiments.) 

I’m sure if you could make such a test while playing a Go game you would be 
surprised about the results.

David O.

PS: It should be said that “feeling” was an inaccurate word here, but I gather 
from your answer that you understood what I meant: i.e. the unconscious 
decision process. In fact, when we get aware of a “feeling”, when defined in 
the stricter sense as a product by the "limbic system”, the decision may 
already have been made.
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Re: [Computer-go] Are the AlphaGols coming?

[David Ongaro]

On Jan 5, 2017, at 10:49 PM, Robert Jasiek <jas...@snafu.de> wrote:
> 
> On 06.01.2017 03:36, David Ongaro wrote:
>> Two amateur players where analyzing a Game and a professional player 
>> happened to come by.
>> So they asked him how he would assess the position. After a quick look he 
>> said “White is
> > leading by two points”. The two players where wondering: “You can count 
> > that quickly?”
> 
> Usually, accurate positional judgement (not only territory but all aspects) 
> takes between a few seconds and 3 minutes, depending on the position and 
> provided one is familiar with the theory.

Believe it or not, you also rely on “feelings” otherwise you wouldn’t be able 
to survive.

Some see DNNs as some kind of “cache” which has knowledge of the world in 
compressed form. Because it's compressed it can’t always reproduce learned 
facts with absolute accuracy but on the other hand it has the much more desired 
feature to even yield reasonable results for states it never saw before.

Mathematically (the approach you seem yourself constrain into) there doesn’t 
seem to be a good reason why this should work. But if you take the physical 
structure of the world into account things change. In fact there is a recent 
pretty interesting paper (not only for you, but surely also for other readers 
in this list) about this topic: https://arxiv.org/abs/1608.08225 
<https://arxiv.org/abs/1608.08225>.

I interpret the paper like this: the number of states we have to be prepared 
for with our neural networks (either electronic or biological) may be huge, but 
compared to all mathematically possible states it's almost nothing. That is due 
to the fact that our observable universe is an emergent result of relatively 
simple physical laws. That is also the reason why deep networks (i.e. with many 
layers) work so well, even though mathematically a one layer network is enough. 
If the emergent behaviours of our universe can be understand in layers of 
abstractions, we can scale our network linearly by the number of layers 
matching the number of abstractions. That’s a huge win over the exponential 
growth required when we need a mathematical correct solution for all possible 
states.

The “physical laws” for Go are also relatively simple and the complexity of Go 
is an emergent result of these. That is also the reason why the DNNs are 
trained with real Go positions not just with random positions, which make up 
the majority of all possible Go positions. Does that mean the DNNs won’t 
perform well when evaluating random positions, or even just the "arcane 
positions” you discussed with Jim? Absolutely! But it doesn’t have to. That’s 
not its flaw but its genius.

David O.

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Re: [Computer-go] Are the AlphaGols coming?

[David Ongaro]

> On Jan 5, 2017, at 2:37 AM, Detlef Schmicker <d...@physik.de> wrote:
> 
> Hi,
> 
> what makes you think the opening theory with reverse komi would be the
> same as with standard komi?
> 
> I would be afraid to invest an enormous amount of time just to learn,
> that you have to open differently in reverse komi games :)

Thats why I used the comparative adjective “less”. It might not be ideal, but 
still much better than changing the fundamental structure of the opening with 
an extra stone. Furthermore the effect might not as big as you think:

1. The stronger player doesn’t have to play overplays when the handicap is 
correct. If the handicap is correct and if AlphaGo “knows” that is another 
question though… Of course the weaker player might play differently (i.e. more 
safely) but at least that is something he or she can control
2. One could even argue the other way around:  we might see more sound 
(theoretically correct) moves from AlphaGo with reverse Komi. If it's seeing 
itself ahead already during the opening it might resort to slack but safe 
moves. Since it’s still winning we can be left wondering if it was actually a 
good move. But if it does an unusual looking move which it can’t be considered 
an overplay but it’s still winning in the end with reverse Komi there should be 
a real insight to gain.

Still, a reverse Komi handicap is rather big, but it might be the next best 
thing we have without retraining the value network from scratch. Furthermore 
retraining the value network will probably affect the playing style even more.

Thanks,

David O.


