lambdamore

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.

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.

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).