Do Chatbots Get Us Any Closer To Human-Level Artificial Intelligence?

For AI to mirror human intelligence, it must perceive the world like we do.

AI chatbots are getting so good people are starting to see them as human. Several users have recently called the bots their best friends, others have professed their love, and a Google engineer even helped one hire a lawyer. From a product standpoint, these bots are extraordinary. But from a research perspective, the people dreaming about AI reaching human-level intelligence are due for a reality check.

Chatbots today are trained only on text, a debilitating limitation. Ingesting mountains of the written word can produce jaw-dropping results — like rewriting Eminem in Shakespearian style — but it prevents the perception of the nonverbal world. Much of human intelligence isn’t marked down. We pick up our innate understanding of physics, craft, and emotion by living, not by reading. And without written material on these topics to train on, AI comes up short.

“The understanding these current systems have of the underlying reality that language expresses is extremely shallow,” said Yann LeCun, Meta’s chief AI scientist and a professor of computer science at New York University. “It’s not a particularly big step towards human-level intelligence.” 

Holding up a sheet of paper, LeCun demonstrated ChatGPT’s limited understanding of the world in a recent Big Technology Podcast episode. The bot, he promised, would not know what would happen if he let go of the paper with one hand. Upon consultation, ChatGPT said the paper would “tilt or rotate in the direction of the hand that is no longer holding it.” For a moment — given its presentation and confidence — the answer seemed plausible. But the bot was dead wrong. 

Lecun’s paper moved toward the hand still holding it, something humans know instinctually. ChatGPT, however, blanked out because people rarely describe the physics of letting go of a paper in text (well, perhaps until now).

“I can come up with a huge stack of similar situations, each one of them will not have been described in any text,” LeCun said. “So then the question you want to ask is, 'How much of human knowledge is present and described in text?' And my answer to this is a tiny portion. Most of human knowledge is not actually language-related.”

Without an innate understanding of the world, AI can’t predict. And without prediction, it can’t plan. “Prediction is the essence of intelligence,” said LeCun. This explains, at least in part, why self-driving cars are still bumbling through a world they don’t completely understand. And why chatbot intelligence remains limited — if still powerful — despite the anthropomorphizing.

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What Else I’m Reading, Listening To, And Watching

Why Google seems flat-footed on AI [Forbes]

Why so many buildings collapsed in Turkey [BBC]

An estate lawyer considers ChatGPT’s implications on the business [After Your Time]

Inside Austin’s transformation [New Yorker]

Satya Nadella talks about AI to The Verge [YouTube]

I spoke with Bradley Tusk about AI and a bunch of other stuff [Firewall Podcast]

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Number Of The Week

$100 billion 

Google lost this amount of its market cap after its Bard chatbot made an error in a demo.

Quote Of The Week 1

Shipping talks and bullshit walks.

John Gruber comparing Google’s chatbot effort vs. Microsoft’s new Bing feature.

Quote Of The Week 2

"Retirement should be a second life, not a waiting room for death."

French protestor decrying his government’s push to raise the retirement age. Millions have taken to the streets in France in recent weeks to push back against the potential changes.

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This Week On Big Technology Podcast: Was The Creator Economy A Farce? — With Kaya Yurieff and Joe Caporoso 

Kaya Yurieff is a reporter at The Information covering the Creator Economy. Joe Caporoso is the President of Team Whistle and founder of Badlands, a subscription podcast covering the New York Jets. Both join Big Technology Podcast to discuss whether the Creator Economy — a term for the online content creator business — was overblown. We dig into the motivations of platforms and VCs looking to build businesses off those who believed they could make a living by posting on social media. And we examine how creators who make it do so successfully.

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