[Computer-go] Replicating AlphaGo results

Hi! Since I didn't say that yet, congratulations to DeepMind! (I guess I'm a bit disappointed that no really new ML models had to be invented for this though, I was wondering e.g. about capsule networks or training simple iterative evaluation subroutines (for semeai etc.) by NTM-based

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

On Thu, Jan 28, 2016 at 3:14 PM, Stefan Kaitschick wrote: > That "value network" is just amazing to me. > It does what computer go failed at for over 20 years, and what MCTS was > designed to sidestep. Thought it worth a mention: Detlef posted about trying to train

Re: [Computer-go] Replicating AlphaGo results

On Thu, Jan 28, 2016 at 10:29:29AM -0600, Jim O'Flaherty wrote: > I think the first goal was and is to find a pathway that clearly works to > reach into the upper echelons of human strength, even if the first version > used a huge amount of resources. Once found, then the approach can be >

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

I always thought the same. But I don't think they tackled the decomposition problem directly. Achieving good(non-terminal) board evaluations must have reduced the problem. If you don't do full playouts, you get much less thrashing between independent problems. It also implies a useful static L

Re: [Computer-go] Replicating AlphaGo results

I think the first goal was and is to find a pathway that clearly works to reach into the upper echelons of human strength, even if the first version used a huge amount of resources. Once found, then the approach can be explored for efficiencies from both directions, top down (take this away and

Re: [Computer-go] Replicating AlphaGo results

> I'd propose these as the major technical points to consider when > bringing a Go program (or a new one) to an Alpha-Go analog: > ... > * Are RL Policy Networks essential? ... Figure 4b was really interesting (see also Extended Tables 7 and 9): any 2 of their 3 components, on a single

Re: [Computer-go] Game Over

I would just mention that Maven/Scrabble truncated rollouts are not comparable to Go/MCTS truncated rollouts. An evaluation function in Scrabble is readily at hand, because scoring points is hugely correlated with winning. There is no evaluation function for Go that is readily at hand. There

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

On 1/27/16 12:08 PM, Aja Huang wrote: 2016-01-27 18:46 GMT+00:00 Aja Huang >: Hi all, We are very excited to announce that our Go program, AlphaGo, has beaten a professional player for the first time. AlphaGo beat the European

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

Hi Simon, do your remember my silly remarks in an email discussion almost a year ago? You had written: >> So, yes, with all the exciting work in DCNN, it is very tempting >> to also do DCNN. But I am not sure if we should do so. And my silly reply had been: > I think that DCNN is somehow in a

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

Congratulations! What I find most impressive is the engineering effort, combining so many different parts, which even standalone would be a strong program. I think the design philosophy of using 3 different sources of "go playing" strength is great in it self (and if you read the paper there

Re: [Computer-go] Game Over

On 28.01.2016 04:57, Anders Kierulf wrote: Please let me know if I misinterpreted anything. You write "Position evaluation has not worked well for Go in the past" but I think you should write "...Computer Go..." because applicable, reasonably accurate theory for human players' positional

Re: [Computer-go] Game Over

I find it interesting that right until he ends his review, Antti only praises White's moves, which are the human ones. When he stops, he even considers a win by White as basically inevitable. Now Fan Hui either blundered badly afterwards, or more promising, it could be hard for humans to evaluate

Re: [Computer-go] Game Over

That would make my writing nonsense of course. :) Thanks for the pointer. On Thu, Jan 28, 2016 at 12:26 PM, Xavier Combelle wrote: > > > 2016-01-28 12:23 GMT+01:00 Michael Markefka : >> >> I find it interesting that right until he ends his

Re: [Computer-go] Game Over

Hi Xavier, Really nice comments by Antti Törmänen, to the point and very clear explanation. Thanks for the pointer. best regards, Jan van der Steen On 28-01-16 11:45, Xavier Combelle wrote: here a comment by Antti Törmänen

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

I think many amateurs would already benefit from a simple blunder check and a short list of viable alternatives and short continuations for every move. If I could leave my PC running over night for a 30s/move analysis at 9d level and then walk through my game with that quality of analysis, I'd be

Re: [Computer-go] Game Over

here a comment by Antti Törmänen http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/ 2016-01-28 11:19 GMT+01:00 Darren Cook : > > If you want to view them in the browser, I've also put them on my blog: > > >

Re: [Computer-go] Game Over

> If you want to view them in the browser, I've also put them on my blog: > http://www.furidamu.org/blog/2016/01/26/mastering-the-game-of-go-with-deep-neural-networks-and-tree-search/ > (scroll down) Thanks. Has anyone (strong) made commented versions yet? I played through the first game, but it

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

I think such analysis might not bee too usefull. At least chess players think it is not very usefull. Usually for learning you need "wake-up" your brains so computer analysis without reasons probabaly on marginally useful. But very entertaining 2016-01-28 13:27 GMT+02:00 Michael Markefka

Re: [Computer-go] Game Over

> here a comment by Antti Törmänen > http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/ Thanks, exactly what I was looking for. He points out black 85 and 95 might be mistakes, but didn't point out any dubious white (computer) moves. He picks out a couple of white moves

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

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