Part of this timing jump is Google throwing hardware at the problem with a large 280 GPU + 1920 CPU cluster. I would venture this is almost 100x bigger than most of the Go AI hardware we've seen to date. The nature paper suggests without this cluster it would be playing competitively with other single workstation Go AI, but nowhere near top level players.

Apparently it is still strong on a single machine https://twitter.com/demishassabis/status/708489093676568576

> throwing hardware at the problem with a large 280 GPU + 1920 CPU cluster.

You have a trillion connection neural net wrapped in 2 pounds of flesh inside your head. This is a massively larger amount of hardware compared to just about every animal out there. Throwing hardware at a problem is a solution to intelligence.

I'm not comparing brain wetware to hardware. The parent's post was interested in how we achieved such great AI go performance today that was supposed to take 10 years. If you look at the components that fueled this, the performance of the system was advanced significantly by having additional hardware; both in training the policy and value networks with billions of synthetic Go games and at runtime.

I don't like the biological comparison, but using your metaphor it would be like God saying "Hey I've created a brain but only have 10 billion synapses. Evolution would normally take 10 years to get to human-scale at our current organic rate but if I throw money at building a bigger brain cavity I can squeeze in the 1 trillion to get there today!"

FWIW Deep Blue was also a Supercomputer at the time it beat Kasparov. The phone in your pocket can now beat a Grandmaster.

Extrapolating Deep Blue's 11GFLOPs supercomputer to today with Moore's law would be equivalent to a 70TFLOPs cluster. AlphaGo is using 1+PFLOPs of compute. While they likely aren't actually achieving that compute throughput, to put this in perspective this is the compute scale used to model huge geophysics simulations covering a 800km x 400km x 100km volume with 8+ billion grid points around the San Andreas.

At the very least, it's interesting to see how much more accessible computation has become. Back when I was in school I could only dream of having a cluster of 280 GPUs. When sometimes the dream would come true and you had access to a cluster you would have to wait your turn in the job queue and hope you had enough compute in our resource quota to prevent your job from being terminated.

Now I could spin up a 280 GPU cluster on AWS (after dealing with pesky utilization limits) for only $182/hour. If researchers at Google have been doing this non stop for the past year they have "racked up" $1.6M on just compute. This is a drop in the bucket for a marketing department and the publicity they have achieved. I don't think normal Go AI developers have access to those resources :)

Yeah but Moore's law is dead now so that really doesn't say much anymore.

Don't underestimate algorithmic improvements. Today's chess engines running on DeepBlue hardware outperform DeepBlue running on today's hardware.

Modern chess engines are built on a testing infrastructure that makes it possible to measure how each potential change affects the playing strength. This "Testing revolution" has brought massive improvements in playing strength.

For AlphaGo, it's probably the training that requires the most computational resources. The 'distilled knowledge' could perhaps run on a desktop PC. The program would search fewer variations and would be weaker, but if AlphaGo improves further, that version might still be stronger than any human.

My understanding is that the significant part was that before this, throwing more hardware at the available Go AIs still didn't make them competitive against high level players.

Also, it feels like training the AI against many games with lots of hardware is somewhat equivalent to a human progressional who engrossed themselves in the game and trained since childhood.