> Am 05.01.2017 um 10:50 schrieb Paweł Morawiecki:
>> 2017-01-04 21:07 GMT+01:00 David Ongaro <david.ong...@hamburg.de>:
>> 
>>> 
>>> [...]So my question is: is it possible to have reverse Komi games
>>> by feeding the value network with reverse colors?
>>> 
>> 
>> In the paper from Nature (subsection "Features for policy/value
>> network"), authors state:
>> 
>> *the stone colour at each intersection was represented as either
>> player or opponent rather than black or white. *
>> 
>> Then, I think the AlphaGo algorithm would be fine with a reverse
>> komi. Namely, a human player takes black and has 7.5 komi. The next
>> step is that AlphaGo gives 2 stones of handicap but keeps 7.5 komi
>> (normally you have 0.5).
>> 
>> Aja, can you confirm this?
>> 
>> 
>>> Also having 2 stone games is not so interesting since it would
>>> reveal less insights for even game opening Theory.
>>> 
>> 
>> I agree with David here, most insights we would get from even
>> games. But we can imagine the following show. Some games are played
>> with a reverse komi, some games would be played with 2 stones (yet,
>> white keeps 7.5 komi) and eventually the main event with normal
>> even games to debunk our myths on the game. Wouldn't be super
>> exciting!?
>> 
>> Best regards, Paweł
>> 
>> 
>> 
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Re: [Computer-go] Are the AlphaGols coming?

[David Ongaro]

After this unbelievable streak of 60 won games (even though we still have to 
see how it holds up with longer time control) it’s not completely unthinkable 
anymore to play top pros with a handicap. Sadly because the Komi is fixed for 
the value network it seems the next bigger handicap is 2 stones with Komi for 
white which seems a big jump. Also having 2 stone games is not so interesting 
since it would reveal less insights for even game opening Theory. So my 
question is: is it possible to have reverse Komi games by feeding the value 
network with reverse colors? Or wouldn’t that work because the value network is 
so fine calibrated that it would throw it off if there is one more white stone 
than usual? I guess the policy network and MTSC should have no problems with a 
changed Komi?

Maybe Aja or some other expert for Value networks can answer that?

Thanks

David O.


> On Jan 4, 2017, at 8:02 AM, Jim O'Flaherty  wrote:
> 
> Tysvm for posting that!
> 
> I had predicted it was AlphaGo from the beginning. If there is a competitor 
> emerging, I think we would have seen some sort of publicity around it, if not 
> just to provoke a response with the AlphaGo team.
> 
> 
> On Wed, Jan 4, 2017 at 9:11 AM, Janzert  > wrote:
> On 1/2/2017 7:05 AM, "Ingo Althöfer" wrote:
> Hello Paweł,
> 
> There have been another 8 games on Foxwq server:
> ...
> Totally, 38-0. It looks like a kind, indirect (yet powerful), message
> from DeepMind to Chinese Go Association: "Please, let us try a real
> challenge, like 3-handicap games, it does not really make much sense
> to play even anymore".
> 
> So, do you want to say that "Master" might be AlphaGo?
> From the disucssion I thought that "Master" was a chinese bot.
> 
> If Aja is reading: can you enlighten us?
> 
> Cheers, Ingo.
> 
> Looks like we have an official answer in the affirmative
> https://twitter.com/demishassabis/status/816660463282954240 
> 
> 
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Re: [Computer-go] do you know King of Tsumego: Panda Sensei on android

[David Ongaro]

> On Oct 26, 2016, at 11:07 AM, Xavier Combelle <xavier.combe...@gmail.com> 
> wrote:
> Le 26/10/2016 à 19:42, David Ongaro a écrit :
>> 
>>> On Oct 26, 2016, at 9:32 AM, Xavier Combelle <xavier.combe...@gmail.com 
>>> <mailto:xavier.combe...@gmail.com>> wrote:
>>> 
>>> but it seems to me there is a problem in this variation proposed by Haylee: 
>>> http://eidogo.com/#3z7gfMaeI:0,7 <http://eidogo.com/#3z7gfMaeI:0,7>
>> 
>> You’re right. Instead of q16 B should play t14 immediately. (Interestingly: 
>> if there would be a white stone on t13 it would be a ‘eternal life' 
>> situation (because of the t14, t17, t15, t16, t14 cycle).)
>> 
> Actually if I don't mistake q16 at t14 don't work it, it was seen in the 
> video and lead to a seki http://eidogo.com/#aA8PgSSk:0,1,0 
> <http://eidogo.com/#aA8PgSSk:0,1,0>

Without the q19, r18 exchange it would be seki, but after this exchange its a 
roku moku nakade (http://senseis.xmp.net/?RabbittySix 
<http://senseis.xmp.net/?RabbittySix>}. I.e. if W answers t14 at q16, B can 
play t17, t15, r17, t19, s17 (for proving purposes, no need to actually play it 
unless B has to take white out during the game.)

