Self-assessment in expert AI predictions

by Stuart_Armstrong 1 min read26th Feb 20139 comments

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This brief post is written on behalf of Kaj Sotala, due to deadline issues.

The results of our prior analysis suggested that there was little difference between experts and non-experts in terms of predictive accuracy. There were suggestions, though, that predictions published by self-selected experts would be different from those elicited from less selected groups, e.g. surveys at conferences.

We have no real data to confirm this, but a single datapoint suggests the idea might be worth taking seriously. Michie conducted an opinion poll of experts working in or around AI in 1973. The various experts predicted adult-level human AI in:

  • 5 years: 0 experts
  • 10 years: 1 expert
  • 20 years: 16 experts
  • 50 years: 20 experts
  • More than 50 years: 26 experts

On a quick visual inspection, these results look quite different from the distribution in the rest of the database giving a much more pessimistic prediction than the more self-selected experts:


But that could be an artifact from the way that the graph on page 12 breaks the predictions down to 5 year intervals while Michie breaks them down into intervals of 10, 20, 50, and 50+ years. Yet there seems to remain a clear difference once we group the predictions in a similar way [1]:

This provides some support for the argument that "the mainstream of expert opinion is reliably more pessimistic than the self-selected predictions that we keep hearing about".

[1] Assigning each prediction to the closest category, so predictions of <7½ get assigned to 5, 7½<=X<15 get assigned to 10, 15<=X<35 get assigned to 20, 35<=X<50 get assigned to 50, and 50< get assigned to over fifty.