This podcast episode from Gordon's STEM Blog explores the AI phenomenon called grokking, a term derived from Heinlein's work describing deep, intuitive understanding. Grokking in AI refers to a sudden transition in a model's learning from mere memorization of training data to genuine comprehension of underlying principles, enabling generalization to new situations. This "aha!" moment often occurs after a period of seemingly stalled progress, challenging the traditional assumption that plateauing performance indicates diminishing returns in training. The episode highlights that grokking involves the AI discovering simpler, more generalizable solutions, evidenced by changes in the neural network's internal structure, and suggests that evaluating AI should go beyond simple accuracy metrics to assess true understanding. Ultimately, grokking implies that achieving advanced AI may require extended training periods to allow for these non-linear breakthroughs in comprehension. Check out Gordon's blog at gordostuff.com

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