The word Artificial Intelligence was coined in 1955, with the original ‘fathers of AI’ Marvin Minsky, Claude Shannon, and John McCarthy, convening the famous Dartmouth Conference in 1956. Another seminal moment was much later in 2017, when the paper ‘Attention Is All You Need’ gave birth to the transformer and Generative AI. There have been other landmark moments, but the technology came into its own in 2024, when its leading lights won two, not one, Nobel prizes for their work in AI. John Hopfield and Geoffrey Hinton won the Physics prize for their work in deep learning and associated fields. The very next day David Baker, Demis Hassabis and John Jumper won the chemistry prize for applying deep learning and AI to solve the intractable problems around protein folding. Two of these names are the leading lights of AI – Hassabis who set up Deep Mind, and Hinton, the ‘father of AI’ who is probably the only person to win both the Turing Award, the highest honour in AI, and the Nobel.

I have written often about them, and count both as inspirations. It gratifies me even more that both are alumni of my own alma mater, Cambridge University. Geoff Hinton from Kings College is also famously the man who does not sit down; his severe back problems prevent him from doing so. I quoted him in an earlier article here, where he says: “I last sat down in 2005, and it was a mistake.” With his trademark wry British humour, he talks of his back being “a long-standing problem.” His fame, however, derives from his storied achievements in AI: he rescued neural networks from an AI winter, invented’ deep learning, tutored a clutch of AI deities which included Ilya Sutskever, and won the fabled Turing Award before winning the first AI Nobel. After studying at Cambridge and Edinburgh, Hinton went to study something different: How the human brain stored memories, and how it worked. He was one of the first researchers who started working on ‘mimicking’ the human brain with computer hardware and software, thus constructing a newer and purer form of AI, which we now call ‘deep learning.’ His PhD thesis demonstrated how deep neural networks outclassed older machine learning models at identifying speech patterns. He invented ‘backpropagation,’ which was one of the concepts that inspired Google’s BackRub search algorithm. By mimicking the brain, he sought to get rid of traditional machine learning techniques, where humans would label pictures, words, and objects; instead, his work copied the brain’s self-learning techniques. He and his team built ‘artificial

neurons’ modelled after the columns of neurons in the brain’s cortex. These neural nets can gather information, react to it, build an understanding of what something looks or sounds like. The AI community did not trust this novel approach; Hinton told Sky News that it was “an idea that almost no one on Earth believed in at that point.” That sentiment has certainly changed – it is deep learning that sit behind the Large Language Models of Generative AI, drive autonomous vehicles, predict the weather and, famously, predict protein folding when not defeating human beings at the game of Go.

And this is where we come to the child prodigy, Demis Hassabis, who graduated from Queens College at Cambridge much later in 2014. He took Hinton’s legacy further and created DeepMind, later bought by Google. His lab was the original OpenAI, in a singular pursuit of ‘solving intelligence’ and build a beneficial AGI, or Artificial General Intelligence. DeepMind achieved global fame when its AlphaGo defeated the Go world champion comprehensively in 2017, but the real breakthrough which led to the chemistry Nobel was AlphaFold. This AI product cracked one of the hardest problems in medical science – predicting how a protein would fold. Every carbon-based life form is made of proteins, and it is how they fold that decides almost everything about our physiology and life. There are over 200 million known proteins today, and each of them folds in a unique three-dimensional shape. It was impossible for scientists to study each one of them, if proteins fold wrongly, for instance, they can cause horrific harm; the accumulation of misshapen proteins is said to cause Alzheimer’s, Parkinson’s etc. DeepMind built on AlphaGo brought it to a level where it can predict the folded shape of a protein, right down to the molecular level. In 2020, the problem was declared solved; a breakthrough as important as mapping the human genome, or the discovery of antibiotics, something that can change medical science forever. As a scientist remarked, “What took us months and years to do, AlphaFold was able to do in a weekend.”

Now the next powerful AI technology, LLMs and Generative AI, is doing equally magical stuff. But it was the yeoman work done against all odds by pioneers like Hinton to make Deep Learning mainstream which made AI claim the biggest prize of them all. Truly, the awards were more for Deep Learning, not AI, pioneered by the restless man who never sits down.