I am cynical for a reason. >They are using games to develop algorithms as they are easily defined problems I believe they use games because it makes good PR.

I am cynical for a reason. >They are using games to develop algorithms as they are easily defined problems I believe they use games because it makes good PR. I believed that before they chose StarCraft, but now I am more convinced in it. Also, because their algorithms are extremely data-hungry and it's easy to generate tons of training data for a game simply by running it. And because it is possible to evaluate performance of an algorithm over and over again. >they have in turn used those algorithms on real world problems, notably AlphaFold AlphaFold does not use their board game AI[1]. Naming it similarly is just a marketing tactic. AlphaFold might be a genuinely useful application of their resources, in which case they deserve praise in credit for it. I have to do more research on it, which is made very hard by all the hyperbole and hype around their work. >Furthermore, their investment into TPU's is going to bring the price down in the long run Overall, they do the exact opposite of democratizing AI. They invest heavily into insanely data-hungry and hardware-hungry approaches that a normal company will not be able to use (directly, not through Google's cloud services) for decades if ever. > And yes we should assume if AGI is possible it will require a lot of compute. What you're saying here (overall) does not make sense. There is absolutely no reason to assume that breaking records in games without substantial improvements in AI theory makes us any closer to "AGI". Ignoring the completely unsubstantiated statements about AGI, we are left with the question of practical applications. I am not convinced that the societal benefits of investing in complex and computationally intensive machine learning is higher than investing in improvements of simpler algorithms. Most practical applications of AI outside of a few ubercorps involve simpler, older algorithms. Even someone as big as SAP relies mostly on decision trees and SVMs. If anything, the hype around deep learning likely means it's harder to get funding for old-school AI research that is far likely to benefit smaller companies. --- [1] http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf Page 11, A7D.