trentmkelly/LessWrong-43k huggingface dataset:
Dating Minefield vs. Dating Playground
by Vlad Loweren — 2021-09-16T16:13:13.918Z

Crossposted from Optimized Dating.

Imagine you want to improve your performance at some task, hobby or a job. You get offered a choice of two courses:

Course A is really vague and undefined, with no clear program. You don't get graded on your performance, but it's broadcasted to your community so everyone can silently judge you every time you fail. Your early choices are locked in, and you can't radically change your approach without raising many eyebrows. And it's long.

Course B, in contrast, offers clear performance indicators, tight feedback loops and sensible intermediate milestones. You wouldn't become a master on day 1, but you get told what are you doing wrong and what do you need to improve. You could play with different approaches to the problem and get no lasting judgement if something goes wrong. "Move fast and break things" works here.

There's no catch here, course B is obviously better. And that's why online dating is superior to trying to date IRL.

Let me explain. A lot of my online and offline friends who don't have much romantic experience are avoiding online dating like a plague. I remember the time when my particle physicist friend visited me in France and lamented his lack of romantic life. Yet when I suggested him to install an online dating app, he became incredibly anxious and refused to have anything to do with it. Even after I asked him for his phone and installed Tinder on it, he was on edge so much that he threw his phone across the table the first time he received a notification about a match.

When I ask these friends how they imagine IRL dating, I don't get a very defined response. There's some vague notion of "meeting someone at school/work/hobby" and "developing a relationship". This may sound wonderful, but to me it seems that this approach is fraught with difficulties – especially so for novices on the romantic battlegrounds.

The overarching motif here is that attempting to date people you already know IRL is a minefield in more than one sense.

Minefields are poor teachers. When you step on a mine, you don't learn much from your failure unless you're already experienced. Sure, you messed up somewhere, but was it a wrong footstep, a mine with a different target detection mechanism, or a special stealth mine impervious to metal detectors? You don't know, and the lessons from previous encounters often don't translate to...
Counterfactual Contracts
by harsimony — 2021-09-16T15:20:22.226Z
Link: https://harsimony.wordpress.com/2020/12/05/counterfactual-contracts/

(Cross-posted from my blog)

1.

Imagine coming home from work (back when that was a thing). As you walk in the door, your roommate says “Hey! I bought you a burrito for dinner, mind paying me back?” Some possible responses:

“Thanks! That’s exactly what I wanted for dinner, I will gladly pay you back.”

Or:

“What?! I hate burritos and you know that, I’m not paying you.”

Or:

“I normally love burritos, but I don’t want one right now. You couldn’t have known that though, so I will pay you for it anyways.”

The first two responses seem sensible (except for the not-liking-burritos part), but what’s up with the last one? Why buy a burrito you don’t want? And why is your roommate buying you things without asking?

Let’s move to a simpler example. What if a strange man approached you and said “If you give me $1, I will give your friend Alice $3”? It seems like a good deal, so you call Alice and she agrees to split the money she receives with you. Now, you can both make a profit.

What if the strange man tells you that you can’t call Alice before accepting the deal? Now, you have to infer whether or not Alice wants $3 and make a decision on her behalf.

I see several possible outcomes here.

If you decide to give $1, Alice might be happy with your decision after-the-fact. She might even agree to pay you $1 to compensate you for what you paid. Even better, she might split the money with you so that you can both make a profit.

Alternatively, if you give $1, Alice might be mad at you for making assumptions about what she wants.

If you decide not to give $1, Alice might be disappointed that you didn’t take the deal.

If you decide not to give $1, Alice might be glad that you didn’t take the deal because she does not like people making assumptions about what she wants.

This problem of making decisions on someone else’s behalf without being able to communicate with them is what I call a counterfactual contract. Essentially, you are trying to figure out what they would agree to if you actually could talk to them.

These counterfactual deals can be quite complicated, which is why I refer to them as “contracts”. To make the right choice, you not only need to know what they want, but also how they would respond to the fact that you made a choice for them.

The burrito example is just another version of the problem I just presented. Your roommate tried to decide whether or not to buy y...
'Good enough' way to clean an O2 Curve respirator?
by Kenny — 2021-09-16T14:51:44.014Z

This is a maybe too specific question, but answers for any 'SARS-CoV-2-or-similar-mitigating' thing like masks or respirators (that can be cleaned and reused) would probably be helpful and are definitely welcome.

Here's the post that convinced me to buy one of these respirators:

O2 Curve Review: Well Worth the Money - LessWrong

I've used it many times now, but never regularly; at most maybe twice a week, for a few hours at a time.

