2min chapter

80,000 Hours Podcast cover image

#78 – Danny Hernandez on forecasting and the drivers of AI progress

80,000 Hours Podcast

CHAPTER

Scaling Laws of Likener Language Models

In a blog post, i'd recommend people look at how ai train scales. They talk about this kind of perimeter they found they call the gradient noise scale. There's just like this aspect of a system that they can take a measurement on. You can measure any m l system on it and that will predict the parallyzability of that task. And we've learned that complex tasks have larger empirical just r re more paralyzable.

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