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 :)
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 :)