Synthetic intelligence fashions are beginning to achieve science. Previously two years, they’ve demonstrated that they’ll analyse knowledge, design experiments and even provide you with new hypotheses. The tempo of progress has some researchers satisfied that synthetic intelligence (AI) might compete with science’s biggest minds within the subsequent few many years.
In 2016, Hiroaki Kitano, a biologist and chief govt at Sony AI, challenged researchers to perform simply that: to develop an AI system so superior that it might make a discovery worthy of a Nobel prize. Calling it the Nobel Turing Problem, Kitano introduced the endeavour because the grand problem for AI in science. A machine wins if it may obtain a discovery on a par with top-level human analysis.
That’s not one thing present fashions can do. However by 2050, the Nobel Turing Problem envisions an AI system that, with out human intervention, combines the abilities of speculation era, experimental planning and knowledge evaluation to make a breakthrough worthy of a Nobel prize.
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It may not even take till 2050. Ross King, a chemical-engineering researcher on the College of Cambridge, UK, and an organizer of the problem, thinks such an ‘AI scientist’ may rise to laureate standing even sooner. “I feel it’s virtually sure that AI methods will get adequate to win Nobel prizes,” he says. “The query is that if it’s going to take 50 years or 10.”
However many researchers don’t see how present AI methods, that are skilled to generate strings of phrases and concepts on the premise of humankind’s present pool of data, might contribute recent insights. Carrying out such a feat may demand drastic modifications in how researchers develop AI and what AI funding goes in direction of. “If tomorrow, you noticed a authorities programme make investments a billion {dollars} in elementary analysis, I feel it might advance a lot sooner,” says Yolanda Gil, an AI researcher on the College of Southern California in Los Angeles.
Others warn that there are looming dangers to introducing AI into the analysis pipeline.
Prize-worthy discoveries
The Nobel prizes have been created to honour those that “have conferred the best profit” to humankind, as its namesake, Alfred Nobel, wrote in his will. For the science prizes, Bengt Nordén, a chemist and former chair of the Nobel Committee for Chemistry, considers three standards: a Nobel discovery should be helpful, be wealthy with affect and open a door to additional scientific understanding, he says.
Though solely residing individuals, organizations and establishments are at the moment eligible for the prizes, AI has had earlier encounters with the Nobel committee. In 2024, the Nobel Prize in Physics went to machine-learning pioneers who laid the groundwork for synthetic neural networks. That very same yr, half of the chemistry prize acknowledged the researchers behind AlphaFold, an AI system from Google DeepMind in London that predicts the 3D buildings of proteins from their amino-acid sequence. However these awards have been for the scientific strides behind AI methods — not for ones made by AI.
Demis Hassabis (left) and John Jumper (center) received a Nobel prize for the AI mannequin AlphaFold.
Jonathan Nackstrand/AFP through Getty
For an AI scientist to say its personal discovery, the analysis would must be carried out “totally or extremely autonomously”, in keeping with the Nobel Turing Problem. The AI scientist would wish to supervise the scientific course of from starting to finish, deciding on inquiries to reply, experiments to run and knowledge to analyse, in keeping with Gil.
Gil says that she has already seen AI instruments helping scientists in virtually each step of the invention course of, which “makes the sector very thrilling”. Researchers have demonstrated that AI may also help to decode the speech of animals, hypothesize on the origins of life within the Universe and predict when spiralling stars may collide. It may forecast deadly mud storms and assist to optimize the meeting of future quantum computer systems.
AI can also be starting to carry out experiments by itself. Gabe Gomes, a chemist at Carnegie Mellon College in Pittsburgh, Pennsylvania, and his colleagues designed a system referred to as Coscientist that depends on massive language fashions (LLMs), the sort behind ChatGPT and comparable methods, to plan and execute complicated chemical reactions utilizing robotic laboratory gear. And an unreleased model of Coscientist can do computational chemistry with outstanding pace, says Gomes.
One in every of Gomes’s college students as soon as complained that the software program took half an hour to work out a transition state for a response. “The issue took me over a yr as a graduate scholar,” he says.
The Tokyo-based firm Sakana AI is utilizing LLMs in an try and automate machine-learning analysis. On the identical time, researchers at Google and elsewhere are exploring how chatbots may work in groups to generate scientific concepts.
Most scientists who’re utilizing AI flip to it as an assistant or collaborator of kinds, usually appointed to particular duties. That is the primary of three waves of AI in science, says Sam Rodriques, chief govt of FutureHouse — a analysis lab in San Francisco, California, that debuted an LLM designed to do chemistry duties earlier this yr. It and different ‘reasoning fashions’ study to imitate step-wise logical thought, utilizing a trial-and-error course of that entails coaching on appropriate examples.
The prevailing fashions are useful collaborators that may make predictions on the premise of information, and speed up in any other case painstaking kinds of computation. However they have an inclination to want a human within the loop throughout at the very least one stage.
Subsequent, says Rodriques, AI will get higher at growing and evaluating its personal hypotheses by looking by literature and analysing knowledge. James Zou, a biomedical knowledge scientist at Stanford College in California, has begun transferring into this realm. He and his colleagues not too long ago confirmed {that a} system constructed on LLMs can scour organic knowledge to search out insights that researchers miss. As an illustration, when given a broadcast paper and a knowledge set of RNA sequences related to it, the system discovered that sure immune cells in people with COVID-19 usually tend to swell up as they die, an concept that hadn’t been explored by the paper’s authors. It’s exhibiting “that the AI agent is starting to autonomously discover new issues,” Zou says.
