Monday AI Brief #18
More questions than answers
Before we get started, I want to let you know that this will be the last issue of Monday AI Brief. As I find my voice and niche, it’s become clear that I’m best suited to providing in-depth analysis rather than general interest pieces about AI. If you’d like to follow me on that journey, I’ll be continuing Monday AI Radar and writing more long-form pieces. And if not, thank you for being part of this early experiment.
And now, let’s talk about AI.
Nobody said the path would be clear. We know we need to prepare for AGI, but how do we do that if we don’t know whether it’s coming in 3 years or in 100? What about recursive self improvement: will that escalate to superintelligence, or fizzle out? And as the White House starts laying out its legislative agenda for AI, should we push for government leadership on existential risk, or merely hope they stay out of the way while we do the heavy lifting?
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Broad Timelines
Toby Ord reviews some of the best-known AGI timelines and concludes that we should prepare for a wide range of possibilities (his 80% probability range is from 3 to 100 years). What does that imply for people who want to work on AI safety—should you rush to have the most impact right away, or invest in building capacity to have more impact later?
Given this deep uncertainty we need to act with epistemic humility. We have to take seriously the possibility it will come soon and hedge against that. But we also have to take seriously the possibility that it comes late and take advantage of the opportunities that would afford us. The world at large is doing too little of the former, but those of us who care most about making the AI transition go well might be doing too little of the latter.
This is exactly correct: the AI future is high variance, and it isn’t enough to have a plan that will work great if everything plays out exactly the way you expect. We need a portfolio of plans and projects that will work in a wide range of possible futures.
My writing
Contra Anil Seth on AI Consciousness. Biological naturalists argue that consciousness is tightly coupled to details of human neurobiology, making it unlikely that AI will achieve consciousness in the foreseeable future. I examine the arguments put forward by a leading biological naturalist and find them unconvincing.
Do we already have AGI?
Even though its meaning has drifted, AGI remains a useful anchoring concept. Benjamin Todd bravely wades into the debate about what it actually means, bringing welcome rigor and clarity. He pulls together four of the most useful definitions of AGI and concludes that current AI doesn’t meet any of them:
Long answer: on the most prominent definitions, current AI is superhuman in some cognitive tasks but still worse than almost all humans at others. That makes it impressively general, but not yet AGI.
No, AI alignment isn’t solved
There’s a common belief that alignment might be easier than we once expected: LLMs are unexpectedly good at generalizing and understanding human values, and current alignment techniques work surprisingly well. Transformer’s Lynette Bye reports on some reasons for optimism, and reminds us that we still have a lot of work to do:
“We’re still doing alignment ‘on easy mode’ since our models aren’t really superhuman yet,” says Leike. Hubinger agrees: the crucial problem will be overseeing systems that are smarter than humans, and we haven’t yet seen how our systems will fare against that problem. As does Greenblatt: “Once the models are qualitatively very superhuman, lots of stuff starts breaking down.”
Save us, Digital Cronkite!
Noah Smith follows up on Dan Williams’ recent piece ($) about AI as a possible source of shared truth. He argues that while social media elevates the most extreme partisan voices, AI might instead empower the moderate majority ($) and thereby strengthen democracy and society at large.
This makes sense, and we can already see early signs of those trends. I’m not convinced, however, that we’re seeing the long-term equilibrium: will current patterns continue, or will we see the emergence of persuasive AIs that have been trained to be highly partisan?