The Best of LessWrong

How does it work to optimize for realistic goals in physical environments of which you yourself are a part? E.g. humans and robots in the real world, and not humans and AIs playing video games in virtual worlds where the player not part of the environment. The authors claim we don't actually have a good theoretical understanding of this and explore four specific ways that we don't understand this process.

The Rocket Alignment Problem

This post is a not a so secret analogy for the AI Alignment problem. Via a fictional dialog, Eliezer explores and counters common questions to the Rocket Alignment Problem as approached by the Mathematics of Intentional Rocketry Institute.

MIRI researchers will tell you they're worried that "right now, nobody can tell you how to point your rocket’s nose such that it goes to the moon, nor indeed any prespecified celestial destination."

Arguments about fast takeoff

Will AGI progress gradually or rapidly? I think the disagreement is mostly about what happens before we build powerful AGI.

I think weaker AI systems will already have radically transformed the world. This is strategically relevant because I'm imagining AGI strategies playing out in a world where everything is already going crazy, while other people are imagining AGI strategies playing out in a world that looks kind of like 2018 except that someone is about to get a decisive strategic advantage.

#14

Coherence arguments do not entail goal-directed behavior

Rohin Shah argues that many common arguments for AI risk (about the perils of powerful expected utility maximizers) are actually arguments about goal-directed behavior or explicit reward maximization, which are not actually implied by coherence arguments. An AI system could be an expected utility maximizer without being goal-directed or an explicit reward maximizer.

#16

Robustness to Scale

You want your proposal for an AI to be robust to changes in its level of capabilities. It should be robust to the AI's capabilities scaling up, and also scaling down, and also the subcomponents of the AI scaling relative to each other.

We might need to build AGIs that aren't robust to scale, but if so we should at least realize that we are doing that.

#20

Paul's research agenda FAQ

Alex Zhu spent quite awhile understanding Paul's Iterated Amplication and Distillation agenda. He's written an in-depth FAQ, covering key concepts like amplification, distillation, corrigibility, and how the approach aims to create safe and capable AI assistants.

#21

An Untrollable Mathematician Illustrated

A hand-drawn presentation on the idea of an 'Untrollable Mathematician' - a mathematical agent that can't be manipulated into believing false things.

#23

Specification gaming examples in AI

A collection of examples of AI systems "gaming" their specifications - finding ways to achieve their stated objectives that don't actually solve the intended problem. These illustrate the challenge of properly specifying goals for AI systems.

#32

Beyond Astronomical Waste

What if our universe's resources are just a drop in the bucket compared to what's out there? We might be able to influence or escape to much larger universes that are simulating us or can otherwise be controlled by us. This could be a source of vastly more potential value than just using the resources in our own universe.

#34

Open question: are minimal circuits daemon-free?

Can the smallest boolean circuit that solves a problem be a "daemon" (a consequentialist system with its own goals)? Paul Christiano suspects not, but isn't sure. He thinks this question, while not necessarily directly important, may yield useful insights for AI alignment.

#39

Challenges to Christiano’s capability amplification proposal

Eliezer Yudkowsky offers detailed critiques of Paul Christiano's AI alignment proposal, arguing that it faces major technical challenges and may not work without already having an aligned superintelligence. Christiano acknowledges the difficulties but believes they are solvable.

15Isnasene

[Disclaimer: I'm reading this post for the first time now, as of 1/11/2020. I also already have a broad understanding of the importance of AI safety. While I am skeptical about MIRI's approach to things, I am also a fan of MIRI. Where this puts me relative to the target demographic of this post, I cannot say.] Overall Summary I think this post is pretty good. It's a solid and well-written introduction to some of the intuitions behind AI alignment and the fundamental research that MIRI does. At the same time, the use of analogy made the post more difficult for me to parse and hid some important considerations about AI alignment from view. Though it may be good (but not optimal) for introducing some people to the problem of AI alignment and a subset of MIRI's work, it did not raise or lower my opinion of MIRI as someone who already understood AGI safety to be important. To be clear, I do not consider any of these weaknesses serious because I believe them to be partially irrelevant to the audience of people who don't appreciate the importance of AI-Safety. Still, they are relevant to the audience of people who give AI-Safety the appropriate scrutiny but remain skeptical of MIRI. And I think this latter audience is important enough to assign this article a "pretty good" instead of a "great". I hope a future post directly explores the merit of MIRI's work on the context AI alignment without use of analogy. Below is an overview of my likes and dislikes in this post. I will go into more detail about them in the next section, "Evaluating Analogies." Things I liked: * It's a solid introduction to AI-alignment, covering a broad range of topics including: * Why we shouldn't expect aligned AGI by default * How modern conversation about AGI behavior is problematically underspecified * Why fundamental deconfusion research is necessary for solving AI-alignment * It directly explains the value/motivation of particular pieces of MIRI work via analogy -- which

