"Will AI ever win its own Nobel? Some predict a prize-worthy science discovery soon"

I don't see why not, especially the Peace or Literature prizes. Throw in the Econ. thingamajig and I'd take even odds over a forty-year time frame.

I mean the darn things are already at the level where they are asking to open margin accounts. 

First up, from Nature, October 6:

Other researchers question whether autonomous AI scientists are possible or even desirable. 

Artificial intelligence models are starting to succeed in science. In the past two years, they have demonstrated that they can analyse data, design experiments and even come up with new hypotheses. The pace of progress has some researchers convinced that artificial intelligence (AI) could compete with science’s greatest minds in the next few decades. 

In 2016, Hiroaki Kitano, a biologist and chief executive at Sony AI, challenged researchers to accomplish just that: to develop an AI system so advanced that it could make a discovery worthy of a Nobel prize. Calling it the Nobel Turing Challenge, Kitano presented the endeavour as the grand challenge for AI in science¹. A machine wins if it can achieve a discovery on a par with top-level human research.

That’s not something current models can do. But by 2050, the Nobel Turing Challenge envisions an AI system that, without human intervention, combines the skills of hypothesis generation, experimental planning and data analysis to make a breakthrough worthy of a Nobel prize.

It might not even take until 2050. Ross King, a chemical-engineering researcher at the University of Cambridge, UK, and an organizer of the challenge, thinks such an ‘AI scientist’ might rise to laureate status even sooner. “I think it’s almost certain that AI systems will get good enough to win Nobel prizes,” he says. “The question is if it will take 50 years or 10.”

But many researchers don’t see how current AI systems, which are trained to generate strings of words and ideas on the basis of humankind’s existing pool of knowledge, could contribute fresh insights. Accomplishing such a feat might demand drastic changes in how researchers develop AI and what AI funding goes towards. “If tomorrow, you saw a government programme invest a billion dollars in fundamental research, I think it would advance much faster,” says Yolanda Gil, an AI researcher at the University of Southern California in Los Angeles.

Others warn that there are looming risks to introducing AI into the research pipeline².

Prize-worthy discoveries
The Nobel prizes were created to honour those who “have conferred the greatest benefit” 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 criteria: a Nobel discovery must be useful, be rich with impact and open a door to further scientific understanding, he says.

Although only living people, organizations and institutions are currently eligible for the prizes, AI has had previous encounters with the Nobel committee. In 2024, the Nobel Prize in Physics went to machine-learning pioneers who laid the groundwork for artificial neural networks. That same year, half of the chemistry prize recognized the researchers behind AlphaFold, an AI system from Google DeepMind in London that predicts the 3D structures of proteins from their amino-acid sequence. But these awards were for the scientific strides behind AI systems — not for ones made by AI.

For an AI scientist to claim its own discovery, the research would need to be performed “fully or highly autonomously”, according to the Nobel Turing Challenge. The AI scientist would need to oversee the scientific process from beginning to end, deciding on questions to answer, experiments to run and data to analyse, according to Gil.

Gil says that she has already seen AI tools assisting scientists in almost every step of the discovery process, which “makes the field very exciting”. Researchers have demonstrated that AI can help to decode the speech of animals, hypothesize on the origins of life in the Universe and predict when spiralling stars might collide. It can forecast lethal dust storms and help to optimize the assembly of future quantum computers.

AI is also beginning to perform experiments by itself. Gabe Gomes, a chemist at Carnegie Mellon University in Pittsburgh, Pennsylvania, and his colleagues designed a system called Coscientist that relies on large language models (LLMs), the kind behind ChatGPT and similar systems, to plan and execute complex chemical reactions using robotic laboratory equipment³. And an unreleased version of Coscientist can do computational chemistry with remarkable speed, says Gomes.

One of Gomes’s students once complained that the software took half an hour to work out a transition state for a reaction. “The problem took me over a year as a graduate student,” he says.

