I’m really interested in the relation between the increasing number of AI researchers and the associated rate of new ideas in AI.
Yeah, that’s an interesting question.
One type of relevant data that’s different from looking at the output distribution across scientists is just looking at the evolution of total researcher hours on one hand and measures of total output on the other hand. Bloom and colleagues’ Are ideas getting harder to find? collects such data and finds that research productivity, i.e. roughly output her researcher hour, has been falling everywhere they look:
The number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than the number required in the early 1970s. More generally, everywhere we look we find that ideas, and the exponential growth they imply, are getting harder to find.
Thanks, yes, that seems much more relevant. The cases in that paper feel slightly different in that I expect AI and ML to currently be much more “open” fields where I expect orders of magnitude more paths of ideas that can lead towards transformative AI than
paths of ideas leading to higher transistor counts on a CPU (hmm, because it’s a relatively narrow technology confronting physical extremes?)
paths of ideas leading to higher crop yields (because evolution already invested a lot of work in optimizing energy conversion?)
paths of ideas leading to decreased mortality of specific diseases (because this is about interventions in extremely complex biochemical pathways that are still not well understood?)
Maybe I could empirically ground my impression of “openness” by looking at the breadth of cited papers at top ML conferences, indicating how highly branched the paths of ideas currently are compared to other fields? And maybe I could look at the diversity of PIs/institutions of the papers that report new state-of-the-art results in prominent benchmarks, which indicates how easy it is to come into the field and have very good new ideas?
Yeah, that’s an interesting question.
One type of relevant data that’s different from looking at the output distribution across scientists is just looking at the evolution of total researcher hours on one hand and measures of total output on the other hand. Bloom and colleagues’ Are ideas getting harder to find? collects such data and finds that research productivity, i.e. roughly output her researcher hour, has been falling everywhere they look:
Thanks, yes, that seems much more relevant. The cases in that paper feel slightly different in that I expect AI and ML to currently be much more “open” fields where I expect orders of magnitude more paths of ideas that can lead towards transformative AI than
paths of ideas leading to higher transistor counts on a CPU (hmm, because it’s a relatively narrow technology confronting physical extremes?)
paths of ideas leading to higher crop yields (because evolution already invested a lot of work in optimizing energy conversion?)
paths of ideas leading to decreased mortality of specific diseases (because this is about interventions in extremely complex biochemical pathways that are still not well understood?)
Maybe I could empirically ground my impression of “openness” by looking at the breadth of cited papers at top ML conferences, indicating how highly branched the paths of ideas currently are compared to other fields? And maybe I could look at the diversity of PIs/institutions of the papers that report new state-of-the-art results in prominent benchmarks, which indicates how easy it is to come into the field and have very good new ideas?