What I’ve learned from 25 years of automated science, and what the future holds: an interview with Ross King

AIhub is excited to launch a new series, speaking with leading researchers to explore the breakthroughs driving AI and the reality of the future promises – to give you an inside perspective on the headlines. The first interviewee is Ross King, who created the first robot scientist back in 2009. He spoke to us about the nature of scientific discovery, the role AI has to play, and his recent work in DNA computing.

Automated science is a really exciting area, and it feels like everyone’s talking about it at the moment – e.g. AlphaFold sharing the 2024 Nobel Prize. But you’ve been working in this field for many years now. In 2009 you developed Adam, the first robot scientist to generate novel scientific knowledge. Could you tell me some more about that?

So the history goes back to before Adam. Back in the late 1990s, I moved from a postdoc at what was then the Imperial Cancer Research Fund – now Cancer Research UK – and got my first academic job at the University of Wales, Aberystwyth. That’s where I had the original idea of trying to automate scientific research.

Our first publication on this was in 2004. It was a paper about robot scientists, published in Nature. That was the start. We showed that the different steps in the scientific method – forming hypotheses, determining experiments to test them, analysis of the results – could all be individually automated. But the whole cycle wasn’t fully automated, and the AI system didn’t do any novel science at that point.

In 2009, we built the Adam system. Adam was a (physically) large laboratory automation system, combined with AI that could perform full cycles of scientific research, and had knowledge about yeast functional genomics. Adam hypothesised and experimentally confirmed novel scientific knowledge about yeast metabolism, which we manually verified in the lab.

How has the field evolved since then?

For many years, not much happened. Funding was difficult due to the financial crisis, which made the British Research Councils much more conservative. Before that period, panels would choose the most exciting science. Afterwards, they focused more on what would help Britain financially in the near term.

We couldn’t get funding for many years, and few others were interested. There was some work in symbolic regression – finding interpretable mathematical models to fit phenomena – but not much automation of science. What changed was the general rise of AI. As AI became more prominent, interest picked up, especially after 2017.

**What are the potential upsides and downsides of AI scientists? **

I’ll start with the big picture: I think that science is positive for humanity. I think our lives in the 21st century are better than those of kings and queens in the 17th century, when modern science started. We have better food from around the world, beautiful fruits for breakfast, and much better healthcare – a 17th-century dentist was not pleasant. My mobile phone can communicate with billions of people at the touch of a button, and I can fly around the world. These are unbelievably good standards of living for billions of people, not just elites. The application of science to technology has provided this. Of course there are downsides – pollution, environmental damage – but generally, for humans, I think life is better than in the 17th century.

However, we still have huge problems. We can’t stop global warming or many diseases, and a billion people still live with food insecurity. I think we have sufficient technology to solve these problems if the nations of the world collaborated and shared resources. But I see no prospect of that happening in the current world situation, and I see no examples from history where these things have happened. So my only hope is that science becomes more efficient. If AI can help achieve that, then perhaps we can overcome these challenges. If we have better technology and we treat people badly after that, then it’s not down to constraints in the world, it’s down to human beings.

As for having AI scientists as colleagues: AI systems don’t understand the big picture. They can’t do really clever things, like Einstein seeing space and time as a four-dimensional continuum as opposed to quite separate things. If you read the 1905 paper by Einstein, it starts off with this philosophical problem about electricity and magnets – AI systems are nowhere near as clever as being able to do anything like that. They can’t see deep analogies or connections, but they are brilliant at other parts of science. They can literally read everything – they have read every paper in the world 1000 times. If you have a small amount of data, machine learning systems can analyze it better than humans would. In this sense, they have superhuman powers.

One interesting thing now is that if you’re a working scientist and you’re not using AI, in almost all fields you’re not going to be competitive anymore. AI on its own is not better than humans – yet. But a human plus AI is better than a human alone. Human scientists need to embrace AI and use it to do better science.

Do you think we’ll reach a point where autonomous AI will be able to generate the research questions and direct the movement of research?

Yes, I think so, although we’re not close to that at the moment. They can generate new ideas in constrained spaces, often better than humans, but they don’t really have the big picture yet.

I think that will come sooner or later. I’m involved in a project called the Nobel Turing Challenge. The goal of that is to build an AI robotic system able to do autonomous science at the level of a Nobel Prize winner, by the year 2050. And if you can do that, we can build two machines, a hundred machines, a million machines – and we’d transform society.

**Do you think that’s feasible by 2050? **

Just before the pandemic and during the pandemic, I thought the probability of hitting that target was dropping. But then there was the breakthrough of large language models, which are amazing in many ways – often remarkably stupid too, but generally very clever. I think that they alone will not be enough to beat the Nobel Turing Challenge, but I think they’ve made the probability of hitting that target much more likely.

What is interesting – and I don’t know the answer to this – is whether you need to solve AI in general to solve science, or whether it’s more like chess, where you can build a special machine which is genius at chess but not anything else. Imagine some machine which is a genius at physics but doesn’t know anything about poetry or history. Would that be enough?

**My instinct would be to say that it’s not, because everything’s so interlinked – poetry has rhythm, music contains mathematical structures. I think an AI scientist would need a broader understanding of reality than just its specific domain. **

People used to think that we needed those things to solve chess, so our human intuition is not very good at these things. For example, I didn’t expect LLMs to work so well, just by building a bigger network and putting in more data. I assumed they’d need some deep internal model of the world, or even that they would need a body to really understand how things move around in the world.

**LLMs raise some interesting questions – are they just mimicking intelligence, as they lack internal models? **

I think AI must have, in some sense, some internal model inside. It’s just we don’t really understand why they work. It’s purely empirical, which is very unusual. I don’t remember a case where we have such an important technology, but we have so little understanding of it.

**It is quite mysterious. Especially because science is always asking “what’s the mechanism?” With AI, it’s the opposite. The question is “does it work?” We don’t know what the mechanism is. **

It’s not even clear what the theory to explain it is. Coming from machine learning, I assumed it would be some sort of Bayesian inference