trentmkelly/LessWrong-43k huggingface dataset:
Superbabies: Putting The Pieces Together
by sarahconstantin — 2024-07-11T20:40:05.036Z

This post was inspired by some talks at the recent LessOnline conference including one by LessWrong user “Gene Smith”.

Let’s say you want to have a “designer baby”. Genetically extraordinary in some way — super athletic, super beautiful, whatever.

6’5”, blue eyes, with a trust fund.

Ethics aside[1], what would be necessary to actually do this?

Fundamentally, any kind of “superbaby” or “designer baby” project depends on two steps:

1.) figure out what genes you ideally want;

2.) create an embryo with those genes.

It’s already standard to do a very simple version of this two-step process. In the typical course of in-vitro fertilization (IVF), embryos are usually screened for chromosomal abnormalities that would cause disabilities like Down Syndrome, and only the “healthy” embryos are implanted.

But most (partially) heritable traits and disease risks are not as easy to predict.

Polygenic Scores

If what you care about is something like “low cancer risk” or “exceptional athletic ability”, it won’t be down to a single chromosomal abnormality or a variant in a single gene. Instead, there’s typically a statistical relationship where many genes are separately associated with increased or decreased expected value for the trait.

This statistical relationship can be written as a polygenic score — given an individual’s genome, it’ll crunch the numbers and spit out an expected score. That could be a disease risk probability, or it could be an expected value for a trait like “height” or “neuroticism.”

Polygenic scores are never perfect — some people will be taller than the score’s prediction, some shorter — but for a lot of traits they’re undeniably meaningful, i.e. there will be a much greater-than-chance correlation between the polygenic score and the true trait measurement.

Where do polygenic scores come from?

Typically, from genome-wide association studies, or GWAS. These collect a lot of people’s genomes (the largest ones can have hundreds of thousands of subjects) and personal data potentially including disease diagnoses, height and weight, psychometric test results, etc. And then they basically run multivariate correlations. A polygenic score is a (usually regularized) multivariate regression best-fit model of the trait as a function of the genome.

A polygenic score can give you a rank ordering of genomes, from “best” to “worst” predicted score; it can also give you a “wish list” o...
Sherlockian Abduction Master List
by Cole Wyeth — 2024-07-11T20:27:00.000Z

[Radically updated with many new entries around 07/10/24]

Epistemic status: The List has been tested in the real world by me (with mixed results) and extensively checked for errors by many commenters. The Background section is mostly speculation and anecdotes, feel free to skip to The List once you understand its reason for existence.

tldr: This is a curated list of observable details about a person's appearance that indicate something useful/surprising about them. Ideally, studying this list will be an efficient way to cultivate more insightful observational/abductive abilities, approaching the fictional example of Sherlock Holmes. Please contribute after reading the Rules - comment your ideas here if you have a lesswrong account, email them to me directly at colewyeth@gmail.com, or join the google group.

Background

Is it possible to develop observational abilities comparable to Sherlock Holmes?

Sir Arthur Conan Doyle's fictional detective has many enviable skills, including mastery of disguise and some expertise at unarmed combat, as well as generally being a genius, but we will focus primarily on his more well known observational power. Though Holmes is often described as a master of logical "deduction," this power is better described as (possibly superhuman) abduction. That is, Holmes perceives tiny details that many people would miss, then constructs explanations for those details. By reasoning through the interacting implications of these explanations, he is able to make inferences that seem impossible to those around him. The final step is actually deductive, but the first two are perhaps more interesting. Holmes' ability to perceive more than others does seem somewhat realistic; it is always possible to actively improve one's situational awareness, at least on a short term basis, simply by focusing on one's surroundings. The trick seems to be the second step, where Holmes is able to work backwards from cause to effect, often leveraging slightly obscure knowledge about a wide variety of topics.

