Last week, the scientific world celebrated a historic event: John Hopfield and Geoffrey Hinton were awarded the 2024 Nobel Prize in Physics for their groundbreaking contributions to neural networks and deep learning. At the same time, Demis Hassabis, head of DeepMind, received the Nobel Prize in Chemistry for the development of AlphaFold, which has revolutionised our understanding of protein structures.
These recognitions not only honour individual achievements but also underscore the monumental impact of artificial intelligence (AI) on science and industry. This milestone marks a turning point in how AI is redefining our understanding of the world and transforming the way we live and work.
Physics and AI: from atoms to neural networks
Richard Feynman, one of the most influential physicists of the 20th century, who famously played bongos in his spare time, summarised the nature of the material world with the words: “All things are made of atoms – small particles that are in constant motion and attract each other when they are a little apart, but repel each other when they are pressed into each other.”
Only a genius could capture the complexity and simplicity of the physical world in such straightforward terms. Apart from its atomic nature, physics is based on two other cornerstones: it is quantum-based, and interactions always tend towards states that minimise the free energy of the system. Using these three principles and a little imagination, these interactions are described by mathematical equations that create bucolic landscapes with valleys and mountains of energy states. In these landscapes, the particles interact with each other and search for the lowest points where the energy is minimised.
Similarly, in machine learning, neural networks attempt to minimise an error function. This process can be visualised as a descent through an error landscape, where the gradient descent algorithm traverses hills and valleys to find the point with the lowest error. By adjusting the weights and parameters of the network, the algorithm learns to make increasingly accurate predictions.
John Hopfield’s work introduced neural networks, which function as associative memory systems, enabling machines to efficiently remember and recognise patterns. Geoffrey Hinton, in turn, revolutionised the field with the development of backpropagation, an algorithm that enables neural networks to learn iteratively and improve their performance through experience. Their joint contributions laid the foundation for deep learning, which is now used in countless applications.
The Transformer and AlphaFold: Solving complex puzzles
The Transformer architecture is one of the most significant advances in the field of deep learning. This model has revolutionised sequence processing and serves as the basis for technologies such as ChatGPT and AlphaFold.
Demis Hassabis and his team at DeepMind applied advanced machine learning principles to develop AlphaFold, which was able to predict the 3D structures of proteins with remarkable accuracy. This breakthrough solved a problem that had challenged scientists for more than 50 years and was computationally intractable. It has accelerated research in molecular biology and has had a direct impact on the development of drugs and therapies.
From Physics to Industry: AI and process transformation
The impact of these advances goes beyond academia and extends to industry. Just as particles in an energy landscape search for states of minimum energy, companies in an optimisation landscape strive to minimise errors and maximise efficiency.
AI automates repetitive and analytical tasks, allowing humans to focus on solving more complex and creative problems. Deep learning algorithms navigate through error landscapes to find optimal solutions, transforming processes in sectors such as manufacturing, finance, and healthcare.
The hybrid intelligence revolution
The recognition of Hopfield, Hinton, and Hassabis highlights the importance of hybrid intelligence, where humans and machines collaborate to achieve unprecedented levels of performance. Machines learn from data and continuously improve, while humans contribute creativity, intuition, and context.
This paradigm is redefining entire industries. In manufacturing, intelligent robots are adapting to new tasks. In finance, algorithms are analysing vast amounts of data to inform strategic decisions. In healthcare, AI is helping to diagnose diseases and personalise treatments for individual patients.
The significance of these awards is not only a recognition of the winners’ contributions but also a symbol of the beginning of a new era, where AI and human intelligence work together to tackle global challenges. By combining the power of algorithms with human intelligence, we are entering an era of hybrid intelligence, where machines do not replace humans but collaborate with them to solve more complex problems and create unprecedented opportunities.
