Chemists weigh in on machine learning + End of theoretical physics as we know it

Chemists weigh in on machine learning

EXCERPT: . . . Ask 10 chemists what they think about the promise of machine learning, and you’ll get 10 different answers. That might make for a lively Twitter debate, but the discussion also has serious implications.

If machine learning is less valuable than it’s been claimed to be, says George Schatz, a physical chemist at Northwestern University, “people end up wasting time and effort” testing it in their labs. Scientists who invest training, lab time, and money into machine learning could find themselves in a difficult position if the tool doesn’t solve a problem as promised.

On the other hand, if machine learning is the wave of the future, chemists who aren’t using it risk falling behind their peers.

It probably won’t be possible to definitively answer the question “Is machine learning overhyped?” without the benefit of hindsight. But after conducting dozens of conversations with chemists, C&EN has found that a consensus about the current state of machine learning emerges.

Machine learning is a category of artificial intelligence that describes a computer’s ability to train on a set of data and then create rules or knowledge from that data. Chemists are often interested in the tool’s predictive power. For instance, if you give a machine-learning algorithm a list of 100 metal alloys and their melting points, can it predict the melting point of an alloy it hasn’t encountered before—potentially even one that’s never been synthesized?

Despite all this promise—or perceived promise—one thing that machine learning isn’t is magic. “Let’s be realistic,” says George Dahl, a computer scientist at Google. “Machine learning is nonlinear regression,” a simple type of statistical analysis in which collected data are “fit” with model parameters. [...]

Making machine learning sound like something it’s not yet could be bad for the technique itself. If it can’t live up to the bar that’s been set, funders and scientists may decide machine learning isn’t worth their time. “We need the most brilliant minds to feel enticed” to study it and explore its benefits for it to be successful, says Nuno Maulide, an organic synthetic chemist at the University of Vienna.

[...] If chemists think they need to use buzzwords like “machine learning” to attract more eyeballs or dollars, [Bartosz] Grzybowski doesn’t blame them. “Impact factor is god,” he says. But he says once the hype of machine learning dies away, valuable tools will remain, as previous fads like combinatorial chemistry or genomics have demonstrated. Each of those had its own ride on the hype roller coaster, and while neither lived up to what some people promised, they both remain in use.

[Matt] Toussant believes we’re near peak hype in machine learning and about to fall into the valley of disillusionment. “But ultimately all technology recovers from the pit of despair,” he says. “I expect machine learning to do the same. I believe in its future.”


The End of Theoretical Physics As We Know It (Hossenfelder)

EXCERPT: . . . Theoretical physics has a reputation for being complicated. I beg to differ. That we are able to write down natural laws in mathematical form at all means that the laws we deal with are simple — much simpler than those of other scientific disciplines.

Unfortunately, actually solving those equations is often not so simple. For example, we have a perfectly fine theory that describes the elementary particles called quarks and gluons, but no one can calculate how they come together to make a proton. The equations just can’t be solved by any known methods. Similarly, a merger of black holes or even the flow of a mountain stream can be described in deceptively simple terms, but it’s hideously difficult to say what’s going to happen in any particular case.

Of course, we are relentlessly pushing the limits, searching for new mathematical strategies. But in recent years much of the pushing has come not from more sophisticated math but from more computing power. [...] Accordingly, theoretical physics now has many subdisciplines dedicated to computer simulations of real-world systems, studies that would just not be possible any other way. [...] In addition, physicists have studied hypothetical fundamental particles by observing stand-ins called quasiparticles. [...]

This line of research raises some big questions. First of all, if we can simulate what we now believe to be fundamental by using composite quasiparticles, then maybe what we currently think of as fundamental — space and time and the 25 particles that make up the Standard Model of particle physics — is made up of an underlying structure, too. Quantum simulations also make us wonder what it means to explain the behavior of a system to begin with. Does observing, measuring, and making a prediction by use of a simplified version of a system amount to an explanation?

But for me, the most interesting aspect of this development is that it ultimately changes how we do physics...


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