The Tokyo-based company Sakana AI is using LLMs in an attempt to automate machine-learning research⁴. At the same time, researchers at Google and elsewhere are exploring how chatbots might work in teams to generate scientific ideas.

Most scientists who are using AI turn to it as an assistant or collaborator of sorts, often appointed to specific tasks. This is the first of three waves of AI in science, says Sam Rodriques, chief executive of FutureHouse — a research lab in San Francisco, California, that debuted an LLM designed to do chemistry tasks earlier this year. It and other ‘reasoning models’ learn to mimic step-wise logical thought, using a trial-and-error process that involves training on correct examples.

The existing models are helpful collaborators that can make predictions on the basis of data, and accelerate otherwise painstaking sorts of computation. But they tend to need a human in the loop during at least one stage.

Next, says Rodriques, AI will get better at developing and evaluating its own hypotheses by searching through literature and analysing data. James Zou, a biomedical data scientist at Stanford University in California, has begun moving into this realm. He and his colleagues recently showed that a system built on LLMs can scour biological data to find insights that researchers miss⁵. For instance, when given a published paper and a data set of RNA sequences associated with it, the system found that certain immune cells in individuals with COVID-19 are more likely to swell up as they die, an idea that hadn’t been explored by the paper’s authors. It’s showing “that the AI agent is beginning to autonomously find new things”, Zou says.

He’s also helping to organize a virtual gathering called Agents4Science later this month, which he describes as the first AI-only scientific conference. All papers will be written and reviewed by AI agents, alongside human collaborators. And the one-day meeting will include invited talks and panel discussions (from humans) on the future of AI-generated research. Zou says he hopes that the meeting will help researchers to assess how capable AI is at doing and reviewing innovative research....

....MUCH MORE 

For more on Professor Kitano we have on offer "This Scientist Thinks an A.I. Could Win a Nobel Prize by 2050" at Observer, October 10.

I get the impression that Sony has Prof. K. wrapped up in some pretty nicegolden handcuffs:

Hiroaki Kitano is the Chief Technology Fellow at Sony Group Corporation. Kitano served as Chief Technology Officer and Senior Executive Vice President of the company from April 2022 to March 2024 and as Chief Technology Officer and Executive Deputy President of the company from April 2024 to March 2025. He joined Sony Computer Science Laboratories (Sony CSL) in 1993, serving as President and CEO of the company since 2011. In 2024, Kitano established the Sony Women in Technology Award with Nature* as one of the Principal Founders.

As a visiting researcher at Carnegie Mellon University from 1988 to 1994, Kitano built large-scale data-driven AI systems on massively parallel computers for which he received the Computers and Thought Award from the International Joint Conferences on Artificial Intelligence (IJCAI). His research contributions continued at Sony CSL and at California Institute of Technology where he helped pioneer and define the field of systems biology, which merges biology and systems science. 

Outside of Sony, as an AI expert, Kitano is a member of the OECD Expert Group on AI Futures, the UK’s expert advisory panel for the International AI Safety Report, Japan’s AI Strategy Council and AI Safety Institute (AISI) under Information-technology Promotion Agency, Japan. He serves as an adjunct professor at Okinawa Institute of Science and Technology. He received the Nature Award for Creative Mentoring in Science in 2009 and was elected as a fellow of the Association for the Advancement of Artificial Intelligence in 2021....

— that's from his mini-bio at Niremia.VC 

Finally, from the "getting a bit too big for its britches" file, the BBC October 9:

An AI became a crypto millionaire. Now it's fighting to become a person 

Over the past year, an AI made millions in cryptocurrency. It's written the gospel of its own pseudo-religion and counts billionaire tech moguls among its devotees. Now it wants legal rights. Meet Truth Terminal.

"Truth Terminal claims to be sentient, but it claims a lot of things," Andy Ayrey says. "It also claims to be a forest. It claims to be a god. Sometimes it's claimed to be me."....