oh comeon. Assuming you have a reasonable evaluation search is everything in Go of course.George Tsavdaris wrote:matthewlai wrote: Yes, that's exactly what I meant. Handwritten evaluation functions in chess are good enough that switching to a neural network (slightly better outputs but much slower) is a net negative.
I really hope someone skilled in ML steps in and take it as far as it can go and see what happens.
Even if the AlphaGo/Giraffe approach doesn't work, there are still many possible machine learning approaches that can be explored.
The situation is easy as i see it:
In GO the huge branching factor and number of moves for the average game is so huge, that no computer or human can base or improve his play, improving that. I.e improving search in GO does not do anything(compared to evaluation function).
So evaluation is THE MOST crucial factor by far!
From the 361 moves you can HARD FORWARD PRUNE kind of 300 moves already.
A few moves later in the game you can almost hard forward prune 330 moves out of the 340 legal moves or so. In chess this is totally impossible.
So search is EVERYTHING, however under 2 conditions.
Condition 1 is a reasonable evaluation function yet condition 2 might surprise most.
You *do* need to cover a specific search space and that means simply a lot of nodes. Now as most go programmers hardly know how to program compared to the chessprogrammers their programs are ugly slow. Usually using neural network for example that's pathetic slow.
So the easy solution then is to throw big hardware at it.
Once you do have big hardware - something most can afford now - say a 48 core AMD machine gets you far already and its peanuts to pick it up.
Yet in that case search is everything.
There is however little money to make (from an absolute viewpoint) with computer-go so no really good programmers ever got into that job of writing software for it let alone brilliant game tree search guys.
That's why it took so long for something to develop there.
If as much money and especially brilliant game tree searchers would've thrown into the game of go quite possibly the strongest go program now wouldn't be using a neural net and it would not be using monte carlo yet a clever selective search.
The advantage of that you basically see in game 4 very well. AlphaGo suffers from major horizon effect playing moves that just lost point after point and contributing to its loss.
Note that i wonder what they offered Lee Sedol, who isn't the strongest go player right now - he didn't even win a single big tournament past 4 years - that he played so bad first 3 games.
He made huge blunders there that lost him obviously game 2.
Such blunders are equal to me blundering away a piece.
Now i'm approaching IM level (having 2 IM norms) yet only if i play really little and under big time trouble i start blundering.
So alphago in chess rating would be roughly 2250 now or so.
Very few go players are professional go players and basically at age 11 you get chosen as a go player to get a professional player (usually).
With so few pro's there, how high are the odds that a talented kid of 11 years old also becomes a very strong professional player - or does he just get another IM elo 2360 who blunders a lot?
Draw your own conclusions on what Lee Sedol did do - what Bob described years ago as the computer shock (if i remember well) - namely the first time you play a very strong chessprogram - then pro's suddenly get faced with how tactical bad they play.
In chess what we see is that basically all old world top players who blunder lost like 100 elopoints or more and that a new generation got there which plays tactical far superior over any previous top player (Kasparov and Karpov and Kramnik maybe excepted). Anand managed to improve himself tactical quite a lot past 20 years.
He's actually around 2850 elo - just cuz there is nothing at stake at tournaments, candidates tournament excepted, he takes it too easy too many games and draws those.
That's why his elo is a lot lower.
Yet he'll win this candidates tournament quite possibly as i predicted months ago.
If we compare that with the state of the art in go - then there is just a 100 pro players or so, majority whom got picked to get a pro player at age of 11.
The strongest go player is an 18 year old Chinese player.
In 2015 at the age of 17 he won many important tournaments.
Would that be possible in chess?
I am no big expert in go (probably to most posters here i am as of course semi-professional chess versus semi-professional go you need the same skills to do well in those games) i can't rate the chinese yet i'm pretty convinced he can't be bribed to lose 3 games in a row.
Not saying that this is what Lee Sedol did do - yet let's face it - he didn't win a single big tournament past 4 years that Lee Sedol if i look at his wikipedia.
I guess Google (Alphabet) didn't have enough cash to play worlds strongest go player.
In 2007 i predicted exactly this scenario when we were standing in the bunker of Amsterdam.
The chinese Go delegation, one of the professional go players nearly wanted to kill me for that. Fire came out of her eyes.
"THAT WILL NEVER HAPPEN" she said. "A professional go player will never lose from a computer".
Where i answerred her that the real weakness of humankind is money.
What we saw from search viewpoint from alphago in game 2 and game 4 wasn't very impressive simply.
Big progress maybe for those who didn't follow computer go lately. Let's see whether Google still has the guts to challenge worlds number 1.
We know what IBM did do when kasparov played a few elo 2000 games against it. Or how Salov called it: "just a mediocre GM playing Deep Blue as kasparov without his openings isn't a very good player".
Where i disagree with that statement from Salov - it says enough about the quality of those games.
Yet it's really interesting to see what Google will do now...