Scientists who built the “foundation” of artificial intelligence received the Nobel Prize

Two scientists credited with laying the foundations for today’s powerful machine learning – University of Toronto professor emeritus Geoffrey Hinton and Princeton University professor John Hopfield – were received the Nobel Prize in Physics Today.

Their discoveries and inventions laid the foundation for many recent breakthroughs in artificial intelligence, said the Nobel Committee of the Royal Swedish Academy of Sciences. Since the 1980s, their work has enabled the creation of artificial neural networks, whose computer architecture is loosely modeled on the structure of the brain.

By mimicking the way our brains make connections, neural networks enable AI tools to essentially “learn by example” Developers can train an artificial neural network to recognize complex patterns by feeding it data, which is the basis for some of today’s best-known applications of artificial intelligence, from language generation to image recognition.

“I had no expectations about it. “I am extremely surprised and honored to be included,” a “stunned” Hinton said at the University of Toronto press release.

Hinton, often called the “godfather of artificial intelligence” he said New York Times last year, “a part of him… now regrets his life’s work.” He reportedly left his position at Google in 2023 so he could draw attention to the potential risk created by technology that he played a key role in bringing to fruition.

“It’s hard to imagine how you can prevent bad actors from using this for evil purposes,” Hinton said in “ NOW interview.

The Nobel Committee appreciated Hinton for developing the so-called Boltzmann machinegenerative model, with colleagues from the 1980s:

Hinton used tools from statistical physics, the study of systems made up of many similar components. The machine is trained by providing examples that are likely to appear during machine operation. A Boltzmann machine can be used to classify images or create new examples of the type of pattern it was trained on. Hinton built on this work to help usher in today’s explosive growth in machine learning.

Hinton’s work is based on fellow award-winning John Hopfield’s Hopfield network, an artificial neural network that can reproduce patterns:

The Hopfield’s network uses physics that describes the properties of a material that result from its atomic spin – the property that makes each atom a tiny magnet. The network as a whole is described in a way equivalent to the spin energy found in physics, and is trained by finding the values ​​of the connections between nodes so that the stored images have low energy. When the Hopfield network receives a distorted or incomplete image, it methodically works through the nodes and updates their values ​​so that the network’s energy decreases. The network therefore works step by step to find the stored image that most closely resembles the imperfect one with which it was fed.

Hinton continues to express his concerns about artificial intelligence, including: call with reporters today. “We don’t have experience of what it’s like when someone is smarter than us. And it will be wonderful in many ways,” he said. “But we also have to worry about a number of possible bad consequences, particularly the threat of these things getting out of hand.”