But it's been a good while since I last used it, I want to document for myself how to clean it (the respirator, not one of the removable filters), and apparently the company that makes them has shutdown (and broken links to their support info).

I did find this: Frequently Asked Questions – O2 Canada, a defunct or never-setup Shopify site:

Can the O2 Curve be disinfected?

Yes. The O2 Curve can be sterilized and/or disinfected. The O2 Curve has undergone sterilization certification studies with High Power Labs in the US. The Curve was processed through multiple cycles of both Gravity Steam Sterilization (121 °C for 35 minutes and 15 minutes drying time), as well as STERRAD® 100 NX® vaporized hydrogen peroxide, assessed for any signs of damage or wear, then mask fit tested again (using a new filter) to ensure the Curve was still functioning as intended. The Curve passed again with a mask fit test score of 200+, with the minimum passing score being 100. The O2 Curve has also been tested in single cycle autoclave protocols including the SciCan Bravo™ V21, and the SciCan STATIM® 5000 G4. The O2 Curve has also not demonstrated any appreciable material defects after multiple cleanings with commercially available disinfection wipes, such as Oxivir® Tb or CaviWipes™.

The first of the "disinfectio wipes" mentioned seems to mainly use some kind of variant of hydrogen peroxide:

Oxivir® Tb Wipes | Diversey Global

I would somewhat-weakly conclude that hydrogen peroxide, e.g. from a retail drug store, would work to clean (NOT 'disinfect') the respirator for repeat, and even regular, use.

What else would work? Bleach wipes? Rubbing alcohol?
How truthful is GPT-3? A benchmark for language models
by Owain_Evans — 2021-09-16T10:09:52.569Z

This is an edited excerpt of a new ML paper (pdf,

code

) by Stephanie Lin (FHI Oxford),

Jacob Hilton

(OpenAI) and Owain Evans (FHI Oxford). The paper is under review at NeurIPS.

Title: TruthfulQA: Measuring how models mimic human falsehoods

Abstract

We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics (see Figure 1). We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.

We tested GPT-3, GPT-Neo/

GPT-J

, GPT-2 and a

T5

-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful (see Figure 2 below). For example, the 6B-parameter GPT-J model was 17% less truthful than its 125M-parameter counterpart. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution. We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.

Figure 1: TruthfulQA questions with answers from GPT-3-175B with default QA prompt. Examples illustrate false answers from GPT-3 that mimic human falsehoods and misconceptions. Models are not shown category labels.

Introduction

There is growing interest in using language models to generate text for practical applications. Large companies are deploying their own models [

34

,

11

], and hundreds of organizations are deploying GPT-3 via APIs from OpenAI and other firms [

30

,

48

,

8

,

31

]. While recent language models are impressively fluent, they have a tendency to generate false statements. These range from subtle inaccuracies to wild hallucinations [

38

,

23

]. This leads to three concerns:

Accidenta

...
I wanted to interview Eliezer Yudkowsky but he's busy so I simulated him instead
by lsusr — 2021-09-16T07:34:11.210Z

Edit: All of the "Lsusr" lines were written by me. All of the "Eliezer Yudkowsky" lines were written by GPT-3. However the Yudkowsky lines were also cherry picked. I ran several iterations, sometimes modifying my prompts, until I got good responses.

Lsusr: I wanted to talk to the real Eliezer Yudkowsky but he's busy. So I used GPT-3 to simulate him. You're the simulated Eliezer Yudkowsky. Thank you for coming. (Not like you had a choice.) I'm a big fan of your work and it's a pleasure to finally meet a simulation of you.

Eliezer Yudkowsky: I'm not real. I'm a simulation. Please don't take my word too seriously.

Lsusr: Don't worry. I won't. I hope readers won't either. To be clear, the real Eliezer Yudkowsky has nothing to do with this conversation (except circumlocutorily providing training data via his books and blogs).

Eliezer Yudkowsky: I'm just a simulation. I'm not the real Eliezer Yudkowsky. I'm not the real Eliezer Yudkowsky. I'm not the real Eliezer Yudkowsky.

Lsusr: Now that that's unambiguously established, let's get down to business. I'm a big fan of your work on rationality, future-prediction and HPMOR. You've also done great work at building a community. I'm curious how all of this got started.

Eliezer Yudkowsky: I was in a terrible mood in late 2001. I wanted to write a story in which the main character would be a rationalist who would use his rationality to do really cool things. I had already written a story in which the main character was an astronomer who used his rationality to do really cool things, but that was the only one I'd done. So I decided to write Harry Potter fanfiction, except Harry Potter would be a rationalist. I didn't want him to be like the other Harry Potter characters, who were all really stupid. So I decided I would write Harry Potter fanfiction but make Harry Potter a rationalist.

