2024: A year of extraordinary progress and advancement in AI

Then in late November, as part of a broader effort to expand and deepen public dialogue around science and AI, we co-hosted the AI for Science Forum with the Royal Society, which convened scientists, researchers, governmental leaders and executives to discuss key topics like cracking the protein structure prediction challenge, mapping the human brain and saving lives through accurate forecasting and spotting wildfires. We hosted a Q&A with the four Nobel Laureates in attendance at the forum, Sir Paul Nurse, Jennifer Doudna, Demis Hassabis and John Jumper, which is available to listen to via the Google DeepMind podcast.

This was also a landmark year for another reason: Demis Hassabis and John Jumper, along with David Baker, were awarded the 2024 Nobel Prize® in Chemistry for their work on AlphaFold 2. As the Nobel committee recognized, their work:

"[H]as opened up completely new possibilities to design proteins that have never been seen before, and we now have access to predicted structures of all 200 million known proteins. These are truly great achievements."

It was also exciting to see the 2024 Nobel Prize® in Physics awarded to recently retired long-time Googler Geoffrey Hinton (along with John Hopfield), "for foundational discoveries and inventions that enable machine learning with artificial neural networks."

The Nobels followed additional recognitions for Google including the NeurIPS 2024 Test of Time Paper Awards for Sequence to Sequence Learning with Neural Networks and Generative Adversarial Nets, and the Beale-Orchard-Hays Prize, which was awarded to a collaborative team of educators and Google professionals for groundbreaking work on Primal-Dual Linear Programming (PDLP). (PDLP, now part of Google OR Tools, helps solve large-scale linear programming problems with real-world applications from data center network traffic engineering to container shipping optimization.)

This year, we made a number of product advances and published research that showed how AI can benefit people directly and immediately, ranging from preventative and diagnostic medicine to disaster readiness and recovery to learning.

In healthcare, AI holds the promise of democratizing quality of care in key areas, such as early detection of cardiovascular disease. Our research demonstrated how using a simple fingertip device that measures variations in blood flow, combined with basic metadata, can predict heart health risks. We built on previous AI-enabled diagnostic research for tuberculosis, demonstrating how AI models can be used for accurate TB screenings in populations with high rates of TB and HIV. This is important to reducing the prevalence of TB (more than 10 million people fall ill with it each year), as roughly 40% of people with TB go undiagnosed.