Isn't this jumping the shark a bit? It's a 5-game match. The first was really, really close.
Jumping the shark and jumping the gun are two different things. You want the second one.
AlphaGo beats the world champion Lee Sedol in first of five matches
531–540 of 596 posts
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#532Earlier quoted context omitted.
>> In case you're not aware, AlphaGo's key component is based on the same type of Deepmind system that learned to play dozens of Atari games, to superhuman levels, by watching the pixels, without any programmatic adaptation to the particular Atari game. The Atari-playing AI watched the pixels indeed, but it was also given a set of actions to choose from and more importantly, a reward representing the change in the ga…
I think you are confusing utility functions with intelligence. All AIs need utility functions. An AI without a utility function would just do nothing. It would have no reason to beat Atari games, because it wouldn't get any reward for doing so. Even humans have utility functions. For example, we get rewards for having sex, or eating food, or just making social relationships with other humans. Or we have negative rein…
>> I think you are confusing utility functions with intelligence.
No, what I'm really saying is that you can't have an autonomous agent that needs to be told what to do all the time. In machine learning, we train algorithms by giving them examples of what we want them to learn, so basically we tell them what to learn. And if we want them to learn something new, we have to train them again, on new data.
Well, that's not conducive to autonomous or "general" intelligence. There may be any number of tasks that your "general" AI will need to perform competently at. What's it gonna do? Come back to you and cry every time it fails at something? So then you have a perpetual child AI that will never stand on its own two feet as an adult, because there's always something new for it to learn. Happy little AI, for sure, but not very useful and not very "general". Except for a general nuisance, maybe.
Edit: I'm saying that machine learning can't possibly lead to general AI, because it's crap at learning useful things on its own.
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#533Can someone explain why this is more impressive than a computer beating top chess players over a decade ago? I'm not very familiar with Go, and while there were far more squares on a Go board, it seems less sophisticated than chess to me. Maybe Go has way more moves possible and emergent strategies or something I'm not taking into account.
Firstly there are vastly more positions in Go. Secondly, it's very hard to evaluate a Go position, especially at the start of the game when there are few stones on the board. In chess you can get a long way using a simple evaluation (K=99, Q=9, R=5, B=3, N=3, P=1).
>In chess you can get a long way using a simple evaluation (K=99, Q=9, R=5, B=3, N=3, P=1).
I guess it depends on your idea of a "long way". Using only your simple evaluation a program would be rated somewhere around 1000-1200. It would lose every single game to an average tournament player.
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#534Earlier quoted context omitted.
Sort-of repeating a comment I made last time AlphaGo came up: As far as I know there is nothing particularly novel about AlphaGo, in the sense that if we stuck an AI researcher from ten years ago in a time machine to today, the researcher would not be astonished by the brilliant new techniques and ideas behind AlphaGo; rather, the time-traveling researcher would probably categorize AlphaGo as the result of ten years'…
I think the thing that would surprise a researcher from ten years ago is mainly the use of graphics cards for general compute. The shader units of 2005 would only be starting to get to a degree of flexibility and power where you could think to use them for gpgpu tasks.
I don't know... CUDA was released almost 9 years ago. So I don't think it's a stretch to suggest that cutting edge researchers from 10 years ago would have been thinking about using GPU's that way.
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#535Earlier quoted context omitted.
The point is that no one could train deep nets 10 years ago. Not just because of computing power, but because of bad initializations, and bad transfer functions, and bad regularization techniques, etc. These things might seem like "small iterative refinements", but they add up to 100x improvement. Even when you don't consider hardware. And you should consider hardware too, it's also a factor in the advancement of AI.…
They could. There was a different set of tricks that didn't work as well (greedy pretraining).
Lots of people tried and failed.
Today lots of people-- ones with even less background and putting in less effort-- try and are successful.
This is not a small change, even if it is the product of small changes.
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#536Earlier quoted context omitted.
