This episode examines how modern artificial intelligence is trained, and why its dominant methods may diverge from what decades of research tell us about effective learning. While contemporary AI systems emphasize mathematical efficiency and backpropagation, human learning relies on biological principles such as error-driven adaptation, productive struggle, interleaved practice, and spaced repetition. The discussion explores emerging research that draws inspiration from cognitive science, including ideas like synthetic sleep, active learning, and training regimes designed to reduce catastrophic forgetting and data inefficiency. By comparing current AI approaches with the mechanisms that support deep, flexible human learning, the episode argues that future progress toward more general and resilient intelligence will require architectures that reflect how brains actually learn. Rather than replacing human thinking, such systems could serve to extend and strengthen it.
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