Nobel Prize In AI
The list of 2024 Nobel Prize winners has brought a lot of attention to AI. First, it was with the Nobel Prize in Physics, which rewarded the fundamental physics-inspired theory and early prototype for the building of neural networks, the basis of most of today’s AI technology. We discussed in detail how these early neural networks worked in “Investing in Nobel Prize Achievements—Artificial Neural Networks, The Basis Of AI.”
Another Nobel Prize, this time in Chemistry, would be awarded to computation-focused research. More precisely, it went half to David Baker “for computational protein design”, and the other half jointly to Google’s AlphaFold key researchers, Demis Hassabis and John M. Jumper, “for protein structure prediction”.
So, it is not an exaggeration to say that 2024 was the AI Nobel Prize year, with two out of three of the Nobel Prizes in “hard sciences” (Physics, Chemistry, Medicine/Biology) going to AI-related projects.
This was obviously praised by researchers in the field and people enthusiastic about the future of AI-driven research. But it also raised a few eyebrows among scientists, especially physicists, who see computing and AI as not “real physics.”
What Should Nobel Prizes Reward?
This is part of a larger and older debate of what constitutes “pure” science worthy of a Nobel Prize. Some will say that only theoretical advancement and true intellectual breakthroughs are worthy of the most prestigious international prize in science.
I’m speechless. I like ML and ANN as much as the next person, but hard to see that this is a Physics discovery. Guess the Nobel got hit by AI hype.
Jonathan Pritchard – Astrophysicist at Imperial College London
Others will say that sciences should also be rewarded for their impact on the real world, especially when it comes to the Nobel Prize, whose founder, Mr. Alfred Nobel made to give a prize “to those who, during the preceding year, have conferred the greatest benefit to humankind” in physics, chemistry, physiology or medicine, literature, and peace.
In that context, it might be argued that neural networks have indeed contributed greatly to the benefit of humankind, will likely continue to do so even more in the future, and are therefore worthy of this year’s Nobel Prize.
Is Computing Physics?
Regarding the 2024 Nobel Prize In Physics specifically, the method developed to create neural networks was deeply rooted in physics. More specifically, it drew heavily from statistical physics, a field describing things with many elements like gases or liquids.
It also took inspiration from the observation that collective properties in many physical systems are robust to changes in model details. Notably magnetic materials derive their special characteristics thanks to their atomic spin – a property that makes each atom a tiny magnet.
Overall, the models rewarded by the Nobel Prize, The Boltzmann machine invented by Hinton and the Hopfield network, “are both energy-based models” that do not differ much from the mathematical description used to understand the physics of real-life material.
Still, some “real” physicists felt some dismay at the idea that computing is taking the spotlight from physics, maybe speaking more about how little society gives respect and attention to this field than about this year’s Nobel Prize in itself.
“It falls into the field of computer science. The annual Nobel prize is a rare opportunity for physics — and physicists with it — to step into the spotlight.
It’s the day when friends and family remember they know a physicist and maybe go and ask him or her what this recent Nobel is all about. But not this year.”
Sabine Hossenfelder- Physicist at the Munich Center for Mathematical Philosophy in Germany
Physics Coming Back Into AI
The Nobel Prize reward also takes into account a growing development in computing to go back to physics-based methods to improve machine learning, neural networks, and AI.
“We need the way of thinking we have in physics to study machine learning.”
Lenka Zdeborová – Statistical physicist at the Swiss Federal Institute of Technology in Lausanne.
The early work on neural networks was truly interdisciplinary, taking also inspiration from the latest advancement in our understanding of how biological neurons work, and bringing together math, physics, computer sciences, and neurobiology.
“I think that the Nobel Prize in Physics should continue to spread into more regions of physics knowledge. Physics is becoming wider and wider, and it contains many areas of knowledge that did not exist in the past, or were not part of physics.”
Giorgio Parisi- Physicist at the Sapienza University of Rome, who shared the 2021 physics Nobel.
So while maybe a little surprising, and to the dismay of the most purist type of physicists, the attribution of the Nobel Prize to John Hopfield and Geoffrey Hinton is not as far from physics and “hard sciences” as it might look at at first glance.
Adding Insult To Injury?
Maybe the reaction of some members of the scientific community would have been more moderate if the Chemistry Nobel Prize had not been computation-focused as well.
Here, the debate is more focused on the real contribution of AI to the field, giving some credit to the “Guess the Nobel got hit by AI hype” criticism.
This is because neural networks like AlphaFold, predicting the 3D configuration of protein, were built on top of a massive treasure trove of data built over decades of practical experiments. This includes especially the Protein Data Bank, a freely available repository of more than 200,000 protein structures.
These structures were determined experimentally by thousands of human researchers over decades, using advanced methods (often themselves Nobel Prize-winning discoveries) like X-ray crystallography, cryo-electron microscopy, etc.
“I don’t think AlphaFold involves any radical change in the underlying science that wasn’t already in place.
It’s just how it was put together and conceived in such a seamless way that allowed AlphaFold to reach those heights.”
David Jones – Bioinformatician at University College London, who collaborated with DeepMind on the first version of AlphaFold
How Impactful Is AlphaFold?
Criticizing AlphaFold as “only building onto previous research” can maybe be seen as a little off the mark. After all, it is a very common pattern that a Nobel Prize-winning discovery is often built on 3-4 other previous Nobel Prize-winning discoveries.
More importantly, it is key to determine if AlphaFold is a true breakthrough, making the previously impossible suddenly achievable.
We previously discussed a scientific paper investigating if AlphaFold can discover new molecules and medicine, instead of just refining the already known data.
And it seems to be the case:
Researchers determined that the proportion of compounds that altered protein activity for each of the models was around 50% and 20% for the sigma-2 receptor and 5-HT2A receptors, respectively. A result greater than 5% is exceptional.
Considering that drug discovery is very much like trying to manually find a needle in a haystack, a 50% success rate of “guessing” where the needle is from the first try is indeed exceptional.
So here too, this year’s Nobel Prize in Chemistry might not fit the bill when it comes to “pure” science and progress in fundamental understanding. But it seems to match well the state goal of the Nobel Prize: “to give a prize to those who, during the preceding year, have conferred the greatest benefit to humankind”.
Closing The Controversy
We can agree that as AI becomes a key tool in most scientific disciplines, we should not see every Nobel Prize rewarding mostly AI moving forward. In the same way, we do not regularly reward “basic” computing (or, for that matter, chemistry or metallurgy) because researchers use computers (and chemicals and metals) on a daily basis.
However, it seems that the reaction against this year’s Nobel Prize attribution was a little exaggerated, maybe illustrating the frustration of scientists to see so much media attention sucked up by AI only despite impressive scientific progress in most fields.
What Can AI Really Do?
In any case, AI is likely to be a truly transformative technology, and a true landmark in the history of mankind in a similar fashion to previous technologies like the steam engine, the telegraph, the internal combustion engine, or the first computers.
Here is a short list of the potential applications of AI:
- Drug discovery & Biological data analysis.
- Diagnosis & automated medical treatment, including surgery.
- Robotics, from domestic servants to automated production.
- Self-driving cars & logistics.
- Personalized learning & knowledge diffusion.
- New materials are being discovered in energy, material sciences, nanotechnology, etc.
- New computing methods, including photonics, quantum computing, etc.
- Space exploration, from automated off-world colonies to asteroid mining.
In that context, the “hype” around AI is totally justified.
But as with most new important technology, the process of turning the initial innovation into practical use cases can take a little longer than hoped, for example, computers in the 1970s, decades before the Internet revolution and even more before AI, while also likely having an impact much larger than anyone could have expected.