21DanielFilan

I think that strictly speaking this post (or at least the main thrust) is true, and proven in the first section. The title is arguably less true: I think of 'coherence arguments' as including things like 'it's not possible for you to agree to give me a limitless number of dollars in return for nothing', which does imply some degree of 'goal-direction'. I think the post is important, because it constrains the types of valid arguments that can be given for 'freaking out about goal-directedness', for lack of a better term. In my mind, it provokes various follow-up questions: 1. What arguments would imply 'goal-directed' behaviour? 2. With what probability will a random utility maximiser be 'goal-directed'? 3. How often should I think of a system as a utility maximiser in resources, perhaps with a slowly-changing utility function? 4. How 'goal-directed' are humans likely to make systems, given that we are making them in order to accomplish certain tasks that don't look like random utility functions? 5. Is there some kind of 'basin of goal-directedness' that systems fall in if they're even a little goal-directed, causing them to behave poorly? Off the top of my head, I'm not familiar with compelling responses from the 'freak out about goal-directedness' camp on points 1 through 5, even though as a member of that camp I think that such responses exist. Responses from outside this camp include Rohin's post 'Will humans build goal-directed agents?'. Another response is Brangus' comment post, although I find its theory of goal-directedness uncompelling. I think that it's notable that Brangus' post was released soon after this was announced as a contender for Best of LW 2018. I think that if this post were added to the Best of LW 2018 Collection, the 'freak out' camp might produce more of these responses and move the dialogue forward. As such, I think it should be added, both because of the clear argumentation and because of the response it is likely to provoke.

12Vika

I've been pleasantly surprised by how much this resource has caught on in terms of people using it and referring to it (definitely more than I expected when I made it). There were 30 examples on the list when was posted in April 2018, and 20 new examples have been contributed through the form since then. I think the list has several properties that contributed to wide adoption: it's fun, standardized, up-to-date, comprehensive, and collaborative. Some of the appeal is that it's fun to read about AI cheating at tasks in unexpected ways (I've seen a lot of people post on Twitter about their favorite examples from the list). The standardized spreadsheet format seems easier to refer to as well. I think the crowdsourcing aspect is also helpful - this helps keep it current and comprehensive, and people can feel some ownership of the list since can personally contribute to it. My overall takeaway from this is that safety outreach tools are more likely to be impactful if they are fun and easy for people to engage with. This list had a surprising amount of impact relative to how little work it took me to put it together and maintain it. The hard work of finding and summarizing the examples was done by the people putting together the lists that the master list draws on (Gwern, Lehman, Olsson, Irpan, and others), as well as the people who submit examples through the form. What I do is put them together in a common format and clarify and/or shorten some of the summaries. I also curate the examples to determine whether they fit the definition of specification gaming (as opposed to simply a surprising behavior or solution). Overall, I've probably spent around 10 hours so far on creating and maintaining the list, which is not very much. This makes me wonder if there is other low hanging fruit in the safety resources space that we haven't picked yet. I have been using it both as an outreach and research tool. On the outreach side, the resource has been helpful for making the arg

17Vanessa Kosoy

In this essay, Rohin sets out to debunk what ey perceive as a prevalent but erroneous idea in the AI alignment community, namely: "VNM and similar theorems imply goal-directed behavior". This is placed in the context of Rohin's thesis that solving AI alignment is best achieved by designing AI which is not goal-directed. The main argument is: "coherence arguments" imply expected utility maximization, but expected utility maximization does not imply goal-directed behavior. Instead, it is a vacuous constraint, since any agent policy can be regarded as maximizing the expectation of some utility function. I have mixed feelings about this essay. On the one hand, the core argument that VNM and similar theorems do not imply goal-directed behavior is true. To the extent that some people believed the opposite, correcting this mistake is important. On the other hand, (i) I don't think the claim Rohin is debunking is the claim Eliezer had in mind in those sources Rohin cites (ii) I don't think that the conclusions Rohin draws or at least implies are the right conclusions. The actual claim that Eliezer was making (or at least my interpretation of it) is, coherence arguments imply that if we assume an agent is goal-directed then it must be an expected utility maximizer, and therefore EU maximization is the correct mathematical model to apply to such agents. Why do we care about goal-directed agents in the first place? The reason is, on the one hand goal-directed agents are the main source of AI risk, and on the other hand, goal-directed agents are also the most straightforward approach to solving AI risk. Indeed, if we could design powerful agents with the goals we want, these agents would protect us from unaligned AIs and solve all other problems as well (or at least solve them better than we can solve them ourselves). Conversely, if we want to protect ourselves from unaligned AIs, we need to generate very sophisticated long-term plans of action in the physical world, possibl

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