Artificial intelligence systems have now become so sophisticated they are being awarded Nobel prizes for their academic achievements, and now AI has gained its second Nobel prize, but this time for protein prediction.
Geoffrey Hinton, a computer scientist whose work on deep learning is the foundation of all AI models currently used today, was awarded a Nobel prize, along with Princeton University professor John Hopfield. Both researchers were awarded the Nobel Prize in physics for their contributions to deep learning technologies, which have become the underpinning technology we now broadly call AI.
Now, AI has done it again, with a Nobel Prize being given to Demis Hassabis, the cofounder and CEO of Google DeepMind, and John M. Jumper, a director at DeepMind, for the creation of an AI capable of accurately predicting the structures of protein. Half of the Nobel Prize is awarded to Hassabis and Jumper, and the other half is awarded to David Baker, a professor of biochemistry at the University of Washington, who was recognized for his work on computational protein design. Each of the prize winners shares a $1 million pot.
Why is this creation important? Being able to accurately predict protein structures has many big implications as it will mean researchers are able to develop a deeper understanding of human health, the emergence of life, and the creation of lifesaving drugs like the cure for cancer - all through understanding how protein structures work.
"[Proteins] evolved over the course of evolution to solve the problems that organisms faced during evolution. But we face new problems today, like covid. If we could design proteins that were as good at solving new problems as the ones that evolved during evolution are at solving old problems, it would be really, really powerful," Baker told MIT Technology Review in 2022
