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.

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.

The 2024 Nobel prize in chemistry has been awarded to David Baker, Demis Hassabis and John Jumper for their work on understanding the structure of proteins, which play vital roles in all living organisms. Hassabis and Jumper, of Google DeepMind, developed an artificial intelligence that predicts the structure of proteins. Baker, at the University of Washington in Seattle, has been recognised for his work on designing new proteins. Proteins are the molecules that make life happen. All of the key machinery of life is made of proteins, from the muscles that power us and the molecules that read and copy DNA to the antibodies that protect us from infections.

Better understanding proteins has driven huge breakthroughs in medicine. Mr Hassabis and Prof Jumper used artificial intelligence to predict the structures of almost all known proteins and created a tool called AlphaFold2. The committee called it a "complete revolution" in chemistry, and the tool is now used for 200 million proteins worldwide. Professor Baker used amino acids to design a new protein, opening the door to the creation of new proteins used in pharmaceuticals, vaccines and other tools. Prof Baker told the committee shortly after the announcement that he was "very excited and very honoured".

Prompt: "An AI learning to crack tough puzzle (with no text on the image)". If you have ever chatted with an AI language model like chatGPT, you might have been impressed by its coherent and well-structured answers. But does that imply these AIs can handle any query? The real challenge begins when we ask them to exercise logic and reason. We tried it on the popular Sudoku puzzle: GPT4-based ChatGPT is perfectly aware of these rules, and confident it can indeed play Sudoku.

Create new proteins in minutes. Two years ago researchers cracked the code on using A.I. to predict the shape of proteins. Creating new proteins can be a critical scientific endeavor: In the past, humans have been able to make insulin analogs for diabetics and immune cells that fight cancer. But all of that is hard. Building a new protein requires determining how a sequence of amino acids will fold up into a final molecular structure, to figure out how the protein actually functions.

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An AI has designed anti-microbial proteins that were then tested in real life and shown to work. The same approach could eventually be used to make new medicines. Proteins are made of chains of amino acids. The sequence of those acids determine the protein's shape and function. Ali Madani at Salesforce Research in California and his colleagues used an AI to design millions of new proteins, then created a small sample of those to test whether they worked.

Last spring, an artificial intelligence lab called OpenAI unveiled technology that lets you create digital images simply by describing what you want to see. Called DALL-E, it sparked a wave of similar tools with names like Midjourney and Stable Diffusion. Promising to speed the work of digital artists, this new breed of AI captured the imagination of both the public and the pundits -- and threatened to generate new levels of online disinformation. Social media is now teeming with the surprisingly conceptual, in which shockingly detailed, often photorealistic images are generated by DALL-E and other tools. "Photo of a teddy bear riding a skateboard in Times Square." "Cute corgi in a house made out of sushi."

Artificial-intelligence tools are helping to scientists to come up with proteins that are shaped unlike anything in nature.Credit: Ian C Haydon/UW Institute for Protein Design In June, South Korean regulators authorized the first-ever medicine, a COVID vaccine, to be made from a novel protein designed by humans. The vaccine is based on a spherical protein'nanoparticle' that was created by researchers nearly a decade ago, through a labour-intensive trial-and error-process1. Now, thanks to gargantuan advances in artificial intelligence (AI), a team led by David Baker, a biochemist at the University of Washington (UW) in Seattle, reports in Science2,3 that it can design such molecules in seconds instead of months. 'The entire protein universe': AI predicts shape of nearly every known protein Such efforts are a part of a scientific sea change, as AI tools such as DeepMind's protein-structure-prediction software AlphaFold are embraced by life scientists. In July, DeepMind revealed that the latest version of AlphaFold had predicted structures for every protein known to science.