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"How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin Jan 11, 2023

Show notes

https://www.lesswrong.com/posts/L4anhrxjv8j2yRKKp/how-discovering-latent-knowledge-in-language-models-without
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

Introduction

A few collaborators and I recently released a new paper: Discovering Latent Knowledge in Language Models Without Supervision. For a quick summary of our paper, you can check out this Twitter thread.

In this post I will describe how I think the results and methods in our paper fit into a broader scalable alignment agenda. Unlike the paper, this post is explicitly aimed at an alignment audience and is mainly conceptual rather than empirical.

Tl;dr: unsupervised methods are more scalable than supervised methods, deep learning has special structure that we can exploit for alignment, and we may be able to recover superhuman beliefs from deep learning representations in a totally unsupervised way.

Disclaimers: I have tried to make this post concise, at the cost of not making the full arguments for many of my claims; you should treat this as more of a rough sketch of my views rather than anything comprehensive. I also frequently change my mind – I’m usually more consistently excited about some of the broad intuitions but much less wedded to the details – and this of course just represents my current thinking on the topic.

"The next decades might be wild" by Marius Hobbhahn Dec 21, 2022

Show notes

https://www.lesswrong.com/posts/qRtD4WqKRYEtT5pi3/the-next-decades-might-be-wild
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

I’d like to thank Simon Grimm and Tamay Besiroglu for feedback and discussions.

This post is inspired by What 2026 looks like and an AI vignette workshop guided by Tamay Besiroglu. I think of this post as “what would I expect the world to look like if these timelines (median compute for transformative AI ~2036) were true” or “what short-to-medium timelines feel like” since I find it hard to translate a statement like “median TAI year is 20XX” into a coherent imaginable world.

I expect some readers to think that the post sounds wild and crazy but that doesn’t mean its content couldn’t be true. If you had told someone in 1990 or 2000 that there would be more smartphones and computers than humans in 2020, that probably would have sounded wild to them. The same could be true for AIs, i.e. that in 2050 there are more human-level AIs than humans. The fact that this sounds as ridiculous as ubiquitous smartphones sounded to the 1990/2000 person, might just mean that we are bad at predicting exponential growth and disruptive technology.

Update: titotal points out in the comments that the correct timeframe for computers is probably 1980 to 2020. So the correct time span is probably 40 years instead of 30. For mobile phones, it's probably 1993 to 2020 if you can trust this statistic.

I’m obviously not confident (see confidence and takeaways section) in this particular prediction but many of the things I describe seem like relatively direct consequences of more and more powerful and ubiquitous AI mixed with basic social dynamics and incentives.

"Lessons learned from talking to >100 academics about AI safety" by Marius Hobbhahn Nov 17, 2022

Show notes

https://www.lesswrong.com/posts/SqjQFhn5KTarfW8v7/lessons-learned-from-talking-to-greater-than-100-academics
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

I’d like to thank MH, Jaime Sevilla and Tamay Besiroglu for their feedback.

During my Master's and Ph.D. (still ongoing), I have spoken with many academics about AI safety. These conversations include chats with individual PhDs, poster presentations and talks about AI safety.

I think I have learned a lot from these conversations and expect many other people concerned about AI safety to find themselves in similar situations. Therefore, I want to detail some of my lessons and make my thoughts explicit so that others can scrutinize them.

TL;DR: People in academia seem more and more open to arguments about risks from advanced intelligence over time and I would genuinely recommend having lots of these chats. Furthermore, I underestimated how much work related to some aspects AI safety already exists in academia and that we sometimes reinvent the wheel. Messaging matters, e.g. technical discussions got more interest than alarmism and explaining the problem rather than trying to actively convince someone received better feedback.

"How my team at Lightcone sometimes gets stuff done" by jacobjacob Nov 10, 2022

Show notes

https://www.lesswrong.com/posts/6LzKRP88mhL9NKNrS/how-my-team-at-lightcone-sometimes-gets-stuff-done
Disclaimer: I originally wrote this as a private doc for the Lightcone team. I then showed it to John and he said he would pay me to post it here. That sounded awfully compelling. However, I wanted to note that I’m an early founder who hasn't built anything truly great yet. I’m writing this doc because as Lightcone is growing, I have to take a stance on these questions. I need to design our org to handle more people. Still, I haven’t seen the results long-term, and who knows if this is good advice. Don’t overinterpret this.

