3min chapter

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Preventing an AI-related catastrophe (Article)

80,000 Hours Podcast

CHAPTER

Scaling Hypothesis - How Much Compute Will Be Needed to Train Machine Learning Models?

Since 2012, the amount of computational power used to train our largest machine learning models has grown by over a billion times. Hernandez and his team also looked at how much compute has been needed to train a neural network to have the same performance as an early image classification algorithm. They found that the amount of compute required for the same performance has been falling exponentially, halving every 16 months. It's hard to say whether these trends will continue, but they speak to incredible gains in what it's possible to do with machine learning.

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