by Ashutosh Jogalekar

As the relentless march of AI in science continues, I found myself pondering the other day whether there ever would be a time when a human scientist would do the last human experiment, after which the machines take over. Of course, we don’t expect such a moment to be an abrupt one. AI is already taking over large parts of scientific work, and the progression from human to AI will be long, haphazard and lying on a continuum. Nevertheless, along with many other professionals, we scientists are facing a reckoning with technology with few parallels in history. So I asked: what would it actually feel like when slowly but quietly, science passes from the hands of humans to the silicon brains of machines. Perhaps, in what’s the ultimate irony, an AI made a substantial contribution in describing this watershed.
Nobody knew at the time that Elena Varga had made the last important scientific discovery in history that could be called entirely human. There was no announcement marking the occasion, no threshold anyone could point to, and certainly nothing about the experiment itself that suggested it belonged to history. It took place late one winter evening in a laboratory much like thousands of others, in a university building old enough that the pipes knocked in cold weather and the wooden drawers still contained pieces of glassware ordered by people who had retired before Elena arrived.
By then she was sixty-eight and had spent most of her adult life doing chemistry. She was not famous, though people who had worked with her tended to remember her as an authority in the field. She possessed the kind of knowledge that accumulated slowly and was difficult to explain: she knew when a vacuum line was leaking from the change in the pitch of the pump, when a crystallization had gone wrong from the way the first grains appeared on the wall of the flask, and when a reaction mixture deserved another hour even though every measurement said it was finished. None of these abilities appeared on her CV. They were simply the residue of forty-five years spent looking closely at matter.
Scientific work had changed enormously during those years. The systems now in routine use could read essentially the entire relevant literature before suggesting an experiment, generate mechanistic hypotheses, search enormous chemical spaces, design the experiments needed to distinguish among competing explanations, control robotic laboratories, interpret the resulting images, spectra and western blots, and decide what should be done next. Read more »

AI is collapsing barriers in scientific disciplines, most notably in math. An 87-year old conjecture named the 












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