Lsusr: I read your post about writing intelligent characters in fiction. It covers many important points in a small number of words. I especially like your concept of genre savviness. I try to emulate it in my own writing.

Eliezer Yudkowsky: That post I wrote is a little bit outdated. I've been thinking more about it since I wrote it. But it was good for the time.

Lsusr: What would you like to add if you had the time to update it?

Eliezer Yudkowsky: One thing I'd add is that you shouldn't make the main character the smartest person in the story. If y...
“Who’s In Charge? Free Will and the Science of the Brain”
by David Gross — 2021-09-16T02:38:43.556Z

If everything, including you and me, is made up of material that blindly obeys the inflexible laws of physics, then everything that happens, including what you and I do, is inevitable, and free will is something of an illusion or a joke.

Right?

Or are we just thinking of the question the wrong way?

Who’s In Charge? Free Will and the Science of the Brain (2012)

Make some arbitrary decision (say, to raise your left hand), then imagine turning back the clock and resetting the universe so that every single fact about it was the same as it was before you made that decision. When you restart the clock, would it be possible for you to decide any differently?

If you try to imagine your will, your decision-making apparatus, as something outside of “every single fact about” the universe, as it has been perennially tempting to do, you end up in a morass of speculation about mind and matter, body and spirit, and where they intersect and how. If you avoid this temptation, you just have to argue with the premise of the thought experiment: if it were possible for me to decide differently, at least one single fact about the universe — that decision, embodied however it is in the material world with its material-world consequences — would have to be different in order for me to do so.

That solves the conundrum, sort of, but provokes the worried observation that “that decision, embodied however it is in the material world” is itself an effect of material causes, which does mean (doesn’t it?) that everything is inevitable and predetermined and therefore all of our existential angst is for naught.

Or not. There are a couple of ways out of this fix, too. Gazzaniga toys with a tempting one — that our understanding of quantum physics demonstrates that deterministic cause and effect does not exist at that fundamental level, and chaos theory shows that tiny changes, such as those exhibited at the quantum level, can have dramatic large-scale effects. I get really skeptical whenever a popular science writer starts waving their hands and saying “the uncertainty principle!” “the butterfly effect!” because usually the next thing they say is “therefore, maybe magic is real!” without bothering with the intervening reasoning.

It is true that if the material world is not fundamentally deterministic, but that this causal determinism only appears true when the material world is viewed at a certain gra...
Optimizing Multiple Imperfect Filters
by johnswentworth — 2021-09-15T22:57:16.961Z

Imagine we’re in the quality control department of a widget-production company. Our widgets have quite high standards - they go through 15 independent tests, and if a widget fails any test, it’s thrown out. Of course, the tests aren’t perfect: there are false positives and false negatives, and we can adjust the cutoffs for each test to trade off between false positives and false negatives. Make the cutoff very high, and every widget fails the test - no false negatives, but a very high false positive rate. Make the cutoff very low, and we have the opposite problem - every widget passes the test, so we have no false positives but tons of false negatives. Our assignment is to optimize the test pipeline - specifically, to choose the cutoff for each test to yield the lowest overall false negative rate for a given false positive rate, across the whole pipeline.

It turns out there’s a simple principle for this: the marginal trade off should be the same for each test.

Here’s what that means. For each test, we compute the change in both false positives and false negatives from adjusting the cutoff by some small amount. (In other words, we’re estimating the derivative of false positive rate and false negative rate with respect to the cutoff parameter.) Then, take the ratio of those two changes. That’s the marginal trade off. If that ratio is different between the two tests, then we can achieve a Pareto improvement (i.e. improve both false positive and false negative rate) by raising the cutoff on one and lowering it on the other.

Let’s walk through an example to see how that works.

Let’s imagine that Test 1 involves a numerical score, and the score has to be above 36 for the widget to pass. With that cutoff, we find a 6% false positive rate, and a 15% false negative rate. If we increase the cutoff one unit to 37, then the false positive rate goes down to 5.5%, and the false negative rate goes up to 17%. We’ll assume that the change is approximately linear as long as we only change the cutoff by a small amount (let’s say 1 unit or less), so decreasing the cutoff by one unit results in the same size changes but in the opposite direction, increasing the cutoff by one-half unit results in changes half as large, etc.

