LessWrong (Curated & Popular)

https://www.lesswrong.com/posts/J3wemDGtsy5gzD3xa/toni-kurz-and-the-insanity-of-climbing-mountains

Content warning: death

I've been on a YouTube binge lately. My current favorite genre is disaster stories about mountain climbing. The death statistics for some of these mountains, especially ones in the Himalayas are truly insane.

To give an example, let me tell you about a mountain most people have never heard of: Nanga Parbat. It's a 8,126 meter "wall of ice and rock", sporting the tallest mountain face and the fastest change in elevation in the entire world: the Rupal Face.

I've posted a picture above, but these really don't do justice to just how gigantic this wall is. This single face is as tall as the largest mountain in the Alps. It is the size of ten empire state buildings stacked on top of one another. If you could somehow walk straight up starting from the bottom, it would take you an entire HOUR to reach the summit.

31 people died trying to climb this mountain before its first successful ascent. Imagine being climber number 32 and thinking "Well I know no one has ascended this mountain and thirty one people have died trying, but why not, let's give it a go!"

The stories of deaths on these mountains (and even much shorter peaks in the Alps or in North America) sound like they are out of a novel. Stories of one mountain in particular have stuck with me: the first attempts to climb tallest mountain face in the alps: The Eigerwand.

The Eigerwand: First Attempt

The Eigerwand is the North face of a 14,000 foot peak named "The Eiger". After three generations of Europeans had conquered every peak in the Alps, few great challenges remained in the area. The Eigerwand was one of these: widely considered to be the greatest unclimbed route in the Alps.

The peak had already been reached in the 1850s, during the golden age of Alpine exploration. But the north face of the mountain remained unclimbed.

Many things can make a climb challenging: steep slopes, avalanches, long ascents, no easy resting spots and more. The Eigerwand had all of those, but one hazard in particular stood out: loose rock and snow.

In the summer months (usually considered the best time for climbing), the mountain crumbles. Fist-sized boulders routinely tumble down the mountain. Huge avalanaches sweep down its 70-degree slopes at incredible speed. And the huge, concave face is perpetually in shadow. It is extremely cold and windy, and the concave face seems to cause local weather patterns that can be completely different from the pass below. The face is deadly.

Before 1935, no team had made a serious attempt at the face. But that year, two young German climbers from Bavaria, both extremely experienced but relatively unknown outside the climbing community, decided they would make the first serious attempt.

https://www.lesswrong.com/posts/gs3vp3ukPbpaEie5L/deliberate-grieving-1

This post is hopefully useful on its own, but begins a series ultimately about grieving over a world that might (or, might not) be doomed. 

It starts with some pieces from a previous  coordination frontier sequence post, but goes into more detail.

At the beginning of the pandemic, I didn’t have much experience with grief. By the end of the pandemic, I had gotten quite a lot of practice grieving for things. I now think of grieving as a key life skill, with ramifications for epistemics, action, and coordination. 

I had read The Art of Grieving Well, which gave me footholds to get started with. But I still had to develop some skills from scratch, and apply them in novel ways.

Grieving probably works differently for different people. Your mileage may vary. But for me, grieving is the act of wrapping my brain around the fact that something important to me doesn’t exist anymore. Or can’t exist right now. Or perhaps never existed. It typically comes in two steps – an “orientation” step, where my brain traces around the lines of the thing-that-isn’t-there, coming to understand what reality is actually shaped like now. And then a “catharsis” step, once I fully understand that the thing is gone. The first step can take hours, weeks or months. 

You can grieve for people who are gone. You can grieve for things you used to enjoy. You can grieve for principles that were important to you but aren’t practical to apply right now. 

Grieving is important in single-player mode – if I’m holding onto something that’s not there anymore, my thoughts and decision-making are distorted. I can’t make good plans if my map of reality is full of leftover wishful markings of things that aren’t there. 

https://www.lesswrong.com/posts/CjFZeDD6iCnNubDoS/humans-provide-an-untapped-wealth-of-evidence-about#fnref7a5ti4623qb

Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

TL;DR: To even consciously consider an alignment research direction, you should have evidence to locate it as a promising lead. As best I can tell, many directions seem interesting but do not have strong evidence of being “entangled” with the alignment problem such that I expect them to yield significant insights. 

For example, “we can solve an easier version of the alignment problem by first figuring out how to build an AI which maximizes the number of real-world diamonds” has intuitive appeal and plausibility, but this claim doesn’t have to be true and this problem does not necessarily have a natural, compact solution. In contrast, there do in fact exist humans who care about diamonds. Therefore, there are guaranteed-to-exist alignment insights concerning the way people come to care about e.g. real-world diamonds.

