[Computer-go] 7.0 Komi and weird deep search result

Aja ajahuang at gmail.com
Wed Apr 6 17:08:45 PDT 2011


Hi Don,

I don't think you were "rambling on". Your words are informative and constructive to me (especially you have very strong background and experience from computer chess). Specifically, I am reminded that "computer go is still in it's infancy and we are still looking for the big fixes and have not yet come to fully appreciate the immense practical power of incremental improvements over time." Also, I will be more careful in measuring the improvement, as exampled a lot in your description (my supervisor Remi Coulom also repeatedly corrects me at this point).

Aja
  ----- Original Message ----- 
  From: Don Dailey 
  To: Aja 
  Cc: computer-go at dvandva.org 
  Sent: Wednesday, April 06, 2011 9:51 PM
  Subject: Re: [Computer-go] 7.0 Komi and weird deep search result




  On Wed, Apr 6, 2011 at 3:32 AM, Aja <ajahuang at gmail.com> wrote:

    Hi Don,
    Thanks for your penetrating ideas. Yes, I would like to reconsider my feeling and hope that it doesn’t misguide anyone.

    We both know the recent controversy between Fruit and Rybka (or Fabien and Vasik), but of course it’s not the issue here right now. Just want to mention in passing that Fabien said he might develop a Go program in the next few years, so we can expect for another open-source strong program.


  I hope he does,  but of course I did not bring this up to talk about the controversy,  just the reality that computer chess software is marching on at a remarkable pace and this was an excellent example to illustrate that.   

    It’s just my guess that it’s very hard for current MCTS to surpass amateur 5d or 6d. One main reason is it’s difficult to solve a lot of different semeai and life-and-death instances in pro level, even if the program is running on a super big hardware (by this point I was impressed by Olivier’s talk in a conference of Taiwan, in which he gave an “easy” semeai example that Mogo cannot solve with very larger number of simulations). 


  I want to point out that in computer chess that this same exact thing was often done not so many years ago.   A relatively simple position would be presented that humans easily understood,  but seemed completely out of reach for computer chess programs to understand.   It was easy to see that computers would need some ridiculous breakthroughs to be able to understand such positions and the conclusion was that computers probably would never be close to the top humans in chess.   


  It's my view that such illustrations tended to cause people to draw the wrong conclusions and sent people off in the wrong direction,  looking for non-existent breakthroughs and concluding that incremental progress was a completely foolish way to proceed. 


  I believe we (as humans) lack a bit of imagination when it comes to these sort of things.   For example the 4 minute mile was consider physiologically unattainable a few years before the first one was run - in other words it was hard to imagine it ever happening.    It's often difficult for us to imagine things that are too different from what we are currently experiencing (especially  once we decide it is "hard.")     Maybe part of the problem is that we live in an instant gratification society and no longer think in terms of hard work and gradual progress,  we want an instant "breakthrough."    


  Progress is a funny thing if you put numbers on it.  If you get 1%, it doesn't seem like hardly anything. But if you add 1% to that, then 1% again,  it's like compound interest in a bank and you look back over just a few of these and are surprised by how much progress you make.     


  I have been surprised that in chess the point of diminishing returns is farther away that it seems and I'm sure in GO it is even more so by a large degree.   In other words ELO progress in software has been more or less steady,  not slowing to a crawl.   Yes, it is punctuated with small spikes but seen over anything more than a couple of years it's remarkably smooth.    As evidence of that,  the program Houdini recently was released that is at least 50 ELO over it's nearest competitor,  but you can be sure that is only a temporary situation - it will look like a weak program in 2 or 3 years.  

    Another aspect is that it’s extremely hard for MCTS to consider/argue for few points in early stages on 19x19 (because it only sees winning rate and dynamic komi is far from enough to fix it) and that is exactly what pros are very able to.


  The only thing you are telling me is that we picked a hard problem.   There is nothing here inherently unsolvable,  we are  just impatient and cannot imagine (yet) how we are going to solve this.   


