cross-posted to Twitter: 
https://twitter.com/DavidSKrueger/status/1573643782377152514

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-What sorts of skills an AI would need to achieve powerbase ability
-Timelines till powerbase ability
-Takeoff speeds
-How things are going to go by default, alignment-wise (e.g. will it be as described in this?)
-How things are going to go by default, governance-wise (e.g. will it be as described in this?)
 

I have two questions I'd love to hear your thoughts about.

1. What is the overarching/high-level research agenda of your group? Do you have a concrete alignment agenda where people work on the same thing or do people work on many unrelated things? 

2. What are your thoughts on various research agendas to solve the alignment that exists today? Why do you think they will fall short of their goal? What are you most excited about?

Feel free to talk about any agendas, but I'll just list a few that come to my mind (in no particular order). 

IDA, Debate, Interpretability (I read a tweet I think, where you said you are rather skeptical about this), Natural Abstraction Hypothesis, Externalized Reasoning Oversight, Shard Theory, (Relaxed) Adversarial Training, ELK, etc.

  1. No overarching agenda, but I do have some directions I'm more keen on pushing, see: https://www.davidscottkrueger.com/
  2. I'm pessimistic overall, and don't find any of the agendas particularly promising. I'm a strong proponent of the portfolio approach (https://futureoflife.org/2017/08/17/portfolio-approach-to-ai-safety-research/).  

    My pessimism means that my success stories usually involve us getting lucky; the main way this can happen is if some aspect of the problem is easier than I expect. From the technical point of view the main candidates ~OTMH ar
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1Jérémy Scheurer1y
Thanks for your thoughts, really appreciate it.  One quick follow-up question, when you say "build powerful AI tools that are deceptive" as a way of "the problem being easier than anticipated", how exactly do you mean that? Do you say that as in, if we can create deceptive or power-seeking tool AI very easily, it will be much simpler to investigate what is happening and derive solutions?  Here are some links to the concepts you asked about.  Externalized Reasoning Oversight:  This was also recently introduced https://www.lesswrong.com/posts/FRRb6Gqem8k69ocbi/externalized-reasoning-oversight-a-research-direction-for . The main idea is to use Chain-of-though reasoning to oversee the thought processes of your model (assuming that those thought processes are complete and straightforward, and the output causally depends on it).    Shard Theory: https://www.lesswrong.com/posts/iCfdcxiyr2Kj8m8mT/the-shard-theory-of-human-values. It was proposed very recently. Their TL;DR is "We propose a theory of human value formation. According to this theory, the reward system shapes human values in a relatively straightforward manner. Human values are not e.g. an incredibly complicated, genetically hard-coded set of drives, but rather sets of contextually activated heuristics which were shaped by and bootstrapped from crude, genetically hard-coded reward circuitry. "    Relaxed Adversarial Training: I think the main post is this one https://www.lesswrong.com/posts/9Dy5YRaoCxH9zuJqa/relaxed-adversarial-training-for-inner-alignment . But I really like the short description by Beth (https://www.lesswrong.com/posts/YQALrtMkeqemAF5GX/another-list-of-theories-of-impact-for-interpretability):  "The basic idea of relaxed adversarial training is something like: * A deceptive model may be waiting for an input that strongly indicates it’s in the deployment environment rather than in a test - for example, one that’s computationally hard to produce * This makes generating adversarial examp
3David Scott Krueger (formerly: capybaralet)1y
ERO is another one of those things that I think has a lot of precedent in ML (There's a paper I was trying to dig up using natural language as the latent space in a variational autoencoder), but doesn't look very promising to me because of "steganography everywhere".  Like other approaches to interpretability, it seems worth pursuing, but I also worry that people believe too much in flawed interpretations. Shard theory sounds like an interesting framing, and again something that a lot of people would already agree with in ML, I'm not sure what it is supposed to be useful for or what sort of testable predictions it makes.  Seems like a perspective worth keeping in mind, But I'm not sure I'd call it a research agenda. RAT: I don't see any way to "search over something like ‘the model’s beliefs about what it has seen’"; This seems like a potential sticking point; There's more foundational research needed to figure out if when how we can even ascribe beliefs to a model etc. As a general comment, I think a lot of the "agendas" That people discuss here are not very well fleshed out, And the details are crucially important.  I'm not even sure whether to call a lot of these ideas "agendas".  To need they strike me as more like "framings".  It is important to note that the ML community doesn't publish "framings", except when they can be supported by concrete results (You can sometimes find people giving their perspective or framing on some problems in machine learning in blogs or keynotes or tutorials, etc.).  So I think that people here often overestimate the novelty of their perspective.  I think it is good to reward people for sharing these things, but given that a lot of other people might have similar thoughts but choose not to share them, I don't think people here have quite the right attitude towards this.  Writing up or otherwise communicating such framings without a strong empirical or theoretical contribution and expecting credit for the ideas / citations of you
1Jérémy Scheurer1y
ERO: I do buy the argument of Steganography everywhere if you are optimizing for outcomes. As described here (https://www.lesswrong.com/posts/pYcFPMBtQveAjcSfH/supervise-process-not-outcomes) outcome-based optimization is an attractor and will make your sub-compoments uninterpretable. While not guaranteed, I do think that process based optimization might suffer less from steganography (although only experiments will eventually show what happens). Any thoughts on process based optimization?   Shard Theory:  Yeah, the word research agenda was maybe wrongly picked. I was mainly trying to refer to research directions/frameworks.  RAT: Agree at the moment this is not feasible. See above, I don't have strong views on how to call this. Probably for some things research agenda might be too strong of a word. I appreciate your general comment, this is helpful in better understanding your view on Lesswrong vs. for example peer-reviewing. I think you are right to some degree. There is a lot of content that is mostly about framing and does not provide concrete results. However, I think that sometimes a correct framing is needed for people to actually come up with interesting results, and for making things more concrete. Some examples I like for example are the inner/outer alignment framing (which I think initially didn't bring any concrete examples), or the recent Simulators (https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators) post. I think in those cases the right framing helps tremendously to make progress with concrete research afterward. Although I agree that grounded, concrete, and result-oriented experimentation is indeed needed to make concrete progress on a problem. So I do understand your point, and it can feel like flag planting in some cases. Note: I'm also coming from academia, so I definitely understand your view and share it to some degree. However, I've personally come to appreciate some posts (usually by great researchers) that allow me to think ab
3David Scott Krueger (formerly: capybaralet)1y
My understanding is that process-based optimization is just another name for supervising intermediary computations -- you can treat anything computed by a network as an "output" in the sense of applying some loss function.   So (IIUC), it is not qualitatively different.
2David Scott Krueger (formerly: capybaralet)1y
That was a typo, should say "are NOT deceptive"
  • importance / difficulty of outer vs inner alignment
  • outlining some research directions that seem relatively promising to you, and explain why they seem more promising than others

I feel like I'm pretty off outer vs. inner alignment.

People have had a go at inner alignment, but they keep trying to affect it by taking terms for interpretability, or modeled human feedbacks, or characteristics of the AI's self-model, and putting them into the loss function, diluting the entire notion that inner alignment isn't about what's in the loss function.

People have had a go at outer alignment too, but (if they're named Charlie) they keep trying to point to what we want by saying that the AI should be trying to learn good moral reasoning, which me... (read more)