I think that if just in 2020 we did a survey about whether the capabilities of (for example) the image generation systems discussed in other today's threads would materialize at 2022, then more than half of deep learning technologists would have said that it's not going to happen so fast, demonstrating underconfidence instead of overconfidence.
For another example, when people in NLP put up tasks like the GLUE dataset, a reasonable expectation was that it would be a meaningful benchmark for some time, but it turned out that the previously too hard tasks became too easy just a year or so later - again, demonstrating underconfidence instead of overconfidence.
IMHO the overconfidence appears when people listen to PR of various companies overpromising products, not to the actual technologists doing research on the technology.
I think that if just in 2020 we did a survey about whether the capabilities of (for example) the image generation systems discussed in other today's threads would materialize at 2022, then more than half of deep learning technologists would have said that it's not going to happen so fast, demonstrating underconfidence instead of overconfidence.
For another example, when people in NLP put up tasks like the GLUE dataset, a reasonable expectation was that it would be a meaningful benchmark for some time, but it turned out that the previously too hard tasks became too easy just a year or so later - again, demonstrating underconfidence instead of overconfidence.
IMHO the overconfidence appears when people listen to PR of various companies overpromising products, not to the actual technologists doing research on the technology.