AI could have 20% chance of sentience in 10 years, says philosopher David Chalmers

"Maybe in 10 years we'll have virtual perception, language action, unified agents with all these features, perhaps exceeding, say, the capacities of something like a fish," suggests NYU philosophy professor David Chalmers. While a fish-level intelligent program wouldn't necessarily be conscious, "there would be a decent chance of it."

NeurIPS 2022

The likelihood that today's most sophisticated artificial intelligence programs are sentient, or conscious, is less than 10 percent, but in a decade from now, the leading AI programs might have a 20 percent or better chance of being conscious.

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That is, if they can achieve fish-level cognition.

That is how NYU philosophy professor David Chalmers on Monday threaded the needle of an extremely controversial topic. 

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(Chalmers said he considers the two terms "conscious" and "sentient" to be "roughly equivalent," at least for the purpose of scientific and philosophical exploration.)

"Thinking constructively, extended language models with sensory, image-related processes and embodiment tied to a virtual or physical body, are developing fast," said Chalmers. He cited as examples Flamingo, the DeepMind text and image network that is a paper at this year's NeurIPS; and Google's SayCan, which involves using language models to control robots. 

Those works are examples of a burgeoning field of "LLM+," things that go beyond just being language models to being "robust perception, language, action models with rich senses and bodies, perhaps in virtual worlds, which are, of course, a lot more tractable than the physical world." 

Chalmers, who has just written a book on virtual worlds, offered, "I think this kind of work in virtual environments is very exciting for issues tied to consciousness."

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Virtual worlds are important, he noted, because they may help to produce "world models," and those might rebut the most serious criticisms against sentience. 

Chalmers cited the criticisms of scholars such as Timnit Gebru and Emily Bender that language models are just "stochastic parrots," regurgitating training data; and of Gary Marcus, who says the programs just do statistical text processing. 

In response to those critiques, said Chalmers, "There's this challenge, I think, to turn those objections into a challenge, to build extended language models with robust world models and self models."

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