He’s additionally serving to to prepare a digital gathering referred to as Agents4Science later this month, which he describes as the primary AI-only scientific convention. All papers shall be written and reviewed by AI brokers, alongside human collaborators. And the one-day assembly will embody invited talks and panel discussions (from people) on the way forward for AI-generated analysis. Zou says he hopes that the assembly will assist researchers to evaluate how succesful AI is at doing and reviewing modern analysis.
There are recognized challenges to such efforts, together with the hallucinations that usually plague LLMs, Zou says. However he says these points might be largely remedied with human suggestions.
Rodriques says that the ultimate stage of AI in science, and what FutureHouse is aiming for, is fashions that may ask their very own questions and design and carry out their very own experiments — no human crucial. He sees this as inevitable, and says that AI might make a discovery worthy of a Nobel “by 2030 on the newest”.
Essentially the most promising areas for a breakthrough — by an AI scientist or in any other case — are in supplies science or in treating ailments resembling Parkinson’s or Alzheimer’s, he says, as a result of these are areas with huge open challenges and an unmet want.
Excited about considering
Many researchers are cautious of such claims, seeing a lot bigger hurdles. Doug Downey, a researcher on the Allen Institute for AI in Seattle, Washington, says he and his colleagues have discovered that their LLM brokers fall flat when trying to finish a analysis mission from starting to finish. In a single research of 57 AI brokers, the crew discovered that though the brokers can totally full particular science-related duties about 70% of the time, that determine drops to simply 1% after they try and generate an thought, plan and execute an experiment and analyse knowledge for a full report (see go.nature.com/4ntxs6q). “Finish-to-end automated scientific discovery stays a formidable problem,” Downey and the opposite authors write.
Though AI appears to have plenty of potential to advance science, it isn’t with out limitations, says Downey. “I feel it’s not clear how lengthy it’s going to take to beat that.”
Even when right this moment’s AI methods make sound predictions in a sure subfield, they don’t essentially study the bigger underlying ideas. One current research, for example, discovered that though an AI mannequin might predict how a planet orbits a star, it couldn’t replicate the elemental legal guidelines of physics that govern these our bodies. It wasn’t studying a scientific precept a lot as mimicking the outcomes of that precept. In one other research, an AI software couldn’t conjure an correct map of New York Metropolis’s streets, regardless of studying the best way to navigate by town.
Subbarao Kambhampati, a pc scientist at Arizona State College in Tempe, says such pitfalls reveal how the lived expertise of a human researcher is vital for understanding fundamental scientific ideas. Against this, AI methods expertise the world solely vicariously by the info units that they’re fed. Some researchers are exploring a melding of AI and robots that may give these methods extra expertise navigating the world.
An absence of real-world expertise will make it tough for AI fashions to pose recent, artistic questions and supply new insights into the human world, says Kambhampati. “I’m very supportive of claims that AI can speed up science,” he says. However “to say that you just don’t want human scientists and that this machine will simply make some Nobel-worthy discovery” feels like nothing greater than hype.
For Gil, growing an AI scientist able to a Nobel-worthy discovery would require investing extra effort in AI instruments with a wider vary of capabilities, together with meta-reasoning. Researchers might want to discover methods to imbue AI with the power to guage and regulate its personal reasoning processes — to consider its personal considering. That shift might allow fashions to weigh up which varieties of experiment will produce the perfect outcomes and to revise their scientific theories on the premise of recent findings.
Gil has lengthy labored on elementary analysis that would grant AI such skills, however she says that LLMs have taken over the highlight. If that continues, she expects Nobel-worthy discoveries to be a distant prospect. “There are such a lot of thrilling outcomes that you could get with generative AI methods,” says Gil. “However there’s plenty of different areas to concentrate to.”
King agrees that there are obstacles forward. LLMs don’t perceive the human world effectively, or what they’re contributing to it, he says: “It doesn’t even know what it’s doing is science.”
Many discussions at conferences held by the Nobel Turing Problem concentrate on what advances AI has but to make and the way it can get there. Does an AI scientist have to obtain synthetic normal intelligence, for example, being as educated and adaptable as a human? Will an AI scientist behave like a human scientist, or will the trail to discovery differ? What are the authorized and moral implications of AI-automated discovery? And the way may a prize for AI scientists be funded?
Figuring out what AI can obtain may come solely with time. “The one solution to get these solutions is to check them — like we do with any speculation,” says Gil.
Different researchers wonder if the scientific neighborhood ought to be pushing for such a discovery in any respect. In a 2024 article, Lisa Messeri, an anthropologist at Yale College in New Haven, Connecticut, and Molly Crockett, a psychologist at Princeton College in New Jersey, argue that over-reliance on AI in science has already begun to introduce extra errors. In addition they be aware that AI might crowd out various approaches and cut back innovation, with scientists starting to “produce extra however perceive much less”.
It’s attainable that automated discovery might include critical downsides for science — and scientists. AI is performing duties that lower alternatives for junior scientists, who may by no means acquire the required expertise to earn their very own Nobel prizes down the road, Messeri says. “Whereas this isn’t a zero-sum sport, given the present shrinking of analysis and college budgets, we’re at a regarding second for evaluating the professionals and cons of this future,” she says.
This text is reproduced with permission and was first revealed on October 6, 2025.
