Katja Grace has analyzed my and Stuart Armstrong’s 2012 paper “How We’re Predicting AI – or Failing To”. She discovered that one of the conclusions, “predictions made by AI experts were indistinguishable from those of non-experts”, is flawed due to “a spreadsheet construction and interpretation error”. In other words, I coded the data in one way, there was a communication error and a misunderstanding about what the data meant, and as a result of that, a flawed conclusion slipped into the paper.
I’m naturally embarrassed that this happened. But the reason why Katja spotted this error was that we’d made our data freely available, allowing her to spot the discrepancy. This is why data sharing is something that science needs more of. Mistakes happen to everyone, and transparency is the only way to have a chance of spotting those mistakes.
I regret the fact that we screwed up this bit, but proud over the fact that we did share our data and allowed someone to catch it.
EDITED TO ADD: Some people have taken this mistake to suggest that the overall conclusion, that AI experts are not good predictors of AI timelines, to be flawed. That would overstate the significance of this mistake. While one of the lines of evidence supporting this overall conclusion was flawed, several others are unaffected by this error. Namely, the fact that expert predictions disagree widely with each other, that many past predictions have turned out to be false, and that the psychological literature on what’s required for the development of expertise suggests that it should be very hard to develop expertise in this domain. (see the original paper for details)
(I’ve added a note of this mistake to my list of papers.)