AI's Growing Influence in Scientific Research: Nobel Prizes Highlight Potential and Challenges

Will AI one day win a Nobel Prize?

Artificial intelligence is already disrupting industries from banking and finance to film and journalism, and scientists are investigating how AI might revolutionize their field -- or even win a Nobel Prize. In 2021, Japanese scientist Hiroaki Kitano proposed what he dubbed the "Nobel Turing Challenge", inviting researchers to create an "AI scientist" capable of autonomously carrying out research worthy of a Nobel Prize by 2050. Some scientists are already hard at work seeking to create an AI colleague worthy of a Nobel, with this year's laureates to be announced between October 7 and 14. And in fact, there are around 100 "robot scientists" already, according to Ross King, a professor of machine intelligence at Chalmers University in Sweden. In 2009, King published a paper in which he and a group of colleagues presented "Robot Scientist Adam" -- the first machine to make scientific discoveries independently. "We built a robot which discovered new science on its own, generated novel scientific ideas and tested them and confirmed that they were correct," King told AFP. The robot was set up to form hypotheses autonomously, and then design experiments to test these out. It would even program laboratory robots to carry out those experiments, before learning from the process and repeating. 'Not trivial' "Adam" was tasked with exploring the inner workings of yeast and discovered "functions of genes" that were previously unknown in the organism. In the paper, the robot scientist's creators noted that while the discoveries were "modest" they were "not trivial" either. Later, a second robot scientist -- named "Eve" -- was set up to study drug candidates for malaria and other tropical diseases. According to King, robot scientists already have several advantages over your average human scientist. "It costs less money to do the science, they work 24/7," he explained, adding that they are also more diligent at recording every detail of the process. At the same time, King conceded that AI is far from being anywhere close to a Nobel-worthy scientist. For that, they would need to be "much more intelligent" and able to "understand the bigger picture". 'Nowhere near' Inga Strumke, an associate professor at the Norwegian University of Science and Technology, said that for the time being the scientific profession is safe. "The scientific tradition is nowhere near being taken over by machines anytime soon," she told AFP. However, Strumke added that "doesn't mean that it's impossible", adding that it's "definitely" clear that AI is having and will have an impact on how science is conducted. One example of how it is already in use is AlphaFold -- an AI model developed by Google DeepMind -- which is used to predict the three-dimensional structure of proteins based on their amino acid. "We knew that there was some relation between the amino acids and the final three-dimensional shape of the proteins... and then we could use machine learning to find it," Strumke said. She explained that the complexity of such calculations was too daunting for humans. "We kind of have a machine that did something that no humans could do," she said. At the same time, for Strumke, the case of AlphaFold also demonstrates one of the weaknesses of current AI models such as so-called neural networks. They are very adept at crunching massive amounts of information and coming up with an answer, but not very good at explaining why that answer is correct. So while the over 200 million protein structures predicted by AlphaFold are "extremely useful", they "don't teach us anything about microbiology", Strumke said. Aided by AI For her, science seeks to understand the universe and is not merely about "making the correct guess". Still, the groundbreaking work done by AlphaFold has led to pundits putting the minds behind it as front-runners for a Nobel Prize. Google DeepMind's director John Jumper and CEO and co-founder Demis Hassabis were already honored with the prestigious Lasker Award in 2023. Analytics group Clarivate, which keeps an eye on potential Nobel science laureates, places the pair among the top picks for the 2024 candidates for the Prize in Chemistry, announced on October 9. David Pendlebury, head of the research group, admits that while a 2021 paper by Jumper and Hassabis has been cited thousands of times, it would be out of character for the Nobel jury to award work so quickly after publication -- as most discoveries that are honored date back decades. At the same time, he feels confident that it won't be too long before research aided by AI will win the most coveted of science prizes. "I'm sure that within the next decade there will be Nobel Prizes that are somehow assisted by computation and computation these days is more and more AI," Pendlebury told AFP.

Will AI one day win a Nobel Prize?

