Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Robert Jasiek
On 20.10.2017 21:12, uurtamo . wrote: do something like really careful experimental design across many dimensions simultaneously (node weights) and several million experiments -- each of which will require hundreds if not tens of thousands of games to find the result of the change. Worse, there

Re: [Computer-go] Zero performance

2017-10-20 Thread Gian-Carlo Pascutto
I agree. Even on 19x19 you can use smaller searches. 400 iterations MCTS is probably already a lot stronger than the raw network, especially if you are expanding every node (very different from a normal program at 400 playouts!). Some tuning of these mini searches is important. Surely you don't

Re: [Computer-go] Zero performance

2017-10-20 Thread John Tromp
> You can also start with 9x9 go. That way games are shorter, and you probably > don't need 1600 network evaluations per move to do well. Bonus points if you can have it play on goquest where many of us can enjoy watching its progress, or even challenge it... regards, -John

Re: [Computer-go] Zero performance

2017-10-20 Thread fotland
The paper describes 20 and 40 block networks, but the section on comparison says AlphaGo Zero uses 20 blocks. I think your protobuf describes a 40 block network. That's a factor of two  If you only want pro strength rather than superhuman, you can train for half their time. Your time looks

Re: [Computer-go] Zero performance

2017-10-20 Thread Sorin Gherman
Training of AlphaGo Zero has been done on thousands of TPUs, according to this source: https://www.reddit.com/r/baduk/comments/777ym4/alphago_zero_learning_from_scratch_deepmind/dokj1uz/?context=3 Maybe that should explain the difference in orders of magnitude that you noticed? On Fri, Oct 20,

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Gian-Carlo Pascutto
On Fri, Oct 20, 2017, 21:48 Petr Baudis wrote: > Few open questions I currently have, comments welcome: > > - there is no input representing the number of captures; is this > information somehow implicit or can the learned winrate predictor > never truly approximate the

Re: [Computer-go] Zero performance

2017-10-20 Thread Álvaro Begué
I suggest scaling down the problem until some experience is gained. You don't need the full-fledge 40-block network to get started. You can probably get away with using only 20 blocks and maybe 128 features (from 256). That should save you about a factor of 8, plus you can use larger

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread uurtamo .
This sounds like a nice idea that is a misguided project. Keep in mind the number of weights to change, and the fact that "one factor at a time" testing will tell you nearly nothing about the overall dynamics in a system of tens of thousands of dimensions. So you're going to need to do something

[Computer-go] Zero performance

2017-10-20 Thread Gian-Carlo Pascutto
I reconstructed the full AlphaGo Zero network in Caffe: https://sjeng.org/dl/zero.prototxt I did some performance measurements, with what should be state-of-the-art on consumer hardware: GTX 1080 Ti NVIDIA-Caffe + CUDA 9 + cuDNN 7 batch size = 8 Memory use is about ~2G. (It's much more for

Re: [Computer-go] Subject: Re: AlphaGo Zero

2017-10-20 Thread Robert Jasiek
On 20.10.2017 16:44, Hendrik Baier wrote: Where is the respect and the appreciation for other people's groundbreaking work without immediately having to make the discussion about your own research, or otherwise derailing it into the irrelevant or fantastical? Instead of joining your

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Gian-Carlo Pascutto
On 19-10-17 13:00, Aja Huang via Computer-go wrote: > Hi Hiroshi, > > I think these are good questions. You can ask them at  > https://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/ It seems the question was indeed asked but not answered:

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Robert Jasiek
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

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Álvaro Begué
When I did something like this for Spanish checkers (training a neural network to be the evaluation function in an alpha-beta search, without any human knowledge), I solved the problem of adding game variety by using UCT for the opening moves. That means that I kept a tree structure with the

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Gian-Carlo Pascutto
On 19-10-17 13:23, Álvaro Begué wrote: > Summing it all up, I get 22,837,864 parameters for the 20-block network > and 46,461,544 parameters for the 40-block network. > > Does this seem correct? My Caffe model file is 185887898 bytes / 32-bit floats = 46 471 974 So yes, that seems pretty close.

[Computer-go] Subject: Re: AlphaGo Zero

2017-10-20 Thread Hendrik Baier
Where is the respect and the appreciation for other people's groundbreaking work without immediately having to make the discussion about your own research, or otherwise derailing it into the irrelevant or fantastical? Congratulations again to the AlphaGo team! Excellent work well described, a

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Petr Baudis
I tried to reimplement the system - in a simplified way, trying to find the minimum that learns to play 5x5 in a few thousands of self-plays. Turns out there are several components which are important to avoid some obvious attractors (like the network predicting black loses on every move from

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Adrian . B . Robert
Robert Jasiek writes: > So there is a superstrong neural net. > > 1) Where is the semantic translation of the neural net to human theory > knowledge? > > 2) Where is the analysis of the neural net's errors in decision-making? > > 3) Where is the world-wide discussion preventing

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Dan Schmidt
On Fri, Oct 20, 2017 at 12:06 AM, Robert Jasiek wrote: > > 3) Where is the world-wide discussion preventing a combination of AI and > (nano-)robots, which self-replicate or permanently ensure energy access, > from causing extinction of mankind? > You will find it if you Google

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Robert Jasiek
On 20.10.2017 09:38, Xavier Combelle wrote: What is currently named nanorobot is simply hand assembled molecules which have mechanical properties and need huge framework to be able simply move. Sure. But we must not wait until such a thing exists. -- robert jasiek

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Xavier Combelle
You seems to lack of knowing what is really a nano robot in current term. They are very far to have the possibility to self replicate them self and far more being able to dissolve the planet by doing that. What is currently named nanorobot is simply hand assembled molecules which have mechanical

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Robert Jasiek
On 20.10.2017 07:10, Petri Pitkanen wrote: >> 3) Where is the world-wide discussion preventing a combination of AI >> and (nano-)robots, which self-replicate or permanently ensure energy >> access, from causing extinction of mankind? 3) Would it be a bad thing? All thing considered, not just

Re: [Computer-go] AlphaGo Zero

2017-10-20 Thread Petri Pitkanen
1) There is no such thing and I do doubt if it ever will exist. Even humans fail elaborate why they know certain things 2) If we are talking about new one. Very few people seen it playing so I guess we lack the data. For the old we know it made errors, dunno if analysis points why. Neural nets