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.
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.
(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.
Granted, chess AI is basically at that point right now. But, go AI has a ways to go.
PS: Honestly, it might be a year or a decade, but I suspect there is plenty of headroom to drastically surpass human play.
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.
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 weren't designed for ML algorithms, which can have high-level deficiencies and behave erratically under certain conditions.
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.
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
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.
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.
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.