Hacker News Reader

Comment by jballanc | Hacker News Reader

So AlphaGo is just a bot after all...

Toward the end AlphaGo was making moves that even I (as a double-digit kyu player) could recognize as really bad. However, one of the commentators made the observation that each time it did, the moves forced a highly-predictable move by Lee Sedol in response. From the point of view of a Go player, they were non-sensical because they only removed points from the board and didn't advance AlphaGo's position at all. From the point of view of a programmer, on the other hand, considering that predicting how your opponent will move has got to be one of the most challenging aspects of a Go algorithm, making a move that easily narrows and deepens the search tree makes complete sense.

View on HN

Or maybe it was just the computer version of grasping at straws. None of the future gamestates looked good, so it ended up picking whatever could at least theoretically lead to a comeback, even if that would require Lee Sedol to miss a completely obvious move.

This looks like a possibility to me. I'm not sure that Alphago has any idea how strong an opponent it is facing at any given moment. It's played games against venues and it's played games against professionals. But if a set of complex advanced moves are still likely to lead to a narrow defeat, it might well pick some stupid moves that in theory could allow it to win back a dominant position if the opponent plays badly.Since it doesn't have a model of the competence of its opponent, that might appear to be a viable strategy because against some opponents and in some past games it's played, that could work.

This is really interesting, because forming a model of our opponent and tailoring our strategies appropriately is fundamental to how humans approach competitions.

> Toward the end AlphaGo was making moves that even I (as a double-digit kyu player) could recognize as really bad.

Maybe its training was focused on winning, not loosing narrowly. So as soon as it became obvious it can't win. It was just making silly moves because it was less researched scenario.

Some people when they see they can't win they do silly moves just for fun.

They're not really silly moves, it's just that in order to get into a good state for AlphaGo, Lee Sedol would have to miss the obvious response to those moves.

If he had made a dumb move and AlphaGo capitalized on it we would be talking about how the AI goaded him into a sense of safety.

If he had made a dumb move in response then we would be talking about how he made a really really dumb move, that even an amateur could have seen.

The moves made by AlphaGo there were very bad.

If the situation was unwinnable then in a certain sense all moves are equally bad.

This is not the reason. Monte Carlo programs typically play bad-looking moves in way ahead / way behind situations. In these situations "bad" moves will sometimes not alter the win probability they measure, so they don't know how to disguinish a more natural move from one that looks bad. This was mentioned in the commentary when one of the DeepMind guys came in.

Go programmers have taken various steps to mitigate this behavior, such as dynamically adjusting komi to trick the engine into thinking it is a closer game, but I don't know if AlphaGo uses any such technique.

A bit like getting a confused parser back on track. We've witnessed the first digital cold sweat.

I'm curious - is this the same thing that it was doing in the matches that it won? People have been talking about how it seemed to be toying with its opponent towards the end of those matches, making moves that didn't gain it much, but maybe it just doesn't actually understand what moves to play some of the time and it simply hasn't mattered before.

I doubt its loss function tries to maximize the prediction of the opponents next move.

I doubt it's considering that directly. What I'm guessing (without having really studied the paper in depth) is that if you have two board positions which would score identically in the end, but where one has fewer remaining liberties than the other (i.e. one is deeper into the tree), then it will be scored more highly.

In other words, the humans commentating the game were evaluating the moves as non-sensical because the outcome (AlphaGo plays here so Lee plays here) is a foregone conclusion and doesn't change the human evaluation of the board position. What it does do is remove uncertainty (AlphaGo plays here, but Lee screws up and plays somewhere else). In their evaluation, humans tend to value that uncertainty (i.e. counting on the possibility of a mistake), but I'd guess that AlphaGo penalizes the uncertainty (i.e. known board positions are scored higher than potential board positions), leading it to over-value simple advancement of the board in the end-game.

The bigger the game board, the more possible states, the better chance a human player has against the AI. Is this the same heuristic in play here - Play a game with more unknowns, gain an edge?

Do you think Lee could use this as a way to crack AlphaGo?

From my limited knowledge of the game, a few of the moves that Lee made before AlphaGo "lost its mind" were a tad on the aggressive side. The conventional wisdom in Go is to prefer more conservative moves (increasingly so as the game progresses). Usually, if your opponent is being overly aggressive then you want to play more conservatively and wait for them to make a mistake, but in AlphaGo's case, it attempted to match Lee's aggressiveness move-for-move, and Lee was able to capitalize.

If I had to guess (and this is pure speculation), AlphaGo has no concept of waiting for its opponent to make a mistake. Instead, it assumes its opponent will continue to make the best possible follow-ups, and so AlphaGo feels overly compelled to "keep up". In this case, that did it in.

