A theoretical physicist known for blunt assessments says, that physicists avoid the term “Artificial Intelligence”:
… not only because it reeks of hype, but because the analogy to natural intelligence is superficial at best, misleading at worst. True, the current models are loosely based on the human brain’s architecture…
This type of iterative learning is certainly one aspect of intelligence, but it leaves much wanting. The current algorithms heavily rely on humans to provide suitable input data. They do not formulate own goals. They do not propose models. They are, as far as physicists are concerned, but elaborate ways of fitting and extrapolating data.Sabine Hossenfelder, “What can artificial intelligence do for physics? And what will it do to physics?” at BackRe(Action)
She is quick to add, however, that new AI techniques, far from heralding the End of Theory, as prophesied in 2008, can be of great help to physicists: “In summary, machine learning rather suddenly allows physicists to tackle a lot of problems that were previously intractable, simply because of the high computational burden.”
One example Sabine Hossenfelder (above left) cites is the analysis of vast numbers of images from outer space in order to find, say, black holes. They can be detected because they bend the spacetime around them but few are enormous like this 2017 find, which is 800 million times more massive than the Sun. Many black holes are small and their signal is often quite faint. Finding them is just the job for a machine.
Will the scientist’s role change? Astrophysicist Brian Nord at Fermilab recalls, without any intense nostalgia, his days as a postdoc when “our team would spend dozens of hours per person scouring large swaths of the sky to identify lenses, often by eye”. As to future changes wrought by AI, he suggests,
It’ll be like when we first started using computers. That happened before my time, but when scientists used to get a paper, they’d have to figure out how to get the data out of the plots. Now we can download the figures onto our computers and extract the data digitally.
AI is going to do something similar. Scientists will still have work to do, but it’ll be different work. Tasks like classifying images will be abstracted away. Maybe more of our time will be spent on hypothesis generation, because that’s hard for AI. A lot of us will also need to learn how these algorithms work so that we can interpret results.Sophia Chen, “Q&A: Paving A Path for AI in Physics Research” at APS Physics
So far from reducing jobs in astrophysics, AI is on target to create more of them. For one thing, as much more information becomes available, talented people will be needed to interpret, teach, and write about it for the public.
Maybe the robot will do you a favor and snatch your job. The historical pattern is that drudgery gets automated, not creativity.
Robot-proofing your job, Peter Thiel’s way. Jay Richards and Larry L. Linenschmidt continue their discussion of what has changed—and what won’t change—when AI disrupts the workplace