̽��ֱ�� of Cambridge - Ross King

Opinion: the future of science is automation

Anonymous — Mon, 26 Feb 2024 13:02:43 +0000

Thanks to the widespread availability of food and medical care, the ability to travel, and many other scientific and technological developments, billions of people today are living better lives than kings of centuries past. It is deeply surprising to me how little appreciated this astonishing fact is.

Of course, despite all the progress we’ve made, the world faces many challenges in the 21st century: climate change, pandemics, poverty and cancer, to name just a few.

If all the countries in the world could join together to share technology and resources, we might be to deal with and overcome these challenges. However, history presents no example of such collaboration, and the current geopolitical situation does not offer much in the way of hope.

Our best hope of dealing with these challenges is to make science and technology more productive. ̽��ֱ��only feasible way to achieve this is through the integration of Artificial Intelligence (AI) and laboratory automation.

AI systems already possess superhuman scientific powers. They can remember massive volumes of facts and learn from huge datasets. They can execute flawless logical reasoning, and near optimal probabilistic reasoning. They are can read every scientific paper, indeed everything ever written. These powers are complimentary to human scientists.

When the scientific method was developed in the 17th century, one of the core insights was the need to conduct experiments in the physical world, not just to think.

Today, laboratory automation is steadily advancing, and robots can now carry out most of the laboratory tasks that humans can. We are also now seeing the emergence of the ‘Cloud Lab’ concept. ̽��ֱ��idea is to provide laboratory automation at scale and remotely, with scientists sending their samples to the cloud lab, using a computer interface to design and execute their experiments.

And then there are AI Scientists: AI systems integrated with laboratory automations that are capable of carrying out the closed-loop automation of scientific research (aka 'Robot Scientists', 'Self-driving Labs'). These systems automatically originate hypotheses to explain observations, devise experiments to test these hypotheses, physically run these experiments using laboratory robotics, interpret the results, and then repeat the cycle.

AI Scientists can work cheaper, faster, more accurately, and longer than humans. They can also be easily multiplied. As the experiments are conceived and executed automatically by computer, it’s possible to completely capture and digitally curate all aspects of the scientific process, making the science more reproducible. There are now around 100 AI Scientists around the world, working in areas from quantum mechanics to astronomy, from chemistry to medicine.

Within the last year or so the world has been stunned by the success of Large Language Models (LLMs) such as ChatGPT, which have achieved breakthrough performance on a wide range of conversation-based tasks. LLMs are surprisingly strong absorbers of technical knowledge, such as chemical reactions and logical expressions. LLMs, and more broadly Foundation Models, show great potential for super-charging AI Scientists. They can act both as a source of scientific knowledge, since they have read all the scientific literature, and a source of new scientific hypotheses.

One of the current problems with LLMs is their tendency to hallucinate, that is to output statements that are not true. While this is a serious problem in many applications, it is not necessarily so in science, where physical experiments are the arbiters of truth. Hallucinations are hypotheses.

AI has been used as a tool in the research behind tens of thousands of scientific papers. We believe this only a start. We believe that AI has the potential to transform the very process of science.

We believe that by harnessing the power of AI, we can propel humanity toward a future where groundbreaking achievements in science, even achievements worthy of a Nobel Prize, can be fully automated. Such advances could transform science and technology, and provide hope of dealing with the formidable challenges that face humankind in the 21st century

̽��ֱ��Nobel Turing Challenge aims to develop AI Scientists capable of making Nobel-quality scientific discoveries at a level comparable, and possibly superior to the best human scientists by 2050.

As well as being a potential transformative power for good, the application of AI to science has potential for harm. As a step towards preventing this harm, my colleagues and I have prepared the Stockholm Declaration on AI for Science. This commits the signees to the responsible and ethical development of AI for science. A copy of the declaration can be signed on ̽��ֱ��Stockholm Declaration on AI for Science website.

We urge all scientists working with AI to sign.

Professor Ross King from Cambridge's Department of Chemical Engineering and Biotechnology, who originated the idea of a 'Robot Scientist', discusses why he believes that AI-powered scientists could surpass the best human scientists by the middle of the century, but only if artificial intelligence for science is developed responsibly and ethically.

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Robot arm handling test tubes.

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‘Robot scientist’ Eve finds that less than one third of scientific results are reproducible

sc604 — Tue, 05 Apr 2022 23:20:02 +0000

̽��ֱ��researchers, led by the ̽��ֱ�� of Cambridge, analysed more than 12,000 research papers on breast cancer cell biology. After narrowing the set down to 74 papers of high scientific interest, less than one-third – 22 papers – were found to be reproducible. In two cases, Eve was able to make serendipitous discoveries.

̽��ֱ��results, reported in the journal Royal Society Interface, demonstrate that it is possible to use robotics and artificial intelligence to help address the reproducibility crisis.

A successful experiment is one where another scientist, in a different laboratory under similar conditions, can achieve the same result. But more than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce some of their own experiments: this is the reproducibility crisis.

