Is running AlphaGo really that expensive? I get that training deep learning systems is very computationally expensive, but my understanding is that running them is orders of magnitude cheaper.
The Economist says "The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games"
If you price by the GCE calculator[0] it's $1920 to rent 1920 CPU cores for 20 hours. This doesn't include GPU costs as they don't seem to have GPUs available on cloud, but I could see that easily doubling the costs.
It would be nice if they preserve a snap shot of Alphago as it was a the start of the Sedol games as a historic thing. Then if they open source it people could go play it too. They have mostly open sourced their code but not I think the learning data.
Not really. Even if it would only take a single dedicated GPU to win in real time against a human opponent you could not offer a service like that for free. If google massively overfits their opponent that equation still holds true so it's not really proof. It would only be proof if they switched off 10% or so of the array that powers their offering right now and it would lose from Lee Sedol consistently. For all you know they have a huge margin of error.
Alas, the software is tied to Google's platform. Just releasing as open source won't do. (That's why they didn't open source Google Reader. The source would have been both useless for running a Reader clone, and given away lots of trade secrets about Google infrastructure.)
I don't doubt there is an a-symmetry between the number of machines / GPUs they throw at the problem during a match and during the run-up to a match but even so they will have to have some margin of error if they expect to win in the first place and besides that whatever that pile of hardware is it, the infrastructure required to run it and the people involved are not free.
