Stockholm, Sweden · 7–9 October 2024 — The 2024 Nobel Prizes in Physiology or Medicine, Physics, and Chemistry recognised a constellation of discoveries that have reshaped modern biology and computing, with artificial neural networks and protein structure prediction sharing the spotlight with a fundamental discovery in gene regulation.
Across three consecutive days of announcements from Stockholm, the Nobel Assembly at Karolinska Institutet and the Royal Swedish Academy of Sciences honoured seven laureates whose work spans from a one-millimetre worm to the architecture of deep learning systems used by billions of people.
Medicine: The Discovery of MicroRNA
On 7 October 2024, the Nobel Assembly at Karolinska Institutet awarded the Nobel Prize in Physiology or Medicine jointly to Victor Ambros and Gary Ruvkun "for the discovery of microRNA and its role in post-transcriptional gene regulation."
Their work revealed a fundamentally new principle of gene regulation — how cells control which genetic instructions are activated. Every cell in the human body contains the same chromosomes and the same genes, yet muscle cells, nerve cells, and intestinal cells develop distinct characteristics. The answer lies in gene regulation, which allows each cell to select only the relevant instructions.
Ambros and Ruvkun made their discovery while studying a small roundworm, Caenorhabditis elegans, in the late 1980s as postdoctoral fellows in the laboratory of Robert Horvitz, himself a Nobel laureate in 2002. They investigated two mutant strains — lin-4 and lin-14 — that displayed defects in the timing of genetic programmes during development.
The results, published in 1993 in two articles in the journal Cell, were initially met with what the Nobel Assembly described as "almost deafening silence." The scientific community considered the mechanism a peculiarity of C. elegans, likely irrelevant to humans. That perception changed in 2000, when Ruvkun's group published the discovery of a second microRNA encoded by the let-7 gene, which was highly conserved across the animal kingdom. Today, more than one thousand microRNAs are known to be encoded in the human genome, and gene regulation by microRNA is understood to be universal among multicellular organisms.
Physics: Foundations of Machine Learning
On 8 October 2024, the Royal Swedish Academy of Sciences awarded the Nobel Prize in Physics jointly to John J. Hopfield of Princeton University and Geoffrey Hinton of the University of Toronto "for foundational discoveries and inventions that enable machine learning with artificial neural networks."
The award surprised many by recognising AI research within physics, but the Academy emphasised that the laureates had used tools from physics to develop methods that underpin modern machine learning.
John Hopfield created the Hopfield network, an associative memory that can store and reconstruct images and other patterns in data. The network utilises physics that describes a material's characteristics due to atomic spin — a property that makes each atom a tiny magnet. The network is described in a manner equivalent to the energy in a spin system, and is trained by finding values for connections between nodes so that saved images have low energy. When fed a distorted or incomplete image, the network methodically works through nodes, updating their values so the network's energy falls, finding the saved image most similar to the imperfect input.
Geoffrey Hinton used the Hopfield network as the foundation for the Boltzmann machine, which can learn to recognise characteristic elements in data. Hinton used tools from statistical physics — the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run, and can be used to classify images or create new examples of the pattern on which it was trained.
"The laureates' work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties." — Ellen Moons, Chair of the Nobel Committee for Physics
Hinton, widely known as a godfather of AI, later expressed ambivalence about the technology he helped create, telling reporters that he feared the consequences of AI systems eventually surpassing human intelligence.
Chemistry: Protein Design and Structure Prediction
On 9 October 2024, the Royal Swedish Academy of Sciences awarded the Nobel Prize in Chemistry in two parts: one half to David Baker of the University of Washington and Howard Hughes Medical Institute "for computational protein design," and the other half jointly to Demis Hassabis and John Jumper of Google DeepMind "for protein structure prediction."
Baker succeeded in 2003 in using amino acids — life's building blocks — to design a new protein unlike any other known protein. Since then, his research group has produced proteins that can be used as pharmaceuticals, vaccines, nanomaterials, and tiny sensors.
Hassabis and Jumper developed AlphaFold2, an AI model that solved a 50-year-old challenge in biology: predicting the complex three-dimensional structures of proteins from their amino acid sequences. With AlphaFold2, they predicted the structure of virtually all 200 million proteins that researchers have identified. The tool has been used by more than two million people from 190 countries, enabling researchers to better understand antibiotic resistance and create images of enzymes that can decompose plastic.
"One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities." — Heiner Linke, Chair of the Nobel Committee for Chemistry
A Year of AI Recognition
The 2024 Nobel Prizes were notable for the prominent role of artificial intelligence across multiple categories. The Physics Prize honoured the foundational physics behind neural networks, while the Chemistry Prize recognised an AI system that solved one of biology's grand challenges. This cross-disciplinary recognition reflected the extent to which AI had become embedded in the fabric of modern scientific research.
| Prize | Laureates | Affiliation | Prize Amount (SEK) |
|---|---|---|---|
| Physiology or Medicine | Victor Ambros, Gary Ruvkun | UMass Chan, Harvard/MGH | 11 million |
| Physics | John J. Hopfield, Geoffrey Hinton | Princeton, Toronto | 11 million |
| Chemistry | David Baker; Demis Hassabis, John Jumper | UW/HHMI; Google DeepMind | 11 million |
Each prize carried an award of 11 million Swedish kronor, an increase from previous years, reflecting the Nobel Foundation's strengthened financial position.
Sources
- Nobel Assembly at Karolinska Institutet, Press release: The Nobel Prize in Physiology or Medicine 2024, 7 October 2024,
- The Royal Swedish Academy of Sciences, Press release: The Nobel Prize in Physics 2024, 8 October 2024,
- The Royal Swedish Academy of Sciences, Press release: The Nobel Prize in Chemistry 2024, 9 October 2024,
- Lee RC, Feinbaum RL, Ambros V. Cell. 1993;75(5):843-854
- Wightman B, Ha I, Ruvkun G. Cell. 1993;75(5):855-862
- Pasquinelli AE et al. Nature. 2000;408(6808):86-89
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Institutional Research Desk · Foresight Institute of Research and Translation
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