  I have discovered that in computer chess (which I have been into for decades) the "unsolvable" problems didn't really make that much difference in the short term.     The solutions come at a natural rate and until programs get a lot better in other areas you will find that some of these "glaring" weaknesses do not make much different in terms of how strong the program is at the moment even when it seems like its a huge deal.    These weaknesses gradually start making a huge difference  when the program is really good and we tend to judge programs more by their weaknesses than their strengths.    So when we see something "ugly" it makes us think the program cannot be as strong as it actually has proved to be.    And computer program have strengths and weakness in different proportions than we do so this tends to distort our own views of how good or bad they play.  


  An example in computer chess is basic endgame knowledge.   It's really ugly to see a chess program trade down from a won ending to a draw because it doesn't understand that certain simple endings cannot be won despite being a piece up. Years ago, after seeing glaring example of this horrible weakness, I took some time and implemented a large number of scoring corrections to deal with this as well as putting in king and pawn versus king perfect play database.   I patted myself on the back and expect to see a decent ELO gain.    However even on modern programs this probably does not add more than 2 or 3 ELO and I'm being generous.     If you show a grandmaster some of these glitches he might conclude that your program plays like an amateur (in fact when programs first became master strength many strong human players would see one of the "ugly" moves and conclude that the program could not play a move like that and even be "expert" strength, let alone master strength.)


  I'm not saying these are not real problem in computer go,  but the point is that there a large number of problems that altogether define exactly where we stand right now and we just have to start making dents (which we actually have been doing to a remarkable degree if you would only look more carefully.)    The bigger problems are just going to take longer to fix than the lesser problems.      Also,  I believe we have to get over this notion that we have to "fix it" completely.  We probably will not fix it suddenly with a one line program change,  but we can and will find ways to minimize the problems, and it may be gradual and incremental.      


  In your example you rightly note that program do very well when in their "sweet spot",  when there are clearly defined goals that affect winning percentages.     In computer chess it used to be believed that no amount of searching could improve the programs "horrible" positional play and that computers only played well if there were immediate tactical considerations,  otherwise they quickly went wrong.     That turned out not to be true, it was just not clearly understood at the time because we were looking at the problem through our own biased eyes and seeing the ugly things.     The truth of the matter is that the tree search and playouts works well in all positions but some more than others and we will find ways to clearly improve the situation in the future with incremental progress (not major breakthroughs.)    Also, we have some clearly wrong things that we will fix (like eye definitions we have are approximations and are sometimes broken.)  


  I'll say it again,  I think computer go is still in it's infancy and we are still looking for the big fixes and have not yet come to fully appreciate the immense practical power of incremental improvements over time.   When the problem looks big we feel like small improvements are a waste of time but nothing is farther from the truth.   




    The progress in hardware by Mogo, Fuego and pachi is well-known and impressive, so that I don’t think the amazing progress in computer Go is mainly due to software. Both hardware and software are important in making a strong Go program for now, as far as I can see.


  I think the pattern is the same as it happened in computer chess.   But I personally believe that software will be a much bigger contributor to progress in the future (even if you ignore the "slowdown" of Moores law.)




    I hope your prediction is right: “without anything really major (but no doubt some new small ideas) we are going to see your KGS 5 and 6 dan and much higher in 5 to 10 years.” If not, then we will have a lot of “interesting” work to do, no matter testing methodology, engineering or academic etc. 


  I am convinced I am right on this one.   I have absolutely nothing against finding major breakthroughs of course but I think what we will call "breakthroughs" will be things that add up to 50 ELO or less.    In chess it was things like check extensions,  null move pruning, futility pruning,  LMR etc.    We called null move pruning a "major breakthrough" but when it was first used it added something like 40 or 50 ELO to the strength of a chess program.    Is that a major breakthrough?  You don't notice 50 ELO right away by watching it play because it still will lose 43 percent of the games and thus still get outplayed in many games,  but  It depends on your definition of "major" I guess.     In go that would be something like 1/2 dan.     I do think there will be a few of these kinds of breakthroughs.    What happens is that these good ideas need to get refined and improved too.   I think we get much more out of null move pruning than we used to.   LMR when first implemented does not give chess program hardly any gain until it's done just right.   But when refined it's pretty huge.   My first LMR implementation was only about 20, now it's like 100 ELO or more.   


  Thanks for listening to me ramble on about this ...


  Don



    Aja

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