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#64 Prof. Gary Marcus 3.0
Machine Learning Street Talk (MLST)
Scaling Laws in AI: Limits and Insights
This chapter explores the correlation between the size of language models and their performance, focusing on the implications of scaling laws in machine learning. It discusses the limitations of scaling, the importance of context over mere memorization, and the ongoing challenges in achieving true AI understanding and fairness.
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