Physics Embraces AI: A Development for One Percent of the One Percenters
July 22, 2020
Say what you like about Newton. Teachers have made gravity “real” to indifferent students with the apple on the noggin metaphor.
Physics teachers today face a different challenge. The “old school” ideas are not going to win promotions, grants, or — even better — a prize. Cash! Fame! The cash thing may work among those who are work from home dads and colleagues without a tenure track ticket.
What can physicists who are the one percent of the one percenters? The answer is to combine esoteric mathematical concepts with the future forward concept “artificial intelligence.”
AI can do physics and “AI in Physics: Are We Facing a Scientific Revolution?” explains this shift. Now between you and me, there are a number of revolutions underway, but the “real life” stuff is of scant interest to physicists in my experience. Einstein anecdotes notwithstanding, physicists are an interesting chunk of the one percent’s one percenters.
Your homework? Verify the over density equation and show each step. No shortcuts! This is forward leaning physics with “real” representations, simulations, and predicted properties. No apple either.
The write up states with significant seriousness that symbolic regression:
can be used to derive mathematical formulas from the internally represented relationships in the network. Symbolic regression is carried out as a genetic algorithm. Equipped with variables and mathematical operators, the algorithm searches for the simplest mathematical formula with which known data can be reproduced.
Like many helpful mathy statements, this statement illuminates the process:
Their result clearly shows that the mixture of data, neural graph networks and symbolic regression is actually suitable for extracting mathematical formulas – in this case an already known natural law – from data with AI.
I enjoy the “clearly.”
But the future is not the stuff one can see, touch, feel, sniff, or think about in substantive ways. The future is tackling Dark Matter with AI.
I learned:
The researchers used the neural grapheme network again. Each node contains information about a dark matter halo such as position, speed and mass and is connected to other halos at a distance of 50 Mpc / h. The network was trained with data from the Quijote Dark Matter Simulation , a collection of generated dark matter structures.
And there is a payoff. Ready?
After the training, the GNN was able to predict the desired property of the halos more accurately than previous models. Using symbolic regression, the researchers were then able to produce a previously unknown mathematical formula that has a lower error rate than the currently most commonly used human-made formula for the same task. The resulting formula was also better able to deal with previously unknown data. For Cranmer, this is a clear sign that the mathematical formula generalizes much better than the neural graph network from which it was derived. This coincides with our previous experience in physics, says Cranmer: “The language of simple symbolic models describes the universe correctly.”
Forget the apple falling on Newton’s slightly addled brain carrier. Think in terms of this metaphor:
If AI is like Columbus, computing power is Santa Maria
And Big Data? Of course, of course. One percent of one percenters know this.
Stephen E Arnold, July 22, 2020
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