“Consider how humans navigate the alignment subproblem you’re worried about” is a habit which I (TurnTrout) picked up from Quintin Pope. I wrote the post, he originated the tactic. 

https://www.lesswrong.com/posts/DdDt5NXkfuxAnAvGJ/changing-the-world-through-slack-and-hobbies

 

Introduction

In EA orthodoxy, if you're really serious about EA, the three alternatives that people most often seem to talk about are

(1) “direct work” in a job that furthers a very important cause;

(2) “earning to give”;

(3) earning “career capital” that will help you do those things in the future, e.g. by getting a PhD or teaching yourself ML.

By contrast, there’s not much talk of:

(4) being in a job / situation where you have extra time and energy and freedom to explore things that seem interesting and important. 

But that last one is really important!

https://www.lesswrong.com/posts/8oMF8Lv5jiGaQSFvo/boundaries-part-1-a-key-missing-concept-from-utility-theory

Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

This is Part 1 of my «Boundaries» Sequence on LessWrong.

Summary: «Boundaries» are a missing concept from the axioms of game theory and bargaining theory, which might help pin-down certain features of multi-agent rationality (this post), and have broader implications for effective altruism discourse and x-risk (future posts).

1. Boundaries (of living systems) 

Epistemic status: me describing what I mean.

With the exception of some relatively recent and isolated pockets of research on embedded agency (e.g., Orseau & Ring, 2012; Garrabrant & Demsky, 2018), most attempts at formal descriptions of living rational agents — especially utility-theoretic descriptions — are missing the idea that living systems require and maintain boundaries.

When I say boundary, I don't just mean an arbitrary constraint or social norm.  I mean something that could also be called a membrane in a generalized sense, i.e., a layer of stuff-of-some-kind that physically or cognitively separates a living system from its environment, that 'carves reality at the joints' in a way that isn't an entirely subjective judgement of the living system itself.  Here are some examples that I hope will convey my meaning:

https://www.lesswrong.com/posts/MdZyLnLHuaHrCskjy/itt-passing-and-civility-are-good-charity-is-bad

I often object to claims like "charity/steelmanning is an argumentative virtue". This post collects a few things I and others have said on this topic over the last few years.

My current view is:

• Steelmanning ("the art of addressing the best form of the other person’s argument, even if it’s not the one they presented") is a useful niche skill, but I don't think it should be a standard thing you bring out in most arguments, even if it's an argument with someone you strongly disagree with.
• Instead, arguments should mostly be organized around things like:
  • Object-level learning and truth-seeking, with the conversation as a convenient excuse to improve your own model of something you're curious about.
  • Trying to pass each other's Ideological Turing Test (ITT), or some generalization thereof. The ability to pass ITTs is the ability "to state opposing views as clearly and persuasively as their proponents".
    • The version of "ITT" I care about is one where you understand the substance of someone's view well enough to be able to correctly describe their beliefs and reasoning; I don't care about whether you can imitate their speech patterns, jargon, etc.
  • Trying to identify and resolve cruxes: things that would make one or the other of you (or both) change your mind about the topic under discussion.
• Argumentative charity is a complete mess of a concept⁠—people use it to mean a wide variety of things, and many of those things are actively bad, or liable to cause severe epistemic distortion and miscommunication.
• Some version of civility and/or friendliness and/or a spirit of camaraderie and goodwill seems like a useful ingredient in many discussions. I'm not sure how best to achieve this in ways that are emotionally honest ("pretending to be cheerful and warm when you don't feel that way" sounds like the wrong move to me), or how to achieve this without steering away from candor, openness, "realness", etc.

https://www.lesswrong.com/posts/mmHctwkKjpvaQdC3c/what-should-you-change-in-response-to-an-emergency-and-ai

Related to: Slack gives you the ability to notice/reflect on subtle things

Epistemic status: A possibly annoying mixture of straightforward reasoning and hard-to-justify personal opinions.

It is often stated (with some justification, IMO) that AI risk is an “emergency.”  Various people have explained to me that they put various parts of their normal life’s functioning on hold on account of AI being an “emergency.”  In the interest of people doing this sanely and not confusedly, I’d like to take a step back and seek principles around what kinds of changes a person might want to make in an “emergency” of different sorts. 

Principle 1: It matters what time-scale the emergency is on

There are plenty of ways we can temporarily increase productivity on some narrow task or other, at the cost of our longer-term resources.  For example:

• Skipping meals
• Skipping sleep
• Ceasing to clean the house or to exercise
• Accumulating credit card debt
• Calling in favors from friends
• Skipping leisure time

https://www.lesswrong.com/posts/3pinFH3jerMzAvmza/on-how-various-plans-miss-the-hard-bits-of-the-alignment

Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

(As usual, this post was written by Nate Soares with some help and editing from Rob Bensinger.)

