AI and Compute: Our analysis showing that the amount of compute used in the largest AI training runs has had a doubling period of 3.5 months since 2012 (net increase of 300,000x):https://blog.openai.com/ai-and-compute/
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Yep, note that this was an initial cut at deep blue and makes some assumptions re: utilization and “thinking time”. As jack said, for this particular analysis we looked at learning systems in particular
Where did the 11 GF / sec number come from? IIRC, the papers says they could evaluate ~200 mill positions per second, and were calculating ~8,000 features per position. That makes it at least 1 TF / sec, and probably at least an order of magnitude higher.
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I checked the numbers. Deep Blue (1997) appears comparable to the 2012 systems:https://twitter.com/michael_nielsen/status/1192667888840142848 …
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It comes from the LINPACK benchmark in 1997: https://www.top500.org/site/48052
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The quoted calculation does ignore the specialized chip for evaluation of positions though
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