Berto Jongman: True Cost of AI Too High….

IO Impotency
Berto Jongman

Can the planet really afford the exorbitant power demands of machine learning?

OpenAI, the San Francisco-based AI research lab, has been trying to track the amount of computing power required for machine learning ever since the field could be said to have started in 1959. What it’s found is that the history divides into two eras. From the earliest days to 2012, the amount of computing power required by the technology doubled every two years – in other words, it tracked Moore’s law of growth in processor power. But from 2012 onwards, the curve rockets upwards: the computing power required for today’s most-vaunted machine-learning systems has been doubling every 3.4 months.

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