Epistemologist specialized in the difficulties of alignment and how to solve AI X-Risks. Currently at Conjecture.
Blogging at For Methods.
adamShimi
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Static Analysis As A Lifestyle
Thanks for the pointer!
Assigning Praise and Blame: Decoupling Epistemology and Decision Theory
Lost in Innovation: The Case of Phlogiston
Oh, didn’t know him!
Thanks for the links!
Thanks for the comment!
I agree with you that there are situations where the issue comes from a cultural norm rather than psychological problems. That’s one reason for the last part of this post, where we point out to generally positive and productive norms that try to avoid these cultural problems and make it possible to discuss them. (One of the issue I see in my own life with cultural norms is that they are way harder to discuss when in addition psychological problems compound them and make them feel sore and emotional). But you might be right that it’s worth highlighting more.
In a more meta point, my model is that we have moved from societies where almost everything is considered ″people’s fault” to societies where almost everything is considered “society’s fault”. And it strikes me that this is an overcorrection, and that actually many issues in day to day life and groups are just people’s problem (here drawing from my experience of realizing in many situations that I was the problem, and in other — less common — that the norms were the problem.)
Group-level Consequences of Psychological Problems
Oh, I definitely agree, this is a really good point. What I was highlighting was an epistemic issue (namely the confusion between ideal and necessary conditions) but there is also a different decision theoretic issue that you highlighted quite well.
It’s completely possible that you’re not powerful enough to work outside the ideal condition. But by doing the epistemic clarification, now we can consider the explicit decision of taking step to become more powerful and being better able to manage non-ideal conditions.
Good point! The difference is that the case explained in this post is one of the most sensible version of confusing the goal and the path, since there the path is actually a really good path. On the other version (like wanting to find a simple theory simply, the path is not even a good one!
Confusing the ideal for the necessary
In many ways, this post is frustrating to read. It isn’t straigthforward, it needlessly insults people, and it mixes irrelevant details with the key ideas.
And yet, as with many of Eliezer’s post, its key points are right.
What this post does is uncover the main epistemological mistakes made by almost everyone trying their hands at figuring out timelines. Among others, there is:
Taking arbitrary guesses within a set of options that you don’t have enough evidence to separate
Piling on arbitrary assumption on arbitraty assumption, leading to completely uninformative outputs
Comparing biological processes to human engineering in term of speed, without noticing that the optimization path is the key variable (and the big uncertainty)
Forcing the prediction to fit within a massively limited set of distributions, biasing it towards easy to think about distributions rather than representative ones.
Before reading this post I was already dubious of most timeline work, but this crystallized many of my objections and issues with this line of work.
So I got a lot out of this post. And I expect that many people would if they spent the time I took to analyze it in detail. But I don’t expect most people to do so, and so am ambivalent on whether this post should be included in the final selection.
I was mostly thinking of the efficiency assumption underlying almost all the scenarios. Critch assumes that a significant chunk of the economy always can and does make the most efficient change (everyone replacing the job, automated regulations replacing banks when they can’t move fast enough). Which neglects many potential factors, like big economic actors not having to be efficient for a long time, backlash from customers, and in general all factors making economic actors and market less than efficient.
I expect that most of these factors could be addressed with more work on the scenarios.
I consider this post as one of the most important ever written on issues of timelines and AI doom scenario. Not because it’s perfect (some of its assumptions are unconvincing), but because it highlights a key aspect of AI Risk and the alignment problem which is so easy to miss coming from a rationalist mindset: it doesn’t require an agent to take over the whole world. It is not about agency.
What RAAPs show instead is that even in a purely structural setting, where agency doesn’t matter, these problem still crop up!
This insight was already present in Drexler’s work, but however insightful Eric is in person, CAIS is completely unreadable and so no one cared. But this post is well written. Not perfectly once again, but it gives short, somewhat minimal proofs of concept for this structural perspective on alignment. And it also managed to tie alignment with key ideas in sociology, opening ways for interdisciplinarity.
I have made every person I have ever mentored on alignment study this post. And I plan to continue doing so. Despite the fact that I’m unconvinced by most timeline and AI risk scenarios post. That’s how good and important it is.
I agree that a lot of science relies on predictive hallucinations. But there are examples that come to mind, notably the sort of phenomenological compression pushed by Faraday and (early) Ampère in their initial exploration of electromagnetism. What they did amounted to vary a lot of the experimental condition and relate outcomes and phenomena to each other, without directly assuming any hidden entity. (see this book for more details)
More generally, I expect most phenomenological laws to not rely heavily on predictive hallucinations, even when they integrate theoretical terms in their formulation. That’s because they are mostly strong experimental regularities (like the ideal gas law or the phenomenological laws of thermodynamics) which tend to be carried to the next paradigm with radically different hidden entities.
The special nature of special relativity
So reification means “the act of making real” in most english dictionaries (see here for example). That’s the meaning we’re trying to evoke here, where the reification bias amounts to first postulate some underlying entity that explain the phenomena (that’s merely a modelling technique), and second to ascribe reality to this entity and manipulate it as if it was real.
Reification bias
Nothing New: Productive Reframing
You use the analogy with sports betting multiple time in this post. But science and sports are disanalogical in almost all the relevant ways!
Notably, sports are incredibly limited and well-defined, with explicit rules that literally anyone can learn, quick feedback signals, and unambiguous results. Completely the opposite of science!
The only way I see for the analogy to hold is by defining “science” in a completely impoverished way, that puts aside most of what science actually looks like. For example, replication is not that big a part of acience, it’s just the visible “clean” one. And even then, I expect the clarification of replication issues and of the original meaning to be tricky.
So my reaction to this proposal, like my reaction to any prediction market for things other than sports and games, is that I expect it to be completely irrelevant to the progress of knowledge because of the weakness of such tools. But I would definitely be curious of attempts to explicitly address all the ambiguities of epistemology and science through betting mechanisms. Maybe you know of some posts/works on that?
Definitely!
Dimensional analysis was the first place this analogy jumped to me when reading Fly By Night Physics, because it truly used dimensions not only to check results, but also to infer the general shape of the answer (which is also something you can do in type systems, for example a function with a generic type a→a can only be populated by the identity function, because it cannot not do anything else than return its input).
Although in physics you need more tricks and feels to do it correctly. Like the derivation of the law of the pendulum just from dimensional analysis requires you to have the understanding of forces as accelerations to know that you can us g here.
Dimensions are also a perennial candidate for things that should be added to type systems, with people working quite hard at implementing it (just found this survey from 2018).
I looked at the repo, and was quite confused how you did it, until I read
That’s a really smart trick! I’m sure there are some super advanced cases where the units might cancel out wrongly, but in practice they shouldn’t, and this let’s you interface with all the random software that exists! (Modulo the run it twice, as you said, because the two runs correspond to two different drawings of the constants)
Another interesting thing with radiants is that when you write a literal expression, it will look quite different than in degrees (involving many more instances of π), so inspection can fix many errors without paying the full price of different types.