Any changes to your median timeline until AGI, i. e., do we actually have these 9-14 years?
Here’s a dump of my current timeline models. (I actually originally drafted this as part of the post, then cut it.)
My current intuition is that deep learning is approximately one transformer-level paradigm shift away from human-level AGI. (And, obviously, once we have human-level AGI things foom relatively quickly.) That comes from an intuitive extrapolation: if something were about as much better as the models of the last 2-3 years, as the models of the last 2-3 years are compared to pre-transformer models, then I’d expect them to be at least human-level. That does not mean that nets will get to human level immediately after that transformer-level shift comes along; e.g. with transformers it still took ~2-3 years before transformer models really started to look impressive.
So the most important update from deep learning over the past year has been the lack of any transformer-level paradigm shift in algorithms, architectures, etc.
There are of course other potential paths to human-level (or higher) which don’t route through a transformer-level paradigm shift in deep learning. One obvious path is to just keep scaling; I expect we’ll see a paradigm shift well before scaling alone achieves human-level AGI (and this seems even more likely post-Chinchilla). The main other path is that somebody wires together a bunch of GPT-style AGIs in such a way that they achieve greater intelligence by talking to each other (sort of like how humans took off via cultural accumulation); I don’t think that’s very likely to happen near-term, but I do think it’s the main path by which 5-year timelines would happen without a paradigm shift. Call it maybe 5-10%. Finally, of course, there’s always the “unknown unknowns” possibility.
How long until the next shift?
Back around 2014 or 2015, I was visiting my alma mater, and a professor asked me what I thought about the deep learning wave. I said it looked pretty much like all the previous ML/AI hype cycles: everyone would be very excited for a while and make grand claims, but the algorithms would be super finicky and unreliable. Eventually the hype would die down, and we’d go into another AI winter. About ten years after the start of the wave someone would show that the method (in this case large CNNs) was equivalent to some Bayesian model, and then it would make sense when it did/didn’t work, and it would join the standard toolbox of workhorse ML algorithms. Eventually some new paradigm would come along, and the hype cycle would start again.
… and in hindsight, I think that was basically correct up until transformers came along around 2017. Pre-transformer nets were indeed very finicky, and were indeed shown equivalent to some Bayesian model about ten years after the excitement started, at which point we had a much better idea of what they did and did not do well. The big difference from previous ML/AI hype waves was that the next paradigm—transformers—came along before the previous wave had died out. We skipped an AI winter; the paradigm shift came in ~5 years rather than 10-15.
… and now it’s been about five years since transformers came along. Just naively extrapolating from the two most recent data points says it’s time for the next shift. And we haven’t seen that shift yet. (Yes, diffusion models came along, but those don’t seem likely to become a transformer-level paradigm shift; they don’t open up whole new classes of applications in the same way.)
So on the one hand, I’m definitely nervous that the next shift is imminent. On the other hand, it’s already very slightly on the late side, and if another 1-2 years go by I’ll update quite a bit toward that shift taking much longer.
Also, on an inside view, I expect the next shift to be quite a bit more difficult than the transformers shift. (I don’t plan to discuss the reasons for that, because spelling out exactly which technical hurdles need to be cleared in order to get nets to human level is exactly the sort of thing which potentially accelerates the shift.) That inside view is a big part of why my timelines last year were 10-15 years, and not 5. The other main reasons my timelines were 10-15 years were regression to the mean (i.e. the transformers paradigm shift came along very unusually quickly, and it was only one data point), general hype-wariness, and an intuitive sense that unknown unknowns in this case will tend to push toward longer timelines rather than shorter on net.
Put all that together, and there’s a big blob of probability mass on ~5 year timelines; call that 20-30% or so. But if we get through the next couple years without a transformer-level paradigm shift, and without a bunch of wired-together GPTs spontaneously taking off, then timelines get a fair bit lot longer, and that’s where my median world is.
Here’s a dump of my current timeline models. (I actually originally drafted this as part of the post, then cut it.)
My current intuition is that deep learning is approximately one transformer-level paradigm shift away from human-level AGI. (And, obviously, once we have human-level AGI things foom relatively quickly.) That comes from an intuitive extrapolation: if something were about as much better as the models of the last 2-3 years, as the models of the last 2-3 years are compared to pre-transformer models, then I’d expect them to be at least human-level. That does not mean that nets will get to human level immediately after that transformer-level shift comes along; e.g. with transformers it still took ~2-3 years before transformer models really started to look impressive.
So the most important update from deep learning over the past year has been the lack of any transformer-level paradigm shift in algorithms, architectures, etc.
There are of course other potential paths to human-level (or higher) which don’t route through a transformer-level paradigm shift in deep learning. One obvious path is to just keep scaling; I expect we’ll see a paradigm shift well before scaling alone achieves human-level AGI (and this seems even more likely post-Chinchilla). The main other path is that somebody wires together a bunch of GPT-style AGIs in such a way that they achieve greater intelligence by talking to each other (sort of like how humans took off via cultural accumulation); I don’t think that’s very likely to happen near-term, but I do think it’s the main path by which 5-year timelines would happen without a paradigm shift. Call it maybe 5-10%. Finally, of course, there’s always the “unknown unknowns” possibility.
How long until the next shift?
Back around 2014 or 2015, I was visiting my alma mater, and a professor asked me what I thought about the deep learning wave. I said it looked pretty much like all the previous ML/AI hype cycles: everyone would be very excited for a while and make grand claims, but the algorithms would be super finicky and unreliable. Eventually the hype would die down, and we’d go into another AI winter. About ten years after the start of the wave someone would show that the method (in this case large CNNs) was equivalent to some Bayesian model, and then it would make sense when it did/didn’t work, and it would join the standard toolbox of workhorse ML algorithms. Eventually some new paradigm would come along, and the hype cycle would start again.
… and in hindsight, I think that was basically correct up until transformers came along around 2017. Pre-transformer nets were indeed very finicky, and were indeed shown equivalent to some Bayesian model about ten years after the excitement started, at which point we had a much better idea of what they did and did not do well. The big difference from previous ML/AI hype waves was that the next paradigm—transformers—came along before the previous wave had died out. We skipped an AI winter; the paradigm shift came in ~5 years rather than 10-15.
… and now it’s been about five years since transformers came along. Just naively extrapolating from the two most recent data points says it’s time for the next shift. And we haven’t seen that shift yet. (Yes, diffusion models came along, but those don’t seem likely to become a transformer-level paradigm shift; they don’t open up whole new classes of applications in the same way.)
So on the one hand, I’m definitely nervous that the next shift is imminent. On the other hand, it’s already very slightly on the late side, and if another 1-2 years go by I’ll update quite a bit toward that shift taking much longer.
Also, on an inside view, I expect the next shift to be quite a bit more difficult than the transformers shift. (I don’t plan to discuss the reasons for that, because spelling out exactly which technical hurdles need to be cleared in order to get nets to human level is exactly the sort of thing which potentially accelerates the shift.) That inside view is a big part of why my timelines last year were 10-15 years, and not 5. The other main reasons my timelines were 10-15 years were regression to the mean (i.e. the transformers paradigm shift came along very unusually quickly, and it was only one data point), general hype-wariness, and an intuitive sense that unknown unknowns in this case will tend to push toward longer timelines rather than shorter on net.
Put all that together, and there’s a big blob of probability mass on ~5 year timelines; call that 20-30% or so. But if we get through the next couple years without a transformer-level paradigm shift, and without a bunch of wired-together GPTs spontaneously taking off, then timelines get a fair bit lot longer, and that’s where my median world is.