The 2024 Nobel Prizes for physics and chemistry were announced on 8 and 9 October respectively. Both prizes were awarded for work enabling or using machine learning. More specifically, Hopfield is recognised for "inventing a network that uses a method for saving and recreating patterns". This Hopfield network utilises physics that describes a material's characteristics due to its atomic spin. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy.

For long periods of its history, artificial intelligence has lurked in the hinterland of science, often unloved and unfunded -- but two Nobel prizes in one week suggest its time in the sunshine has finally arrived. First on Tuesday, Geoffrey Hinton and John Hopfield won the physics prize for their pioneering work in creating the foundations of modern AI. Then on Wednesday, David Baker, John Jumper and Demis Hassabis shared the chemistry prize for work revealing the secrets of proteins through AI.

Demis Hassabis didn't know he was getting the Nobel Prize in chemistry from the Royal Swedish Academy of Sciences until his wife started being bombarded with calls from a Swedish number on Skype. "She would put it down several times, and then they kept persisting," Hassabis said today in a press conference convened to celebrate the awarding of the prize, alongside John Jumper, his colleague at Google DeepMind. "Then I think she realized it was a Swedish number, and they asked for my number." That he won the prize--the most prestigious in science--may not have been all that much of a shock: A day earlier, Geoffrey Hinton, often called one of the "godfathers of AI," and Princeton University's John Hopfield were awarded the Nobel Prize in physics for their work on machine learning. "Obviously the committee decided to kind of make a statement, I guess, when having the two together," said Hassabis in a press conference organized after his win. In case it wasn't clear: AI is here, and it's now possible to win a Nobel Prize by studying it and contributing to other fields--whether physics in the case of Hinton and Hopfield or chemistry in the case of Hassabis and Jumper, who won alongside David Baker, a University of Washington genome scientist.

You may know that biochemist David Baker shares the 2024 Nobel Prize in chemistry with two researchers from Google's DeepMind. But a video game helped get them there, and you can still play -- and contribute to science -- even today. Baker and the University of Washington's Institute for Protein Design led the team to create fold.it, an online puzzle game in which players actually design synthetic proteins. Originally, the game was coded to allow players to help determine the structure of existing proteins, beginning in 2008. In 2019, the project expanded to allow gamers to actually design new proteins that had never before existed.

Alex Galvagni, CEO of Age of Learning and a former artificial intelligence researcher with NASA, says advances in AI now make it possible to deliver to children "a personalized and supportive" experience in education. This photo combo shows the 2024 Nobel Prize winners in Physics, professor John Hopfield, left, of Princeton University, and professor Geoffrey Hinton, of the University of Toronto, on Tuesday, Oct. 8, 2024. FOUNDATIONAL WORK: Two pioneers of artificial intelligence -- John Hopfield and Geoffrey Hinton -- won the Nobel Prize in physics Tuesday for helping to create the building blocks of machine learning that are revolutionizing the way we work and live, but also create new threats for humanity. UBER EV: Ride-sharing platform Uber on Tuesday announced that the company is taking new steps to promote the use of electric vehicles (EVs) on its platform. 'BETTER JOB': The United Nations (U.N.) advisory body on artificial intelligence (AI) last week issued seven recommendations to address AI-related risks, but an expert told Fox News Digital the points do not cover critical areas of concern.

A day after recognizing former Google vice president and engineering fellow Geoffrey Hinton for his contributions to the field of physics, the Royal Swedish Academy of Sciences has honored a pair of current Google employees. On Wednesday, DeepMind CEO Demis Hassabis and senior research scientist John Jumper won half of the 2024 Nobel Prize in Chemistry, with the other half going to David Baker, a professor at the University of Washington. Baker, Hassabis and Jumper all advanced our understanding of those essential building blocks of life that are responsible for functions both inside and outside the human body. The Nobel Committee cited Baker's seminal work in computational protein design. Since 2003, Baker and his research team have been using amino acids and computers to design entirely new proteins.

Most 17-year-olds spend their days playing video games, but Britain's latest Nobel prize winner spent his teenage years developing them. Sir Demis Hassabis, who was jointly awarded the chemistry prize on Wednesday, got his big break in the tech world as co-designer of 1994's hit game Theme Park, where players create and operate amusement parks. Born in London to a Greek Cypriot father and Singaporean mother, Hassabis went on to gain a double first in computer science at Cambridge University, complete a PhD in cognitive neuroscience and co-found the artificial intelligence startup DeepMind, which Google bought for 400m in 2014. The 48-year-old was knighted for services to AI this year. He is the chief executive of Google's AI unit, Google DeepMind, and its achievements in using AI to predict and design the structure of proteins has spurred the award of the Nobel to Hassabis and his colleague John Jumper, who are sharing half of the award with the other half going to the US academic David Baker.

Google DeepMind founder Demis Hassabis has won a joint Nobel Prize for Chemistry for using artificial intelligence to predict the structures of proteins. Hassabis shares half the prize with John M. Jumper, a director at Google DeepMind, while the other half has been awarded to David Baker, a professor in biochemistry at the University of Washington for his work on computational protein design. The potential impact of this research is enormous. Proteins are fundamental to life, but understanding what they do involves figuring out their structure--a very hard puzzle that once took months or years to crack for each type of protein. By cutting down the time it takes to predict a protein's structure, computational tools such as those developed by this year's award winners are helping scientists gain a greater understanding of how proteins work and opening up new avenues of research and drug development.

Hassabis and Jumper created AlphaFold, which in 2020 solved a problem scientists have been wrestling with for decades: predicting the three-dimensional structure of a protein from a sequence of amino acids. The AI tool has since been used to predict the shapes of all proteins known to science. Their latest model, AlphaFold 3, can predict the structures of DNA, RNA, and molecules like ligands, which are essential to drug discovery. DeepMind has also released the source code and database of its results to scientists for free. "I've dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people," said Demis Hassabis.

The 2024 Nobel Prize in Chemistry has been awarded to a trio of scientists for their breaththrough work into protein structures. London-born Demis Hassabis, CEO of British AI firm, DeepMind, is one of the three given the prize, along with his colleague John M. Jumper and American biochemist David Baker. Together, they cracked the code for proteins' amazing structures, which had previously been much of a mystery. 'One of the discoveries being recognised this year concerns the construction of spectacular proteins,' said Heiner Linke, Chair of the Nobel Committee for Chemistry. 'The other is about fulfilling a 50-year-old dream – predicting protein structures from their amino acid sequences.