Nobel Prizes Embrace AI Innovation

You’re going to want to sit down for this one. The 2024 Nobel Prize committee just did something unprecedented–that is, two groups of AI researchers took home the respective Physics and Chemistry awards. And no, this isn’t science fiction; it’s a watershed moment that’s going to forever change how we make scientific discoveries.

Let me tell you about the brilliant minds in science who just made history in Physics by winning the Nobel Prize for “foundational discoveries and inventions that enable machine learning with artificial neural networks.” In simple terms, they discovered patterns in how information forms using physics.

Back in the 1980s, when computers were basically glorified calculators, John Hopfield and Geoffrey Hinton were already thinking way ahead of their time. Hopfield looked at how atoms act as tiny magnets and thought, “Hey, we could use this to make computers learn.” Pretty wild, right? He created networks that could actually remember and fix broken patterns—something your brain does naturally, but for computers, this was revolutionary.

Then there’s Hinton (aka the “Godfather of AI,” and yes, he’s earned that title). He took Hopfield’s ideas and supercharged them with his Boltzmann machine. So, every time your phone recognizes your face or your car spots a stop sign, you’re seeing the descendants of their work in action. Not bad for something that started with a guy looking at magnets.

The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties

– Ellen Moons, Chair of the Nobel Committee for Physics.

But here’s where it gets really interesting. Over in the Chemistry department, something equally amazing was happening. David Baker was busy designing entirely new proteins—you know, those tiny molecular machines that keep all of us alive. Meanwhile, Demis Hassabis and John Jumper tackled a problem that had been driving scientists crazy for 50 years: predicting how proteins fold. As everyone who has taken ABS (or Molecular and Cellular Biology) at IMSA knows—protein folding is a big deal. It’s one of the greatest achievements of humankind that we can now predict protein structures and even design our own proteins. These molecules are impossibly complex, and understanding their structure is crucial for everything—including why some diseases occur, how antibiotic resistance develops, or why certain microbes can break down plastic. Fun fact—my SIR lab has been researching various aspects of just one protein for the last 30+ years!

Before Hassabis and Jumper’s AI system, AlphaFold2, figuring out a single protein structure could take years. Now? It takes hours. Let that sink in. Here’s a real-world example that’ll blow your mind: A scientist working on a critical cellular signaling protein spent years trying to understand its structure. AlphaFold2 solved it in hours. That’s not just faster–it’s like jumping from a horse-drawn carriage to a supersonic jet. 

The Royal Swedish Academy of Sciences awarded the Nobel Prize in Chemistry 2024 with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John Jumper “for protein structure prediction.” The trio brilliantly cracked the code for proteins’ astonishing structures. 

The best part? This doesn’t just impact the lives of scientists. Every new drug being developed, every breakthrough in understanding diseases, every advancement in creating sustainable materials—they’re all being accelerated by these discoveries. Your future healthcare, the food you eat, even solutions to climate change could come from these innovations.

Twenty years ago, people would have laughed if you’d said AI researchers would win Nobel Prizes in both Physics and Chemistry. Now? We’re watching it happen.