"How We're Predicting AI — or Failing to"

lukeprog

The new paper by Stuart Armstrong (FHI) and Kaj Sotala (SI) has now been published (PDF) as part of the Beyond AI conference proceedings. Some of these results were previously discussed here. The original predictions data are available here.

Abstract:

This paper will look at the various predictions that have been made about AI and propose decomposition schemas for analysing them. It will propose a variety of theoretical tools for analysing, judging and improving these predictions. Focusing speciﬁcally on timeline predictions (dates given by which we should expect the creation of AI), it will show that there are strong theoretical grounds to expect predictions to be quite poor in this area. Using a database of 95 AI timeline predictions, it will show that these expectations are born out in practice: expert predictions contradict each other considerably, and are indistinguishable from non-expert predictions and past failed predictions. Predictions that AI lie 15 to 25 years in the future are the most common, from experts and non-experts alike.

Abstract:

This paper will look at the various predictions that have been made about AI and propose decomposition schemas for analysing them. It will propose a variety of theoretical tools for analysing, judging and improving these predictions. Focusing speciﬁcally on timeline predictions (dates given by which we should expect the creation of AI), it will show that there are strong theoretical grounds to expect predictions to be quite poor in this area. Using a database of 95 AI timeline predictions, it will show that these expectations are born out in practice: expert predictions contradict each other considerably, and are indistinguishable from non-expert predictions and past failed predictions. Predictions that AI lie 15 to 25 years in the future are the most common, from experts and non-experts alike.

Overall, a very good paper, both from an AI perspective and in terms of demonstrating how to apply various epistemic techniques that aren't nearly as widespread as they should be. However, I have seen a few typos and other problems. The bottom of page 64 says,"Moore’s law could be taken as an ultimate example of grid: " I think that should be grind. Also, I liked

Care must be taken when applying this method: the point is to extract a useful verifiable prediction, not to weaken or strengthen a reviled or favoured argument. The very first stratagems in Shopenhauer’s “The Art of Always being Right” [17] are to extend and over-generalise the consequences of your opponent’s argument; conversely, one should reduce and narrow down one’s own arguments. There is no lack of rhetorical tricks to uphold one’s own position, but if one is truly after the truth, one must simply attempt to find the most reasonable empirical version of the argument; the truth-testing will come later.

But wish the paper had been slightly more specific about how the authors avoided this failure mode.

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"How We're Predicting AI — or Failing to"

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"How We're Predicting AI — or Failing to"

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