Murphyjitsu: Conditional on TAI being built in 2025, what happened? (i.e. how was it built, what parts of your model were wrong, what do the next 5 years look like, what do the 5 years after 2025 look like?)
On the object level, I think it would probably turn out to be the case that a) I was wrong about horizon length and something more like ~1 token was sufficient, b) I was wrong about model size and something more like ~10T parameter was sufficient. On a deeper level, it would mean I was wrong about the plausibility of ultra-sudden takeoff and shouldn’t have placed as much weight as I did on the observation that AI isn’t generating a lot of annual revenue right now and its value-added seems to have been increasing relatively smoothly so far.
I would guess that the model looks like a scaled-up predictive model (natural language and/or code), perhaps combined with simple planning or search. Maybe a coding model rapidly trains more-powerful successors in a pretty classically Bostromian / Yudkowskian way.
Since this is a pretty Bostromian scenario, and I haven’t thought deeply about those scenarios, I would default to guessing that the world after looks fairly Bostromian, with risks involving the AI forcibly taking control of most of the world’s resources, and the positive scenario involving cooperatively using the AI to prevent other x-risks (including risks from other AI projects).
Re why AI isn’t generating much revenue—have you considered the productivity paradox? It’s historically normal that productivity slows down before steeply increasing when a new general purpose technologies arrives.
the observation that AI isn’t generating a lot of annual revenue right now
Not sure how relevant, but I saw that Gwern seems to think this comes from a bottleneck of people who can apply AI, not from current AI being insufficient:
But how absurd—to a first approximation, ML/DL has been applied to 𝘯𝘰𝘵𝘩𝘪𝘯𝘨 thus far. We’re 𝘵𝘩𝘢𝘵 bottlenecked on coders!
And the lack of coders may rapidly disappear soon-ish, right? At least in Germany studying ML seems very popular since a couple of years now.
In some sense I agree with gwern that the reason ML hasn’t generated a lot of value is because people haven’t put in the work (both coding and otherwise) needed to roll it out to different domains, but (I think unlike gwern) the main inference I make from that that it wouldn’t have been hugely profitable to put in the work to create ML-based applications (or else more people would have been diverted from other coding tasks to the task of rolling out ML applications).
I mostly agree with that with the further caveat that I tend to think the low value reflects not that ML is useless but the inertia of a local optima where the gains from automation are low because so little else is automated and vice-versa (“automation as colonization wave”). This is part of why, I think, we see the broader macroeconomic trends like big tech productivity pulling away: many organizations are just too incompetent to meaningful restructure themselves or their activities to take full advantage. Software is surprisingly hard from a social and organizational point of view, and ML more so. A recent example is coronavirus/remote-work: it turns out that remote is in fact totally doable for all sorts of things people swore it couldn’t work for—at least when you have a deadly global pandemic solving the coordination problem...
As for my specific tweet, I wasn’t talking about making $$$ but just doing cool projects and research. People should be a little more imaginative about applications. Lots of people angst about how they can possibly compete with OA or GB or DM, but the reality is, as crowded as specific research topics like ‘yet another efficient Transformer variant’ may be, as soon as you add on a single qualifier like, ‘DRL for dairy herd management’ or ‘for anime’, you suddenly have the entire field to yourself. There’s a big lag between what you see on Arxiv and what’s out in the field. Even DL from 5 years ago, like CNNs, can be used for all sorts of things which they are not at present. (Making money or capturing value is, of course, an entirely different question; as fun as This Anime Does Not Exist may be, there’s not really any good way to extract money. So it’s a good thing we don’t do it for the money.)
Ah yeah, that makes sense—I agree that a lot of the reason for low commercialization is local optima, and also agree that there are lots of cool/fun applications that are left undone right now.
On the object level, I think it would probably turn out to be the case that a) I was wrong about horizon length and something more like ~1 token was sufficient, b) I was wrong about model size and something more like ~10T parameter was sufficient. On a deeper level, it would mean I was wrong about the plausibility of ultra-sudden takeoff and shouldn’t have placed as much weight as I did on the observation that AI isn’t generating a lot of annual revenue right now and its value-added seems to have been increasing relatively smoothly so far.
I would guess that the model looks like a scaled-up predictive model (natural language and/or code), perhaps combined with simple planning or search. Maybe a coding model rapidly trains more-powerful successors in a pretty classically Bostromian / Yudkowskian way.
Since this is a pretty Bostromian scenario, and I haven’t thought deeply about those scenarios, I would default to guessing that the world after looks fairly Bostromian, with risks involving the AI forcibly taking control of most of the world’s resources, and the positive scenario involving cooperatively using the AI to prevent other x-risks (including risks from other AI projects).
Re why AI isn’t generating much revenue—have you considered the productivity paradox? It’s historically normal that productivity slows down before steeply increasing when a new general purpose technologies arrives.
See “Why Future Technological Progress Is Consistent with Low Current Productivity Growth” in “Artificial Intelligence and the Modern Productivity Paradox”
Not sure how relevant, but I saw that Gwern seems to think this comes from a bottleneck of people who can apply AI, not from current AI being insufficient:
And the lack of coders may rapidly disappear soon-ish, right? At least in Germany studying ML seems very popular since a couple of years now.
In some sense I agree with gwern that the reason ML hasn’t generated a lot of value is because people haven’t put in the work (both coding and otherwise) needed to roll it out to different domains, but (I think unlike gwern) the main inference I make from that that it wouldn’t have been hugely profitable to put in the work to create ML-based applications (or else more people would have been diverted from other coding tasks to the task of rolling out ML applications).
I mostly agree with that with the further caveat that I tend to think the low value reflects not that ML is useless but the inertia of a local optima where the gains from automation are low because so little else is automated and vice-versa (“automation as colonization wave”). This is part of why, I think, we see the broader macroeconomic trends like big tech productivity pulling away: many organizations are just too incompetent to meaningful restructure themselves or their activities to take full advantage. Software is surprisingly hard from a social and organizational point of view, and ML more so. A recent example is coronavirus/remote-work: it turns out that remote is in fact totally doable for all sorts of things people swore it couldn’t work for—at least when you have a deadly global pandemic solving the coordination problem...
As for my specific tweet, I wasn’t talking about making $$$ but just doing cool projects and research. People should be a little more imaginative about applications. Lots of people angst about how they can possibly compete with OA or GB or DM, but the reality is, as crowded as specific research topics like ‘yet another efficient Transformer variant’ may be, as soon as you add on a single qualifier like, ‘DRL for dairy herd management’ or ‘for anime’, you suddenly have the entire field to yourself. There’s a big lag between what you see on Arxiv and what’s out in the field. Even DL from 5 years ago, like CNNs, can be used for all sorts of things which they are not at present. (Making money or capturing value is, of course, an entirely different question; as fun as This Anime Does Not Exist may be, there’s not really any good way to extract money. So it’s a good thing we don’t do it for the money.)
Ah yeah, that makes sense—I agree that a lot of the reason for low commercialization is local optima, and also agree that there are lots of cool/fun applications that are left undone right now.