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Challenges and Speculations in Scaling AI Models
The chapter explores the challenges related to scaling AI models and the uncertainty surrounding the future trajectory of AI development. It discusses limitations in increasing training data size, potential roadblocks in achieving significant performance improvements, and the speculative views on the future scaling of AI. The chapter also touches upon the debates around optimizing gradient descent, exploring different architectures, and the potential timeline for developing super intelligences in the next few decades.