Stockholm (AFP) - Artificial intelligence is already disrupting industries from banking and finance to film and journalism, and scientists are investigating how AI might revolutionise their field -- or even win a Nobel Prize. In 2021, Japanese scientist Hiroaki Kitano proposed what he dubbed the "Nobel Turing Challenge", inviting researchers to create an "AI scientist" capable of autonomously carrying out research worthy of a Nobel Prize by 2050. Some scientists are already hard at work seeking to create an AI colleague worthy of a Nobel, with this year's laureates to be announced between October 7 and 14. And in fact, there are around 100 "robot scientists" already, according to Ross King, a professor of machine intelligence at Chalmers University in Sweden. In 2009, King published a paper in which he and a group of colleagues presented "Robot Scientist Adam" -- the first machine to make scientific discoveries independently. "We built a robot which discovered new science on its own, generated novel scientific ideas and tested them and confirmed that they were correct," King told AFP. The robot was set up to form hypotheses autonomously, and then design experiments to test these out. It would even program laboratory robots to carry out those experiments, before learning from the process and repeating. 'Not trivial' "Adam" was tasked with exploring the inner workings of yeast and discovered "functions of genes" that were previously unknown in the organism. In the paper, the robot scientist's creators noted that while the discoveries were "modest" they were "not trivial" either. Later, a second robot scientist -- named "Eve" -- was set up to study drug candidates for malaria and other tropical diseases. According to King, robot scientists already have several advantages over your average human scientist. "It costs less money to do the science, they work 24/7," he explained, adding that they are also more diligent at recording every detail of the process. At the same time, King conceded that AI is far from being anywhere close to a Nobel-worthy scientist. For that, they would need to be "much more intelligent" and able to "understand the bigger picture". 'Nowhere near' Inga Strumke, an associate professor at the Norwegian University of Science and Technology, said that for the time being the scientific profession is safe. "The scientific tradition is nowhere near being taken over by machines anytime soon," she told AFP. However, Strumke added that "doesn't mean that it's impossible", adding that it's "definitely" clear that AI is having and will have an impact on how science is conducted. One example of how it is already in use is AlphaFold -- an AI model developed by Google DeepMind -- which is used to predict the three-dimensional structure of proteins based on their amino acid. "We knew that there was some relation between the amino acids and the final three-dimensional shape of the proteins... and then we could use machine learning to find it," Strumke said. She explained that the complexity of such calculations was too daunting for humans. "We kind of have a machine that did something that no humans could do," she said. At the same time, for Strumke, the case of AlphaFold also demonstrates one of the weaknesses of current AI models such as so-called neural networks. They are very adept at crunching massive amounts of information and coming up with an answer, but not very good at explaining why that answer is correct. So while the over 200 million protein structures predicted by AlphaFold are "extremely useful", they "don't teach us anything about microbiology", Strumke said. Aided by AI For her, science seeks to understand the universe and is not merely about "making the correct guess". Still, the groundbreaking work done by AlphaFold has led to pundits putting the minds behind it as front-runners for a Nobel Prize. Google DeepMind's director John Jumper and CEO and co-founder Demis Hassabis were already honoured with the prestigious Lasker Award in 2023. Analytics group Clarivate, which keeps an eye on potential Nobel science laureates, places the pair among the top picks for the 2024 candidates for the Prize in Chemistry, announced on October 9. David Pendlebury, head of the research group, admits that while a 2021 paper by Jumper and Hassabis has been cited thousands of times, it would be out of character for the Nobel jury to award work so quickly after publication -- as most discoveries that are honoured date back decades. At the same time, he feels confident that it won't be too long before research aided by AI will win the most coveted of science prizes. "I'm sure that within the next decade there will be Nobel Prizes that are somehow assisted by computation and computation these days is more and more AI," Pendlebury told AFP.

Will AI one day win a Nobel Prize?

