The timelines-relevant milestone of AGI is ability to autonomously research, especially AI’s ability to develop AI that doesn’t have particular cognitive limitations compared to humans. Quickly giving AIs experience at particular jobs/tasks that doesn’t follow from general intelligence alone is probably possible through learning things in parallel or through AIs experimenting with greater serial speed than humans can. Placing that kind of thing into AIs is the schlep that possibly stands in the way of reaching AGI (even after future scaling), and has to be done by humans. But also reaching AGI doesn’t require overcoming all important cognitive shortcomings of AIs compared to humans, only those that completely prevent AIs from quickly researching their way into overcoming the rest of the shortcomings on their own.
It’s currently unclear if merely scaling GPTs (multimodal LLMs) with just a bit more schlep/scaffolding won’t produce a weirdly disabled general intelligence (incapable of replacing even 50% of current fully remote jobs at a reasonable cost or at all) that is nonetheless capable enough to fix its disabilities shortly thereafter, making use of its ability to batch-develop such fixes much faster than humans would, even if it’s in some sense done in a monstrously inefficient way and takes another couple giant training runs (from when it starts) to get there. This will be clearer in a few years, after feasible scaling of base GPTs is mostly done, but we are not there yet.
The timelines-relevant milestone of AGI is ability to autonomously research, especially AI’s ability to develop AI that doesn’t have particular cognitive limitations compared to humans. Quickly giving AIs experience at particular jobs/tasks that doesn’t follow from general intelligence alone is probably possible through learning things in parallel or through AIs experimenting with greater serial speed than humans can. Placing that kind of thing into AIs is the schlep that possibly stands in the way of reaching AGI (even after future scaling), and has to be done by humans. But also reaching AGI doesn’t require overcoming all important cognitive shortcomings of AIs compared to humans, only those that completely prevent AIs from quickly researching their way into overcoming the rest of the shortcomings on their own.
It’s currently unclear if merely scaling GPTs (multimodal LLMs) with just a bit more schlep/scaffolding won’t produce a weirdly disabled general intelligence (incapable of replacing even 50% of current fully remote jobs at a reasonable cost or at all) that is nonetheless capable enough to fix its disabilities shortly thereafter, making use of its ability to batch-develop such fixes much faster than humans would, even if it’s in some sense done in a monstrously inefficient way and takes another couple giant training runs (from when it starts) to get there. This will be clearer in a few years, after feasible scaling of base GPTs is mostly done, but we are not there yet.