I spent several of my naive teenage years trying to become more like Holmes. I carefully examined people's shoes (often I actually requested that the shoes be handed over) for numerous features: mud and dirt from walking outside, the apparent price of the shoe, the level of wear and tear, and more specifically the distribution of wear between heel and toe...
Thoughts to niplav on lie-detection, truthfwl mechanisms, and wealth-inequality
by Emrik — 2024-07-11T18:55:46.687Z

Emrik

Hi, niplav! You might be wondering, "what's this about?", and you would be right.

You see, I was going to write this to you on Schelling.pt (⛯) in response to our chat about lie-detection and the future of human civilization, but instead I am writing it here. I also considered writing it as an email, and I really liked that idea. But I changed my mind about that too. "Nah", I said. "I'll write it here instead."

Postscript

In the context of trying to make society more honest, I wonder to what extent clever truthfwl mechanisms (re mechanism design) could be important for facilitating the civilizational adoption of / managing the transition to brain-scanning credibility-tech.

What I actually should have written in that sentence is: "I have some maybe-interesting maybe-important things which I felt inspired to say to you, but I don't have a smooth-simple way to make them seem coherently relevant, so I'll just say the things." But instead I tried really hard to find a sentence which would justify why I was talking about the things in the first place, especially since I now decided to write it on LessWrong!

Tangentially relevant background preposterous claims which I will not be defending here:

Epistemic status… checks out!

The magic shop in the middle of everywhere: cheap spells against egregores, ungods, akrasia, and you-know-whos!

Anyway, Vickrey auctions feels to me like an almost magical solvent for many societal problems. It's got more practical applications than assurance contracts, and I'm already excited about those. So I asked Sonnet whether there were more patterns like that where it came from:

👀

Idk what those things are yet, but I smell structure within which I intuit a kernel with which I can generate many such solutions to the problems I care about. If the textbook offers a variety of applications for which there are proofs, the real-life applicability of the kernel likely expands beyond that (once adequately grokked). I suspect I may already be unhappily familiar with problem(s) "Strategyproof Task Scheduling" aims to solve.

Deceptive strategy

Trying to make other people believe false things about your preferences.

The meta-pattern is: If a game has a Nash equilibrium for deceptive strategies, it is sometimes/often feasible to change the game rules/mechanics such that the decep

...
Games for AI Control
by charlie_griffin — 2024-07-11T18:40:50.607Z

[UPDATE: This post is now updated to reflect our conference-length preprint, available on arXiv. Louis Thomson helped with this update. New results are marked "NEW".]

This post summarises our new workshop paper: Games for AI Control. The paper applies AI- and game-theoretic tools to the AI Control framework introduced by Redwood Research [1, 2].

We'll be extending this work and submitting it to a conference, so we welcome suggestions! Please reach out if you're excited about developing these ideas. This post is written from Charlie’s perspective.

AI Control Refresher

The AI Control methodology aims to design and assess protocols[1] that AI labs could use to deploy powerful but untrusted advanced AI systems. For a full summary, see Redwood's blog post, but I’ll try to recap here.

A primary goal of AI Control is to design protocols that are safe in that they can reduce the probability of some unacceptable outcome, conditional on the untrusted AI system being a schemer. [2] However, protocols must also be useful, harnessing a significant amount of the benefit of the untrusted system in cases where it is not scheming.

After defining a particular use case and unacceptable outcome, evaluating a protocol involves iteratively engineering the best attack strategies for the red team (a human/AI hybrid acting as a proxy for the untrusted AI) and the blue team (which develops the protocol). This red-teaming exercise is the bulk of the work. It involves coming up with protocols and gathering empirical data about key questions, such as how well GPT-3.5 can spot backdoors inserted by GPT-4 (see Figure 4 of the original paper):

(Left) GPT-3.5 is deployed as a “Trusted Monitor” to assess programming solutions generated by GPT-4. It is asked to output scores expressing the likelihood that a programming solution contains a backdoor. For evaluation, GPT-4 is sometimes prompted to submit a backdoor. (Right) Using the empirical data and simulation, Redwood shows the tradeoff between the safety and usefulness across a range of protocols.

