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There's no doubt that with further refinement, we'll soon see AI play Go at a level well beyond human

No doubt? Seriously? What kind of knowledge do you have to make such statements? There are plenty of examples where technology has rapidly advanced to some remarkable level, but then almost completely plateaued. For example, space travel or Tesla's work on applications of electromagnetism. Heck, even other areas of AI research.

I really don't see why people here readily assume that this particular approach to computers playing Go is easily improvable. Neither do I see why everyone assumes there will be no discoveries of anti-AI strategies that will work well against it.

With neural networks involved, it's hard to say. And all we have so far is information about about, what, 15 games? Some of which were won by people. Mind you, those people never played AlphaGo before, while the bot benefited from a myriad of training samples, as well as from Go expertise of some of its creators.

I'm also tired of all the statements about "accelerating progress". It's not like all the AI research of the past was useless until DNNs came along. That's the narrative I often get from the media, but it misrepresents the history of the field. There was no shortage of working ML/AI algorithms in the past decades. The main problem was always at applying them to real-world things in useful ways. And in that sense, AlphaGo isn't much different from Deep Blue.

One big shift in the field is that these days a lot of AI research is done by corporations rather than universities. Corporations are much better at selling whatever they do as "useful", which isn't such a good thing in the long run. We're redefining progress as we go and moving goalposts for every new development.




> No doubt? Seriously? What kind of knowledge do you have to make such statements?

Uh, click the link in the OP and find out? AI just beat a top 5 human professional 4-1. Go rankings put that AI at #2 in the world.

If AlphaGo improves at all at this point it will have achieved a level well beyond any human.

It is incredibly, ludicrously unlikely that AlphaGo has achieved the absolute peak of its design given that it went from an elo of ~2900 to ~3600 in just a few months.


There are actually a lot of room for improvement. Just some of the things:

(1) Better timing control. Maybe when the probability of winning reaches below say, 50% but has not hit the losing threshold, spend extra time.

(2) Introducing "anti-fragility". Maybe even train the net asymmetrically to play from losing positions to gain more experience with that.

(3) Debug and find out why it plays what looks like non-sense forcing moves when it thinks it is behind (assuming that is what is actually happening).

There's another interesting thing. Among the Go community, there might have been initially some misplaced pride. But the pros and the community very quickly changed their attitude about AlphaGo (as they have in the past when something that seems to not work, yet proves itself in games). They are seeing an opportunity for the advancement of Go as a game. I think a lot of the pros are very curious, even excited, and might be knocking on Google's doors to try to get access to AlphaGo.


To be fair, I think a larger sample size of human vs computer games are needed. Let the top pros train with the computers and we can measure what level is beyond any human.


Being the best ranked player != playing well beyond humans. When the AI can play 1,000 games and never lose that's well beyond people.

Granted, chess AI is basically at that point right now. But, go AI has a ways to go.


Given the leaps of progress made between this series of games and the previous series in only a few months, I'd expect "never lose" will become a recognized reality in about a year.


Possibly, it's not clear if AlphaGo is playing better or simply approaching the game differently. Game five was close and AlphaGo seemed to mostly win due to time considerations.

PS: Honestly, it might be a year or a decade, but I suspect there is plenty of headroom to drastically surpass human play.


When AlphaGo does lose, it seems to happen when outright bugs cause it to make moves that are readily recognizable as mistakes. It doesn't seem to happen because it's not quite "smart" enough, or because its underlying algorithms are fundamentally flawed.

That's a big difference. Bugs can be identified and fixed. By the time AlphaGo faces another top professional (Ke Jie?) we can safely assume that whatever went wrong in Game 4 won't happen again.

Consider how much stronger the system has become in the few months since the match against Fan Hui. Another advance like that will place it far beyond the reach of anything humans will ever be able to compete with.


> When AlphaGo does lose, it seems to happen when outright bugs cause it to make moves that are readily recognizable as mistakes

I'm not sure this is true. It made the wrong move at move 79 in game 4, but I'm not sure that should be considered an obvious mistake.