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Re: [Computer-go] do you know King of Tsumego: Panda Sensei on android

[David Ongaro]

> On Oct 26, 2016, at 9:32 AM, Xavier Combelle  
> wrote:
> 
> but it seems to me there is a problem in this variation proposed by Haylee: 
> http://eidogo.com/#3z7gfMaeI:0,7 

You’re right. Instead of q16 B should play t14 immediately. (Interestingly: if 
there would be a white stone on t13 it would be a ‘eternal life' situation 
(because of the t14, t17, t15, t16, t14 cycle).)

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[Computer-go] GoGui dead?

[David Ongaro]

The GoGui Website on "http://gogui.sourceforge.net 
“ states: "This software is no longer actively 
developed or supported.” And Indeed there doesn’t seem to be any updates since 
beginning of the year (and that is after a 2 year hiatus).

So what are you using as an open source GTP client?

Thanks,

David O.

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Re: [Computer-go] US Go Congress AlphaGo Keynote speech uploaded to AGA Youtube Channel

[David Ongaro]

> On Aug 3, 2016, at 7:24 PM, Chun Sun  wrote:
> 
> This is the first talk by Aja and Fan Hui on Saturday 7/30 night.
> 
> https://www.youtube.com/watch?v=LX8Knl0g0LE 
> 

Thanks for the link, that was really revealing. So we can in fact say that 
there was no “bug" fixed in "AlphaGo 18" to fix the Problem of Game 4? It just 
went away due to more training?

So there is still training going on for AlphaGo. Why is it still on version 18 
then? What does this imply? It’s still trained but not as much as before? How 
much training would justify a new version number? Or is a new version number 
only given for actual code changes? Does that mean besides training there is no 
actual software development going on anymore for AlphaGo? I thought there are a 
lot of ideas which couldn’t go into AlphaGo 18 due to the "code freeze” for the 
Lee Sedol match. Are these ideas are not going to be implemented?

It’s a pity that these questions didn’t came up at the USGC but I hope Aja can 
answer them here?

Thanks,

David O.

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Re: [Computer-go] Commercial Go software and high-end users

[David Ongaro]

On 01 Jun 2016, at 00:45, Gian-Carlo Pascutto <g...@sjeng.org> wrote:

> On 31-05-16 22:56, David Ongaro wrote:
>> Isn't e.g. TensorFlow Apache 2.0 license and would allow its
>> inclusion in commercial products?
> 
> TensorFlow relies on CuDNN for good GPU performance. Almost all
> libraries do, because CuDNN is hand optimized by NVIDIA, and hence
> rather hard to beat.

Ok, the CuDNN license might not be as convenient as the Apache 2.0 license, but 
to quote from 
https://devblogs.nvidia.com/parallelforall/accelerate-machine-learning-cudnn-deep-neural-network-library/:

> Note that the cuDNN license allows you to install and use as many copies of 
> the software as you need, for both individual and corporate use.  This 
> intentionally permissive license is designed to allow cuDNN to be useful in 
> conjunction with open-source frameworks.

So it seems nvidia got out of his way to make cuDNN useable for commercial 
purposes without imposing extra license costs.

>> I might overestimating it, but on the other hand I guess a Professor
>> like Rémi has much more obligations other then writing Go Software.
>> So anything which could save time helps.
> 
> I don't really want to answer in Remi's place, but I think he's working
> fulltime on Go now. His page states:
> 
> "In the *past*, I was Associate Professor of Computer Science..."

I see, it must be great to be able to just focus on Computer Go these days… But 
I still would argue that Rémi probably shouldn't waste his time in reinventing 
the wheel.

David O.

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Re: [Computer-go] Commercial Go software and high-end users

[David Ongaro]

> On 31 May 2016, at 13:11, Gian-Carlo Pascutto <g...@sjeng.org> wrote:
> 
> On 31/05/2016 20:45, David Ongaro wrote:
>> I suspect Aja is right and Remi should go the path of integrating the
>> GPU even if it's just to get more "oomph" for CS. That he tried to
>> learn GPU programming from scratch is a noble attempt but I guess
>> it's just to ambitious to accomplish in a reasonable timeframe. Using
>> one of the ready to use frameworks should make it feasible though.
> 
> They're a pretty annoying burden if you want to make the engine
> commercial. I'm not even sure CuDNN can be bundled with the engine?

