20min chapter

AXRP - the AI X-risk Research Podcast cover image

37 - Jaime Sevilla on AI Forecasting

AXRP - the AI X-risk Research Podcast

CHAPTER

Optimizing Machine Learning Training and Inference

This chapter explores the balance between computational inference and training in machine learning, emphasizing how companies can enhance efficiency by aligning these processes. It discusses the implications of longer training durations for smaller models and the varying computational spending practices across companies like Meta and OpenAI. The conversation also highlights advancements in algorithmic training, the significance of scaling laws, and the challenges of establishing effective benchmarks for AI impact.

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