Suppose you went up on stage in front of a company you founded, that now had grown to 100, or 1000, 10 000+ people. You were going to give a talk about your company values. You can say things like “We care about moving fast, taking responsibility, and being creative” -- but I expect these words would mostly fall flat. At the end of the day, the path the water takes down the hill is determined by the shape of the territory, not the sound the water makes as it swooshes by. To manage that many people, it seems to me you need clear, concrete instructions. What are those? What are things you could write down on a piece of paper and pass along your chain of command, such that if at the end people go ahead and just implement them, without asking what you meant, they would still preserve some chunk of what makes your org work?

"Decision theory does not imply that we get to have nice things" by So8res Nov 08, 2022

Show notes

https://www.lesswrong.com/posts/rP66bz34crvDudzcJ/decision-theory-does-not-imply-that-we-get-to-have-nice
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

(Note: I wrote this with editing help from Rob and Eliezer. Eliezer's responsible for a few of the paragraphs.)

A common confusion I see in the tiny fragment of the world that knows about logical decision theory (FDT/UDT/etc.), is that people think LDT agents are genial and friendly for each other.[1]

One recent example is Will Eden’s tweet about how maybe a molecular paperclip/squiggle maximizer would leave humanity a few stars/galaxies/whatever on game-theoretic grounds. (And that's just one example; I hear this suggestion bandied around pretty often.)

I'm pretty confident that this view is wrong (alas), and based on a misunderstanding of LDT. I shall now attempt to clear up that confusion.

To begin, a parable: the entity Omicron (Omega's little sister) fills box A with $1M and box B with $1k, and puts them both in front of an LDT agent saying "You may choose to take either one or both, and know that I have already chosen whether to fill the first box". The LDT agent takes both.

"What?" cries the CDT agent. "I thought LDT agents one-box!"

LDT agents don't cooperate because they like cooperating. They don't one-box because the name of the action starts with an 'o'. They maximize utility, using counterfactuals that assert that the world they are already in (and the observations they have already seen) can (in the right circumstances) depend (in a relevant way) on what they are later going to do.

A paperclipper cooperates with other LDT agents on a one-shot prisoner's dilemma because they get more paperclips that way. Not because it has a primitive property of cooperativeness-with-similar-beings. It needs to get the more paperclips.

"What 2026 looks like" by Daniel Kokotajlo Nov 06, 2022

Show notes

https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-looks-like#2022
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

This was written for the Vignettes Workshop.[1] The goal is to write out a detailed future history (“trajectory”) that is as realistic (to me) as I can currently manage, i.e. I’m not aware of any alternative trajectory that is similarly detailed and clearly more plausible to me. The methodology is roughly: Write a future history of 2022. Condition on it, and write a future history of 2023. Repeat for 2024, 2025, etc. (I'm posting 2022-2026 now so I can get feedback that will help me write 2027+. I intend to keep writing until the story reaches singularity/extinction/utopia/etc.)

What’s the point of doing this? Well, there are a couple of reasons:

Sometimes attempting to write down a concrete example causes you to learn things, e.g. that a possibility is more or less plausible than you thought.
Most serious conversation about the future takes place at a high level of abstraction, talking about e.g. GDP acceleration, timelines until TAI is affordable, multipolar vs. unipolar takeoff… vignettes are a neglected complementary approach worth exploring.
Most stories are written backwards. The author begins with some idea of how it will end, and arranges the story to achieve that ending. Reality, by contrast, proceeds from past to future. It isn’t trying to entertain anyone or prove a point in an argument.
Anecdotally, various people seem to have found Paul Christiano’s “tales of doom” stories helpful, and relative to typical discussions those stories are quite close to what we want. (I still think a bit more detail would be good — e.g. Paul’s stories don’t give dates, or durations, or any numbers at all really.)[2]
“I want someone to ... write a trajectory for how AI goes down, that is really specific about what the world GDP is in every one of the years from now until insane intelligence explosion. And just write down what the world is like in each of those years because I don't know how to write an internally consistent, plausible trajectory. I don't know how to write even one of those for anything except a ridiculously fast takeoff.” --Buck Shlegeris

This vignette was hard to write. To achieve the desired level of detail I had to make a bunch of stuff up, but in order to be realistic I had to constantly ask “but actually though, what would really happen in this situation?” which made it painfully obvious how little I know about the future. There are numerous points where I had to conclude “Well, this does seem implausible, but I can’t think of anything more plausible at the moment and I need to move on.” I fully expect the actual world to diverge quickly from the trajectory laid out here. Let anyone who (with the benefit of hindsight) claims this divergence as evidence against my judgment prove it by exhibiting a vignette/trajectory they themselves wrote in 2021. If it maintains a similar level of detail (and thus sticks its neck out just as much) while being more accurate, I bow deeply in respect!

