Google has unveiled an experimental artificial intelligence system that “uses advanced reasoning to help scientists synthesize vast amounts of literature, generate novel hypotheses, and suggest detailed research plans”, according to its press release. “The idea with [the] ‘AI co-scientist’ is to give scientists superpowers,” says Alan Karthikesalingam at Google.

The tool, which doesn’t have an official name yet,Ìębuilds on Google’s Gemini large language models. When a researcher asks a question or specifies a goal – to find a new drug, say – the tool comes up with initial ideas within 15 minutes. Several Gemini agents then “debate” these hypotheses with each other, ranking them and improving them over the following hours and days, says Vivek Natarajan at Google.

During this process, the agents can search the scientific literature, access databases and use tools such as Google’s AlphaFold system for predicting the structure of proteins. “They continuously refine ideas, they debate ideas, they critique ideas,” says Natarajan.

Google has already made the system available to a few research groups, which have released short papers describing their use of it. The teams that tried it are enthusiastic about its potential, and these examples suggest the AI co-scientist will be helpful for synthesising findings. However, it is debatable whether the examples support the claim that the AI can generate novel hypotheses.

For instance, Google says one team used the system to find “new” ways of potentially treating liver fibrosis. However, the drugs proposed by the AI have previously been studied for this purpose. “The drugs identified are all well established to be antifibrotic,” says at UK biotech company Alcyomics. “There is nothing new here.”

While this potential use of the treatments isn’t new, team member at Stanford University School of Medicine in California says two out of three drugs selected by the AI co-scientist showed promise in tests on human liver organoids, whereas neither of the two he personally selected did – despite there being more evidence to support his choices. Peltz says Google gave him a small amount of funding to cover the costs of the tests.

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In another paper, at Imperial College London and his colleagues describe how the co-scientist proposed a hypothesis matching an unpublished discovery. He and his team study mobile genetic elements – bits of DNA that can move between bacteria by various means. Some mobile genetic elements hijack bacteriophage viruses. These viruses consist of a shell containing DNA plus a tail that binds to specific bacteria and injects the DNA into it. So, if an element can get into the shell of a phage virus, it gets a free ride to another bacterium.

One kind of mobile genetic element make its own shells. This type is particularly widespread, which puzzled Penadés and his team, because any one kind of phage virus can infect only a narrow range of bacteria. The answer, they recently discovered, is that these shells can hook up with the tails of different phages, allowing the mobile element to get into a wide range of bacteria.

While that finding was still unpublished, the team asked the AI co-scientist to explain the puzzle – and its number one suggestion was stealing the tails of different phages.

“We were shocked,” says PenadĂ©s. “I sent an email to Google saying, you have access to my computer. Is that right? Because otherwise I can’t believe what I’m reading here.”

However, the team did publish a paper in 2023 – which was fed to the system – about how this family of mobile genetic elements At the time, the researchers thought the elements were limited to acquiring tails from phages infecting the same cell. Only later did they discover the elements can pick up tails floating around outside cells, too.

So one explanation for how the AI co-scientist came up with the right answer is that it missed the apparent limitation that stopped the humans getting it.

What is clear is that it was fed everything it needed to find the answer, rather than coming up with an entirely new idea. “Everything was already published, but in different bits,” says PenadĂ©s. “The system was able to put everything together.”

The team tried other AI systems already on the market, none of which came up with the answer, he says. In fact, some didn’t manage it even when fed the paper describing the answer. “The system suggests things that you never thought about,” says PenadĂ©s, who hasn’t received any funding from Google. “I think it will be game-changing.”

Whether it really is game-changing will become clearer over time. Google’s track record when it comes to claims about AI tools to help scientists is mixed. Its AlphaFold system is living up to the hype, winning the team behind it a Nobel prize last year.

In 2023, however, the company announced thatÌę had been synthesised with the help of its GNoME AI. Yet, according to a 2024 analysis by at University College London, .

Despite his findings, Palgrave thinks AI can help scientists. “In general, I think AI has a huge amount to contribute to science if it is implemented in collaboration with experts in the respective fields,” he says.

Google DeepMind says its artificial intelligence has helped design chips that are already being used in data centres and even smartphones. But some chip design experts are sceptical of the company’s claims that such AI can plan new chip layouts better than humans can.

The newly named AlphaChip method can design “superhuman chip layouts” in hours, rather than relying on weeks or months of human effort, said and , researchers at Google DeepMind, in a . This AI approach uses reinforcement learning to figure out the relationships among chip components and gets rewarded based on the final layout quality. But independent researchers say the company has not yet proven such AI can outperform expert human chip designers or commercial software tools – and they want to see AlphaChip’s performance on public benchmarks involving current, state-of-the-art circuit designs.

“If Google would provide experimental results for these designs, we could have fair comparisons, and I expect that everyone would accept the results,” says at Binghamton University in New York. “The experiments would take at most a day or two to run, and Google has near-infinite resources – that these results have not been offered speaks volumes to me.”

Google DeepMind’s blog post accompanies an to Google’s 2021 Nature journal paper about the company’s AI process. Since that time, Google DeepMind says that AlphaChip has helped design three generations of Google’s Tensor Processing Units (TPU) – specialised chips used to train and run generative AI models for services such as Google’s Gemini chatbot.

The company also claims that the AI-assisted chip designs perform better than those designed by human experts and have been improving steadily. The AI achieves this by reducing the total length of wires required to connect chip components – a factor that can lower chip power consumption and potentially improve processing speed. And Google DeepMind says that AlphaChip has created layouts for general-purpose chips used in Google’s data centres, along with helping the company MediaTek develop a chip used in Samsung mobile phones.

“We really don’t know what AlphaChip is today, what it does and what it doesn’t do,” says , a chip design researcher at a competing firm. “We do know that reinforcement learning takes two to three orders of magnitude greater compute resources than methods used in commercial tools and is usually behind [in terms of] results.”

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Markov and Madden critiqued theÌęoriginal paper’s claimsÌęabout AlphaChip outperforming unnamed human experts. “Comparisons to unnamed human designers are subjective, not reproducible, and very easy to game. The human designers may be applying low effort or be poorly qualified – there is no scientific result here,” says Markov. “Imagine if AlphaGo reported wins over unnamed Go players.” A Google DeepMind spokesperson described the experts as members of Google’s TPU chip design team using the best available commercial tools.

In 2023, an independent expert who had reviewed Google’s paper his Nature commentary article that had originally praised Google’s work but had also urged replication. That expert, at the University of California, San Diego, also ran a that tried to replicate Google’s AI method and found it did not consistently outperform a human expert or conventional computer algorithms. The best-performing methods used for comparison were commercial software or internal research tools for chip design from companies such as Cadence and NVIDIA. In a , Goldie and Mirhoseini disputed Kahng’s benchmarking results. They said his tests had not pretrained the AI method on specific chip designs – a crucial factor in its performance – and relied upon “far fewer compute resources” than Google DeepMind’s team to train the AI.Ìę

“On every benchmark where there’s what I would consider a fair comparison, it seems like reinforcement learning lags behind the state of the art by a wide margin,” says Madden. “For circuit placement, I don’t believe that it’s a promising research direction.”