AI gets its Nobel moment

• It's solving intractable problems and analyzing vast troves of scientific data. At the same time, it's raising concerns about the ways it might put cutting-edge science in the hands of bad actors.

The big picture: The technical foundations of AI were laid over decades, but its advances have only received wide recognition more recently with the advent of chatbots and the popularization of generative AI.

Driving the news: Geoffrey Hinton and John Hopfield were awarded the Nobel Prize in Physics on Tuesday for their work on AI in the late 1970s into the 1980s.

• Hopfield and Hinton each drew on concepts in physics to invent artificial neural networks that sparked and influenced the development of AI. Hopfield is an emeritus professor at Princeton University, and Hinton is a professor at the University of Toronto.

The Nobel committee presented the prize in chemistry on Wednesday to Google DeepMind CEO Demis Hassabis, DeepMind director John Jumper and University of Washington professor David Baker for their work on proteins that are crucial to life.

• Hassabis and Jumper were recognized for the development of an AI system that cracked one of biology's toughest problems: predicting the structure of a protein.

Between the lines: The Nobel prize is often awarded for research done decades ago, after its impact can be clearly assessed as having "the greatest benefit to humankind."

• In one of the quicker reaction times in the Nobel annals, the committee cited the AlphaFold2 system that was first demonstrated just four years ago and has been used by scientists around the world to tackle a range of scientific problems.
• AlphaFold2 has been used by "more than two million people from 190 countries," according to the Nobel committee, to explore antibiotic resistance, drug design, crop resilience and other scientific questions. The DeepMind team continues to expand its scope and increase its power.
• Baker worked on another AI-driven protein prediction tool called RoseTTAFold and also designed altogether new proteins.

Hassabis' "longstanding passion and motivation for doing AI" was to one day be able to "build learning systems that are able to help scientists accelerate scientific discovery," he told me last year.

• AI has reached the point where scientists can "design better experiments because of the insights provided by AI predictions," Jumper told Axios.

Yes, but: "It's far too premature to talk about AI being involved in all prizes," Hassabis said in a press conference on Wednesday.

• "The human ingenuity comes in first — asking the question, developing the hypothesis — and AI systems can't do any of that. It just sort of analyses data right now," he said.
• "It's interesting the committee decided to make a statement by having the two AI-linked prizes together."

Zoom in: Three of the Nobel prize winners have ties to Google — Hinton left the company last year, saying he wanted to speak freely about what he and others see as the dangers of AI.

• The winners' private-sector ties speak to the enormous resources needed for AI research today, which some researchers warn runs the risk of consolidating the power of the technology and its development to profit-focused companies.

What to watch: AI critic Gary Marcus writes that Hinton (and others) have favored advancing AI through ever-expanding neural networks that learn from vast troves of data — the approach that fuels generative AI.

• But Hassabis and others are exploring what's known as neurosymbolic AI, a technique that combines neural networks and hard-wired, or symbolic, knowledge. In July, DeepMind announced the approach was used to build a math-savvy AI system that made Silicon Valley buzz.
• It's unclear which path will ultimately yield the "greatest benefit to humankind." And of course there's no guarantee either will prove a boon.