= 27d567bc2d48d9f40e9ccc20ea370b15)
Jan_Rzymkowski comments on [LINK] The errors, insights and lessons of famous AI predictions: preprint - Less Wrong Discussion
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
Comments (14)
Isn't this article highly susceptible to hindsight bias? For example, the reason authors analyse Dreyfus's prediction is that, he was somewhat right. If he weren't, authors woudn't include that data-point. Therefore it skewes the data, even if it is not their intention.
It's hard to take valuable assessements from the text, when it would be naturally prone to highlight mistakes of the experts and correct predictions by laymen.
The Dartmouth conference was very wrong, and is also famous. Not sure hindsight points in a particular direction.
Now I think I shouldn't mention hindsight bias, it doesn't really fit here. I'm just saying that some events would be more probably famous, like: a) laymen posing extraordinary claim and ending up being right b) group of experts being spectacularly wrong
If some group of experts met in 1960s and pose very cautious claims, chances are small that it would end up being widely known. And ending up in above paper. Analysing famous predictions is bound to end up with many overconfident predictions - they're just more flashy. But it doesn't yet mean most of predictions are overconfident.
Very valid point. But overconfidence is almost universal, and estimates where selection bias isn't an issue (duck as polls at conferences) seem to show it as well.