Not exactly what you’re asking for, but maybe a 2x2 could be food for thought.
Stefan_Schubert
Realist and pragmatist don’t seem like the best choices of terms, since they pre-judge the issue a bit in the direction of that view.
Thanks.
I think psychologists-scientists should have unusually good imaginations about the potential inner workings of other minds, which many ML engineers probably lack.
That’s not clear to me, given that AI systems are so unlike human minds.
tell your fellow psychologist (or zoopsychologist) about this, maybe they will be incentivised to make a switch and do some ground-laying work in the field of AI psychology
Do you believe that (conventional) psychologists would be especially good at what you call AI psychology, and if so, why? I guess other skills (e.g. knowledge of AI systems) could be important.
I think that’s exactly right.
I think that could be valuable.
It might be worth testing quite carefully for robustness—to ask multiple different questions probing the same issue, and see whether responses converge. My sense is that people’s stated opinions about risks from artificial intelligence, and existential risks more generally, could vary substantially depending on framing. Most haven’t thought a lot about these issues, which likely contributes. I think a problem problem with some studies on these issues is that researchers over-generalise from highly framing-dependent survey responses.
I wrote an extended comment in a blog post.
Summary:
Summing up, I disagree with Hobbhahn on three points.
I think the public would be more worried about harm that AI systems cause than he assumes.
I think that economic incentives aren’t quite as powerful as he thinks they are, and I think that governments are relatively stronger than he thinks.
He argues that governments’ response will be very misdirected, and I don’t quite buy his arguments.
Note that 1 and 2⁄3 seem quite different: 1 is about how much people will worry about AI harms, whereas 2 and 3 are about the relative power of companies/economic incentives and governments, and government competency. It’s notable that Hobbhahn is more pessimistic on both of those relatively independent axes.
Another way to frame this, then, is that “For any choice of AI difficulty, faster pre-takeoff growth rates imply shorter timelines.”
I agree. Notably, that sounds more like a conceptual and almost trivial claim.
I think that the original claims sound deeper than they are because they slide between a true but trivial interpretation and a non-trivial interpretation that may not be generally true.
Thanks.
My argument involved scenarios with fast take-off and short time-lines. There is a clarificatory part of the post that discusses the converse case, of a gradual take-off and long time-lines:
Is it inconsistent, then, to think both that take-off will be gradual and timelines will be long? No – people who hold this view probably do so because they think that marginal improvements in AI capabilities are hard. This belief implies both a gradual take-off and long timelines.
Maybe a related clarification could be made about the fast take-off/short time-line combination.
However, this claim also confuses me a bit:
No – people who hold this view probably do so because they think that marginal improvements in AI capabilities are hard. This belief implies both a gradual take-off and long timelines.
The main claim in the post is that gradual take-off implies shorter time-lines. But here the author seems to say that according to the view “that marginal improvements in AI capabilities are hard”, gradual take-off and longer timelines correlate. And the author seems to suggest that that’s a plausible view (though empirically it may be false). I’m not quite sure how to interpret this combination of claims.
For every choice of AGI difficulty, conditioning on gradual take-off implies shorter timelines.
What would you say about the following argument?
Suppose that we get AGI tomorrow because of a fast take-off. If so timelines will be extremely short.
If we instead suppose that take-off will be gradual, then it seems impossible for timelines to be that short.
So in this scenario—this choice of AGI difficulty—conditioning on gradual take-off doesn’t seem to imply shorter timelines.
So that’s a counterexample to the claim that for every choice of AGI difficulty, conditioning on gradual take-off implies shorter timelines.
I’m not sure whether it does justice to your reasoning, but if so, I’d be interested to learn where it goes wrong.
- 21 Apr 2022 12:52 UTC; 4 points) 's comment on For every choice of AGI difficulty, conditioning on gradual take-off implies shorter timelines. by (
Holden Karnofsky defends this view in his latest blog post.
I think it’s too quick to think of technological unemployment as the next problem we’ll be dealing with, and wilder issues as being much further down the line. By the time (or even before) we have AI that can truly replace every facet of what low-skill humans do, the “wild sci-fi” AI impacts could be the bigger concern.
A related view is that less advanced/more narrow AI will do be able to do a fair number of tasks, but not enough to create widespread technological unemployment until very late, when very advanced AI quite quickly causes lots of people to be unemployed.
One consideration is how long time it will take for people to actually start using new AI systems (it tends to take some time for new technologies to be widely used). I think that some have speculated that that time lag may be shortened as AI become more advanced (as AI becomes involved in the deployment of other AI systems).