Some scientists are already hard at work seeking to create an AI colleague worthy of a Nobel, with this year's laureates to be announced between October 7 and 14. And in fact, there are around 100 "robot scientists" already, according to Ross King, a professor of machine intelligence at Chalmers University in Sweden.Stockholm, Oct 03, 2024 -Artificial intelligence is already disrupting industries from banking and finance to film and journalism, and scientists are investigating how AI might revolutionise their field -- or even win a Nobel Prize. In 2021, Japanese scientist Hiroaki Kitano proposed what he dubbed the "Nobel Turing Challenge", inviting researchers to create an "AI scientist" capable of autonomously carrying out research worthy of a Nobel Prize by 2050. Some scientists are already hard at work seeking to create an AI colleague worthy of a Nobel, with this year's laureates to be announced between October 7 and 14. And in fact, there are around 100 "robot scientists" already, according to Ross King, a professor of machine intelligence at Chalmers University in Sweden. In 2009, King published a paper in which he and a group of colleagues presented "Robot Scientist Adam" -- the first machine to make scientific discoveries independently. "We built a robot which discovered new science on its own, generated novel scientific ideas and tested them and confirmed that they were correct," King told AFP. The robot was set up to form hypotheses autonomously, and then design experiments to test these out. It would even program laboratory robots to carry out those experiments, before learning from the process and repeating. Not trivial "Adam" was tasked with exploring the inner workings of yeast and discovered "functions of genes" that were previously unknown in the organism. In the paper, the robot scientist's creators noted that while the discoveries were "modest" they were "not trivial" either. Later, a second robot scientist -- named "Eve" -- was set up to study drug candidates for malaria and other tropical diseases. According to King, robot scientists already have several advantages over your average human scientist. "It costs less money to do the science, they work 24/7," he explained, adding that they are also more diligent at recording every detail of the process. At the same time, King conceded that AI is far from being anywhere close to a Nobel-worthy scientist. For that, they would need to be "much more intelligent" and able to "understand the bigger picture". Nowhere near Inga Strumke, an associate professor at the Norwegian University of Science and Technology, said that for the time being the scientific profession is safe. "The scientific tradition is nowhere near being taken over by machines anytime soon," she told AFP. However, Strumke added that "doesn't mean that it's impossible", adding that it's "definitely" clear that AI is having and will have an impact on how science is conducted. One example of how it is already in use is AlphaFold -- an AI model developed by Google DeepMind -- which is used to predict the three-dimensional structure of proteins based on their amino acid. "We knew that there was some relation between the amino acids and the final three-dimensional shape of the proteins... and then we could use machine learning to find it," Strumke said. She explained that the complexity of such calculations was too daunting for humans. "We kind of have a machine that did something that no humans could do," she said. At the same time, for Strumke, the case of AlphaFold also demonstrates one of the weaknesses of current AI models such as so-called neural networks. They are very adept at crunching massive amounts of information and coming up with an answer, but not very good at explaining why that answer is correct. So while the over 200 million protein structures predicted by AlphaFold are "extremely useful", they "don't teach us anything about microbiology", Strumke said. Aided by AI For her, science seeks to understand the universe and is not merely about "making the correct guess". Still, the groundbreaking work done by AlphaFold has led to pundits putting the minds behind it as front-runners for a Nobel Prize. Google DeepMind's director John Jumper and CEO and co-founder Demis Hassabis were already honoured with the prestigious Lasker Award in 2023. Analytics group Clarivate, which keeps an eye on potential Nobel science laureates, places the pair among the top picks for the 2024 candidates for the Prize in Chemistry, announced on October 9. David Pendlebury, head of the research group, admits that while a 2021 paper by Jumper and Hassabis has been cited thousands of times, it would be out of character for the Nobel jury to award work so quickly after publication -- as most discoveries that are honoured date back decades. At the same time, he feels confident that it won't be too long before research aided by AI will win the most coveted of science prizes. "I'm sure that within the next decade there will be Nobel Prizes that are somehow assisted by computation and computation these days is more and more AI," Pendlebury told AFP.

