You can see a full transcript of this conversation, or listen to an audio recording, on our website.

Summary

We spoke with Robin Hanson on September 5, 2019. Here is a brief summary of that conversation:

Hanson thinks that now is the wrong time to put a lot of effort into addressing AI risk:
- We will know more about the problem later, and there’s an opportunity cost to spending resources now vs later, so there has to be a compelling reason to spend resources now instead.
- Hanson is not compelled by existing arguments he’s heard that would argue that we need to spend resources now:
  - Hanson famously disagrees with the theory that AI will appear very quickly and in a very concentrated way, which would suggest that we need to spend resources now because won’t have time to prepare.
  - Hanson views the AI risk problem as essentially continuous with existing principal agent problems, and disagrees that the key difference—the agents being smarter—should clearly worsen such problems.
- Hanson thinks that we will see concrete signatures of problems before it’s too late– he is skeptical that there are big things that have to be coordinated ahead of time.
  - Relatedly, he thinks useful work anticipating problems in advance usually happens with concrete designs, not with abstract descriptions of systems.
- Hanson thinks we are still too far away from AI for field-building to be useful.
Hanson thinks AI is probably at least a century, perhaps multiple centuries away:
- Hanson thinks the mean estimate for human-level AI arriving is long, and he thinks AI is unlikely to be ‘lumpy’ enough to happen without much warning :
  - Hanson is interested in how ‘lumpy’ progress in AI is likely to be: whether progress is likely to come in large chunks or in a slower and steadier stream.
    - Measured in terms of how much a given paper is cited, academic progress is not lumpy in any field.
    - The literature on innovation suggests that innovation is not lumpy: most innovation is lots of little things, though once in a while there are a few bigger things.
- From an outside view perspective, the current AI boom does not seem different from previous AI booms.
- We don’t have a good sense of how much research needs to be done to get to human-level AI.
- If we don’t expect progress to be particularly lumpy, and we don’t have a good sense of exactly how close we are, we have good reason to think we are not e.g. five-years away rather th

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