The 2024 Nobel Prize for Physics has been a trend breaker with computer scientists being awarded the prestigious prize.
Geoffrey Hinton, one of this year’s laureates, was previously awarded the 2018 Turing Prize, arguably the most prestigious prize in computer science.
John Hopfield, the other laureate, and Hinton, were amongst the early generation of computer scientists in the 1980s, who’d set the foundations for machine learning, a technique used to train artificial intelligence. These techniques shaped modern AI models, to take up the mantle from us, to discover patterns within reams of data, which otherwise would take humans arguably forever.
Until the last mid-century, computation was a task that required manual labor. Then, Alan Turing, the British inventor and scientist, who’d rose to fame during World War 2, having helped break the Enigma code, would conceive the theoretical basis for modern computers. It was when he tried to push further, he came up with, arguably a thought, that led to publication of “Can machines think?” Seemingly an innocuous question, but with radical consequences if it really took shape, Turing, through his conceptions of algorithms, laid the foundation of artificial intelligence.
Why the physics prize?
Artificial neural networks, particularly, form the basis for today’s much popular OpenAI’s ChatGPT, and numerous other facial, image and language translational software. But these machine learning models have broken the ceiling with regards to their applications in numerous disciplines: from computer science, to finance to physics.
Physics did form the bedrock in AI research, particularly that of condensed matter physics. Particularly of relevance is spin glass – a phenomena in condensed matter physics, that involves quantum spins behaving randomly when it’s not supercooled, when it rather becomes orderly. Their applications to AI is rather foundational.
John Hopfield and Geoff Hinton are pioneers of artificial neural networks. Hopfield, an American, and Hinton, from Britain, came from diverse disciplines. Hopfield trained as a physicist. But Hinton was a cognitive psychologist. The burgeoning field of computer science, needed interdisciplinary talent, to attack a problem that no single physicist, logician, mathematician could solve. To construct a machine that can think, it will have to learn to make sense of reality. Learning is key, and computer scientists took inspiration from across statistical and condensed matter physics, psychology and neuroscience to come up with the neural network.
Inspired by the human brain, it involves artificial neurons, that holds particular values. This takes shape when the network would be initially fed data as part of a training program before it’s trained further on unfamiliar data. These values would update upon subsequent passes with more data; forming the crux of the learning process. The potential for this to work happened though with John Hopfield constructing a simple neural network in 1982.
Hopfield network, with neurons forming a chain of connections. Credit: Wikimedia Commons
Neurons pair up with one another, to form a long chain. Hopfield would then feed an image, training it by having these neurons passing along information, but only one-way at a time. Patterns of neurons that fire together, wire together, responding to particular patterns that it formerly trained with. Known as the Hebbian postulate, it actually forms the basis for learning in the human brain. It was when the Hopefield network was able to identify even the most distorted version of the original image, did AI take its baby steps. But then to train the network to learn robustly across a swathe of more data, required additional layers of neurons, and wasn’t an easy goal to achieve. There was a need for an efficient method of learning.
Artificial neural network, with neurons forming connections. The information can go across in both directions (though not indicated in the representation). Credit: Wikimedia Commons
That’s when Geoff Hinton entered the picture at around the same timeframe, helping conceive backpropagation, a technique that’s now mainstream and is the key to machine learning models that we use today. But in 2000, Hinton conceived the multi-layered version of the “Boltzmann machine”, a neural network founded on the Hopfield network. Geoff Hinton was featured in Ed Publica‘s Know the Scientist column.