Isn't e.g. TensorFlow Apache 2.0 license and would allow its inclusion in 
commercial products?

> Additionally, not all customers might have a GPU that is enough faster
> than the CPU (i.e. not the built-in one in modern CPUs, save maybe AMD's
> APU units), so you need a good CPU fallback anyway. Oh, and if you use
> CUDA, you lose about 1/3rd of your customers, again.

Again, I think TensorFlow (but there might be others) is in big parts CPU/GPU 
agnostic, so one could make flexible choices here (even at runtime) without 
rewriting code. I'm aware that abstractions are leaking and not always 
desirable (e.g. if you want to take explicit advantage of the actual 
differences), but it should make things much easier to start with.

> You're overestimating the difficulty of programming a GPU though. Yes,
> if you've never done it before the programming takes some adjustment,
> but the SIMT model is very convenient to write code in, IMHO much easier
> than trying to coerce things to SIMD layouts.

I might overestimating it, but on the other hand I guess a Professor like Rémi 
has much more obligations other then writing Go Software. So anything which 
could save time helps.

David O.

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Re: [Computer-go] Commercial Go software and high-end users

[David Ongaro]

Hi Ingo,

> On 31 May 2016, at 00:07, Ingo Althöfer <3-hirn-ver...@gmx.de> wrote:
> 
> Hi Petr,
> 
> "Petr Baudis" 
>>> ... It is enough that the [CHESS] program is tactically strong.
>> 
>>  But strong Go programs are traditionally strategically strong, but
>> tactically *weak*. 
> 
> "tactical" was meant for Chess. In Go, players may use
> "the other strengths" of go programs. For instance, in November
> Benjamin Teuber (6d, one of the top German players) was impresssed
> by CrazyStone's analysis of one of his games (against FJ Dickhut).
> Teuber: "Some of CS' moves were eye-openers for me. I had never
> thought about those interesting moves."

But I guess you already have to be a quite strong player in order to see these 
nuggets in a sea of weaker moves. I wasn't very impressed by the CS analysis 
done by Michael Markefka from the German go forum 
(http://www.dgob.de/yabbse/index.php?action=dlattach;topic=6039.0;attach=4930). 
Maybe its acceptable that it didn't found r8 and preferred q7 because r8 is not 
easy to see (should be a no brainer for 7d though) but that it preferred e.g. 
m3 over r2 keeps me wondering. Then at later points in the game it likes to 
peep at q7 at random times for no reason. It actually found that d15 is better 
than c15 which CS played in the actual game but instead of the deciding mistake 
b3 it suggests c9, which, while much better than the game since its not a total 
blunder, is clearly inferior to the Joseki move c7.

Sure a strong player like Benjamin can distinguish between just bad and 
interesting suggestions and might learn from such analyses. But weaker players 
might end up confused and weaker than before.

Note that I'm not singing in this strange chorus of disappointment over CS 
performance in the game against Haylee. I think it's way to early for todays 
computer programs (besides AlphaGo) to attempt even games against Pros. So not 
only the loss but also the way of loss was to be expected. I only want to put 
CS capabilities as a Go tutor in perspective.

I suspect Aja is right and Remi should go the path of integrating the GPU even 
if it's just to get more "oomph" for CS. That he tried to learn GPU programming 
from scratch is a noble attempt but I guess it's just to ambitious to 
accomplish in a reasonable timeframe. Using one of the ready to use frameworks 
should make it feasible though.

David O.

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Re: [Computer-go] OmegaGo

[David Ongaro]

Some of "Dr. Browns" pamphlets remind me of Herman Hesse's Steppenwolf. But 
Hesse's text was much more refined and enjoyable to read, so I think "SPAM" is 
a fitting categorisation.



