
#8 – Sara Hooker: Big AI, The Compute Frenzy, and Grumpy Models
Scaling Theory
Scaling Laws and Model Optimization in AI
This chapter explores the intricate relationship between computing power and model performance in artificial intelligence, questioning traditional scaling laws. It emphasizes the role of high-quality data and innovative optimization strategies over sheer compute resources, illustrating diverse impacts on model capabilities. The discussion also critiques current regulatory frameworks, urging adaptability amid rapid technological advancements, particularly in contexts influenced by evolving architectures.
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