Author has it completely backward. [edit: on further reading, the title is clickbait but the article content is consistent with my point below)
With only a few exceptions, ML is incredibly simple (there is no AI). The math is simple, the mechanics of evaluating it is simple, the reason it works is simple, and it only really works well if you have absurd amounts of data and CPU time.
Physics is... determining the mathematics you need to know on the fly while discovering and explaining many phenomena.
You can spend decades focusing on matrix multiplications and other fairly straightforward trivia to analyze your particle trajectories, and then suddenly, you need to know group theory or some completely different field of math just to understand the basic modelling.
Personally I think the most impressive thing in physics and stats so far is our ability to predict the trajectories of solar system objects far into the future. After some very serious numerical analysis over the past 50 years, we've reached the point where there aren't many improvements we can make, and most of them come from identifying new objects, their position, and mass (data/parameters), and the real argument is about whether the underlying behavior is truly unpredictable even if you have perfect information.
Of course, the last best work in this area was done by Sussman who has been an ML researcher for some time: (https://www.researchgate.net/publication/6039194_Chaotic_Evo...)
As you can see, physicists pretty much invented all the math to do ML whilst solving other problems along the way: https://en.wikipedia.org/wiki/Stability_of_the_Solar_System
in fact many of my friends who were physics people, when I show them the code of a large scale batch training system they wonder why they did physics instead of CS because the math is so unbelivably simple compared ot the tensor path integrals they had to learn in Senior Physics.
With only a few exceptions, ML is incredibly simple (there is no AI). The math is simple, the mechanics of evaluating it is simple, the reason it works is simple, and it only really works well if you have absurd amounts of data and CPU time.
Physics is... determining the mathematics you need to know on the fly while discovering and explaining many phenomena. You can spend decades focusing on matrix multiplications and other fairly straightforward trivia to analyze your particle trajectories, and then suddenly, you need to know group theory or some completely different field of math just to understand the basic modelling.
Personally I think the most impressive thing in physics and stats so far is our ability to predict the trajectories of solar system objects far into the future. After some very serious numerical analysis over the past 50 years, we've reached the point where there aren't many improvements we can make, and most of them come from identifying new objects, their position, and mass (data/parameters), and the real argument is about whether the underlying behavior is truly unpredictable even if you have perfect information.
Of course, the last best work in this area was done by Sussman who has been an ML researcher for some time: (https://www.researchgate.net/publication/6039194_Chaotic_Evo...)
As you can see, physicists pretty much invented all the math to do ML whilst solving other problems along the way: https://en.wikipedia.org/wiki/Stability_of_the_Solar_System
in fact many of my friends who were physics people, when I show them the code of a large scale batch training system they wonder why they did physics instead of CS because the math is so unbelivably simple compared ot the tensor path integrals they had to learn in Senior Physics.