Zach Furman

If correct, this would reframe deep learning's success as an instance of something we understand in principle, while pointing toward what we would need to formalize to make the connection rigorous.

I first review the theoretical ideal of Solomonov induction and the empirical surprise of deep learning success.

Next, mechanistic interpretability provides direct evidence that networks learn algorithm-like structures.

I examine the cases of grokking and vision circuits in detail.

Broader patterns provide indirect support.

How networks evade the curse of dimensionality, generalize despite overparameterization, and converge on similar representations.

Finally, I discuss what formalization would require, why it's hard, and the path forward it suggests.

Heading Background Quote Whether we are a detective trying to catch a thief, a scientist trying to discover a new physical law, or a businessman attempting to understand a recent change in demand, we are all in the process of collecting information and trying to infer the underlying causes.

Chain leg.

End quote.

Early in childhood, human babies learn object permanence that unseen objects nevertheless persist even when not directly observed.

In doing so, their world becomes a little less confusing.

It is no longer surprising that their mother appears and disappears by putting hands in front of her face.

They move from raw sensory perception towards interpreting their observations as coming from an external world.

A coherent, self-consistent process which determines what they see, feel, and hear.

As we grow older, we refine this model of the world.

We learn that fire hurts when touched.

Later, that one can create fire with wooden matches.

Eventually, that fire is a chemical reaction involving fuel and oxygen.

At each stage, the world becomes less magical and more predictable.