Experts who made AI possible get Physics Nobel

The Nobel Foundation had announced on Tuesday that American Chicago-born bio-physicist John Hopfield of Princeton University in the United States and Wimbledon-born British-Canadian physicist Geoffrey Hinton of the University of Toronto in Canada won the Physics Prize for 2024. It is in recognition of their “foundation discoveries and inventions that enable machine learning with artificial neural networks.”

This was both a direct and indirect acknowledgement of the tremendous success of Artificial Intelligence (AI). The Nobel committee went back to the “foundations” of AI, and used the accurate term, “artificial neural network (ANN)”. As a matter of fact, Hopfield used the neural network which is how the brain is patterned and functions to create an artificial model of nodes to imitate the pattern of the neural network.

Then he fell back on the methods of physics to see whether the artificial neural network can recognise patterns and store them. He called it associative memory. And he translated this into terms of energy that were distributed among the nodes and how the energy signals were distributed and created repeated patterns on the basis of memory.

Hinton used the Hopfield network to apply the Boltzmann’s statistical pattern to create learning patterns. It is this combined work of Hopfield and Boltzmann that is at the heart of the popular AI products like the ChatGPT and the many large language models (LLMs) that have spread like wildfire in all fields of science of the day.

The Nobel committee in its statement strikes an ecstatic note when it declares, “With their breakthroughs, that stand on the foundations of physical sciences, they have showed a completely new way for us to use to computers to aid and to guide us to tackle many of the challenges our society face. Simply put, thanks to their work Humanity now has a new item in its toolbox, which we can choose to use for good purposes. Machine learning based on ANNs is currently revolutionising science, engineering and daily life. The field is already on its way to enable breakthroughs toward building a sustainable society, e.g. by helping to identify new functional materials. How deep learning by ANNs will be used in the future depends on how we humans choose to use these incredibly potent tools, already present in many aspects of our life.”

Hinton has struck the note of caution though he said that he was happy to have got the prize. He said, “We have no experience of what it’s like to have things smarter than us. It’s going to be wonderful in many respects, in areas like healthcare. But we also have to worry about a number of possible bad consequences. Particularly the threat of these things getting out of control.”

It is interesting that Hinton resigned from his job in Google so that he was free to speak about the dangers of AI. Ellen Moons, chair of the Nobel Committee for Physics had already said, “While machine learning has enormous benefits, its rapid development has also raised concern about our future. Collectively, humans carry the responsibility for using this new technology in a safe and ethical way, for the greatest benefit of humankind.”

Hinton sounds the alarm in a sophisticated manner. He does not regret that the contribution he has made to the development of AI, and he says, “In the same circumstances I would do the same again.” Then he pronounces the almost apocalyptic warning: “But I am worried that the overall consequence of this might be systems more intelligent than us that eventually take control.” It is for the first time since the making of the atom bomb, that some of the scientists at least are truly worried about what may come of AI.