
AlphaGo and AI Progress - mjn
http://www.milesbrundage.com/blog-posts/alphago-and-ai-progress
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euyyn
> comparing AlphaGo Distributed to, e.g. CrazyStone, is to compare two
> distinct states of the art – performance given small computational power
> (and a small team, for that matter) and performance given massive
> computational power and the efforts of over a dozen of the best AI
> researchers in the world.

I know the author probably doesn't have any stake on it, but defending that
the state of the art should be categorized by the size of the team of
researchers behind each product sounds like just being butt-hurt. "Look what
this team accomplished with only two people" only compares the researchers,
not the end results.

~~~
nullc
I didn't read it that way. That authors' goal is towards assessing progress in
the field. To some extent, adding more researchers is like adding more CPUs;
fuel for an industrial process, which should be corrected for when trying to
gauge the rate of return on those investments at the current level of
technology.

~~~
euyyn
> when trying to gauge the rate of return on those investments

Exactly, when trying to gauge your team. Not the end product they produced.

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gizmo686
>Hassabis at AAAI indicated DeepMind’s intent to try to train AlphaGo entirely
with self-play. This would be more impressive, but until that happens, we may
not know how much of AlphaGo’s performance depended on the availability of
this dataset, which DeepMind gathered on its own from the KGS servers.

As an amateur (10k) go player who watch the Fan Hui games, I can say that
AlphaGo seems to rely heavily on the training data. This is because all of its
moves feel very human, even in circumstances where strong humans find better,
weird looking moves. This is in contrast to chess AI (and even other go AI)
that feel distinctly roboty in how they play. In watching a professional (9p)
commentary of the games, it seems that Fan Hui not lose because of particuarly
good moves of AlphaGo, but because of some specific mistakes that he made
(this is not unusually, as most professional games come down to loosing moves,
instead of winning moves). In this sense, AlphaGo seems to be playing at human
level, but with fewer mistakes.

This is certainly impressive, but unfortunately AlphaGo currently seems to be
a demonstration of synthesizing and automating expert human knowledge, instead
of creating new knowledge.

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Houshalter
I don't know what his point was that it was predictable. If you were closely
following the field, sure. There were some _super_ promising papers published
at the end of 2014. Heck if you just saw the wave of deep learning results
that were crushing various AI domains, you could have predicted Go would be on
the chopping block soon. I was really sure that Go would be beaten by the end
of 2015, which didn't happen. However almost everyone I spoke to was skeptical
of that, and the outside world was totally unaware. And it still represents
relatively fast progress, even if the progress is predictable.

Anyway I think drawing strong conclusions about AI progress from this single
example is wrong. You need to look at general trends, which is definitely
rapid improvement.

Here is a study about how fast algorithms are improving in general, after
accounting for hardware speedups:
[https://intelligence.org/files/AlgorithmicProgress.pdf](https://intelligence.org/files/AlgorithmicProgress.pdf)

>Chess programs have improved by around fifty Elo points per year over the
last four decades. Estimates for the significance of hardware improvements are
very noisy but are consistent with hardware improvements being responsible for
approximately half of all progress. Progress has been smooth on the scale of
years since the 1960s, except for the past five.

>Go programs have improved about one stone per year for the last three
decades. Hardware doublings produce diminishing Elo gains on a scale
consistent with accounting for around half of all progress.

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pmontra
Very interesting analysis, I recommend everybody to read it.

A note to the author: I had to change the text color to #333 before being able
to read it comfortably; #888 was too light.

~~~
miles_b
Thanks for the note - it should be easier to read now.

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joe_the_user
While I think I actually said similar things about this being incremental
progress when AlphaGo came out, what I would really enjoy would be an
evaluation of how much progress the enhanced techniques represent _as AI
techniques_. AlphaGo involved a Convolutional Neural Network driving Monte
Carlo Tree Search with a linear approximator thing to speed things up.
Convnets are themselves something of a hybrid system.

So, will the future look like combining together more and more hybrid levels?
Is there a limit to the number of layers or pieces? Maybe only someone Yann
Lecun could comment. Still, I'd be curious.

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hyh1048576
Anyone know how many GPUs will be used for the game vs. Lee Sedol? That
distributed AlphaGo with more GPUs really scales up is impressive and scary.

~~~
kailuowang
My understanding is that once the neural network is trained you don't need as
much computation power to put it in use.

~~~
gizmo686
In general, training requires far more neural networks requires far more
computational power than using them does. However, AlphaGo combines neural
networks with a traditional Monte Carlo search. This means that you can
improve the performance of a trained AlphaGo by just giving it more processing
power. Indeed, with a sufficient amount of processing power, AlphaGo would
converge to fully optimal play.

~~~
pigscantfly
It's not really the traditional Monte Carlo search; IIRC they're using UCT
weighting for MCTS. Apologies if that's what you meant, but I think to most
people "traditional Monte Carlo" means something different (probably uniform
depth charges).

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eli_gottlieb
You're not going to be able to "rigorously model AI progress" at all well if
you don't start by quantifying exactly what deep learning techniques are
actually doing, and what resources they require to do it.

