Model Performance Insights
The discussion highlights how model performance improves when trained on similar tasks, revealing a drop in effectiveness when faced with entirely new concepts. Tim points out the limitations of frontier models in generalizing tasks like multiplication, while Daniel emphasizes the importance of tokenization in handling numerical inputs, noting adjustments made to enhance model accuracy. The conversation suggests that online learning and active fine-tuning could bridge the gap between specialized training and broader applicability.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners
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