This conversation introduces Nested Learning (NL), a sophisticated framework designed to move artificial intelligence beyond static, pre-trained models toward systems capable of continuous adaptation. By organizing neural networks into a hierarchy of optimization problems that operate at different speeds, this paradigm mimics biological memory to prevent the loss of old information while acquiring new skills. The source highlights how this approach addresses the high costs and operational delays associated with retraining large-scale models in industries like healthcare and finance. It specifically examines innovations such as deep optimizers and continuum memory systems that allow AI to process information across immediate, tactical, and strategic timescales. Ultimately, the text argues that shifting toward temporal depth enables more efficient, reliable, and evolving AI integration within complex organizational environments.

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