Google’s ‘Nested Learning’ Signals Smarter AI Agents and Models That Stay Current

Welcome to this Cloud Wars Agent and Copilot Update. In these discussions, I’ll be analyzing opportunities, impact, and outcomes possible with AI; today’s focus is Google nested learning and its potential to make AI models and agents smarter and more stable.

Highlights

00:25 — Nested learning is a new approach by Google to address the issue of AI models catastrophically forgetting, which is the tendency to forget previously learned information when trained on new data. The current solutions to this problem include retraining or fine-tuning in large chunks or freezing parts of the model, which are costly and inefficient. Nested learning aims to create a model that can continuously learn without forgetting what it already knows.

01:10 — Nested learning involves treating a model as a series of learners nested inside each other, learning at different speeds. The analogy of learning in three layers (scratch pad, notebook, and textbook) shows how nested learning works. The concept is likened to cooking a Thanksgiving dinner, where different dishes cook at different speeds but contribute to the final meal.

02:17 — If nested learning works in practice, it could enable more lifelong learning for AI models. The need for full retraining when new tasks or data arrive would be reduced, making long-term processing more stable.

03:10 — Nested learning could lead to agents that improve over time through continuous learning from interactions, logs, and outcomes. Business leaders and product teams would benefit from more stable domain copilots that can absorb new regulations and procedures without losing their baseline expertise. There are questions about what the model can learn autonomously and what requires human approval.

04:12 — Nested learning is a strong signal that the future of AI is not just about bigger models but smarter memory and continual learning.

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