Counterarguments to the basic AI x-risk case Nov 04, 2022

Show notes

"Introduction to abstract entropy" by Alex Altair Oct 29, 2022

Show notes

https://www.lesswrong.com/posts/REA49tL5jsh69X3aM/introduction-to-abstract-entropy#fnrefpi8b39u5hd7
This post, and much of the following sequence, was greatly aided by feedback from the following people (among others): Lawrence Chan, Joanna Morningstar, John Wentworth, Samira Nedungadi, Aysja Johnson, Cody Wild, Jeremy Gillen, Ryan Kidd, Justis Mills and Jonathan Mustin. Illustrations by Anne Ore.

Introduction & motivation

In the course of researching optimization, I decided that I had to really understand what entropy is.[1] But there are a lot of other reasons why the concept is worth studying:

Information theory:
- Entropy tells you about the amount of information in something.
- It tells us how to design optimal communication protocols.
- It helps us understand strategies for (and limits on) file compression.
Statistical mechanics:
- Entropy tells us how macroscopic physical systems act in practice.
- It gives us the heat equation.
- We can use it to improve engine efficiency.
- It tells us how hot things glow, which led to the discovery of quantum mechanics.
Epistemics (an important application to me and many others on LessWrong):
- The concept of entropy yields the maximum entropy principle, which is extremely helpful for doing general Bayesian reasoning.
Entropy tells us how "unlikely" something is and how much we would have to fight against nature to get that outcome (i.e. optimize).
It can be used to explain the arrow of time.
It is relevant to the fate of the universe.
And it's also a fun puzzle to figure out!

I didn't intend to write a post about entropy when I started trying to understand it. But I found the existing resources (textbooks, Wikipedia, science explainers) so poor that it actually seems important to have a better one as a prerequisite for understanding optimization! One failure mode I was running into was that other resources tended only to be concerned about the application of the concept in their particular sub-domain. Here, I try to take on the task of synthesizing the abstract concept of entropy, to show what's so deep and fundamental about it. In future posts, I'll talk about things like:

"Consider your appetite for disagreements" by Adam Zerner Oct 25, 2022

Show notes

https://www.lesswrong.com/posts/8vesjeKybhRggaEpT/consider-your-appetite-for-disagreements
Poker

There was a time about five years ago where I was trying to get good at poker. If you want to get good at poker, one thing you have to do is review hands. Preferably with other people.

For example, suppose you have ace king offsuit on the button. Someone in the highjack opens to 3 big blinds preflop. You call. Everyone else folds. The flop is dealt. It's a rainbow Q75. You don't have any flush draws. You missed. Your opponent bets. You fold. They take the pot and you move to the next hand.

Once you finish your session, it'd be good to come back and review this hand. Again, preferably with another person. To do this, you would review each decision point in the hand. Here, there were two decision points.

The first was when you faced a 3BB open from HJ preflop with AKo. In the hand, you decided to call. However, this of course wasn't your only option. You had two others: you could have folded, and you could have raised. Actually, you could have raised to various sizes. You could have raised small to 8BB, medium to 10BB, or big to 12BB. Or hell, you could have just shoved 200BB! But that's not really a realistic option, nor is folding. So in practice your decision was between calling and raising to various realistic sizes.

"My resentful story of becoming a medical miracle" by Elizabeth Oct 21, 2022

Show notes

https://www.lesswrong.com/posts/fFY2HeC9i2Tx8FEnK/my-resentful-story-of-becoming-a-medical-miracle
This is a linkpost for https://acesounderglass.com/2022/10/13/my-resentful-story-of-becoming-a-medical-miracle/

You know those health books with “miracle cure” in the subtitle? The ones that always start with a preface about a particular patient who was completely hopeless until they tried the supplement/meditation technique/healing crystal that the book is based on? These people always start broken and miserable, unable to work or enjoy life, perhaps even suicidal from the sheer hopelessness of getting their body to stop betraying them. They’ve spent decades trying everything and nothing has worked until their friend makes them see the book’s author, who prescribes the same thing they always prescribe, and the patient immediately stands up and starts dancing because their problem is entirely fixed (more conservative books will say it took two sessions). You know how those are completely unbelievable, because anything that worked that well would go mainstream, so basically the book is starting you off with a shit test to make sure you don’t challenge its bullshit later?

Well 5 months ago I became one of those miraculous stories, except worse, because my doctor didn’t even do it on purpose. This finalized some already fermenting changes in how I view medical interventions and research. Namely: sometimes knowledge doesn’t work and then you have to optimize for luck.

I assure you I’m at least as unhappy about this as you are.

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