I do, and I consider jupyter to copy mathematica notebook, without seriously admitting it.

If you could take a Bayesian perspective toward the super-resolution problem, things will make sense: given a low-res image, it corresponds to a distribution of corresponding high-res images. Which one is more likely? It depends on the prior and the likelihood. The right figure is a possible outcome, however, if we have strong prior toward the possibility of well-known people, we would be biased toward those people. It's not wrong, it is just not comprehensive.

I think you're being a little too charitable to the AI in this case.

Even without "famous person bias", it's fair to say that the skin tone of the resolved image is slightly but measurably lighter than the skin tone of the blurred original (I'm curious what the blurred version of the resolved image looks like, btw).

Occam's razor says the model simply wasn't trained with enough ethnically diverse images, for whatever reason.

Deepmind does not foster its future PhD, but, yes, they offer a better rewarding environment for those PhDs to flourish after they get the basic training.

I consider many of these ideas have been well exploited by Wolfram Mathematica since 1988. The idea of computational thinking has been there since then. It's unfair not to give a historical review of these ideas in the beginning of the lecture.

I think Mathematica is kind of a killer app for lisp.

Mathematica wasn't written in Lisp. You might be thinking of Macsyma.

My understanding is that Wolfram rewrote Macsyma in C but that Macsyma was his direct inspiration for Mathematica.

We can compute the radiative transfer processes to infer the physical processes from the satellite images.

I could recommend this paper: Schneider, Tapio, et al. "Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high‐resolution simulations." Geophysical Research Letters 44.24 (2017).