AI was central to two of 2024's Nobel prize categories. It's a sign of things to come

University of Bath provides funding as a member of The Conversation UK. The 2024 Nobel Prizes in physics and chemistry have given us a glimpse of the future of science. Artificial intelligence (AI) was central to the discoveries honoured by both awards. You have to wonder what Alfred Nobel, who founded the prizes, would think of it all. We are certain to see many more Nobel medals handed to researchers who made use of AI tools. As this happens, we may find the scientific methods honoured by the Nobel committee depart from straightforward categories like "physics", "chemistry" and "physiology or medicine". We may also see the scientific backgrounds of recipients retain a looser connection with these categories. This year's physics prize was awarded to the American John Hopfield, at Princeton University, and British-born Geoffrey Hinton, from the University of Toronto. While Hopfield is a physicist, Hinton studied experimental psychology before gravitating to AI. The chemistry prize was shared between biochemist David Baker, from the University of Washington, and the computer scientists Demis Hassabis and John Jumper, who are both at Google DeepMind in the UK. There is a close connection between the AI-based advances honoured in the physics and chemistry categories. Hinton helped develop an approach used by DeepMind to make its breakthrough in predicting the shapes of proteins. The physics laureates, Hinton in particular, laid the foundations of the powerful field known as machine learning. This is a subset of AI that's concerned with algorithms, sets of rules for performing specific computational tasks. Hopfield's work is not particularly in use today, but the backpropagation algorithm (co-invented by Hinton) has had a tremendous impact on many different sciences and technologies. This is concerned with neural networks, a model of computing that mimics the human brain's structure and function to process data. Backpropagation allows scientists to "train" enormous neural networks. While the Nobel committee did its best to connect this influential algorithm to physics, it's fair to say that the link is not a direct one. Training a machine-learning system involves exposing it to vast amounts of data, often from the internet. Hinton's advance ultimately enabled the training of systems such as GPT (the technology behind ChatGPT), and the AI algorithms AlphaGo and AlphaFold, developed by Google DeepMind. So, backpropagation's impact has been enormous. DeepMind's AlphaFold 2 solved a 50-year-old problem: predicting the complex structures of proteins from their molecular building blocks, amino acids. Every two years, since 1994, scientists have been holding a contest to find the best ways to predict protein structures and shapes from the sequences of their amino acids. The competition is called Critical Assessment of Structure Prediction (CASP). For the past few contests, CASP winners have used some version of DeepMind's AlphaFold. There is, therefore, a direct line to be drawn from Hinton's backpropagation to Google DeepMind's AlphaFold 2 breakthrough. David Baker used a computer program called Rosetta to achieve the difficult feat of building new kinds of proteins. Both Baker's and DeepMind's approaches hold enormous potential for future applications. Attributing credit has always been controversial aspect of the Nobel prizes. A maximum of three researchers can share a Nobel. But big advances in science are collaborative. Scientific papers may have 10, 20, 30 authors or more. More than one team might contribute to the discoveries honoured by the Nobel committee. This year we may have further discussions about the attribution of the research on backpropagation algorithm, which has been claimed by various researchers, as well as for the general attribution of a discovery to a field like physics. We now have a new dimension to the attribution problem. It's increasingly unclear whether we will always be able to distinguish between the contributions of human scientists and those of their artificial collaborators - the AI tools that are already helping push forward the boundaries of our knowledge. In the future, could we see machines take the place of scientists, with humans being consigned to a supporting role? If so, perhaps the AI tool will get the main Nobel prize with humans needing their own category.

Do the 2024 Nobel prizes show that AI is the future of science?

It is a common refrain that artificial intelligence is coming to take all our jobs, and now it seems that Nobel prizewinners are no exception. Two of the awards this year, for physics and chemistry, have been claimed by people working in the field of AI - much to the chagrin of some researchers in areas more traditionally recognised by these categories. What does the rise of the AI Nobel mean for the future of science? "These prizes reflect two different ways of reckoning with the relationship between AI and science:...