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> Natural data is "generated" from a constrained hierarchical / compositional function. > Deep networks learns that hidden structure from polynomially few examples and creatively generate exponentially many valid new ones. > The depth of the network is what's important to overcome the curse of dimensionality, and potentially invalidate...

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Machine Learning Street Talk

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13,826 Aufrufe • vor 4 Monaten •via X (Twitter)

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Profilbild von Machine Learning Street Talk
Machine Learning Street Talkvor 4 Monaten

> For data with hidden hierarchy, the data gap is exponentially large. > How could this be better? - models that predict their own abstractions/latents. > Naturally, @ylecun agrees!

Profilbild von Machine Learning Street Talk
Machine Learning Street Talkvor 4 Monaten

> Generic high-dimensional data is unlearnable, you need more data than atoms in the universe just to interpolate. > The way out of this is coarse graining, which is often exemplified by word2vec i.e. the concept of a "street" emerges from locality statistics. > But the most abstract concepts are the hardest to learn.

Profilbild von Matthieu wyart
Matthieu wyartvor 4 Monaten

Great fun, Tim. The part I'd underline: the same compositional structure that makes data learnable from polynomially few examples is what lets a model recombine them into exponentially many new valid ones: creativity and sample-efficiency are two sides of one coin.

Profilbild von Lonely nerd.
Lonely nerd.vor 4 Monaten

The one take away from the post AI hype is that LLMs and most researchers are interpolators. Very impressive ones. =-) 👇

Profilbild von Warley Vital
Warley Vitalvor 4 Monaten

Interestingly, the previous post in my timeline was this one that points to a similar idea

Profilbild von Mike Pearson
Mike Pearsonvor 4 Monaten

Processes can't be represented by single passes through a network. They're picking up the outputs of processes - which do have some stable patterns. Like memorizing times tables as opposed to learning multiplication as an algorithm. Recursion is required.

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