Latest news with #JoséPenadés
Yahoo
17-03-2025
- Science
- Yahoo
Google AI solves 10-year problem in two days
A new AI tool developed by Google has taken just two days to solve a problem that took human scientists a decade to figure out. The breakthrough was made by researchers at Imperial College London, who were testing out Google's latest 'co-scientist' artificial intelligence model on a subject that had puzzled them for years. After inputting a short prompt about how some superbugs gain resistance to antibiotics, the scientists received several suggestions from the AI – including one answer that they knew to be correct. "This effectively meant that the algorithm was able to look at the available evidence, analyse the possibilities, ask questions, design experiments and propose the very same hypothesis that we arrived at through years of painstaking scientific research, but in a fraction of the time,' said Professor José Penadés, from Imperial's Department of Infectious Disease . 'This type of AI 'co-scientist' platform is still at an early stage, but we can already see how it has the potential to supercharge science.' Dr Tiago Dias da Costa, who co-led the research, said the AI tool will allow scientists to identify 'experimental dead ends' that consume valuable time and resources. 'What our findings show is that AI has the potential to synthesise all the available evidence and direct us to the most important questions and experimental designs,' he said. 'If the system works as well as we hope it could, this could be game-changing; ruling out 'dead ends' and effectively enabling us to progress at an extraordinary pace.' The new AI tool does not negate the need for experiments, but the researchers believe it will help accelerate scientific discoveries by coming up with the most probably hypotheses. Early discoveries will likely involve antimicrobial resistance (AMR), which is currently one of the biggest global healthcare challenges due to the increasing rates of infections and deaths from so-called superbugs. 'The world is facing multiple complex challenges – from pandemics to environmental sustainability and food security,' said Professor Mary Ryan from Imperial College London. 'To address these urgent needs means accelerating traditional R&D processes and artificial intelligence will increasingly support scientific discovery and pioneering developments. 'Our scientists are among the most talented in the world, with the curiosity and lateral thinking needed to exploit AI technologies for societal good. Starting with new avenues for biomedical research and sowing the seeds for greater scientific efficiency – the prospects could be game-changing.' The findings, which are yet to be peer-reviewed, are detailed in a study, titled 'AI mirrors experimental science to uncover a novel mechanism of gene transfer crucial to bacterial evolution', which is available in the preprint server bioRxiv. Sign in to access your portfolio
Yahoo
16-03-2025
- Health
- Yahoo
Scientists spent 10 years cracking superbug problem. It took Google's 'co-scientist' a lot less.
When you buy through links on our articles, Future and its syndication partners may earn a commission. Google's new artificial intelligence (AI) tool has cracked a problem that took scientists a decade to solve in just two days. José Penadés and his colleagues at Imperial College London spent 10 years figuring out how some superbugs gain resistance to antibiotics — a growing threat that claims millions of lives each year. But when the team gave Google's "co-scientist" — an AI tool designed to collaborate with researchers — this question in a short prompt, the AI's response produced the same answer as their then-unpublished findings in just two days. Astonished, Penadés emailed Google to check if they had access to his research. The company responded that it didn't. The researchers published their findings Feb. 19 on the preprint server bioRxiv, so they have not been peer reviewed yet. "What our findings show is that AI has the potential to synthesise all the available evidence and direct us to the most important questions and experimental designs," co-author Tiago Dias da Costa, a lecturer in bacterial pathogenesis at Imperial College London, said in a statement. "If the system works as well as we hope it could, this could be game-changing; ruling out 'dead ends' and effectively enabling us to progress at an extraordinary pace." Antimicrobial resistance (AMR) occurs when infectious microbes — such as bacteria, viruses, fungi and parasites — gain resistance to antibiotics, rendering essential drugs