AI researchers receive the Nobel prize for physics
NOBEL SCIENCE prizes are awarded in three areas: physics, chemistry and physiology or medicine. But occasionally some noteworthy discovery comes along that does not really fit into any of them. In 1973, for example, three pioneering students of animal behaviour, who worked on honeybees, geese and sticklebacks, were shoehorned into the physiology category.
Similar flexibility, though in an area with far more profound consequences than ethology, has been demonstrated with regard to this year’s physics prize. Showing a sense of timeliness not always apparent in its deliberations, Sweden’s Royal Academy of Science has stretched the definition of physics to include computer science, and given its imprimatur to two of the progenitors of the artificial-intelligence (AI) revolution that is currently sweeping all before it.
John Hopfield of Princeton University and Geoffrey Hinton of the University of Toronto both did their crucial work in the early 1980s, at a time when computer hardware was unable to take full advantage of it. Dr Hopfield was responsible for what has become known as the Hopfield network—a type of artificial neural network that behaves like a physical structure called a spin glass, which gave the academy a tenuous reason to call the field “physics”. Dr Hinton’s contribution was to use an algorithm known as backpropagation to train neural networks.
Artificial neural networks are computer programs based loosely on the way in which real, biological networks of nerve cells are believed to work. In particular, the strengths of the connections (known as weights) between “nodes” (the equivalent of neurons) in such networks are plastic. This plasticity grants a network the ability to process information differently in response to past performance; or, in other words, to learn. Hopfield networks, in which each node is connected to every other except itself, are particularly good at learning to extract patterns from sparse or noisy data.
Dr Hinton’s algorithm turbocharged neural networks’ learning ability by letting them work, in effect, in three dimensions. Hopfield networks and their ilk are, in essence, two-dimensional. Though they actually exist only as simulations in software, they can be thought of as physical layers of nodes. Stack such layers on top of one another, though, and train them by tweaking the weights as signals move both backward and forward between the layers (ie, back-propagated as well as forward-propagated) and you have a much more sophisticated learning system.
Dr Hinton also, for good measure, tweaked Dr Hopfield’s networks using a branch of maths called statistical mechanics to create what are known as Boltzmann machines. (Statistical mechanics, which underlies modern understanding of the second law of thermodynamics, was invented by Ludwig Boltzmann, a near contemporary of Alfred Nobel.) Boltzmann machines can be used to create systems that learn in an unsupervised manner, spotting patterns in data without having to be explicitly taught.
It is, then, the activities of these two researchers which have made machine learning really sing. AI models can now not only learn, but create (or, for sceptics, reorganise and regurgitate in a most sophisticated manner). Such tools have thus gone from being able to perform highly specific tasks, such as recognising cancerous cells in pictures of tissue samples or streamlining particle-physics data, to anything from writing essays for lazy undergraduates to running robots.
Dr Hinton, whom the academy’s detectives tracked down to a hotel in California to deliver the glad tidings, and who gamely agreed to answer questions from the press, despite the time difference, seemed both proud and worried about his achievements. He mused that by assisting mental labour, generative AI might have as big an effect on society as the industrial revolution’s assistance of physical labour has done. But he also fretted, as many in the field do, about how machine intelligence that outstripped the human variety would then go on to treat its creators. For now, though, those creators are being treated very nicely indeed. ■
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