Tired of the AI ​​hype? Why the Nobel Prize in Physics went to AI scholars.

In Wednesday’s Future Perfect newsletter, my colleague Dylan Matthews wrote about the arguments for skepticism about this year’s Nobel Prize winners in economics. His argument was that although his theories are interesting, there are many reasons to doubt how correct these theories are.

However, regarding several other Nobel Prizes this year, my skepticism runs in the opposite direction. The Nobel Prize in Physics was awarded this year to John J. Hopfield and Geoffrey E. Hinton “for fundamental discoveries and inventions that enable machine learning with artificial neural networks”.

The award unquestionably reflects serious, impressive and revolutionary work in its research topics, almost certainly some of the most impactful work in existence. The hotly debated question is, well, should this Nobel Prize in Physics really count as physical.

Together, Hopfield and Hinton did much of the fundamental work on neural networks, which store new information by changing the weights between neurons. The Nobel committee argues that Hopfield and Hinton’s background in physics provided inspiration for their fundamental work in AI, and that they reasoned by analogies with molecular interactions and statistical mechanics when developing the first neural networks.

Some people aren’t buying it. “Initially, I was happy to see them recognized with such a prestigious award, but when I read further and saw it was for Physics, I was a little confused,” Andrew Lensen, an artificial intelligence researcher, told Cosmos magazine. “I think it is more accurate to say that his methods may have been inspired for physical research.”

“I’m speechless. I like ML (machine learning) and ANN (artificial neural networks) as much as the next person, but it’s hard to see that this is a discovery in physics,” tweeted physicist Jonathan Pritchard. “I think the Nobel got hit by the AI ​​hype.”

Resentment over AI stealing the spotlight only intensified when the Nobel Prize in Chemistry was announced. It was in part to Google DeepMind founder Demis Hassabis and his colleague John Jumper for AlphaFold 2, a machine learning protein structure predictor.

One of the most difficult problems in biology is anticipating the many molecular interactions that influence how a protein printed from a given chain of amino acids will fold. Better understanding the structure of proteins will dramatically accelerate drug development and fundamental research.

AlphaFold, which can reduce the time needed to understand protein structure by orders of magnitude, is a huge achievement and very encouraging about the eventual ability of AI models to make important contributions in this field. It is certainly worthy of a Nobel Prize – if there were a Nobel Prize in biology. (It doesn’t exist, so Chemistry had to do.)

The Nobel Prize for Chemistry seems much less exaggerated to me than the Nobel Prize for Physics; to the extent that it inspired resentful resentment, I suspect it was mainly because, along with the Physics prize, it was starting to look like a trend. “Computer science appeared to be completing its Nobel takeover,” Nature wrote after the chemistry prize was announced.

The Nobels went all in on AI, declaring on one of the world’s most prestigious stages that the achievements of AI researchers with machine learning constituted serious, respectable, world-class contributions to the fields that vaguely inspired them. In a world where AI is an increasingly important business and where many people find it overblown and extremely annoying, this is a worrying statement.

Is AI overkill? Yes, absolutely. There is a constant barrage of unpleasant and exaggerated claims about what AI can do. There are people who raise absurd amounts of money by adding “AI” to business models that don’t have much to do with AI. Enthusiasm for “AI-based” solutions often exceeds any understanding of how they actually work.

But all of this can – and does – co-exist with the fact that AI is genuinely a big deal. AlphaFold’s achievements in protein folding happened in the context of pre-existing competitions about better protein folding prediction, because it was well understood that solving this problem really mattered. Whether or not you’re enthusiastic about chatbots and generative art, the same techniques have brought the world cheap, fast, and effective transcription and translation – making all kinds of research and communication tasks much easier.

And we are still in the early days of using the machine learning systems for which Hinton and Hopfield laid the framework. I believe that some people who position themselves as “against the AI ​​hype” are effectively standing against the wall of an early 20th century factory, saying, “Have you ever gotten electricity to solve all your problems? No? Hmmm, I guess it wasn’t a big deal.”

At the beginning of the 20th century, it was difficult to predict where electricity would take us, but it was actually quite easy to see that the ability to transfer large portions of human labor to machines would be very important.

Likewise, it’s not hard to see that AI will matter. So while it’s true that there’s a nasty, enthusiastic bunch of clueless investors and dishonest fundraisers eager to label everything with AI, and while it’s true that companies often systematically exaggerate how cool their latest models are, we don’t It is an “exaggeration” to see AI as a huge business and one of the main scientific and intellectual contributions of our day. It’s simply necessary.

The Nobel Prize committee may or may not have tried to jump on the bandwagon — they’re just regular people with the same range of motivations as everyone else — but the work they identified really matters, and we all live in a world that’s been enriched by it.