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There\\u2019s an idea in machine learning that most of the progress we see in AI doesn\\u2019t come from new algorithms of model architectures. instead, some argue, progress almost entirely comes from scaling up compute power, datasets and model sizes \\u2014 and besides those three ingredients, nothing else really matters.

Through that lens the history of AI becomes the history f processing power and compute budgets. And if that turns out to be true, then we might be able to do a decent job of predicting AI progress by studying trends in compute power and their impact on AI development.

And that\\u2019s why I wanted to talk to Jaime Sevilla, an independent researcher and AI forecaster, and affiliate researcher at Cambridge University\\u2019s Centre for the Study of Existential Risk, where he works on technological forecasting and understanding trends in AI in particular. His work\\u2019s been cited in a lot of cool places, including Our World In Data, who used his team\\u2019s data to put together an expos\\xe9 on trends in compute. Jaime joined me to talk about compute trends and AI forecasting on this episode of the TDS podcast.

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Intro music:

- Artist: Ron Gelinas

- Track Title: Daybreak Chill Blend (original mix)

- Link to Track: https://youtu.be/d8Y2sKIgFWc

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Chapters:

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• 2:00 Trends in compute
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• 4:30 Transformative AI
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• 13:00 Industrial applications
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• 19:00 GPT-3 and scaling
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• 25:00 The two papers
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• 33:00 Biological anchors
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• 39:00 Timing of projects
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• 43:00 The trade-off
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• 47:45 Wrap-up
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