[onhn]

The author is talking about how a given physics model appears simple when they are presented with it, e.g. a particular quantum field theory. This is the kind of limited perspective about research that an undergraduate physicist may develop simply by solving the hand crafted problems that are presented to them.

However, the true difficulty in physics is arriving at that model in the first place. Decades of work offered up against experiment, the associated conceptual leaps in understanding required to get to e.g. a quantum field theory which succesfully predicts things are nothing short of a monumental achievement. To say that physics is simple is ludicrous.

[concreteblock]

You are missing the point the article. Author is not trying to argue that AI is 'harder' than physics, like a freshman cs major might argue with their physics friends.

Author is talking about how our physical theories, such as QFT, currently have more predictive power than any theories we currently have about machine learning/deep learning.

(Author has a PhD in theoretical physics).

[aeternum]

While QFT makes some amazingly precise predictions in certain areas like the fine structure constant, it is nearly useless for predicting even most chemistry.

In practice, the computations required to use the QFT model are just too complex for modern computers when it comes to single atoms with more than a few protons, not to mention larger molecules. Instead, we must use simplified models like the Bohr model to make predictions about molecular bonds.

This actually seems to be very similar to AI where we understand not everything, but a lot about basic neurons, yet the emergent phenomena of intelligence is very difficult to predict due to the explosion of computational complexity.

[concreteblock]

That's a good point. I guess our current mathematics is not good enough to say much about the macroscopic behaviour of large interacting models.

[onhn]

I think the article misses the point of what physics is. It is not a collection of "sparse" models and principles, rather, it is a scientific discipline from which such models have emerged.

You will notice the article conflates the two things: physics and the known laws of physics (e.g. first para in section 1.2). Simplicity of the latter does not imply simplicity of the former, but the article assumes that it does in order to tackle/state the question as posed: "Why is AI hard and physics simple?".

[concreteblock]

IMO if all it takes is a few simple substitutions like “physics” -> “known laws of physics” to make the article or title make sense, then it’s unfair to say that the author has missed the point. C.f. Reading the strongest possible interpretation and all that.