So at the end of November, as part of a broader effort to expand and elaborate on public dialogue around science and AI, we hosted AI for the Science Forum with the Royal Society, who called scientists, researchers, government leaders and leaders To discuss key topics such as breaking the challenge of predicting protein structures, mapping the human brain and saving lives through accurate forecasts and mocking forest fires. We were hosting a Q&A with the four Nobel laureates present in the forum, Sir Paul Nurse, Jennifer Dudna, Demis Hassabis and John Jumper, which is available to listen to through the Google Deepmind podcast.
This was also a landmark year for another reason: Demis Hassabis and John Jumper, together with David Baker, were awarded 2024 Nobel Prize® in chemistry for their work at Alphafold 2. As the Nobel Committee recognized, their work became:
“[H]which opened up completely new opportunities to design proteins that have never been seen before, and we now have access to predicted structures of all 200 million known proteins. It’s really great achievements. ”
It was also exciting to see the 2024 Nobel Prize® in physics awarded to the newly retired long -time Googler Geoffrey Hinton (along with John Hopfield), “for basic discoveries and inventions that enable machine learning with artificial neural networks.”
The Nobels followed further accolades to Google, including Neurips 2024 Test of Time Paper Awards for Sequence to Sequence Learning with Neural Networks and Generative Adversarial Nets and Beale-Orchard-Hays Prize, which was awarded a collaborative team of teachers and Google professionals for pioneering work With Primal-Dual Linear Programming (PDLP). (PDLP, which is now part of Google or Tools, helps solve major linear programming problems with real -world applications from data center network penalties to container shipping optimization.)
AI for the benefit of humanity
This year we have done a number of product progress and published research that showed how artificial intelligence can benefit people directly and instantly, from preventive and diagnostic medicine to disaster preparedness and improvement to learning.
In the health care system, AI has promised to democratize the quality of care in key areas, such as early detection of cardiovascular disease. Our research showed how the use of a simple fingertip unit measuring variations in blood flow, combined with basic metadata, can predict heart health risks. We were based on previously AI activated diagnostic research for tuberculosis and demonstrated how AI models can be used for accurate TB screenings in populations with high occurrences of TB and HIV. This is important to reduce the occurrence of tuberculosis (more than 10 million people get sick of it every year) as about 40 % of people with tuberculosis are not diagnosed.