It's easy to say that, in the same way that people now wouldn't be surprised if we could factor large numbers in linear time if we had a functional quantum computer !! 10 years ago no one believed it was possible to train deep nets[1]. It wasn't until the current "revolution" that people learned how important parameter initialization was. Sure, it's not a new algorithm, but it made the problem tractable. So far as al…
It's only difficult because no one threw money at it. It's like saying going to Mars is difficult. It is - but most of the technology is there already, just need money to improve what was used to go to the moon. If you asked people 10 years ago before the moon landing if it was possible, I too would agree it's impossible. But after that breakthrough it opened up the realm is possibilities. I see AlphaGo more of an in…
So are you arguing that superhuman-level performance in just a matter of engineering effort? Or am I missing something?
I'm generally considered to be way over optimistic in my assessment of AI progress. But wow.. that's pretty optimistic!
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#537Earlier quoted context omitted.
It was live streamed and the archive is now up on The Official AGA Youtube Channel at: AlphaGo ?p vs Lee Sedol 9p, 0400 UTC (8pm PST)[1] [1] https://www.youtube.com/watch?v=6ZugVil2v4w
Thanks for the link. Myungwan Kim's commentary is superb, am I the only one thinking the american guy speaks a bit too much though?
It can be hard to bite your tongue when you see the other (non-native) speaker struggling for words...
Andrew Jackson's role is invaluable in clarifying MyungWan Kim's thoughts: the infamously opaque "play this one, and then this one", or his white/black colour mix ups...
I personally think they're a good combo. Andrew is getting gradually better at only jumping in when necessary.
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#538Earlier quoted context omitted.
I disagree with this due to the rate of AlphaGo's progress. Consider CrazyStone which was the previous state of the art in Go computers. That program reached 5dan after many years of development and has not shown any signs of being able to reach Lee Sedol level (9dan). In October of this year AlphaGo beat a 5dan player, bringing it into the range of CrazyStone. Only ~6 months later it beats a 9dan player which means…
Edit: Fan Hui was only 2dan so this is even more insane.
Yes, AlphaGo's progress is amazing. I don't think there's any disagreement there :)
But I don't think you know much about Go, if you can say Fan Hui is "just" 2 dan professional. What do you reckon the strength difference is between 2p and 9p?
Nitpick: while AlphaGo today is certainly stronger than AlphaGo last October, it doesn't follow in any way from the fact that both programs beat their respective opponents. A > B, C > D, D > B, therefore C > A? By "400 ELO", no less?
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#539Earlier quoted context omitted.
The human can run off resources that are available "in the wild", self-repair, and self-replicate at better than 1:1 (that is, a group of n humans produce >n offspring), whereas the smartphone needs a huge amount of infrastructure to repair it and produce new ones. I don't think any mass comparison is really meaningful, mind, but it's not that simple.
Advanced chess players require a society which produces enough surplus to afford enough leisure to allow someone to not only produce a brain not damaged by starvation, but to allow them to use that brain to learn chess at a high level. It took a very long time for humans to get to that level, even though chess is a fairly old game. My point is, humans "in the wild" likely didn't have any equivalent to chess, because…
remember that chess is a war game and that war is most often fought over resources and territory, so they had their "chess" alright.
Re: AlphaGo beats the world champion Lee Sedol in first of five matches
#540Earlier quoted context omitted.
Chess engines and processing power have since then advanced to a point where my phone can now reliably beat Carlsen. There is no reason to suppose Go is different in that respect. In 10 years, DeepMind will fit into a phone.
It's way more about algorithmic improvements than hardware improvements though. Deep Blue evaluated 200 million positions per second. I don't think top programs of today could get to 2 million positions per second on a smartphone (I get about 10 million pos/second on my i7 3770 quad). It's all about improvements in search algorithms as well as position evaluation.
True. Without hardware improvements the processors that can evaluate 2 million positions per second while 'fitting' into a phone (qua processing power and power usage) would not exist though.