Scaling Language Models Past Certain Sizes Can Improve Performance

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GPT-3 was unveiled in 2020, which relates to what we talked about earlier with the Bitter lesson. Just doing that and training these models on more data seems to be sufficient to unlock new capacities. You get these non-linear transition phases as you scale them up. And so anthropic was interested in whether there are also some inverse scaling phenomena where scaling the model instead of just improving the performance might also lead to either degradation of performance or lead to unwanted behavior. But I don't think we see any evidence of that.

Welcome to another episode of Sean Carroll's Mindscape. Today, we're joined by Raphaël Millière, a philosopher and cognitive scientist at Columbia University. We'll be exploring the fascinating topic of how artificial intelligence thinks and processes information. As AI becomes increasingly prevalent in our daily lives, it's important to understand the mechanisms behind its decision-making processes. What are the algorithms and models that underpin AI, and how do they differ from human thought processes? How do machines learn from data, and what are the limitations of this learning? These are just some of the questions we'll be exploring in this episode. Raphaël will be sharing insights from his work in cognitive science, and discussing the latest developments in this rapidly evolving field. So join us as we dive into the mind of artificial intelligence and explore how it thinks.

[The above introduction was artificially generated by ChatGPT.]

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Raphaël Millière received a DPhil in philosophy from the University of Oxford. He is currently a Presidential Scholar in Society and Neuroscience at the Center for Science and Society, and a Lecturer in the Philosophy Department at Columbia University. He also writes and organizes events aimed at a broader audience, including a recent workshop on The Challenge of Compositionality for Artificial Intelligence.

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