"We Need to Talk About How Good A.I. Is Getting"
Posted: Sat Aug 27, 2022 1:38 pm
This NYT article is paywalled, so here's a way to see it for free: https://archive.ph/CbYzu#selection-313.0-313.46
The first half of the article recaps the last five years of progress in machine learning, and goes over things we've been discussing at length on this forum (GPT-3, DALL-E 2, etc.). Here are the bits that were interesting to me:
The first half of the article recaps the last five years of progress in machine learning, and goes over things we've been discussing at length on this forum (GPT-3, DALL-E 2, etc.). Here are the bits that were interesting to me:
Ajeya Cotra, a senior analyst with Open Philanthropy who studies A.I. risk, estimated two years ago that there was a 15 percent chance of “transformational A.I.” — which she and others have defined as A.I. that is good enough to usher in large-scale economic and societal changes, such as eliminating most white-collar knowledge jobs — emerging by 2036.
But in a recent post, Ms. Cotra raised that to a 35 percent chance, citing the rapid improvement of systems like GPT-3.
“A.I. systems can go from adorable and useless toys to very powerful products in a surprisingly short period of time,” Ms. Cotra told me. “People should take more seriously that A.I. could change things soon, and that could be really scary.”
Even if the skeptics are right, and A.I. doesn’t achieve human-level sentience for many years, it’s easy to see how systems like GPT-3, LaMDA and DALL-E 2 could become a powerful force in society. In a few years, the vast majority of the photos, videos and text we encounter on the internet could be A.I.-generated. Our online interactions could become stranger and more fraught, as we struggle to figure out which of our conversational partners are human and which are convincing bots. And tech-savvy propagandists could use the technology to churn out targeted misinformation on a vast scale, distorting the political process in ways we won’t see coming.
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 Too often, journalists — and I admit I’ve been a guilty party here — rely on outdated sci-fi shorthand to translate what’s happening in A.I. to a general audience.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.