In my last post, I described a “hard bit” of the challenge of aligning AGI—the sharp left turn that comes when your system slides into the “AGI” capabilities well, the fact that alignment doesn’t generalize similarly well at this turn, and the fact that this turn seems likely to break a bunch of your existing alignment properties.

Here, I want to briefly discuss a variety of current research proposals in the field, to explain why I think this problem is currently neglected.

I also want to mention research proposals that do strike me as having some promise, or that strike me as adjacent to promising approaches.

Before getting into that, let me be very explicit about three points:

1. On my model, solutions to how capabilities generalize further than alignment are necessary but not sufficient. There is dignity in attacking a variety of other real problems, and I endorse that practice.
2. The imaginary versions of people in the dialogs below are not the same as the people themselves. I'm probably misunderstanding the various proposals in important ways, and/or rounding them to stupider versions of themselves along some important dimensions.[1] If I've misrepresented your view, I apologize.
3. I do not subscribe to the Copenhagen interpretation of ethics wherein someone who takes a bad swing at the problem (or takes a swing at a different problem) is more culpable for civilization's failure than someone who never takes a swing at all. Everyone whose plans I discuss below is highly commendable, laudable, and virtuous by my accounting.

https://www.lesswrong.com/posts/28zsuPaJpKAGSX4zq/humans-are-very-reliable-agents

Over the last few years, deep-learning-based AI has progressed extremely rapidly in fields like natural language processing and image generation. However, self-driving cars seem stuck in perpetual beta mode, and aggressive predictions there have repeatedly been disappointing. Google's self-driving project started four years before AlexNet kicked off the deep learning revolution, and it still isn't deployed at large scale, thirteen years later. Why are these fields getting such different results?

Right now, I think the biggest answer is that ML benchmarks judge models by average-case performance, while self-driving cars (and many other applications) require matching human worst-case performance. For MNIST, an easy handwriting recognition task, performance tops out at around 99.9% even for top models; it's not very practical to design for or measure higher reliability than that, because the test set is just 10,000 images and a handful are ambiguous. Redwood Research, which is exploring worst-case performance in the context of AI alignment, got reliability rates around 99.997% for their text generation models.

By comparison, human drivers are ridiculously reliable. The US has around one traffic fatality per 100 million miles driven; if a human driver makes 100 decisions per mile, that gets you a worst-case reliability of ~1:10,000,000,000 or ~99.999999999%. That's around five orders of magnitude better than a very good deep learning model, and you get that even in an open environment, where data isn't pre-filtered and there are sometimes random mechanical failures. Matching that bar is hard! I'm sure future AI will get there, but each additional "nine" of reliability is typically another unit of engineering effort. (Note that current self-driving systems use a mix of different models embedded in a larger framework, not one model trained end-to-end like GPT-3.)

https://www.lesswrong.com/posts/2GxhAyn9aHqukap2S/looking-back-on-my-alignment-phd

The funny thing about long periods of time is that they do, eventually, come to an end. I'm proud of what I accomplished during my PhD. That said, I'm going to first focus on mistakes I've made over the past four[1] years.

Mistakes

I think I got significantly smarter in 2018–2019, and kept learning some in 2020–2021. I was significantly less of a fool in 2021 than I was in 2017. That is important and worth feeling good about. But all things considered, I still made a lot of profound mistakes over the course of my PhD. 

Social dynamics distracted me from my core mission I focused on "catching up" to other thinkers

I figured this point out by summer 2021. 

I wanted to be more like Eliezer Yudkowsky and Buck Shlegeris and Paul Christiano. They know lots of facts and laws about lots of areas (e.g. general relativity and thermodynamics and information theory). I focused on building up dependencies (like analysis and geometry and topology) not only because I wanted to know the answers, but because I felt I owed a debt, that I was in the red until I could at least meet other thinkers at their level of knowledge. 

But rationality is not about the bag of facts you know, nor is it about the concepts you have internalized. Rationality is about how your mind holds itself, it is how you weigh evidence, it is how you decide where to look next when puzzling out a new area. 

If I had been more honest with myself, I could have nipped the "catching up with other thinkers" mistake in 2018. I could have removed the bad mental habits using certain introspective techniques; or at least been aware of the badness.

But I did not, in part because the truth was uncomfortable. If I did not have a clear set of prerequisites (e.g. analysis and topology and game theory) to work on, I would not have a clear and immediate direction of improvement. I would have felt adrift.