Explainability, Reasoning, Priors and GPT-3
Machine Learning Street Talk (MLST)
Intro
This chapter explores the intricacies of machine learning interpretability, contrasting inductive priors with experiential knowledge in model training. It also critiques current model explanation methods and discusses GPT-3's reasoning capabilities, emphasizing the challenges in making complex models comprehensible to the general public.
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