> On 20 Apr 2016, at 00:57, Michael Markefka  wrote:
> 
> Can I flag this as spam?
> 
> On Tue, Apr 19, 2016 at 11:23 PM, djhbrown .  wrote:
>> 6D out of the blue is no mean achievement,...  60+ years ago, the
>> market for gizmos in UK was flooded with cheap Japanese copies of
>> European products; but whilst innovation and product quality
>> improvement by European manufacturers faded as their fat cat owners
>> complacently went cocacola-soaked soft,  Japanese industry, unlike its
>> USA counterpart, was listening attentively to the wise words of
>> W.Edwards Deming (eg [1,2]) and beginning to improve the reliability,
>> efficiency and efficacy of its products, and by about 30 years ago,
>> Japanese engineering was the equal or better of even German
>> technology.
>> 
>> Korean, Formosan and Hong Kong e-tigers followed hotfoot in Japan's
>> footsteps, and now the same thing is happening in China, so we can
>> expect to see a vast array of Shanghai-teenager-bedroom-produced
>> shanghaied miniclones of Alpha, most with unimaginative copycat names
>> like Beta, Eta, Theta, AIota etc, skulking around the corridors of the
>> Internet, all of which will at first be cheap imitations, but sowing
>> the seeds of in-house and inter-house R quality circles, so that
>> their own descendants will before very long become to Californian IT
>> as Japanese fuel-efficient reliable engines are to US unreliable
>> gas-guzzlers.
>> 
>> Watch out Google Cloud byte-guzzlers, teenage rebels with the lessons
>> of Deming in their notebooks, who have learned from history and from
>> the sterling modus operandi of Steve Jobs and Uncle Tom Cobley et al,
>> are on their way up your Jacob's ladder...
>> 
>> 1.  Charles A. Barclay (1993) Quality Strategy and TQM Policies:
>> Empirical Evidence.
>> MIR: Management International Review Vol. 33, Strategic Quality Management.
>> 2.  
>> http://asq.org/learn-about-quality/total-quality-management/overview/deming-points.html
>> 
 Anybody knows who is the author of BetaGo? It is playing with account
 GoBeta on KGS, and is 6d.
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Re: [Computer-go] Congratulations to Zen!

[David Ongaro]

Hi Nick

> On 07 Apr 2016, at 12:47, Nick Wedd <mapr...@gmail.com> wrote:
> 
> (What I meant was that some of the people posting to the list that you gave a 
> link to have failed to understand it.)

Yes that's why I gave you a direct link to my comment. But the confusion is 
probably my fault, because I failed to mention that you have to press the 
spoiler button to see the solution (When I see a button I press it, so I didn't 
think about it. In that regard I'm no different to my 14 Month old son…)

To be more specific I think a correction could look like this:

 x . x x . x 9 6 .
 . x o o x 8 5 4 7
 x o o x x 1 o 3 x
 x o x x . 2 x x o
 . o x o x x o o o
 o o o o x o o . o
 o x o x . x o o .
 . x o x x x o o .
 x . x . x x o . o


In round 6 AyaMC played the move above against Fuego9. SGF. This looks hopeless 
for White because his left group has only two liberties. But when black played 
4 he suffers a damezumari himself and he gets only a Seki and therefore losing 
the game.

 x 7 x x 5 x 1 2 6
 9 x o o x a 8 4 3
 x o o x x o o o x
 x o x x . x x x o
 b o x o x x o o o
 o o o o x o o . o
 o x o x . x o o .
 . x o x x x o o .
 x . x . x x o . o

The correct but difficult to find move for black would have been 1 which makes 
miai of 2 and 3 creating another damezumari, this time for white, since he can 
not approach at a before black plays b.

Now that we already spend so much time on an old game it would be interesting 
to know if the current programs can get this right?

Thanks,

David



> On 7 April 2016 at 20:15, David Ongaro <david.ong...@hamburg.de> wrote:
> Thanks, but what do you mean by that cheesy comment "Human players can find 
> this hard to understand"? If you want to make a meaningful comment you could 
> say that Fuego didn't understand the situation by playing h8 instead of g9.
> 
> 
> > On 07 Apr 2016, at 11:31, Nick Wedd <mapr...@gmail.com> wrote:
> >
> > Hi David,
> >
> > Thank you for telling me about this.  I have added a link to the article.
> >
> > Best,
> > Nick
> >
> >
> > On 4 April 2016 at 16:53, David Ongaro <david.ong...@hamburg.de> wrote:
> > Sorry for being late to the "game" (but you never specified a deadline till 
> > when you accept corrections). From: 
> > http://www.weddslist.com/kgs/past/41/index.html
> >
> > > Also in round 6, AyaMC played the move shown to the left, against Fuego9. 
> > > SGF. When I saw this move, I thought "this is hopeless for White, his 
> > > left group only has two liberties". But Black can never quite put the 
> > > two-liberty group in atari, and the marked move ensures that he never 
> > > will be able to. What is particularly impressive is that White knows 
> > > this, and has been working towards it for several moves, ignoring a ko 
> > > threat at a9 and sacrificing stones at b1 and d1.
> >
> >
> > I showed already 2008 in a german Go forum that your intuition was right 
> > and the position was indeed hopeless for white: 
> > http://www.dgob.de/yabbse/index.php?topic=3314.msg153307#msg153307
> >
> >
> > > On 04 Apr 2016, at 06:33, Nick Wedd <mapr...@gmail.com> wrote:
> > >
> > > Thank you for noticing this. I will correct it when I am back at home and 
> > > can use a real computer.
> > >
> > > Nick
> > >
> > > On Apr 4, 2016 2:09 PM, "Seo Sanghyeon" <sanx...@gmail.com> wrote:
> > > 2016-03-31 0:17 GMT+09:00 Nick Wedd <mapr...@gmail.com>:
> > > > My report is at  http://www.weddslist.com/kgs/past/S16.1/index.html
> > > > As usual, I look forward to hearing your comments and criticisms; 
> > > > though I
> > > > may not respond for a while, I am about to leave for a week's holiday.
> > >
> > > "In round 6, after 87 moves, Zen19S and JulieBot reached the position 
> > > shown
> > > to the right (JulieBot is Black)."
> > >
> > > It seems that Zen19S should be ManyFaces1 instead.
> > >
> > > --
> > > Seo Sanghyeon
> > > ___
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> >
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Re: [Computer-go] Congratulations to Zen!