If this is what happened, then yes, I would expect Lee to be able to capitalize.

In that case, theoretically at least, we could train AlphaGo by getting top Go players to play many games against each other where one or both players is making very aggressive moves when reasonable?

Well, that's where AlphaGo and the progress in Go AI that it represents is so exciting! The game of Go is so fluid with such a huge number of possible positions that players tend to adopt certain styles of play en masse. I've heard it said that you can identify a Go player's mentor or "house" just by the style of play they use.

This has also resulted in larger shifts in playing style over time. Studying very old (and I mean very old...700+ years old) games can be entertaining and even educational in the abstract, but you won't want to directly adopt the style of play because the game has evolved.

It's already been mentioned a couple of times that AlphaGo almost certainly represents just such a shift. Top players will learn from it, and I'd even be willing to bet they will beat it with some regularity once they do!

Ultimately, what sets apart Go geniuses is their ability to play creatively in the face of seemingly insurmountable challenges. So the big question is how "creative" AlphaGo can be. Is it merely synthesizing strong play from known positions? Can it introduce novel strategies? And if it does, will it be able to adjust as other Go masters adjust to it and bring their own brand of creativity to play?

To answer your original question, this very well could introduce a new era of more aggressive play to the world of Go. Only time will tell...

AlphaGo will learn from any new styles and apply them effectively without mistakes from fatigue or inattention.

This incarnation of AI is not creative, it wont generate new play styles, that is still the domain of top human players for now. But it will ruthlessly learn and adopt any new and improved strategies. That's really the point to take away from its success so far.

AlphaGo mostly plays against itself, meaning it learns in a very separate environment. It certainly might come up with novel strategies.

I suppose that's possible. However Demis Hassabis has said many people have noted that AlphaGo makes human-like moves. He commented that it made sense since AlphaGo taught itself from the games of human players.

It could also be that human-looking moves are already close to the best possible. Neither AlphaGo nor human players can play god's hand, but they're approaching the same location.

Is Go-Space understood well enough to know either way?

The OMG-AI people claim that AGI would be dangerous because it would reliably innovate in new spaces and out-predict humans.

So a true super-AGI would make go moves that were unexpected and incomprehensible with some percentage of misleading fake-outs, but it would still win most or all of the time.

If the human exploration of Go-Space is close to the god's hand bounds, this can't be true.

My intuition (and it's really only just that) is that Go space is large enough that AI would be able to outplay humans while still not even beginning to approach "perfect" play. If so, then I would also expect that humans should be able to follow the lead of AI into new areas of Go space, and outplay the AI (at least until the AI has a chance to learn and catch up).

We'll know if this is the case in a couple of years, if the competition between human and AI goes back-and-forth (unlike Chess, where after AI was good enough to beat humans, it could do so reliably).

Either way, it's interesting to note that AlphaGo had literally thousands of games to learn from to find weaknesses in human play, but Lee Sedol seems to have only needed 3 before he was able to find weaknesses in AlphaGo's play.

> Either way, it's interesting to note that AlphaGo had literally thousands of games to learn from to find weaknesses in human play, but Lee Sedol seems to have only needed 3 before he was able to find weaknesses in AlphaGo's play.

To be fair we can't know how many games Sodol played in his own head to figure this out.

Yes, the starting kernel of its learning this time was a collection of human games. This has caused the Policy Network (which says "given this board state, these moves are worth investigating in more detail") to be biased towards more human-like moves.

But they're already working on a new version of AlphaGo which isn't trained on any human data at all. It starts by making truly random moves and improves from there. This will require much more processing time and probably an order of magnitude more "self-play", but it will probably result in truly novel strategies that aren't part of the current human metagame.

> If I had to guess (and this is pure speculation), AlphaGo has no concept of waiting for its opponent to make a mistake. Instead, it assumes its opponent will continue to make the best possible follow-ups

One of the DeepMind guys just confirmed that this is how AlphaGo operates in the press conference.

Which makes sense. It doesn't even have a concept of a human opponent or human-style mistakes - it plays against Lee just like it plays against itself when training.

"When you're up against someone smarter than you are, do something insane and let them think themselves to death."

-- Pyanfar Chanur (C.J. Cherryh)

This is by design and admitted by the developers - in fact it plans only for what it judges optimal opponent moves. A modification to the game may be to keep on reserve a set of options for nonoptimal opponent play although in actual practice the simplicity of design here (maximize probability of winning and only plan on optimal opponent moves) may be one reason why it plays as well as it does.

Step 1 of exploiting this is "play a brilliant move to win a fight in the middle game", which is not the easiest thing to repeat.

Reply on news.ycombinator.com