ineffective. Dubbed a "silent pandemic," AMR represents one of the biggest health threats facing humanity as the overuse and misuse of antibiotics in both medicine and agriculture accelerate its prevalence. According to a 2019 report by the Centers for Disease Control and Prevention (CDC), drug-resistant bacteria killed at least 1.27 million people globally that year. About 35,000 of those deaths were in the U.S. alone, meaning that U.S. fatalities from the issue had spiked by 52% since the CDC's last AMR report, in 2013. To investigate the problem, Penadés and his team began searching for ways one type of superbug — a family of bacteria-infecting viruses known as capsid-forming phage-inducible chromosomal islands (cf-PICIs) — acquire their ability to infect diverse species of bacteria. Related: Dangerous 'superbugs' are a growing threat, and antibiotics can't stop their rise. What can? The scientists hypothesized that these viruses did this by taking tails, which are used to inject the viral genome into the host bacterial cell, from different bacteria-infecting viruses. Experiments proved their hunch to be correct, revealing a breakthrough mechanism in horizontal gene transfer that the scientific community was previously unaware of. RELATED STORIES —Scientists create 'toxic AI' that is rewarded for thinking up the worst possible questions we could imagine —Want to ask ChatGPT about your kid's symptoms? Think again — it's right only 17% of the time —Just 2 hours is all it takes for AI agents to replicate your personality with 85% accuracy Before anyone on the team shared their findings publicly, the researchers posed this same question to Google's AI co-scientist tool. After two days, the AI returned suggestions, one being what they knew to be the correct answer. "This effectively meant that the algorithm was able to look at the available evidence, analyse the possibilities, ask questions, design experiments and propose the very same hypothesis that we arrived at through years of painstaking scientific research, but in a fraction of the time," Penadés, a professor of microbiology at Imperial College London, said in the statement. The researchers noted that using the AI from the start wouldn't have removed the need to conduct experiments but that it would have helped them come up with the hypothesis much sooner, thus saving them years of work. Despite these promising findings and others, the use of AI in science remains controversial. A growing body of AI-assisted research, for example, has been shown to be irreproducible or even outright fraudulent. To minimize these problems and maximize the benefits AI could bring to research, scientists are proposing tools to detect AI misconduct and establishing ethical frameworks to assess the accuracy of findings.
Yahoo
19-02-2025
- Science
- Yahoo
AI solves superbug mystery in two days after scientists took 10 years
A scientific mystery that took 10 years to solve was cracked in two days by Google's artificial intelligence. The tech giant's latest AI development is dubbed 'co-scientist' and is designed to act as a colleague for researchers, with its own ideas, theories and analysis. Scientists at Imperial College London had spent a decade solving a mystery in the field of antimicrobial resistance (AMR), which creates superbugs that are immune to antibiotics and are expected to kill millions of people a year by 2050. Using traditional research methods, the team had theorised and then proved how different bacteria are able to accrue new DNA which can make them more dangerous, and its study is now in the process of being published by Cell, the peer-reviewed journal. After the work was finished, the scientists at Imperial partnered with Google to help test out the AI co-scientist feature. The researchers asked the co-scientist – which uses many of Google's Gemini AI models to pit various existing data and novel theories against each other – for ideas on how bacteria become immune to antibiotics. Prof José Penadés, who co-led the experimental work at Imperial, told The Telegraph: 'We worked for many years to understand this thing and we found the mechanism. 'Capsids (the protein shell of a virus) are produced with DNA inside and no tails. They have the ability to take a tail from different viruses and affect different species.' While the team knew about this tail-gathering process, nobody else in the world did. Imperial's revelations were private, there was nothing publicly available, and nothing was written online about it. The scientists then asked the co-scientist AI, using a couple of written sentences, if it had any ideas as to how the bacteria operated. Two days later, the AI made its own suggestions, which included what the Imperial scientists knew to be the right answer. 