Scott Alexander has written an in-depth article about Hreha’s article:
The article itself mostly just urges behavioral economists to do better, which is always good advice for everyone. But as usual, it’s the inflammatory title that’s gone viral. I think a strong interpretation of behavioral economics as dead or debunked is unjustified.
See also Alex Imas’s and Chris Blattman’s criticisms of Hreha (on Twitter).
I think that though there’s been a welcome surge of interest in conceptual engineering in recent years, the basic idea has been around for quite some time (though under different names). In particular, Carnap argued that we should “explicate” rather than “analyse” concepts already in the 1940s and 1950s. In other words, we shouldn’t just try to explain the meaning of pre-existing concepts, but should develop new and more useful concepts that partially replace the old concepts.
Carnap’s understanding of explication was influenced by Karl Menger’s conception of the methodological role of definitions in mathematics, exemplified by Menger’s own explicative definition of dimension in topology.
...
Explication in Carnap’s sense is the replacement of a somewhat unclear and inexact concept C, the explicandum, by a new, clearer, and more exact concept , the explicatum.See also Logical Foundations of Probability, pp. 3-20.
The link doesn’t seem to work.
Potentially relevant new paper:
The logic of universalization guides moral judgment
To explain why an action is wrong, we sometimes say: “What if everybody did that?” In other words, even if a single person’s behavior is harmless, that behavior may be wrong if it would be harmful once universalized. We formalize the process of universalization in a computational model, test its quantitative predictions in studies of human moral judgment, and distinguish it from alternative models. We show that adults spontaneously make moral judgments consistent with the logic of universalization, and report comparable patterns of judgment in children. We conclude that alongside other well-characterized mechanisms of moral judgment, such as outcome-based and rule-based thinking, the logic of universalizing holds an important place in our moral minds.
A new paper may give some support to arguments in this post:
The smart intuitor: Cognitive capacity predicts intuitive rather than deliberate thinking
Cognitive capacity is commonly assumed to predict performance in classic reasoning tasks because people higher in cognitive capacity are believed to be better at deliberately correcting biasing erroneous intuitions. However, recent findings suggest that there can also be a positive correlation between cognitive capacity and correct intuitive thinking. Here we present results from 2 studies that directly contrasted whether cognitive capacity is more predictive of having correct intuitions or successful deliberate correction of an incorrect intuition. We used a two-response paradigm in which people were required to give a fast intuitive response under time pressure and cognitive load and afterwards were given the time to deliberate. We used a direction-of-change analysis to check whether correct responses were generated intuitively or whether they resulted from deliberate correction (i.e., an initial incorrect-to-correct final response change). Results showed that although cognitive capacity was associated with the correction tendency (overall r = .13) it primarily predicted correct intuitive responding (overall r = .42). These findings force us to rethink the nature of sound reasoning and the role of cognitive capacity in reasoning. Rather than being good at deliberately correcting erroneous intuitions, smart reasoners simply seem to have more accurate intuitions.
An economist friend said in a discussion about sleepwalk bias 9 March:
In the case of COVID, this led me to think that there will not be that much mortality in most rich countries, but only due to drastic measures.
The rest of the discussion may also be of interest; e.g. note his comment that “in economics, I think we often err on the other side—people fully incorporate the future in many models.”
I agree people often underestimate policy and behavioural responses to disaster. I called this “sleepwalk bias”—the tacit assumption that people will sleepwalk into disaster to a greater extent than is plausible.
Jon Elster talks about “the younger sibling syndrome”:
A French philosopher, Maurice Merleau-Ponty, said that our spontaneous tendency is to view other people as ‘‘younger siblings.’’ We do not easily impute to others the same capacity for deliberation and reflection that introspection tells us that we possess ourselves, nor for that matter our inner turmoil, doubts, and anguishes. The idea of viewing others as being just as strategic and calculating as we are ourselves does not seem to come naturally.
- 2 Nov 2023 12:55 UTC; 5 points) 's comment on My thoughts on the social response to AI risk by (EA Forum;
- 20 Jun 2020 12:42 UTC; 5 points) 's comment on Relevant pre-AGI possibilities by (
Yeah, I think so. But since those people generally find AI less important (there’s both less of an upside and less of a downside) they generally participate less in the debate. Hence there’s a bit of a selection effect hiding those people.
There are some people who arguably are in that corner who do participate in the debate, though—e.g. Robin Hanson. (He thinks some sort of AI will eventually be enormously important, but that the near-term effects, while significant, will not be at the level people on the right side think).
Looking at the 2x2 I posted I wonder if you could call the lower left corner something relating to “non-existential risks”. That seems to capture their views. It might be hard to come up with a catch term, though.
The upper left corner could maybe be called “sceptics”.