U of T computational imaging researchers harness AI to fly with light in motion

Your suspicion is warranted, but it really depends on what "AI" is being used (I'd rather call it ML. As a ML researcher myself, and who publicly criticizes LLMs[0]).

The reasoning for this is that in essence, ML is curve fitting data from high "polynomial" functions (approximately accurate). But there are many things like density estimators which are very good in statistical settings where you cannot access the density function directly (called "intractable") and so all you can deal with is samples (e.g. you can sample examples of human faces, but we have no mathematical equation to describe all variations and in what likelihood). This is not too different from Monte Carlo Sampling and is often used in variational inference. When you are doing density estimation you can have a lot more confidence in your results as you can actually do things like building proper confidence intervals and you can test likelihood (how well does your model explain the data).

So yeah, keep the skepticism up. There's a lot of snake-oil in ML and these days it is probably good to default to that position. Especially since a lot of ML people are not well versed in math and there's a growing sentiment of not needing math (you'll even find that common around here. It is a reliance upon empirical results and not understanding "Elephant fitting"). FWIW here they're using NeRF and it looks like they are using it to tune parameters of their physical model. I'd have to take a deeper look but at a quick glance I'd let down my guard a bit.

[0] Worth noting that "AI" used to be the typical signal that some thing was snake oil. Now everything is called AI. I'll leave it to the reader to determine if this is still a strong signal or not.