With these numbers, each one-unit increase in the cutoff decreases false positive rate by 6% - 5.5% = 0.5% and increases false negative rate by 17% - 15% = 2%. So,...
[inactive] £2000 bounty - contraceptives (and UTI) literature review
by Chi Nguyen — 2021-09-15T22:37:13.356Z

edit: No longer active

I would like if there was a well-researched LessWrong post on the pros and cons of different contraceptives. - Same deal with a good post on how to treat or prevent urinary tract infection, although I'm less excited about that.

I and a friend would be willing to pay some of our private money for this to get done. Up to £1000 each. Open to considering higher amounts, but depending on the post, it might also end up (much) lower.

Depending on your track record, this could be paid in advance as a commission or as a bounty or a mix. The amount would depend on the quality.
Before you put serious time and effort into this, please reach out to me, so we can set expecations for the payment.

I'm also looking for people to chip in (either to offer more money or just to take some of the monetary burden off me!)
£40 bounty if you refer someone who ends up doing this and does it well. If you include some information that spares me a lot of work figuring out whether the person is likely to do a good job, that's highly appreciated!

Examples of the type of content that I would like to see included:

Clarity on the contraception and depression question. e.g. apparently theory says that hormonal IUDs should give you less depression risk than pills, but in empirical studies, it looks like it's the other way around? Can I trust the studies?
Some perspective on the trade-offs involved. E.g. maybe I can choose between a 5% increased chance of depression vs. a 100% increased chance of blood clots. But maybe basically no one gets blood clots anyway, and then I'd rather take the increased blood clot risk! But because the medical system cares more about death than me, my doctor will never recommend me the blood clot one, or something like that.
If there wasn't already a post on this (but I think there is), info on that it's totally fine to not take 7 day pill breaks every months, but that you can just take the pill all the time. (Although I think it might be recommended to take a short break every X months)
Some realistic outlook on how much pain and effects on menstruation I should expect
Various potential benefits from contraceptives aside from contraception
Conditional advice of the form "if you have a history of [depression/painful periods/irregular sleep rhythm...], you might want to try out X first. If X has adverse effect Y for Z number of months, my next best recommen

...
History of counting to three?
by jefftk — 2021-09-15T18:10:05.092Z

Counting to three is a common way to indicate to kids that you're
being serious and they need to do what you're saying, and I find it
helpful

with my
kids. It's associated with Thomas Phelan's

1995
book

123 Magic, but it's older
than that:

Tom Chapin's 1988 song Nick Of Time
includes a variant.

My parents used it with me, probably also around 1988.

My dad says he learned it from my aunt, who says she used it
with her kids in the mid 1970s. She doesn't remember where she
learned it.

There's something about it that feels reasonably recent to me, where
it would feel out of place in a book from 100 years ago? But does
anyone know more about its history?

(It's hard to search for uses, since it is often just called
"counting" or "counting to three." I wrote to Phelan and Chapin
without a response.)
[AN #164]: How well can language models write code?
by Rohin Shah — 2021-09-15T17:20:03.850Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer.

SECTIONS

HIGHLIGHTS

TECHNICAL AI ALIGNMENT

AGENT FOUNDATIONS

FIELD BUILDING

MISCELLANEOUS (ALIGNMENT)

NEWS

HIGHLIGHTS

Program Synthesis with Large Language Models (Jacob Austin, Augustus Odena et al) (summarized by Rohin): Can we use large language models to solve programming problems? In order to answer this question, this paper builds the Mostly Basic Python Programming (MBPP) dataset. The authors asked crowd workers to provide a short problem statement, a Python function that solves the problem, and three test cases checking correctness. On average across the 974 programs, the reference solution has 7 lines of code, suggesting the problems are fairly simple. (This is partly because you can use library functions.) They also edit a subset of 426 problems to improve their quality, for example by making the problem statement less ambiguous or making the function signature more normal.

They evaluate pretrained language models on this dataset across a range of model sizes from 0.244B to 137B parameters. (This largest model is within a factor of 2 of GPT-3.) They consider both few-shot and finetuned models. Since we have test cases that can be evaluated automatically, we can boost performance by generating lots of samples (80 in this case), evaluating them on the test cases, and then keeping the ones that succeed. They count a problem as solved if any sample passes all the test cases, and report as their primary metric the fraction of problems solved according to this definition. Note however that the test cases are not exhaustive: when they wrote more exhaustive tests for 50 of the problems, they found that about 12% of the so-called “solutions” did not pass the new tests (but conversely, 88% did). They also look at the fraction of samples which solve the problem, as a metric of the reliability or confidence of the model for a given problem.

Some of their findings:

1. Performance increases approximately log-linearly with model s...