'This was the top one, it was the first hypothesis it suggested. It was, as you can imagine, quite shocking,' said Prof Penadés. Dr Tiago Dias da Costa, a bacterial pathogenesis expert at Imperial and co-author of the study, added: 'It's about 10 years of research which was condensed in two days by co-scientist.' While the AI was able to spit out the correct hypothesis within 48 hours of being asked, it was unable to do the experiments to prove it, which themselves took years of work. However, the experts say if they had been given the hypothesis at the start of their project, before they drew up the theory themselves, it would have saved years of work. 'The system gives you an answer and that needs to be experimentally validated,' added Dr da Costa. 'You cannot take the answer as a universal truth, so the scientific process would still have to happen. 'But 90 per cent of our experiments in the lab are failed experiments, and imagine if we have an AI collaborator that could guide us in reducing the failed experiments. 'Imagine how much time, grant money and, ultimately, taxpayer money we could save.' The Google AI co-scientist system is still in its infancy and will continue to be refined with further work. But it is quick, easy to use, and simple, the Imperial researchers said. The Imperial scientists were given a host of other ideas by the technology as to what may be driving AMR, some of which are now the focus of real-world research to see if they are also correct. This includes a suggested explanation for a 70-year biological mystery, which preliminary experimental data suggest holds promise. When the scientists, who have spent their entire careers trying to understand and unpick the mysteries of the microbial world, saw the results of the Google AI, they were astonished. Prof Penadés was shopping on a weekend when the email came through from Google with the suggested hypotheses from co-scientist. 'I said to the person I was with to leave me alone for one hour in order to digest this,' he told The Telegraph. 'Half of me was thinking that this cannot be true and it is amazing, and the other half found it very scary. I have this feeling that we are involved in something that will change the way we do science. This is my personal feeling.' AI is already widely used in science. It includes the Nobel Prize-winning AlphaFold technology, developed at Google DeepMind, which uses AI to correctly predict the shapes, structures and behaviours of proteins. Scientists can now see, just from DNA code, how a protein looks and how it will interact with the body, drugs and other entities. A tester version of co-scientist is now to be made freely available to researchers, and an application programming interface (API)I to allow websites to use the base technology is also to be published. The co-scientist was also tested with researchers at Stanford University and Houston Methodist in the US to see if it could identify new targets to treat disease, and if any pre-existing drugs could treat other diseases. The AI found a new target to try to treat liver fibrosis, and suggested that the drug Vorionostat, which is used to treat cancer of immune cells, could help treat the condition. The Government is currently in the process of trying to ramp up the UK's own AI infrastructure, with a focus on turning world-leading academic research into new uses for AI and commercial applications. Last week, the Department for Science, Innovation and Technology approved a new project to use AI in science conducted in the UK. This includes world-first trials that will integrate AI into the peer-review process to try to free up researchers from some of the more time-consuming tasks which distract from doing actual research. A total of £4.8 million of taxpayers' money has been shared among 23 research projects dedicated to using AI in science, including at Bath and Sheffield, to see if AI can improve peer-review. Lord Vallance, the science minister, told The Telegraph last week: 'AI presents new opportunities in a range of sectors, and if researchers can demonstrate its potential to increase transparency, robustness and trust in science, then this could pave the way to freeing them up from mundane paperwork tasks while driving growth.' Broaden your horizons with award-winning British journalism. Try The Telegraph free for 1 month with unlimited access to our award-winning website, exclusive app, money-saving offers and more.
