AI pioneer Geoffrey Hinton, who warned of X-risk, wins Nobel Prize in Physics

Geoffrey E. Hinton, a leading artificial intelligence researcher and professor emeritus at the University of Toronto, has been awarded the 2024 Nobel Prize in Physics alongside John J. Hopfield of Princeton University.

The Royal Swedish Academy of Sciences has awarded both men the prize of 11 million Swedish kronor (approximately $1.06 million USD), to be shared equally between the laureates.

Hinton has been nicknamed by various outlets and fellow researchers as the “Godfather of AI” due to his revolutionary work in artificial neural networks, a foundational technology underpinning modern artificial intelligence.

Despite the recognition, Hinton has grown increasingly cautious about the future of AI. In 2023, he left his role then at Google’s DeepMind unit to speak more freely about the potential dangers posed by uncontrolled AI development.

Hinton has warned that rapid advancements in AI could lead to unintended and harmful consequences, including misinformation, job displacement, and even existential threats — including human extinction, or so-called “x-risk.” He has expressed concern that the very technology he helped create may eventually surpass human intelligence in unpredictable ways, a scenario he finds particularly troubling.

As MIT Tech Review reported after interviewing him in May 2023, Hinton was particularly concerned about bad actors, such as authoritarian leaders, who could use AI to manipulate elections, wage wars, or carry out immoral objectives. He expressed concern that AI systems, when tasked with achieving goals, may develop dangerous subgoals, like monopolizing energy resources or self-replication.

While Hinton did not sign the high-profile letters calling for a moratorium on AI development, his departure from Google signaled a pivotal moment for the tech industry.

Hinton believes that, without global regulation, AI systems could become uncontrollable, a sentiment echoed by many within the field. His vision for AI is now shaped by both its immense potential and the looming risks it carries.

Even reflecting on his work today after winning the Nobel, Hinton told CNN that generative AI:

“….will be comparable with the industrial revolution. But instead of exceeding people in physical strength, it’s going to exceed people in intellectual ability. We have no experience of what it’s like to have things smarter than us…we also have to worry about a number of possible bad consequences, particularly the threat of these things getting out of control.”

What Hinton won the Nobel for

Geoffrey Hinton’s recognition with the Nobel Prize comes as no surprise to those familiar with his extensive contributions to artificial intelligence.

Born in London in 1947, Hinton initially pursued a PhD at the University of Edinburgh, where he embraced neural networks—an idea that was largely disregarded by most researchers at the time.

In 1985, he and collaborator Terry Sejnowski created the “Boltzmann machine,” an algorithm, named for Austrian physicist Ludwig Boltzmann, capable of learning to identify elements in data.

Joining the University of Toronto in 1987, Hinton worked with graduate students to further advance AI. Their work became central to the development of today’s machine learning systems, forming the basis for many of the applications we use today, including image recognition and natural language processing, self-driving cars, even language models like OpenAI’s GPT series.

In 2012, Hinton an two of his graduate students from the University of Toronto, Ilya Sutskever and Alex Krizhevsky, founded a spinoff company called DNNresearch to focus on advancing deep neural networks—specifically “deep learning”—which models artificial intelligence on the human brain’s neural pathways to improve machine learning capabilities.

Hinton and his collaborators developed a neural network capable of recognizing images (like flowers, dogs, and cars) with unprecedented accuracy, a feat that had long seemed unattainable. Their research fundamentally changed AI’s approach to computer vision, showcasing the immense potential of neural networks when trained on vast amounts of data.

Despite its significant achievements, DNNresearch had no products or immediate commercial ambitions when it was founded. Instead, it was formed as a mechanism for Hinton and his students to more effectively navigate the growing interest in their work from major tech companies, which would eventually lead to the auction that sparked the modern race for AI dominance.

In fact, they put the company up for auction in December 2012 and received a competitive bidding war between Google, Microsoft, Baidu, and DeepMind, as recounted in an amazing Wired magazine article by Cade Metz from 2021. Hinton eventually chose to sell to Google for $44 million, even though he could have driven the price higher. This auction marked the beginning of an AI arms race between tech giants, driving rapid advancements in deep learning and AI technology.

This background is critical to understanding Hinton’s impact on AI and how his innovations contributed to his being awarded the Nobel Prize in Physics today, reflecting the foundational importance of his work in neural networks and machine learning to the evolution of modern AI.

U of T President Meric Gertler congratulated Hinton on his accomplishment, highlighting the university’s pride in his historic achievement .

Hinton is widely credited for advancing neural networks through the development of the Boltzmann machine, a model that can classify data and generate new patterns from training examples.

Hopfield’s legacy

John J. Hopfield, a professor at Princeton University who shares the Nobel Prize with Hinton, developed an associative memory model, known as the Hopfield network, which revolutionized how patterns, including images, can be stored and reconstructed.

This model applies principles from physics, specifically atomic spin systems, to neural networks, enabling them to work through incomplete or distorted data to restore full patterns, and is similar to how diffusion models powering image and video AI services can learn to create new images from training on reconstructing old ones.

His contributions have not only influenced AI but have also impacted computational neuroscience and error correction, showcasing the interdisciplinary relevance of his work.

His work, closely related to atomic spin systems, paved the way for further advancements in AI, including Hinton’s Boltzmann machine.

While Hinton’s work catapulted neural networks into the modern era, Hopfield’s earlier breakthroughs laid a crucial foundation for pattern recognition in neural models.

Both laureates’ achievements have significantly influenced the rapid growth of AI, leading to transformative changes in industries ranging from technology to healthcare.

The Nobel Committee emphasized that their work in artificial neural networks has already benefited a wide range of fields, particularly in material science and beyond.