<\/p>\n
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first,\u00a0<\/em>sign up here<\/em>.<\/em><\/p>\n Last Wednesday, Anthropic announced that earlier this year it had launched a molecular biology lab, where Claude agents read and conjecture about hard biology problems and human scientists run experiments on what they report. And this AI-powered lab, the company said, had made its first discovery. <\/p>\n To understand what Anthropic says its system did, imagine you\u2019re flipping through a library of millions of DNA sequences, amassed as scientists sequence more and more of the living world. One step toward a breakthrough might be finding a peculiar sequence that encodes an interesting enzyme, perhaps. Then you\u2019d need to figure out what that enzyme does and, eventually, how to manipulate it to do something useful.<\/p>\n What Anthropic says its system of 950 agents found after 21 hours was not a brand-new sequence. The agents instead flagged a repeating pattern surrounding a known enzyme, a particular pattern Anthropic said hadn\u2019t been catalogued before. But if you read through Anthropic\u2019s announcement, which calls this pattern \u201creminiscent\u201d of what led to the gene-editing technology CRISPR that \u201chas already transformed science and medicine,\u201d it sounds as if this army of agents really found something of note. <\/p>\n These claims have angered some biologists. A viral post from one, subsequently endorsed by the chair and CEO of the drugmaker Eli Lilly, said that \u201cfinding a weird cluster of genes and repeats is often the easy part. The hard part, and where the real discoveries come from, is figuring out what the system actually does.\u201d The agents helped with some laboratory grunt work, in other words. But a discovery it is not. <\/p>\n It\u2019s a reminder that even if AI does something impressive\u2014like finding a pattern in a mass of biological data that would be difficult to perceive with human eyes alone\u2014the result itself may not constitute a breakthrough for science. What is novel for AI may be routine, unsurprising, or simply not that consequential to a biologist.<\/p>\n Muddying the issue further, Mario Rodr\u00edguez Mestre, a biologist at the University of Copenhagen, said over the weekend that his team had already discovered this particular pattern, the New York Times<\/em> reported. Mestre, who regularly chatted with Claude in his work, wondered whether Anthropic\u2019s team had learned from his conversations. Anthropic denies this, but Mestre says he\u2019s stopping all use of Claude anyway.<\/p>\n Part of the problem here is that AI companies aren\u2019t presenting their systems simply as tools scientists can use, like microscopes or supercomputers. They\u2019re insisting that the AI systems are making discoveries themselves. To some, that approach is incompatible with how science actually works, with new knowledge more typically emerging from collaboration and an ever-growing arsenal of tools. <\/p>\n It\u2019s also making people more skeptical of genuine progress when it happens. Whittling 200,000 candidates down to a few worth exploring is no small feat; it is legitimate scientific work. The fact that a general-purpose chatbot could do that work is notable, even if humans helped steer it and ultimately ran the experiments. But once the standard is whether Claude itself made a discovery, all that becomes evidence for one side or the other in a debate that has only two answers: breakthrough or bust.<\/p>\n Once we\u2019re judging AI by whether it has made a discovery, it\u2019s also tempting to shift the goalposts even after it really does seem to notch a win. Earlier this month, OpenAI said its own team agents had cracked a million-dollar problem in mathematics. But a couple of weeks later, nearly every AI skeptic in my feed was sharing an article asking whether it was the math problem that really mattered<\/em>. <\/p>\n To be clear, the piece did not argue that OpenAI\u2019s solution was wrong. Instead, it argued that the particular result may not be the one mathematicians care most about. Throw in the accusation by a mathematician that the models may have used some of his work without credit, and people are left thinking either OpenAI cheated or the solution wasn\u2019t important anyway. Or both.<\/p>\n That\u2019s part of what concerns Lucas Harrington, the biologist who wrote the post critiquing Anthropic\u2019s announcement. He closed with a suggestion: AI companies, he said, should \u201cset the bar high now, so that when an AI actually discovers a fundamentally new biological mechanism, everyone appreciates how big a deal it is.\u201d But as OpenAI\u2019s Sam Altman and Anthropic\u2019s Dario Amodei race to one-up each other, raising<\/em> the bar for scientific breakthroughs by AI might be the last thing on their minds.<\/p>\n #scientific #discovery<\/p>\n","protected":false},"excerpt":{"rendered":" This story originally appeared in The Algorithm, our weekly newsletter on AI. 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