Link post Robin Hanson Robert Long and I recently talked to Robin Hanson—GMU economist, prolific blogger, and longtime thinker on the future of AI—about the amount of futurist effort going into thinking about AI risk.
It was noteworthy to me that Robin thinks human-level AI is a century, perhaps multiple centuries away— much longer than the 50-year number given by AI researchers. I think these longer timelines are the source of a lot of his disagreement with the AI risk community about how much of futurist thought should be put into AI.
Robin is particularly interested in the notion of ‘lumpiness’– how much AI is likely to be furthered by a few big improvements as opposed to a slow and steady trickle of progress. If, as Robin believes, most academic progress and AI in particular are not likely to be ‘lumpy’, he thinks we shouldn’t think things will happen without a lot of warning.
The full recording and transcript of our conversation can be found here.
I’ll respond to comments here, at least for a few days.
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You previously wrote:
The conclusions of those models seem very counterintuitive to me. I think the most likely explanation is that they make some assumptions that I do not expect to apply to the default scenarios involving humans and AGI. To check this, can you please reference some of the models that you had in mind when you wrote the above? (This might also help people construct concrete models that they would consider more realistic.)
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The literature is vast, but this gets you started: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C47&q=%22principal+agent%22&btnG=
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Robin, I’m very confused by your response. The question I asked was for references to the specific models you talked about (with boundedly rational principals and perfectly rational agents), not how to find academic papers with the words "principal" and "agent" in them.
Did you misunderstand my question, or is this your way of saying "look it up yourself"? I have searched through the 5 review papers you cited in your blog post for mentions of models of this kind, and also searched on Google Scholar, with negative results. I can try to do more extensive searches but surely it’s a lot easier at this point if you could just tell me which models you were talking about?
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If you specifically want models with "bounded rationality", why do add in that search term: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C47&as_vis=1&q=bounded+rationality+principal+agent&btnG= See also: https://onlinelibrary.wiley.com/doi/abs/10.1111/geer.12111 https://www.mdpi.com/2073-4336/4/3/508 https://etd.ohiolink.edu/!etd.send_file?accession=miami153299521737861&disposition=inline
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Note that all three of the linked paper are about "boundedly rational agents with perfectly rational principals" or about "equally boundedly rational agents and principals". I have been so far unable to find any papers that follow the described pattern of "boundedly rational principals and perfectly rational agents".
It seems you consider previous AI booms to be a useful reference class for today’s progress in AI.
Suppose we will learn that the fraction of global GDP that currently goes into AI research is at least X times higher than in any previous AI boom. What is roughly the smallest X for which you’ll change your mind (i.e. no longer consider previous AI booms to be a useful reference class for today’s progress in AI)?
[EDIT: added "at least"]
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I’d also want to know that ratio X for each of the previous booms. There isn’t a discrete threshold, because analogies go on a continuum from more to less relevant. An unusually high X would be noteworthy and relevant, but not make prior analogies irrelevant.
Other than, say looking at our computers and comparing them to insects, what other signposts should we look for, if we want to calibrate progress towards domain-general artificial intelligence?
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The % of world income that goes to computer hardware & software, and the % of useful tasks that are done by them.
Recent paper that might be relevant:
https://arxiv.org/abs/1911.01547
Mostly unrelated to your point about AI, your comments about the 100,000 fans having the potential to cause harm rang true to me.
Are there other areas in which you think the many non-expert fans problem is especially bad (as opposed to computer security, which you view as healthy in this respect)?
Would you consider progress on image recognition and machine translation as outside view evidence for lumpiness? Accuracies on ImageNet, an image classification benchmark, dropped by >10% over a 4-year period (graph below) mostly due to the successful scaling up of a type of neural network.
This also seems relevant to your point about AI researchers who have been in the field for a long time being more skeptical. My understanding is that most AI researchers would not have predicted such rapid progress on this benchmark before it happened.
That said, I can see how you still might argue this is an example of over-emphasizing a simple form of perception, which in reality is much more complicated and involves a bunch of different interlocking pieces.
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My understanding is that this progress looks much less of a trend deviation when you scale it against the hardware and other resources devoted to these tasks. And of course in any larger area there are always subareas which happen to progress faster. So we have to judge how large is a subarea that is going faster, and is that size unusually large. Life extension also suffers from the 100,000 fans hype problem.