Telegraph
19-02-2025
- Science
- Telegraph
AI solves superbug mystery in two days after scientists took 10 years
A scientific mystery that took 10 years to solve was cracked in two days by Google's artificial intelligence. The tech giant's latest AI development is dubbed 'co-scientist' and is designed to act as a colleague for researchers, with its own ideas, theories and analysis. Scientists at Imperial College London had spent a decade solving a mystery in the field of antimicrobial resistance (AMR), which creates superbugs that are immune to antibiotics and are expected to kill millions of people a year by 2050. Using traditional research methods, the team had theorised and then proved how different bacteria are able to accrue new DNA which can make them more dangerous, and its study is now in the process of being published by Cell, the peer-reviewed journal. After the work was finished, the scientists at Imperial partnered with Google to help test out the AI co-scientist feature. The researchers asked the co-scientist – which uses many of Google's Gemini AI models to pit various existing data and novel theories against each other – for ideas on how bacteria become immune to antibiotics. Speed and accuracy of results 'quite shocking' Prof José Penadés, who co-led the experimental work at Imperial, told The Telegraph: 'We worked for many years to understand this thing and we found the mechanism. 'Capsids (the protein shell of a virus) are produced with DNA inside and no tails. They have the ability to take a tail from different viruses and affect different species.' While the team knew about this tail-gathering process, nobody else in the world did. Imperial's revelations were private, there was nothing publicly available, and nothing was written online about it. The scientists then asked the co-scientist AI, using a couple of written sentences, if it had any ideas as to how the bacteria operated. Two days later, the AI made its own suggestions, which included what the Imperial scientists knew to be the right answer. 'This was the top one, it was the first hypothesis it suggested. It was, as you can imagine, quite shocking,' said Prof Penadés. Dr Tiago Dias da Costa, a bacterial pathogenesis expert at Imperial and co-author of the study, added: 'It's about 10 years of research which was condensed in two days by co-scientist.' While the AI was able to spit out the correct hypothesis within 48 hours of being asked, it was unable to do the experiments to prove it, which themselves took years of work. However, the experts say if they had been given the hypothesis at the start of their project, before they drew up the theory themselves, it would have saved years of work. 'Imagine how much time and money we could save' 'The system gives you an answer and that needs to be experimentally validated,' added Dr da Costa. 'You cannot take the answer as a universal truth, so the scientific process would still have to happen. 'But 90 per cent of our experiments in the lab are failed experiments, and imagine if we have an AI collaborator that could guide us in reducing the failed experiments. 'Imagine how much time, grant money and, ultimately, taxpayer money we could save.' The Google AI co-scientist system is still in its infancy and will continue to be refined with further work. But it is quick, easy to use, and simple, the Imperial researchers said. The Imperial scientists were given a host of other ideas by the technology as to what may be driving AMR, some of which are now the focus of real-world research to see if they are also correct. This includes a suggested explanation for a 70-year biological mystery, which preliminary experimental data suggest holds promise. When the scientists, who have spent their entire careers trying to understand and unpick the mysteries of the microbial world, saw the results of the Google AI, they were astonished. 'It was amazing – and very scary' Prof Penadés was shopping on a weekend when the email came through from Google with the suggested hypotheses from co-scientist. 'I said to the person I was with to leave me alone for one hour in order to digest this,' he told The Telegraph. 'Half of me was thinking that this cannot be true and it is amazing, and the other half found it very scary. I have this feeling that we are involved in something that will change the way we do science. This is my personal feeling.' AI is already widely used in science. It includes the Nobel Prize-winning AlphaFold technology, developed at Google DeepMind, which uses AI to correctly predict the shapes, structures and behaviours of proteins. Scientists can now see, just from DNA code, how a protein looks and how it will interact with the body, drugs and other entities. A tester version of co-scientist is now to be made freely available to researchers, and an application programming interface (API)I to allow websites to use the base technology is also to be published. The co-scientist was also tested with researchers at Stanford University and Houston Methodist in the US to see if it could identify new targets to treat disease, and if any pre-existing drugs could treat other diseases. The AI found a new target to try to treat liver fibrosis, and suggested that the drug Vorionostat, which is used to treat cancer of immune cells, could help treat the condition. Government investing in AI The Government is currently in the process of trying to ramp up the UK's own AI infrastructure, with a focus on turning world-leading academic research into new uses for AI and commercial applications. Last week, the Department for Science, Innovation and Technology approved a new project to use AI in science conducted in the UK. This includes world-first trials that will integrate AI into the peer-review process to try to free up researchers from some of the more time-consuming tasks which distract from doing actual research. A total of £4.8 million of taxpayers' money has been shared among 23 research projects dedicated to using AI in science, including at Bath and Sheffield, to see if AI can improve peer-review. Lord Vallance, the science minister, told The Telegraph last week: 'AI presents new opportunities in a range of sectors, and if researchers can demonstrate its potential to increase transparency, robustness and trust in science, then this could pave the way to freeing them up from mundane paperwork tasks while driving growth.'