[David Ongaro]

Thanks, but what do you mean by that cheesy comment "Human players can find 
this hard to understand"? If you want to make a meaningful comment you could 
say that Fuego didn't understand the situation by playing h8 instead of g9.


> On 07 Apr 2016, at 11:31, Nick Wedd <mapr...@gmail.com> wrote:
> 
> Hi David,
> 
> Thank you for telling me about this.  I have added a link to the article.
> 
> Best,
> Nick
> 
> 
> On 4 April 2016 at 16:53, David Ongaro <david.ong...@hamburg.de> wrote:
> Sorry for being late to the "game" (but you never specified a deadline till 
> when you accept corrections). From: 
> http://www.weddslist.com/kgs/past/41/index.html
> 
> > Also in round 6, AyaMC played the move shown to the left, against Fuego9. 
> > SGF. When I saw this move, I thought "this is hopeless for White, his left 
> > group only has two liberties". But Black can never quite put the 
> > two-liberty group in atari, and the marked move ensures that he never will 
> > be able to. What is particularly impressive is that White knows this, and 
> > has been working towards it for several moves, ignoring a ko threat at a9 
> > and sacrificing stones at b1 and d1.
> 
> 
> I showed already 2008 in a german Go forum that your intuition was right and 
> the position was indeed hopeless for white: 
> http://www.dgob.de/yabbse/index.php?topic=3314.msg153307#msg153307
> 
> 
> > On 04 Apr 2016, at 06:33, Nick Wedd <mapr...@gmail.com> wrote:
> >
> > Thank you for noticing this. I will correct it when I am back at home and 
> > can use a real computer.
> >
> > Nick
> >
> > On Apr 4, 2016 2:09 PM, "Seo Sanghyeon" <sanx...@gmail.com> wrote:
> > 2016-03-31 0:17 GMT+09:00 Nick Wedd <mapr...@gmail.com>:
> > > My report is at  http://www.weddslist.com/kgs/past/S16.1/index.html
> > > As usual, I look forward to hearing your comments and criticisms; though I
> > > may not respond for a while, I am about to leave for a week's holiday.
> >
> > "In round 6, after 87 moves, Zen19S and JulieBot reached the position shown
> > to the right (JulieBot is Black)."
> >
> > It seems that Zen19S should be ManyFaces1 instead.
> >
> > --
> > Seo Sanghyeon
> > ___
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> > Computer-go@computer-go.org
> > http://computer-go.org/mailman/listinfo/computer-go
> > ___
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> 
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> 
> 
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Re: [Computer-go] new challenge for Go programmers

[David Ongaro]

On 30 Mar 2016, at 03:04, djhbrown .  wrote:
> 
> as to preconceived notions, my own notions are postconceived, having
> studied artificial intelligence and biological computation over 40
> post-doctoral years during which i have published 50 or so
> peer-reviewed scientific papers, some in respectable journals,
> including New Scientist.

And there might be even some valuable research in these papers. If so please 
don't continue to spoil the reputation of your earlier years by pamphlets of 
the recent kind.

Btw: arguments by authority might work once or even twice, but not constantly.