“Good science relies on results being reproducible: otherwise, the results are essentially meaningless,” said Professor Ross King from Cambridge’s Department of Chemical Engineering and Biotechnology, who led the research. “This is particularly critical in biomedicine: if I’m a patient and I read about a promising new potential treatment, but the results aren’t reproducible, how am I supposed to know what to believe? ̽��ֱ��result could be people losing trust in science.”

Several years ago, King developed the robot scientist Eve, a computer/robotic system that uses techniques from artificial intelligence (AI) to carry out scientific experiments.

“One of the big advantages of using machines to do science is they’re more precise and record details more exactly than a human can,” said King. “This makes them well-suited to the job of attempting to reproduce scientific results.”

As part of a project funded by DARPA, King and his colleagues from the UK, US and Sweden designed an experiment that uses a combination of AI and robotics to help address the reproducibility crisis, by getting computers to read scientific papers and understand them, and getting Eve to attempt to reproduce the experiments.

For the current paper, the team focused on cancer research. “ ̽��ֱ��cancer literature is enormous, but no one ever does the same thing twice, making reproducibility a huge issue,” said King, who also holds a position at Chalmers ̽��ֱ�� of Technology in Sweden. “Given the vast sums of money spent on cancer research, and the sheer number of people affected by cancer worldwide, it’s an area where we urgently need to improve reproducibility.”

From an initial set of more than 12,000 published scientific papers, the researchers used automated text mining techniques to extract statements related to a change in gene expression in response to drug treatment in breast cancer. From this set, 74 papers were selected.

Two different human teams used Eve and two breast cancer cell lines and attempted to reproduce the 74 results. Statistically significant evidence for repeatability was found for 43 papers, meaning that the results were replicable under identical conditions; and significant evidence for reproducibility or robustness was found in 22 papers, meaning the results were replicable by different scientists under similar conditions. In two cases, the automation made serendipitous discoveries.

While only 22 out of 74 papers were found to be reproducible in this experiment, the researchers say that this does not mean that the remaining papers are not scientifically reproducible or robust. “There are lots of reasons why a particular result may not be reproducible in another lab,” said King. “Cell lines can sometimes change their behaviour in different labs under different conditions, for instance. ̽��ֱ��most important difference we found was that it matters who does the experiment, because every person is different.”

King says that this work shows that automated and semi-automated techniques could be an important tool to help address the reproducibility crisis, and that reproducibility should become a standard part of the scientific process.

“It’s quite shocking how big of an issue reproducibility is in science, and it’s going to need a complete overhaul in the way that a lot of science is done,” said King. “We think that machines have a key role to play in helping to fix it.”

̽��ֱ��research was also funded by the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI), and the Wallenberg AI, Autonomous Systems and Software Program (WASP)

Reference:
Katherine Roper et al. ‘Testing the reproducibility and robustness of the cancer biology literature by robot.’ Royal Society Interface (2022). DOI: 10.1098/rsif.2021.0821

Researchers have used a combination of automated text analysis and the ‘robot scientist’ Eve to semi-automate the process of reproducing research results. ̽��ֱ��problem of lack of reproducibility is one of the biggest crises facing modern science.

One of the big advantages of using machines to do science is they’re more precise and record details more exactly than a human can

Ross King

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Breast Cancer Cell

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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‘Transformational’ approach to machine learning could accelerate search for new disease treatments

sc604 — Mon, 29 Nov 2021 20:00:00 +0000

̽��ֱ��method, called transformational machine learning (TML), was developed by a team from the UK, Sweden, India and Netherlands. It learns from multiple problems and improves performance while it learns.

TML could accelerate the identification and production of new drugs by improving the machine learning systems which are used to identify them. ̽��ֱ��results are reported in the Proceedings of the National Academy of Sciences.

Most types of machine learning (ML) use labelled examples, and these examples are almost always represented in the computer using intrinsic features, such as the colour or shape of an object. ̽��ֱ��computer then forms general rules that relate the features to the labels.

“It’s sort of like teaching a child to identify different animals: this is a rabbit, this is a donkey and so on,” said Professor Ross King from Cambridge’s Department of Chemical Engineering and Biotechnology, who led the research. “If you teach a machine learning algorithm what a rabbit looks like, it will be able to tell whether an animal is or isn’t a rabbit. This is the way that most machine learning works – it deals with problems one at a time.”

However, this is not the way that human learning works: instead of dealing with a single issue at a time, we get better at learning because we have learned things in the past.

“To develop TML, we applied this approach to machine learning, and developed a system that learns information from previous problems it has encountered in order to better learn new problems,” said King, who is also a Fellow at ̽��ֱ��Alan Turing Institute. “Where a typical ML system has to start from scratch when learning to identify a new type of animal - say a kitten - TML can use the similarity to existing animals: kittens are cute like rabbits, but don’t have long ears like rabbits and donkeys. This makes TML a much more powerful approach to machine learning.”

̽��ֱ��researchers demonstrated the effectiveness of their idea on thousands of problems from across science and engineering. They say it shows particular promise in the area of drug discovery, where this approach speeds up the process by checking what other ML models say about a particular molecule. A typical ML approach will search for drug molecules of a particular shape, for example. TML instead uses the connection of the drugs to other drug discovery problems.

