Continual learning and sample efficiency as future directions

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One of the most powerful AI systems we’ve ever built is succeeding for reasons we still don’t understand. And worse, they may succeed for reasons that might lock us into the wrong future for humanity.

Today’s guest is Anil Ananthaswamy, an award-winning science writer and one of the clearest thinkers on the mathematical foundations of machine learning.

In this conversation, we’re not just talking about new demos, incremental improvements, or updates on new models being released. We’re asking even harder questions: Why does the mathematics of machine learning work at all?

How do these models succeed when they suffer from problems like overparameterization and lack of training data?

And are large language models revealing deep structure, or are they just producing very convincing illusions and causing us to face an increasingly AI-slop-driven future?

KEY TAKEAWAYS

00:00 — Book explores why ML works through math

02:47 — Perceptron proof shows simple math guarantees learning

05:11 — Early AI failed due to single-layer limits

07:12 — Nonlinear limits caused the first AI winter

09:04 — Backpropagation revived neural networks

10:59 — GPUs + big data enabled deep learning

15:25 — AI success risks technological lock-in

17:30 — LLMs lack human-like learning and embodiment

22:57 — High-dimensional spaces power ML behavior

27:36 — Data saturation may slow future gains

31:11 — Continual learning is still missing in AI

33:46 — Neuromorphic chips promise energy efficiency

41:49 — Overparameterized models still generalize well

45:05 — SGD succeeds via randomness in complex landscapes

48:27 — Perceptrons remain the core of modern neural net

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