We’ve seen a few efforts to close the gap — Stanford’s Institute for Human-Centered Artificial Intelligence recently held a three-day “A.I. boot camp” for congressional staff members, for example — but we need more politicians and regulators to take an interest in the technology. (And I don’t mean that they need to start stoking fears of an A.I. apocalypse, Andrew Yang-style. Even reading a book like Brian Christian’s “The Alignment Problem” or understanding a few basic details about how a model like GPT-3 works would represent enormous progress.)
Otherwise, we could end up with a repeat of what happened with social media companies after the 2016 election — a collision of Silicon Valley power and Washington ignorance, which resulted in nothing but gridlock and testy hearings.
Second, big tech companies investing billions in A.I. development — the Googles, Metas and OpenAIs of the world — need to do a better job of explaining what they’re working on, without sugarcoating or soft-pedaling the risks. Right now, many of the biggest A.I. models are developed behind closed doors, using private data sets and tested only by internal teams. When information about them is made public, it’s often either watered down by corporate P.R. or buried in inscrutable scientific papers.
Downplaying A.I. risks to avoid backlash may be a smart short-term strategy, but tech companies won’t survive long term if they’re seen as having a hidden A.I. agenda that’s at odds with the public interest. And if these companies won’t open up voluntarily, A.I. engineers should go around their bosses and talk directly to policymakers and journalists themselves.
Third, the news media needs to do a better job of explaining A.I. progress to nonexperts. Too often, journalists — and I admit I’ve been a guilty party here — rely on outdated sci-fi shorthand to translate what’s happening in A.I. to a general audience. We sometimes compare large language models to Skynet and HAL 9000, and flatten promising machine learning breakthroughs to panicky “The robots are coming!” headlines that we think will resonate with readers. Occasionally, we betray our ignorance by illustrating articles about software-based A.I. models with photos of hardware-based factory robots — an error that is as inexplicable as slapping a photo of a BMW on a story about bicycles.
In a broad sense, most people think about A.I. narrowly as it relates to us — Will it take my job? Is it better or worse than me at Skill X or Task Y? — rather than trying to understand all of the ways A.I. is evolving, and what that might mean for our future.
I’ll do my part, by writing about A.I. in all its complexity and weirdness without resorting to hyperbole or Hollywood tropes. But we all need to start adjusting our mental models to make space for the new, incredible machines in our midst.