> On 30/03/2016, Stefan Kaitschick  wrote:
>> Your lack of respect for task performance is misguided imo. Your
>> preconceived notions of what intelligence is, will lead you astray.
>> 
> 
> 
> -- 
> patient: "whenever i open my mouth, i get a shooting pain in my foot"
> doctor: "fire!"
> http://sites.google.com/site/djhbrown2/home
> https://www.youtube.com/user/djhbrown
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Re: [Computer-go] Nice graph

[David Ongaro]

That would mean 3 stones if the "4 stone handicap" has the same definition as 
in the paper (7.5 Komi for white and 3 extra moves for black after the first 
move. Yes that implies that a traditional 4 stone handicap (without Komi for 
white) is in fact 3.5 Stones).


> On 25 Mar 2016, at 17:23, Rémi Coulom  wrote:
> 
> AlphaGo improved 3-4 stones:
> 
> http://i.imgur.com/ylQTErVl.jpg
> 
> (Found in the Life in 19x19 forum)
> 
> Rémi
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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] CNN with 54% prediction on KGS 6d+ data

[David Ongaro]

Did everyone forget the fact that stronger playouts don't necessarily lead to 
an better evaluation function? (Yes, that what playouts essential are, a 
dynamic evaluation function.) This is even under the assumption that we can 
reach the same number of playouts per move.


> On 08 Dec 2015, at 10:21, Álvaro Begué  wrote:
> 
> I don't think the CPU-GPU communication is what's going to kill this idea. 
> The latency in actually computing the feed-forward pass of the CNN is going 
> to be in the order of 0.1 seconds (I am guessing here), which means finishing 
> the first playout will take many seconds.
> 
> So perhaps it would be interesting to do something like this for 
> correspondence games, but not for regular games.
> 
> 
> Álvaro.
> 
> 
> 
> On Tue, Dec 8, 2015 at 12:03 PM, Petr Baudis  > wrote:
>   Hi!
> 
>   Well, for this to be practical the entire playout would have to be
> executed on the GPU, with no round-trips to the CPU.  That's what my
> email was aimed at.
> 
> On Tue, Dec 08, 2015 at 04:37:05PM +, Josef Moudrik wrote:
> > Regarding full CNN playouts, I think that problem is that a playout is a
> > long serial process, given 200-300 moves a game. You need to construct
> > planes and transfer them to GPU for each move and read result back (at
> > least with current CNN implementations afaik), so my guess would be that
> > such playout would take time in order of seconds. So there seems to be a
> > tradeoff, CNN playouts are (probably much) better (at "playing better
> > games") than e.g. distribution playouts, but whether this is worth the
> > implied (probably much) lower height of the MC tree is a question.
> >
> > Maybe if you had really a lot of GPUs and very high thinking time, this
> > could be the way.
> >
> > Josef
> >
> > On Tue, Dec 8, 2015 at 5:17 PM Petr Baudis  > > wrote:
> >
> > >   Hi!
> > >
> > >   In case someone is looking for a starting point to actually implement
> > > Go rules etc. on GPU, you may find useful:
> > >
> > >
> > > https://www.mail-archive.com/computer-go@computer-go.org/msg12485.html 
> > > 
> > >
> > >   I wonder if you can easily integrate caffe GPU kernels in another GPU
> > > kernel like this?  But without training, reimplementing the NN could be
> > > pretty straightforward.
> > >
> > > On Tue, Dec 08, 2015 at 04:53:14PM +0100, Michael Markefka wrote:
> > > > Hello Detlef,
> > > >
> > > > I've got a question regarding CNN-based Go engines I couldn't find
> > > > anything about on this list. As I've been following your posts here, I
> > > > thought you might be the right person to ask.
> > > >
> > > > Have you ever tried using the CNN for complete playouts? I know that
> > > > CNNs have been tried for move prediction, immediate scoring and move
> > > > generation to be used in an MC evaluator, but couldn't find anything
> > > > about CNN-based playouts.
> > > >
> > > > It might only be feasible to play out the CNN's first choice move for
> > > > evaluation purposes, but considering how well the performance of batch
> > > > sizes scales, especially on GPU-based CNN applications, it might be
> > > > possible to setup something like 10 candidate moves, 10 reply
> > > > candidate moves and then have the CNN play out the first choice move
> > > > for those 100 board positions until the end and then sum up scores
> > > > again for move evaluation (and/or possibly apply some other tried and
> > > > tested methods like minimax). Given that the number of 10 moves is
> > > > supposed to be illustrative rather than representative, other
> > > > configurations of depth and width in position generation and
> > > > evaluation would be possible.
> > > >
> > > > It feels like CNN can provide a very focused, high-quality width in
> > > > move generation, but it might also be possible to apply that quality
> > > > to depth of evaluation.
> > > >
> > > > Any thoughts to share?
> > > >
> > > >
> > > > All the best
> > > >
> > > > Michael
> > > >
> > > > On Tue, Dec 8, 2015 at 4:13 PM, Detlef Schmicker  > > > > wrote:
> > > > > -BEGIN PGP SIGNED MESSAGE-
> > > > > Hash: SHA1
> > > > >
> > > > > Hi,
> > > > >
> > > > > as somebody ask I will offer my actual CNN for testing.
> > > > >
> > > > > It has 54% prediction on KGS 6d+ data (which I thought would be state
> > > > > of the art when I started training, but it is not anymore:).
> > > > >
> > > > > it has:
> > > > > 1
> > > > > 2
> > > > > 3
> > > > >> 4 libs playing color
> > > > > 1
> > > > > 2
> > > > > 3
> > > > >> 4 libs opponent color
> > > > > Empty points
> > > > > last move
> > > > > second last move
> > > > > third last move
> > > > > forth last move
> > > > >
> > > > > input layers, and it is fully convolutional, so with just editing the
> > > > > golast19.prototxt file you can use it for 