My understanding is that the moves that people said were most obviously mistakes later in the game were a result of it being behind (and desperately trying to swing the lead back in its favor), rather than a cause.


Go rankings put that AI at #2 in the world.

Go rankings weren't designed for ML algorithms, which can have high-level deficiencies and behave erratically under certain conditions.


It would be a bizarre coincidence for the technology to advance so quickly and then stop right at the level of the best human players. That's especially so when there are so many big, lucrative applications for the underlying technology.


A critical component of AlphaGo's success is the massive training database comprising of the entire history of documented professional Go games. So while AlphaGo may play the game with an inhuman clarity of reading, it is less clear that it can strategically out-match professionals in the long term who may have an opportunity to find and exploit weaknesses in AlphaGo's process. Lee Sedol had that opportunity, of course, and he was not able to defeat AlphaGo. And how will AlphaGo improve, now that there are no stronger players from whom to train?

Will AlphaGo show us better strategies that have never been done before? In other words, can AlphaGo exhibit creative genius? It may have, but that's rather hard for us to observe.

In any case, I am looking forward to future AI vs AI games. It is still fundamentally a human endeavor.


Can't find the reference now, but in recent interviews the AlphaGo team claimed that one of their next steps would involve training a system without that training database, from scratch (simply by playing lots of games against different versions of itself), and that they estimate that it would be just a bit weaker.


Most of AlphaGo's learning came from self-play. Hence how it was able to vastly exceed the skill level of its initial training data which were amateur, not professional, games.


I don't know if it would be that bizarre. Once AlphaGo can beat the best humans on Earth, what motivation is there to keep improving it? Wasn't that the goal of the project?


Advances in deep learning in general should apply here, and there's a big motivation to keep improving that. Also, Go is popular enough that it should experience the same sort of commoditization drive that advanced Chess engines did, where Deep Blue level play went from being on a supercomputer to being on a smartphone. Then, since this approach scales up with more computing power, running a hypothetical future smartphone-Go engine on a big cluster like AlphaGo has here should put it way beyond the human level.


AlphaGo is still a monstrosity in terms of the hardware it requires. Improvements in AlphaGo will be reflected in the fact that it or something like it will soon sit on a tiny little computer near you. See also: what happened after the chess world champion lost to a computer.


>> Corporations are much better at selling whatever they do as "useful", which isn't such a good thing in the long run.

Yep. There's a grave risk that funding to AI research ends up being slashed just as badly as in the last AI winter, if people start thinking that Google has eaten AI researchers' lunch with its networks and there's no point in trying anything else.

Incidentally, Google would be the first to pay the price of that, since they rely on a steady stream of PhDs to do the real research for them but now I'm just being mean. The point is, we overhype the goose that lays the golden eggs, we run out of eggs.


I like that analogy; we have a perfectly good goose, laying nice, valuable eggs and people keep shouting "they're gold!".


The deepmind team has mentioned that the technique they used to improve AlphaGo's play from October 2015 (when it beat the European champion, who was ranked #600 at that time) until now has not reached the point of diminishing returns yet.

Many go professionals, after reviewing the 2 sets of games, have stated that is quite clear how much AlphaGo has improved in those 4 months.


Well, little doubt. When did any technology suddenly stop improving when it reached human levels?


> There are plenty of examples where technology has rapidly advanced to some remarkable level, but then almost completely plateaued.

And that's why you assume that it does not skyrocket in the future? Predicting the future is hard either way, ask a turkey before he gets his head chopped off.

> I'm also tired of all the statements about "accelerating progress". It's not like all the AI research of the past was useless until DNNs came along.

It's not that it was useless, but AI is improving as any other field is, some say faster than most other fields, and it's becoming more useful from day to day.

My guess would also be that "with further refinement, we'll soon see AI play Go at a level well beyond human", but it's just a guess.


I have almost no doubt. A few months ago they have beaten a weaker pro, and judging from the improvements in such a short time I am fairly certain it will be unbeatable in a few months, if they continue working on it.




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