This work

By introducing “AI-Control Games”, an extension of Markov Decision Processes and normal-form games[3], we model the interaction between a protocol and an untrusted AI system to explore safety and usefulness across a range of protocols and games. This is similar to a discrete-time a...
Video Intro to Guaranteed Safe AI
by Mike Vaiana — 2024-07-11T17:53:47.630Z
Link: https://youtu.be/mwUfAvzjd8o

Many thanks to Evan Miyazono, Nora Amman, Philip Gubbins, and Judd Rosenblatt for valuable feedback towards making this video.

We created a video introduction to the paper Towards Guaranteed Safe AI to highlight its concepts[1] and make them more accessible through a visual medium. We believe the framework introduced in the paper and the broader pursuit of guaranteed safe AI are important but overlooked areas of alignment research, which could have significant implications if proven true. Academic publications don't always reach a broad audience, so by presenting the paper's ideas in a different format, we aim to increase their visibility.

This video is part of our ongoing effort to develop educational materials that encourage and inspire people to engage in alignment research. For example, you can also check out our brief Autoformalism Tutorial, where we reproduce the methods from the paper Autoformalism with Large Language Models, or our pedagogical implementation of Reinforcement Learning from Human Feedback (RLHF).
Guaranteed Safe AI[2] was one of the topics we identified as needing more attention in our 'Neglected Approaches' approach post. We’re excited about the significant developments in this area. We believe that the range of plausible research directions contributing to solving alignment is vast and that the still-evolving state of alignment research means only a small portion has been adequately explored. If there’s a chance that the current dominant research agendas have reached local maxima in the possible approaches, we suspect that pursuing a diverse set (or a mix) of promising neglected approaches would provide greater exploratory coverage of this space.

^

also see this introductory post

^

although at the time we used the term "provably safe" since the umbrella term "guaranteed safe" hadn't been coined yet
Effective Empathy
by  — 2024-07-11T15:14:22.430Z

I have just summarised a video by Psychiatrist Alok Kanija for myself, which is about maximising the benefits of empathy and lowering the drawbacks. It generally pleads the case, that people are getting more selfish and naricicistic, but the rarer empathy becomes the bigger the benefits become, sort of in a market supply/demand function.
In order to actually practise empathy well and dampen yourself of the negative sideeffect you need to:
1. Leave the stress with them after helping them
Mentally seperate the action of helping and your own life, you are not them
2. Help them help themselves, instead of solving their problem
A bit of a video gamey metaphor, but they need the experience they get from completing the skill check of fixing their life, in order to fix future issues. You are not supposed to roll the check for them, just give them a buff on the roll.
3. Be able to step out of your own perspective
The more closely you can render other people in your head and visualise their perspective, the more effective your help will become, and somewhat surprisingly, the less you will be emotionally affected by them
4. Be able to lower your own stress yoursef
Even with all these techniques, helping others is stressfull. You need to be good at standard coping mechanisms, journaling, walks in nature, video games, meditation, whatever works best for you.
5. Actually want to help them
He cites some fascinating research, that the motive has massive outcomes on the effect of helping others on yourself! You only reap the neurobiological rewards of empathy (higher dopamine/serotonin etc), if you are primarily motivated by actually bettering their life, not by being interested in return rewards, or even in those very biological rewards you get for helping others.
AI #72: Denying the Future
by Zvi — 2024-07-11T15:00:05.865Z

The Future. It is coming.

A surprising number of economists deny this when it comes to AI. Not only do they deny the future that lies in the future. They also deny the future that is here, but which is unevenly distributed. Their predictions and projections do not factor in even what the AI can already do, let alone what it will learn to do later on.

