I’d expect that most “AI capabilities research” that goes on today isn’t meaningfully moving us towards AGI at all, let alone aligned AGI. For example, applying reinforcement learning to hospital data. So “how much $ went to AI in 2018″ would be a sloppy upper bound on “important thoughts/ideas/tools on the path to AGI”.
There’s a lot of non-capabilities non-AGI research targeted at “making the thing better for humanity, not more powerful”. For example, interpretability work on models simpler than convnets, or removing bias from word embeddings. If by “AI safety” you mean “technical AGI alignment” or “reducing x-risk from advanced AI” this category definitely isn’t that, but it also definitely isn’t “AI capabilities” let alone “AGI capabilities”.
Nod. Definitely open to better versions of the question that carve at more useful joints. (With a caveat that the question is more oriented towards “what are the easiest street lamps to look under” than “what is the best approximation”)
So, I guess my return question is: do you have suggestions on subfields to focus on, or exclude, from “AI capabilities research” that more reliably points to “AGI”, that you think there’s likely to exist public data on? (Or some other way to carve up AI research space)
It does seem good to have a separate category for “things like removing bias from word embeddings” that is separate from “Technical AGI alignment”. (I think it’s still useful to have a sense of how much effort humanity is putting into that, just as a rough pointer at where our overall priorities are)
Two observations:
I’d expect that most “AI capabilities research” that goes on today isn’t meaningfully moving us towards AGI at all, let alone aligned AGI. For example, applying reinforcement learning to hospital data. So “how much $ went to AI in 2018″ would be a sloppy upper bound on “important thoughts/ideas/tools on the path to AGI”.
There’s a lot of non-capabilities non-AGI research targeted at “making the thing better for humanity, not more powerful”. For example, interpretability work on models simpler than convnets, or removing bias from word embeddings. If by “AI safety” you mean “technical AGI alignment” or “reducing x-risk from advanced AI” this category definitely isn’t that, but it also definitely isn’t “AI capabilities” let alone “AGI capabilities”.
Nod. Definitely open to better versions of the question that carve at more useful joints. (With a caveat that the question is more oriented towards “what are the easiest street lamps to look under” than “what is the best approximation”)
So, I guess my return question is: do you have suggestions on subfields to focus on, or exclude, from “AI capabilities research” that more reliably points to “AGI”, that you think there’s likely to exist public data on? (Or some other way to carve up AI research space)
It does seem good to have a separate category for “things like removing bias from word embeddings” that is separate from “Technical AGI alignment”. (I think it’s still useful to have a sense of how much effort humanity is putting into that, just as a rough pointer at where our overall priorities are)