Re: [Computer-go] Congratulations to CrazyStone!

[David Ongaro]

> On looking at the position later, I found that White would get two more ko 
> threats by starting at c2, they are at c1 and then at e2, while Black has two 
> ko threats in the upper left corner. So by playing at c2 instead of a2, White 
> could have won the ko fight and the game. (I realise that CGI9's thought 
> processes are entirely unlike mine; but it is now so rare for me to notice an 
> error in play that I wanted to mention this one.)
> 
I don't agree to that assessment. Black had three more threads on the upper 
right corner (f9, j9, h9) if he needs to, so there was no way White could have 
won the game. Of course Sa6 was also a thread removing thread, but it didn't 
mattered anymore since white removed threads first (so they both agreed more or 
less to finish the Ko early).



> On 05 Oct 2015, at 11:28, Nick Wedd  wrote:
> 
> Congratulations to CrazyStone, winner of yesterday's 20-round 9x9 KGS bot 
> tournament, with one loss, two jigoes, and 17 wins.
> 
> My report at http://www.weddslist.com/kgs/past/116/index.html 
>  is longer than usual, so 
> is likely to contain mistakes. If you have any comments on it, I hope you 
> will let me know.
> 
> Nick
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Re: [computer-go] Great Wall Opening by Bruce Wilcox

[David Ongaro]

Ingo Althöfer schrieb:

Now I made some autoplay tests, starting from the end position
given in the appendix of this mail.
* one game with Leela 3.16; Black won.
* four games with MFoG 12.016; two wins each for Black and White.
So there is some indiciation that the Great Wall works even
for bots, who are not affected by psychology.

I would like to know how other bots perform in autoplay
after this opening.
  
Have you tried some random Setup for the first 5 stones from Black and 
compared the results? If there's no significant difference, I can't see 
the point in your question.


Regards

David

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Re: [computer-go] Rating variability on CGOS

[David Ongaro]

David Fotland schrieb:

Many Faces also had more trouble against pachi than you would expect from
its rating.  Perhaps Pachi is generally stronger, but throws away some
percentage of games (even against weak players) because of some bug.
  


Seems plausible. But instead of guessing, the standard deviation of the 
rating should give a good indication of such problems. So why doesn't 
CGOS provide the standard deviation of ranks? Should be easy enough to 
calculate and it provides valuable information about the buggyness of 
a program.


In physics, a measured value without standard deviation is useless. For 
good reasons.


David


-Original Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of Brian Sheppard
Sent: Thursday, October 08, 2009 12:48 PM
To: computer-go@computer-go.org
Subject: [computer-go] Rating variability on CGOS

About two weeks ago I took Pebbles offline for an extensive overhaul of
its
board representation. At that time Valkyria 3.3.4 had a 9x9 CGOS rating
of
roughly 2475.

When I looked today, I saw Valkyria 3.3.4 rated at roughly 2334, so I
wondered what was going on.

I found a contributing factor: Valkyria has massively different results
against Pachi than against Pebbles. It happens that Pachi started
playing a
day or two after Pebbles went offline.

Pebbles and Pachi are both rated around 2200, but Valkyria shreds
Pebbles a
lot more often than Pachi:

Pachi:   185 / 273 = 67.8%
Pebbles: 429 / 503 = 85.3%

There are a lot of lessons here...

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