The Future of Life Institute would like to congratulate John Hopfield and Geoffrey Hinton receiving the 2024 Nobel Prize in Physics, and Demis Hassabis, David Baker, and John Jumper for winning the 2024 Nobel Prize in Chemistry. 

Recognized for their contributions to creating the foundations of today’s AI models, both Hinton and Hopfield continue to warn the public about the risks of AI development.

Please see below for a statement from FLI Executive Director Anthony Aguirre – who also holds the Faggin Presidential Chair for the Physics of Information at the University of California, Santa Cruz – on the recipients: 

“The Future of Life Institute congratulates Geoffrey Hinton and John Hopfield for their well-deserved 2024 Nobel Prize in Physics. Their work applying core physics concepts to neural networks to create machine learning paved the way for modern AI, which promises to transform our society, economy, and world. 

“But just as importantly, they have been critical in sounding the alarm around the potential risks of AI. In March 2023, Hopfield signed our Open Letter calling for a pause on the training of the most powerful AI systems, until they could be safely and securely developed and deployed. Two months later, Hinton left his role at Google to speak freely on the dangers of increasingly powerful AI, the growing risk of losing human control, and the urgent need to take action to prevent disaster. Hinton and Hopfield embody the potential of AI to grant incredible benefits to all of humanity – but only if the technology is developed safely and securely. Without meaningful accountability and oversight, AI threatens to cause irrevocable harm to millions of lives and livelihoods.”

FLI would also like to congratulate Demis Hassabis, Professor David Baker, and Dr. John Jumper, who were awarded the 2024 Nobel Prize in Chemistry. Their groundbreaking work using AI to decode the structure of proteins and create new ones was a gamechanger in drug discovery and other areas. Crucially, innovations like AlphaFold reveal the incredible benefits if we develop narrow AI and existing general-purpose systems safely to solve specific problems – instead of racing to deploy increasingly powerful and risky models that we don’t understand.”