“I was surprised how well it works – better than anything else we know for drug design,” said King. “It’s better at choosing drugs than humans are – and without the best science, we won’t get the best results.”

Reference:
Ivan Olier et al. ‘Transformational Machine Learning: Learning How to Learn from Many Related Scientific Problems.’ Proceedings of the National Academy of Sciences (2021). DOI: 10.1073/pnas.2108013118

Researchers have developed a new approach to machine learning that ‘learns how to learn’ and out-performs current machine learning methods for drug design, which in turn could accelerate the search for new disease treatments.

I was surprised how well it works – better than anything else we know for drug design

Ross King

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Woman in grey shirt

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AI 'scientist' finds that toothpaste ingredient may help fight drug-resistant malaria

cjb250 — Thu, 18 Jan 2018 10:00:15 +0000

When a mosquito infected with malaria parasites bites someone, it transfers the parasites into their bloodstream via its saliva. These parasites work their way into the liver, where they mature and reproduce. After a few days, the parasites leave the liver and hijack red blood cells, where they continue to multiply, spreading around the body and causing symptoms, including potentially life-threatening complications.

Malaria kills over half a million people each year, predominantly in Africa and south-east Asia. While a number of medicines are used to treat the disease, malaria parasites are growing increasingly resistant to these drugs, raising the spectre of untreatable malaria in the future.

Now, in a study published today in the journal Scientific Reports, a team of researchers employed the Robot Scientist ‘Eve’ in a high-throughput screen and discovered that triclosan, an ingredient found in many toothpastes, may help the fight against drug-resistance.

When used in toothpaste, triclosan prevents the build-up of plaque bacteria by inhibiting the action of an enzyme known as enoyl reductase (ENR), which is involved in the production of fatty acids.

Scientists have known for some time that triclosan also inhibits the growth in culture of the malaria parasite Plasmodium during the blood-stage, and assumed that this was because it was targeting ENR, which is found in the liver. However, subsequent work showed that improving triclosan’s ability to target ENR had no effect on parasite growth in the blood.

Working with ‘Eve’, the research team discovered that in fact, triclosan affects parasite growth by specifically inhibiting an entirely different enzyme of the malaria parasite, called DHFR. DHFR is the target of a well-established antimalarial drug, pyrimethamine; however, resistance to the drug among malaria parasites is common, particularly in Africa. ̽��ֱ��Cambridge team showed that triclosan was able to target and act on this enzyme even in pyrimethamine-resistant parasites.

“Drug-resistant malaria is becoming an increasingly significant threat in Africa and south-east Asia, and our medicine chest of effective treatments is slowly depleting,” says Professor Steve Oliver from the Cambridge Systems Biology Centre and the Department of Biochemistry at the ̽��ֱ�� of Cambridge. “ ̽��ֱ��search for new medicines is becoming increasingly urgent.”

Because triclosan inhibits both ENR and DHFR, the researchers say it may be possible to target the parasite at both the liver stage and the later blood stage.

Lead author Dr Elizabeth Bilsland, now an assistant professor at the ̽��ֱ�� of Campinas, Brazil, adds: “ ̽��ֱ��discovery by our robot ‘colleague’ Eve that triclosan is effective against malaria targets offers hope that we may be able to use it to develop a new drug. We know it is a safe compound, and its ability to target two points in the malaria parasite’s lifecycle means the parasite will find it difficult to evolve resistance.”

Robot scientist Eve was developed by a team of scientists at the Universities of Manchester, Aberystwyth, and Cambridge to automate – and hence speed up – the drug discovery process by automatically developing and testing hypotheses to explain observations, run experiments using laboratory robotics, interpret the results to amend their hypotheses, and then repeat the cycle, automating high-throughput hypothesis-led research.

Professor Ross King from the Manchester Institute of Biotechnology at the ̽��ֱ�� of Manchester, who led the development of Eve, says: “Artificial intelligence and machine learning enables us to create automated scientists that do not just take a ‘brute force’ approach, but rather take an intelligent approach to science. This could greatly speed up the drug discovery progress and potentially reap huge rewards.”

̽��ֱ��research was supported by the Biotechnology & Biological Sciences Research Council, the European Commission, the Gates Foundation and FAPESP (São Paulo Research Foundation).

Reference
Bilsland, E et al. Plasmodium dihydrofolate reductase is a second enzyme target for the antimalarial action of triclosan. Scientific Reports; 18 Jan 2018; DOI: 10.1038/s41598-018-19549-x

An ingredient commonly found in toothpaste could be employed as an anti-malarial drug against strains of malaria parasite that have grown resistant to one of the currently-used drugs. This discovery, led by researchers at the ̽��ֱ�� of Cambridge, was aided by Eve, an artificially-intelligent ‘robot scientist’.

Drug-resistant malaria is becoming an increasingly significant threat in Africa and south-east Asia, and our medicine chest of effective treatments is slowly depleting

Steve Oliver

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Toothpaste

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