From the journal Science, June 4:
Amid a flood of AI advances, astrophysicists are questioning the soul of their field
One afternoon in April, Cecilia Garraffo settled down at the head of a conference room table in Cambridge, Massachusetts, and gazed out at what might be the last astrophysicists of their kind.
The walls of this room had, in the past, reverberated with the din of thousands of other groups of scientists. Now, as streaks of sunlight poured in, the discussions turned to nonhuman collaborators. One by one, the gathered researchers discussed how they planned to apply machine learning to problems in astronomy. Observing an interstellar comet. Discerning wispy filaments of galaxies at the universe’s largest scales. Developing a new “tokenizer” that can translate astrophysical images into a form more readable by artificial intelligence (AI). “Sometimes models will be overconfident,” Garraffo warned a junior team member.
Afterward, as everyone filed out, black hole researcher Daniel Palumbo made a brief announcement. Representatives from AI chipmaker NVIDIA were on campus in search of scientists who wanted to solve problems using their hardware. To anyone who might need extra processing power, “today’s the day,” he said.
The Center for Astrophysics | Harvard & Smithsonian employs more than 600 astronomers in Cambridge, Massachusetts, and more than 800 overall, making it one of the world’s largest concentrations of professional stargazers. Garraffo heads its AstroAI group, which is charged with leading the center’s approach to applying machine learning to various problems. In just 4 years since they first proposed this specialized team, Garraffo and her colleagues have forged collaborations throughout the building and with industry teams such as Google DeepMind and Anthropic.
Originally, their goal was to use machine learning and AI to remove the technical barriers of math and computation while preserving what Garraffo considers the fun part of physics: honing scientific questions. Their toolbox did not include chatbots. Despite the buzz around ChatGPT, which was released within months of AstroAI’s first proposal, Garraffo had thought her group would steer clear of the headline-grabbing tool and other large language models (LLMs).
At least, until recently.
Now, stories of miraculous progress were starting to spread across the institution. As her MacBook Air pinwheeled to a crawl thanks to an AI agent running software locally, Garraffo’s colleague Alyssa Goodman showed me a data-fitting problem. She wanted to understand how the spiral arms of a distant galaxy were moving. But isolating just that motion from other patterns imparted into her data by the spin and the geometry of that distant galaxy had thwarted her group for years. She asked ChatGPT, which resolved the problem in a few minutes. Now, her research group was planning to write several papers on the resulting data set, “the single best map of spiral arm kinematics ever—like, by a factor of 100.”....
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