Another likely future event is the repeal of the Biden Executive Order. That repeal is part of the Republican platform, and Trump is the favorite to win the election. We must act on the assumption that the order likely will be repealed, with no expectation of similar principles being enshrined in federal law.

Then there are the other core problems we will have to solve, and other less core problems such as what to do about AI companions. They make people feel less lonely over a week, but what do they do over a lifetime?

Also I don’t have that much to say about it now, but it is worth noting that this week it was revealed Apple was going to get an observer board seat at OpenAI… and then both Apple and Microsoft gave up their observer seats. Presumably that is about antitrust and worrying the seats would be a bad look. There could also be more to it.

Table of Contents

Introduction.

Table of Contents.

Language Models Offer Mundane Utility. Long as you avoid GPT-3.5.

Language Models Don’t Offer Mundane Utility. Many mistakes will not be caught.

You’re a Nudge. You say it’s for my own good.

Fun With Image Generation. Universal control net for SDXL.

Deepfaketown and Botpocalypse Soon. Owner of a lonely bot.

They Took Our Jobs. Restaurants.

Get Involved. But not in that way.

Introducing. Anthropic ships several new features.

In Other AI News. Microsoft and Apple give up OpenAI board observer seats.

Quiet Speculations. As other papers learned, to keep pace, you must move fast.

The AI Denialist Economists. Why doubt only the future? Doubt the present too.

The Quest for Sane Regulation. EU and FTC decide that things are their business.

Trump Would Repeal the Biden Executive Order on AI. We can’t rely on it.

Ordinary Americans Are Worried About AI. Every poll says the same thing.

The Week in Audio. Carl Shulman on 80,000 hours was a two parter.

The Wikipedia War. One obsessed man can do quite a lot of damage.

Rhetorical Innovation. Yoshua Bengio gives a stron

...
The Best Bits From Build, Baby, Build
by Maxwell Tabarrok — 2024-07-11T14:09:10.131Z
Link: https://www.maximum-progress.com/p/the-best-bits-from-build-baby-build

Build, Baby, Build

is George Mason Economist Bryan Caplan’s latest book and

second

graphic novel. This book makes the case that government regulation of housing is at the center of our most important economic and social problems. It’s a fun book and a quick read. If you’re already involved in research or debates about housing, much of the book will be familiar to you. The content and format is geared towards a layman audience, so this is a great place to send people for an introduction to the economic importance of housing and housing regulation.

Here are a few of the most interesting pieces from the book:

Inequality

One of the several problems which Bryan argues housing deregulation can solve, or at least aid, is rising inequality and falling social mobility. Bryan brings some interesting stats to bear on this claim.

Citing

Peter Ganong and Daniel Shoag (2017)

he shows that gains from rural to urban migration for low-skilled workers have dropped from 70% in 1960 to -7% in 2010, net of housing costs. High skilled workers see essentially the same net gains in 2010 as in 1960, though only because the nominal gains have increased to offset rising costs. In response, poorer workers no longer migrate to richer states on net.

Bryan also cites

a paper by Matthew Rognlie

which shows that the rise in the net capital share since 1950 is entirely explained by rising returns to housing wealth. Thomas Piketty has connected a rising capital share to rising inequality in his work, but this connection is not made clear in the book. Richer people get more of their income from capital investments than from labor, so increasing returns to capital help the rich get richer. Rognlie’s paper shows that all of these increasing returns are coming from housing though, not from e.g stock market investments.

Fertility

Fertility gets only one page in Bryan’s book. The main graph is this one:

Source

Which shows a large secular decline in fertility, but also a divergence between rural and urban rates. The implication being that rising housing prices due to regulation explain this divergence.

Bryan has written more about

housing and fertility

elsewhere, but this does not address criticisms (e.g here from

.@morebirths

) which claim that density, not high prices, is the cause of low fertility, and that housing deregulation will increase density and urbanization, thus decreasing fertility further. The l...
What Other Lines of Work are Safe from AI Automation?
by RogerDearnaley — 2024-07-11T10:01:12.616Z

TL;DR: Post-AGI career advice needed (asking for a friend).

