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This isn't about Moore's Law though. From the AlphaGo paper:

    > During the match against Fan Hui, AlphaGo evaluated thousands of times
    > fewer positions than Deep Blue did in its chess match against
    > Kasparov; compensating by selecting those positions more intelli-
    > gently, using the policy network, and evaluating them more precisely,
    > using the value network—an approach that is perhaps closer to how
    > humans play. Furthermore, while Deep Blue relied on a handcrafted
    > evaluation function, the neural networks of AlphaGo are trained
    > directly from gameplay purely through general-purpose supervised and
    > reinforcement learning methods

I know that. You didn't read my comment very thoroughly.

My understanding is that it is much more expensive for AlphaGo to evaluate a position than it was for Deep Blue. I'm not certain, but I would be surprised if AlphaGo did not need significantly more computation than Deep Blue.

edit: some actual estimates. Deep Blue had 11.38 GFLOPS[1]. According to the paper in Nature, distributed AlphaGo used 1202 CPUs and 176 GPUs. A single modern GPU can do between 100 and 2000 double precision GFLOPS[2]. So from GPUs alone AlphaGo had access to 4-5 orders of magnitude more computing power than Deep Blue did.

1] https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)

2] https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces...

A brute force approach for Go doesn't work. It doesn't work for humans (deep reading skills) and doesn't work for computers. The Monte Carlo approach was the first one that allowed Go AIs to scale with the hardware. This was at least two years ago.

AlphaGo went way beyond that. It actually learned more like how a Go player does. It was able to examine and play a lot of games. That's why it was able to beat a 2p pro, and within less than half a year, challenge a 9p world-class player at least on even terms.

The big thing isn't that AlphaGo is able to play Go at all at that level, but that learned a specific subject much faster than a human.

Strong agreement that it wasn't purely about computational power, and that there were significant software advances. I just want to make the point that hardware has advanced considerably as well.

This 1000 times. Extrapolating Deep Blue's 11GFlop supercomputer to today with Moore's law would be a 70TFlop cluster. AlphaGo is using 1+PFlops of compute (280GPUs referenced for competition in [0]). That's an insane amount of compute.

While it's fun to hate on IBM, it's not really fair to say Deep Blue was throwing hardware at the problem but AlphaGo isn't. Based on the paper AlphaGo will perform much worse in terms of ELO ranking on a smaller cluster.

[0] http://www.economist.com/news/science-and-technology/2169454...

And just to emphasize the big point here:

The AlphaGo that beat the 2p European champion five months ago was not as strong as the AlphaGo that beat Lee Sedol (9p). I don't think this was just the AlphaGo team throwing more hardware. I think they had been constantly running the self-training during the intervening months so that AlphaGo was improving itself.

If that is so, then the big thing here isn't that AlphaGo is the first AI to win an official match with the currently world's strongest Go player. It's that within less than half a year, AlphaGo was able to learn and grow to go from challenging a 2p to challenging the world's strongest player. Think about that.

I think it's fair to say that in the future, people will look back and wonder how it was possible to live without having a good AI.. similar to how we look past at caveman and wonder how they could live without electricity. AI is really just a tool that we leverage, the same was as we leveraged the wheel or electricity.

Yep. I was just talking with the founder of a startup I work with. His son was born in the past 5 months or so. The son is never going to live in a world that doesn't have deep learning. Like the kids who never knew what the world was like before the smartphone. Like the kids who never knew what the world was like before the web browser.

And AI is just one strand. There are several strands that are as deeply changing, that is happening simultaneously.

I remember someone speaking about the shift between classical hard sci fi and more current sci-fi authors like Neal Stephenson or Peter Hamilton. The classical authors like Heinlein or Asimov might do world building where they just change one thing. What would the world be like if that one thing changed? After a certain point though, things were changing so fast that later authors didn't do that. There were too many things that changed at the same time.

Here's a video where teens discover Windows 95: https://www.youtube.com/watch?v=8ucCxtgN6sc It gives a visual analogy of what you're saying about the new generation and AI!

The son is never going to live in a world that doesn't have deep learning.

Except if a big solar flare hits us ;}

Or a bunch of other things. Maybe our civilization collapses from peak oil or something.

At the moment, we seem to be having too much oil.

That could be true, but according to some articles I was reading at the time (sorry no src), the nature of their engine is that you cannot determine just how strong it is by playing one game because it plays to "just win", and not to win by as much as possible (paraphrase). So maybe it was barely good enough to be 2p, maybe it was already much stronger.

6 months ago it was clearly stronger than Fan Hui 2p. You don't beat someone at that level 5-0 without being consistently stronger.

Fan Hui said the machine played extremely consistently 6 months ago. He said playing the computer was "like pushing against a wall" - just very strong, very consistent performance.

Correct. It played like a top level human player, pretty evenly matched with Lee Sedol. AlphaGo from yesterday would have wiped the floor with AlphaGo from 6 months ago.

Various commentators mentioned how both players, human and synthetic, made a few mistakes. Even I caught a slow move made by the AI. So whether Lee Sedol was at the top of his peformance, or not, is a bit of a debate. But the AI was clearly on the same level, whatever that means.

It was an intense fight throughout the game, with both players making bold moves and taking risks. Fantastic show.

The AI only cares about winning, not about winning by a huge margin.

The slow move might just mean that this was sufficiently big and safer.

Is there any evidence that Sedol was anywhere close to winning?

This is misleading. AlphaGo beat a 2p player five months ago. Now it has beaten a 9p player. That tells is nothing about it's improvement in the intervening time. Given only this information, however unlikely, AlphaGo could have actually been stronger before.

AlphaGo lost a few of the informal matches.

Also, the people working on it flat out told the world that today's version of AlphaGo beats October's version literally all the time.

Question: is ability to win Go matches ordered?

It's more that there are different styles of winning, and a human will tend to specialize in such a style. But generally, someone who is consistently stronger will win over someone who is consistently weaker, regardless of style.

Replying to scarmig, No. Player A may consistently beat Player B who may consistently beat Player C who consistently beats Player A.

There are different strategies depending upon how much emphasis is placed upon early territorial gains as opposed to "influence" which is used for later later territorial gains.

Similarly, playing "passive" moves that make territory without starting a "fight" versus agressively contesting for every piece of territory available.

What I always think about with AI, and speaking to the "man's programming vs man" higher up, is what we'll get when we can teach computers how to solve these problems so that e.g. They could come up with the solution for + implement something like this on their own.

Is the Monte Carlo approach specific to Go in terms of A.I. challenges? Or is the Monte Carlo approach gaining traction in other A.I. problems as well?

I am tremendously unfamiliar with recent A.I developments.

Monte Carlo Tree Search is a key technique in previous computer Go programs (and other games). The improvement here was using the deep learning network as the value function to evaluate nodes in the tree and the policy network to determine the search order.

MC tree search isn't specific to Go, no. It's been used for other games including imperfect information ones like poker. I believe that the main reasons for using MC search is that it does not require an evaluation function, and it acts as an anytime algorithm so you can get a "good enough" answer within arbitrary time constraints.