Hours after the artificial intelligence pioneer Geoffrey Hinton won a Nobel Prize in physics, he drove a rented car to Google’s California headquarters to celebrate.
Hinton doesn’t work at Google anymore. Nor did the longtime professor at the University of Toronto do his pioneering research at the tech giant.
But his impromptu party reflected AI’s moment as a commercial blockbuster that has also reached the pinnacles of scientific recognition.
That was Tuesday. Then, early Wednesday, two employees of Google’s AI division won a Nobel Prize in chemistry for using AI to predict and design novel proteins.
“This is really a testament to the power of computer science and artificial intelligence,” said Jeanette Wing, a professor of computer science at Columbia University.
Asked about the historic back-to-back science awards for AI work in an email Wednesday, Hinton said only: “Neural networks are the future.”
It didn’t always seem that way for researchers who decades ago experimented with interconnected computer nodes inspired by neurons in the human brain. Hinton shares this year’s physics Nobel with another scientist, John Hopfield, for helping develop those building blocks of machine learning.
Neural network advances came from “basic, curiosity-driven research,” Hinton said at a press conference after his win. “Not out of throwing money at applied problems, but actually letting scientists follow their curiosity to try and understand things.”
Such work started well before Google existed. But a bountiful tech industry has now made it easier for AI scientists to pursue their ideas even as it has challenged them with new ethical questions about the societal impacts of their work.
One reason why the current wave of AI research is so closely tied to the tech industry is that only a handful of corporations have the resources to build the most powerful AI systems.