5min chapter

Machine Learning Street Talk (MLST) cover image

ROBERT MILES - "There is a good chance this kills everyone"

Machine Learning Street Talk (MLST)

CHAPTER

The Importance of Unpredictability in Alignment Research

Ryland Schaffert et al. at Stanford thought that sharp and unpredictable changes in model outputs as a function of model scale and specific tasks could be a mirage. They said this is in spite of the per token error rate changing smoothly and continuously with respect to increasing model size. So they intentionally induced emergent abilities in neural networks on different architectures of multiple vision tasks. This raises some very interesting questions, like how do you think the idea of like a sudden capability acquisition should be treated in the context of alignment research? And do you think that somehow this concept of an emergent capability and this self-improving intelligence is related?

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