Are we in an AI overhang?


Are we in an AI overhang?

Are we in an AI overhang? – LessWrong

Over on Developmental Stages of GPTs, orthonormal mentions it at least reduces the chance of a hardware overhang.An overhang is when you have had the ability to build transformative AI for quite some time, but you haven’t because no-one’s realised it’s possible. Then someone does and surprise! It’s a lot more capable than everyone expected. I am worried we’re in an overhang right now. I think we right now have the ability to build an orders-of-magnitude more powerful system than we already have, and I think GPT-3 is the trigger for 100x larger projects at Google, Facebook and the like, with timelines measured in months. INVESTMENT BOUNDS GPT-3 is the first AI system that has obvious, immediate, transformative economic value. While much hay has been made about how much more expensive it is than a typical AI research project, in the wider context of megacorp investment, its costs are insignificant. GPT-3 has been estimated to cost $5m in compute to train, and – looking at the author list and OpenAI’s overall size – maybe another $10m in labour. Google, Amazon and Microsoft each spend about $20bn/year on R&D and another $20bn each on capital expenditure. Very roughly, it totals to $100bn/year. Against this budget, dropping $1bn or more on scaling GPT up by another factor of 100x is entirely plausible right now. All that’s necessary is that tech executives stop thinking of natural language processing as cutesy blue-sky research and start thinking in terms of quarters-till-profitability. A concrete example is Waymo, which is raising $2bn investment rounds – and that’s for a technology with a much longer road to market. COMPUTE COST The other side of the equation is compute cost. The $5m GPT-3 training cost estimate comes from using V100s at $10k/unit and 30 TFLOPS, which is the performance without tensor cores being considered. Amortized over a year, this gives you about $1000/PFLOPS-day…


October 17, 2020
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