Let's assume, for the sake of discussion, that Leopold Aschenbrenner is correct that at some point in the fairly near future (possibly even, as he claims, this decade) AI will be capable of acting as a drop-in remote worker as intelligent as the smartest humans and capable of doing basically any form of intellectual work that doesn't have in-person requirements, and that it can do so as well or better than pretty-much all humans, plus that it's at least two or three orders of magnitude cheaper than current pay for intellectual work (so at least an order of magnitude cheaper than a subsistence income) — and probably decreasing in cost as well.

Let's also assume for this discussion that at some point after that (perhaps not very long after that, given the increase in capacity to do intellectual work on robotics), that developments in robotics overcome Moravec's paradox, and mass production of robots greatly decreases their cost, to the point where a robot (humanoid or otherwise) can do basically every job that requires manual dexterity, hand-eye-coordination, and/or bodily agility, again for significantly less than a human subsistence wage. Let's further assume that some of the new robots are now well-waterproofed, so that even plumbers, lifeguards, and divers are out of work, and also that some of them can be made to look a lot like humans, for tasks where that appearance is useful or appealing.

I'd also like to also assume for this discussion that the concept of a "human job" is still meaningful, thus the human race doesn't go extinct or get entirely disempowered, and that we don't to any great extent merge with machines: some of us may get very good at using AI-powered tools or collaborating with AI co-workers, but we don't effectively plug AI in as a third hemisphere of our brain to the point where it dramatically increases our capabilities.

So, under this specific set of assumptions, what types of paying jobs (other than being on UBI) will then still be available to humans, even if only to talented ones? How long-term are the prospects for these jobs (after the inevitable economic transition period)?

[If you instead want to discuss the probability/implausibility/timelines of any or all of my three assumptions, rather than the economic/career consequences if all three of them occurred, then that's not ...
Decomposing Agency — capabilities without desires
by owencb — 2024-07-11T09:38:48.509Z
Link: https://strangecities.substack.com/p/decomposing-agency

What is an agent? It’s a slippery concept with no commonly accepted formal definition, but informally the concept seems to be useful. One angle on it is Dennett’s Intentional Stance: we think of an entity as being an agent if we can more easily predict it by treating it as having some beliefs and desires which guide its actions. Examples include cats and countries, but the central case is humans.

The world is shaped significantly by the choices agents make. What might agents look like in a world with advanced — and even superintelligent — AI? A natural approach for reasoning about this is to draw analogies from our central example. Picture what a really smart human might be like, and then try to figure out how it would be different if it were an AI. But this approach risks baking in subtle assumptions — things that are true of humans, but need not remain true of future agents.

One such assumption that is often implicitly made is that “AI agents” is a natural class, and that future AI agents will be unitary — that is, the agents will be practically indivisible entities, like single models. (Humans are unitary in this sense, and while countries are not unitary, their most important components — people — are themselves unitary agents.)

This assumption seems unwarranted. While people certainly could build unitary AI agents, and there may be some advantages to doing so, unitary agents are just an important special case among a large space of possibilities for:

Components which contain important aspects of agency (without necessarily themselves being agents);
Ways to construct agents out of separable subcomponents (none, some, or all of which may be reasonably regarded agents in their own right).

We’ll begin an exploration of these spaces. We’ll consider four features we generally expect agents to have[1]:

Goals

Things they are trying to achieve
e.g. I would like a cup of tea

Implementation capacity

The ability to act in the world
e.g. I have hands and legs

Situational awareness

Understanding of the world (relevant to the goals)
e.g. I know where I am, where the kettle is, and what it takes to make tea

Planning capacity

The ability to choose actions to effectively further their goals, given their available action set and their understanding of the situation
e.g. I’ll go downstairs and put the kettle on

We don’t necessarily expect to be able to poi...