"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.

Since the Chinese Room argument does refute the strong AI hypothesis no AI will be possible on current hardware. An artificial brain that duplicates the causal functioning of an organic brain is necessary before an AI can be constructed.

There's a lot of objections to the Chinese room, but in this context, the primary issue is that the Chinese room doesn't matter: Even if the AI isn't conscious in some deep philosophical sense, it has all the same results then for humans the dangers and promises of strong AI are identical.

I further predict that AI researchers will continue to predict immanent AI in direct proportion to research grant dollars they are able to attract.

Continue implies this is currently the case. Do you have evidence for this? My impression is that most AI research is going to practical machine learning which is currently being used for many real world applications. Many people in the machine learning world state that any form of general AI is extremely unlikely to happen soon, so what evidence for this claimed proportion is there?

Corollary: A stable nuclear fusion reactor will be built before a truly conscious artificial mind is.

I don't see how this is a corollary. If you mean to state it as an example of a comparison to what sort of technology would be needed that might make some sense. However, we actually already have stable fusion reactors. Examples include tabtletop designs that can be made by hobbyists. Do you mean something like a fusion reactor that produces more useful energy than is inputted?

Is there something that it is like to be Siri? Still, Siri is a tool and potentially a powerful one. But I feel no need to be afraid of Siri as Siri any more than I am afraid of nuclear weapons in themselves. What frightens me is how people might misuse them. Not the tools themselves. Focusing on the tools then does not address the root issue. Which is human nature and what social structures we have in place to make sure some clown doesn't build a nuke in his basement.

Did ELIZA present the "dangers and promises" of AI? Weizenbaum's secretary thou... (read more)

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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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