AI takes a central role in the 2024 Nobel Prizes. What next?

The 2024 Nobel Prizes in Physics and Chemistry put the spotlight on AI. While the Physics laureates, John Hopfield and Geoffrey Hinton, contributed to its theoretical foundations, two of the three Chemistry laureates – specifically, Demis Hassabis and John Jumper – were rewarded for putting it into use.

John Hopfield developed the Hopfield network in 1982, a form of recurrent artificial neural network that can store and retrieve patterns, mimicking how human memory works. It operates by processing and recognizing patterns even when presented with incomplete or distorted data. His work was significant because it helped bridge the gap between biology and computer science, showing how computational systems could simulate the way the human brain stores and retrieves information.

Geoffrey Hinton co-invented the Boltzmann Machines, a type of neural network that played an important role in understanding how networks can be trained to discover patterns in data. He also popularized the use of backpropagation, an algorithm for training multi-layer neural networks, which considerably improved their capacity to learn complex patterns. Hinton’s contributions ultimately led to AI systems like GPT (Generative Pre-trained Transformers), which underpins ChatGPT, and AlphaFold the AI program that earned Demis Hassabis and John Jumper their Nobel prize in Chemistry.

AlphaFold solved one of biology’s greatest challenges: accurately predicting the 3D structure of proteins from their amino acid sequences. This problem had stumped scientists for decades, as protein folding is essential to understanding how proteins function, which is crucial for drug discovery, disease research, and biotechnology. AlphaFold’s predictions were so accurate that they matched experimental results with near-perfect precision, revolutionizing the field of biology. This breakthrough has wide-ranging implications for medicine and has already begun to accelerate research into diseases, drug discovery, and bioengineering.

Towards AI-driven disruption of traditional business models

Beyond the world of academia and frontier research, the AI techniques developed by the 2024 laureates are permeating the business world too. For one, the capabilities to analyse, identify patterns, and make sense of vast datasets, particularly unstructured data, rely at least partially on them.

From supply chain optimization to consumer behaviour analysis, AI holds the promise of making data-driven decisions faster, and automating a growing range of tasks. Large companies have already launched initiatives to capitalize on this, with some notable successes. Witness the case of a telecom company that generated an ROI 2.5x higher than average thanks to the judicious use of AI; or the case of an energy provider that delivered savings for consumers while increasing its own revenues; or this Supply Chain example that minimized waste and lost sales, while reducing the need for manual intervention at store level. These cases are no exceptions. Increasingly, the deployment of advanced algorithms and data management techniques play a central role in gaining competitive advantage.

Ultimately, AI ability to make sense of vast quantities of data will accelerate innovation and paves the way for new business models that will disrupt existing ones. From biotech to finance and manufacturing, the possibilities are endless, and all industries will eventually be impacted. More prosaically, the breakthroughs of the 2024 Nobel laureates herald an era when AI is not just a futuristic concept, but a key driver of competitiveness right now.