The specific upper bound does matter if we’re worried about superintelligence. If easy-to-get data instead capped out at 10 quadrillion tokens, it’d be easy to blow past 10T-param models; if we conveniently threshold around human-level params, we might be more likely to be dealing with “fast parallel Von Neumanns” than a basilisk, at least initially.
Just to register a prediction: I would be very surprised if photos have anywhere near as much information content as text/video, given their relative lack of long-term causal structure.
I wouldn’t go that far; using these systems to do recursive self-improvement via different learning paradigms (e.g. by designing simulators) could still get FOOM; it just seems less likely to me to happen by accident in the ordinary coarse of SSL training.
Two more points:
The specific upper bound does matter if we’re worried about superintelligence. If easy-to-get data instead capped out at 10 quadrillion tokens, it’d be easy to blow past 10T-param models; if we conveniently threshold around human-level params, we might be more likely to be dealing with “fast parallel Von Neumanns” than a basilisk, at least initially.
Just to register a prediction: I would be very surprised if photos have anywhere near as much information content as text/video, given their relative lack of long-term causal structure.
In short, while concerted effort could plausibly give us human intelligence, it is likely not to go superhuman and FOOM.
I wouldn’t go that far; using these systems to do recursive self-improvement via different learning paradigms (e.g. by designing simulators) could still get FOOM; it just seems less likely to me to happen by accident in the ordinary coarse of SSL training.