Robin, I still don’t understand why economic models predict only modest changes in agency problems, as you claimed here, when the principal is very limited and the agent is fully rational. I attempted to look through the literature, but did not find any models of this form. This is very likely because my literature search was not very good, as I am not an economist, so I would appreciate references. That said, I would be very surprised if these references convinced me that a strongly superintelligent expected utility maximizer with a misaligned utility function (like "maximize the number of paperclips") would not destroy almost all of the value from our source (assuming the AI itself is not valuable). To me, this is the extreme example of a principal-agent problem where the principal is limited and the agent is very capable. When I hear "principal-agent problems are not much worse with a smarter agent", I hear "a paperclip maximizer wouldn’t destroy most of the value", which seems crazy. Perhaps that is not what you mean though. (Of course, you can argue that this scenario is not very likely, and I agree with that. I point to it mainly as a crystallization of the disagreement about principal-agent problems.)
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I’ve also found it hard to find relevant papers. Behavioural Contract Theory reviews papers based on psychology findings and notes:
Most models have an agent who is fully rational, but I’m not sure what you mean by "principal is very limited".
I haven’t finished listening to the whole interview yet, but just so I don’t forget, I want to note that there’s some new stuff in there for me even though I’ve been following all of Robin’s blog posts, especially ones on AI risk. Here’s one, where Robin clarifies that his main complaint isn’t too many AI safety researchers, but that too large of a share of future-concerned altruists are thinking about AI risk.
That’s why it would be hard to give a very precise answer there about how many. But I actually am less concerned about the number of academics working on it, and more about sort of the percentage of altruistic mind space it takes. Because it’s a much higher percentage of that than it is of actual serious research. That’s the part I’m a little more worried about. Especially the fraction of people thinking about the future. I think of, just in general, very few people seem to be that willing to think seriously about the future. As a percentage of that space, it’s huge.
That’s where I most think, "Now, that’s too high." If you could say, "100 people will work on this as researchers, but then the rest of the people talk and think about the future." If they can talk and think about something else, that would be a big win for me because there are tens and hundreds of thousands of people out there on the side just thinking about the future and so, so many of them are focused on this AI risk thing when they really can’t do much about it, but they’ve just told themselves that it’s the thing that they can talk about, and to really shame everybody into saying it’s the priority. Hey, there’s other stuff.
Now of course, I completely have this whole other book, Age of Em, which is about a different kind of scenario that I think doesn’t get much attention, and I think it should get more attention relative to a range of options that people talk about. Again, the AI risk scenario so overwhelmingly sucks up that small fraction of the world. So a lot of this of course depends on your base. If you’re talking about the percentage of people in the world working on these future things, it’s large of course.
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Would you be up for copying over the summary portion of the transcript here?
I was struck by how much I broadly agreed with almost everything Robin said. ETA: The key points of disagreement are a) I think principal-agent problems with a very smart agent can get very bad, see comment above, and b) on my inside view, timelines could be short (though I agree from the outside timelines look long). To answer the questions:
Thanks a lot for doing this! I had more to say than fit in a comment … check out my Thoughts on Robin Hanson’s AI Impacts interview
Associate professor, not assistant professor.
From the transcript: > Robin Hanson: Well, even that is an interesting thing if people agree on it. You could say, "You know a lot of people who agree with you that AI risk is big and that we should deal with something soon. Do you know anybody who agrees with you for the same reasons?"> It’s interesting, so I did a poll, I’ve done some Twitter polls lately, and I did one on "Why democracy?" And I gave four different reasons why democracy is good. And I noticed that there was very little agreement, that is, relatively equal spread across these four reasons. And so, I mean that’s an interesting fact to know about any claim that many people agree on, whether they agree on it for the same reasons. And it would be interesting if you just asked people, "Whatever your reason is, what percentage of people interested in AI risk agree with your claim about it for the reason that you do?" Or, "Do you think your reason is unusual?"> Because if most everybody thinks their reason is unusual, then basically there isn’t something they can all share with the world to convince the world of it. There’s just the shared belief in this conclusion, based on very different reasons. And then it’s more on their authority of who they are and why they as a collective are people who should be listened to or something.I think there’s something to this idea. It also reminds me of the principle that one should beware surprising and suspicious convergence, as well as of the following passage from Richard Ngo: