trentmkelly/LessWrong-43k huggingface dataset:
[Meta?] Using the LessWrong codebase for a blog
by George3d6 — 2020-12-20T03:05:55.462Z

I enjoy the experience of using less-wrong. I believe it's one of the best-designed websites I've ever had the pleasure of stumbling upon.

I also have a personal blog, and I've been considering revamping it. While I do enjoy coding from scratch, I've been considering using a pre-existing platform.

Using WordPress plus some themes is always an option, but I feel like this would be an overkill since the "feel" of LW so closely aligns to my own tastes.

I'm curios if anyone else is doing this (using the LW codebase for their own project, be it a blog, a discussion form, or something else), and how it's going thus far ?

More specifically, I'd be curious:

What kind of machine (very roughly speaking) would you need to handle top volumes ~20,000 visitors/hr (say, max 3,000/min) without the core functionality breaking and ~500 visitors/hr (say, max 100/min) with top-notch user experience (assuming optimal db, distro and reverse proxy choices)
How hard is it to setup the sys admin side of things ? Deploying a prod server behind an nginx with a non-sqlite db and pointing it to your own cdn
How hard is it to modify the theme (e.g. fonts, color-scheme, icons) ?
How hard is it to integrate your own 3rd party service or get rid of them ? Specifically, add your own app cred for the signups and remove google analytics, intercom and all other 3rd party integrations that would make Richard stallman cry which aren't critical to the commenting experience.
Are the makers of LW explicitly fine and open to it being used by other people or is it open source mainly for the sake of community debugging?
What are particularly difficult/annoying/deal-breaking parts of the setup that were unexpected?

... Hopefully the question is not too out of place, it seemed to fit the form better than the repo.
Opposite attractions
by KatjaGrace — 2020-12-19T21:30:15.793Z

Is the opposite of what you love also what you love?

I think there’s a general pattern where if you value A you tend to increase the amount of it in your life, and you end feeling very positively about various opposites of A—things that are very unlike A, or partially prevent A, or undo some of A’s consequences—as well. At least some of the time, or for some parts of you, or in some aspects, or when your situation changes a bit. Especially if you contain multitudes.

Examples:

Alice values openness, so tends to be very open: she tells anyone who asks (and many people who don’t) what’s going on in her life, and writes about it abundantly on the internet. But when she is embarrassed about something, she feels oppressed by everyone being able to see her so easily. So then she hides in her room, works at night when nobody is awake to think of her, and writes nothing online. Because for her, interacting with someone basically equates to showing them everything, her love of openness comes with a secondary love of being totally alone in her room.

Bob values connecting with people, and it seems hard in the modern world, but he practices heartfelt listening and looking people in the eye, and mentally jumping into their perspectives. He often has meaningful conversations in the grocery line, which he enjoys and is proud of. He goes to Burning Man and finds thousands of people desperate to connect with him, so that his normal behavior is quickly leading to an onslaught of connecting that is more than he wants. He finds himself savoring the impediments to connection—the end of an eye-gazing activity, the chance to duck out of a conversation, the walls of his tent—in a way that nobody else at Burning Man is.

An extreme commitment to honesty and openness with your partner might leads to a secondary inclination away from honesty and openness with yourself.

A person who loves travel also loves being at home again afterward, with a pointed passion absent from a person who is a perpetual homebody.

A person who loves jumping in ice water is more likely to also love saunas than someone who doesn’t.

A person who loves snow is more likely to love roaring fires.

A person who loves walking has really enjoyed lying down at the end of the day.

A person who surrounds themselves with systems loves total abandonment of them during holiday more than he who only had an appoi

...
Hierarchical planning: context agents
by Charlie Steiner — 2020-12-19T11:24:09.064Z

If you end up applying this post, please do it in the name of safety research.

I - intro

Suppose I need groceries. When I make a plan to get more groceries, what I don't do is fully model the world (e.g. as a Markov process), find a policy that leads to me getting groceries, and then execute that policy. Among the reasons why not, there are two at the top of the heap both in importance and ridiculousness. First, this requires imagining every situation I can think of and making a plan in case of every eventuality. Imagine me sitting at home, thinking "Yes, well if the peanut butter is on the third shelf, I'll do this, but if it's on the second shelf, I'll do that..." Second, every plan I make, for all of these situations, would have to be in the most exacting detail possible. Not "Then I'll take the peanut butter," but "Then I will activate muscle group #1938 at 43% intensity and muscle group #552 at 9% intensity, and..."

What I actually do is think of some abstract plan like "I'm going to bike to the supermarket down the street, then get some veggies and tofu and stuff, and come home." Then to get started, I figure out how to bike to the supermarket in terms of actions like "put on my shoes and a coat, and grab my bike." Then to get started on that, I make a plan to put on my shoes in terms of actions like "Insert feet into shoes," which itself is done with a plan that looks like "Activate muscle group #698 at 35% intensity, and..."

This sort of hierarchical planning is vital to operating in the real world with any sort of efficiency. In this post I will verbally outline some hierarchical planners - "context agents" - followed by a more precise definition, and finally talk about what the values of context agents look like and why I think that's interesting.

Context agents as stated are going to be pretty impractical, but if you're clever there are ways to make them cheaper. The main point of introducing them is to talk about what values and Goodhart's law look like in the context of hierarchical planning - I'm not wedded to this exact formulation by any means, but I hope you'll agree they help describe something interesting.

II - description

The basic part of a context agent is the contexts. A context is mainly just an MDP-type model of the world. It models the world as having states and actions - at each state within the context, there is some set of actions...
We desperately need to talk more about human challenge trials.
by DirectedEvolution — 2020-12-19T10:00:42.109Z

Note: Thanks to Peter McCluskey, whose comment below helped me develop my thoughts further and which is worth reading in addition to this post.

On March 11, 4,611 people had died of COVID-19, and the WHO declared the disease a pandemic. To date, COVID-19 has killed over 1.6 million people. It may cause lasting injury to many more. The COVID-19 vaccine was designed in two days, and finished on January 13th, 2020. We have waited almost a year to test the now two approved vaccines for safety and efficacy. One idea that could have allowed us to complete testing much more quickly, potentially saving an enormous number of lives, was human challenge trials.

On May 6, 2020, the WHO published Key criteria for the ethical acceptability of COVID-19 human challenge studies.

1DaySooner is a nonprofit advocating for human challenge trials, and estimates that "thousands or millions" of lives could be spared by a human challenge trial. In an HCT, informed volunteers are willingly infected with the virus in order to speedily test the efficacy of a vaccine. This gives results faster than waiting for enough conventional study participants to get accidentally infected by COVID-19 to test whether the vaccine works.

Given that millions of lives were potentially at stake, have we talked about it much?

Let's first consider the scientific discussion. In 2020, the number of articles referencing "human challenge trials" or "human challenge studies" more than doubled over the previous year, as measured by Google Scholar hits, to 171 and 512 articles, respectively. There is an all-time total of 493 and 2,200 articles containing these two terms.

This is a pressing moral issue. Yet, for perspective, there were almost 2,200 papers in English this year alone on the major 18th century philosopher Immanuel Kant's "Metaphysics of Morals."

I'm very proud of the paper I wrote on the categorical imperative in college, but is this really the crucial moral issue of 2020?

There were also well over 10,000 scholarly articles this year referencing Zika virus, a disease which, though terrible in its own right, is dwarfed by the magnitude of COVID-19. We had plenty of moral philosophers and infectious disease experts on hand. It seems conceivable that much more scholarly discussion of this important topic was possible both prior to and during the pandemic.

To those who have treated this issue, I offer my si...
Theasauring
by KatjaGrace — 2020-12-19T08:20:13.000Z

Sometimes I really enjoy opening a thesaurus. I don’t know why. It’s just a moment of opening it, anticipating feeling around amongst the meanings of different words, weighing their rightnesses, which seems like a kind of heavenliness, sometimes. I think it was better in 2018, and now I’m mostly remembering that.
Extrapolating GPT-N performance
by Lukas Finnveden — 2020-12-18T21:41:51.647Z

Brown et al. (2020) (which describes the development of GPT-3) contains measurements of how 8 transformers of different sizes perform on several different benchmarks. In this post, I project how performance could improve for larger models, and give an overview of issues that may appear when scaling-up. Note that these benchmarks are for ‘downstream tasks’ that are different from the training task (which is to predict the next token); these extrapolations thus cannot be directly read off the scaling laws in OpenAI’s Scaling Laws for Neural Language Models (Kaplan et al., 2020) or Scaling Laws for Autoregressive Generative Modelling (Henighan et al., 2020).

(If you don’t care about methodology or explanations, the final graphs are in Comparisons and limits .)

Methodology

Brown et al. reports benchmark performance for 8 different model sizes. However, these models were not trained in a compute-optimal fashion. Instead, all models were trained on 300B tokens (one word is roughly 1.4 tokens), which is inefficiently much data. Since we’re interested in the best performance we can get for a given amount of compute, and these models weren’t compute-optimally trained, we cannot extrapolate these results on the basis of model-size.

Instead, I fit a trend for how benchmark performance (measured in % accuracy) depends on the cross-entropy loss that the models get when predicting the next token on the validation set. I then use the scaling laws from Scaling Laws for Neural Language Models to extrapolate this loss. This is explained in the Appendix.

Plotting against loss

In order to get a sense of how GPT-3 performs on different types of tasks, I separately report few-shot progress on each of the 11 different categories discussed in Brown et al. For a fair comparison, I normalize the accuracy of each category between random performance and maximum performance; i.e., for each data point, I subtract the performance that a model would get if it responded randomly (or only responded with the most common answer), and divide by the difference between maximum performance and random performance. The black line represents the average accuracy of all categories. This implicitly gives less weights to benchmarks in larger categories, which I think is good; see the Appendix for more discussion about this and the normalization procedure.

Note that the x-axis is logarithmic. For reference,...
Notes on Duty
by David Gross — 2020-12-18T18:54:29.179Z

This post examines the virtue of duty. It is meant mostly as an exploration of what other people have learned about this virtue, rather than as me expressing my own opinions about it, though I’ve been selective about what I found interesting or credible, according to my own inclinations. I wrote this not as an expert on the topic, but as someone who wants to learn more about it. I hope it will be helpful to people who want to know more about this virtue and how to nurture it.

What good is duty?

“If we have our own why of life, we shall get along with almost any how.” ―Nietzsche[1]

A duty is often described as something of a burden (e.g. in phrases like “it is my unfortunate duty…”, “duty-bound”). A duty can be a sort of lingering, unfinished business: When one “discharges” a duty, it implies a sort of relief.

So it’s worth asking: in what sense is duty a virtue? Why is it a good thing you would want to seek out in your life?

To say that you have a duty is another way of saying that you have a purpose and that that purpose involves your agency (it’s not a passive purpose). To lack a purpose of this sort can lead to despair, while having a purpose can contribute to hope.[2] People who do not know their duty are more likely to be plagued by questions like “what am I here for?” “why bother to get up in the morning?” They may wallow in frivolity or a “bullshit job,” wondering vaguely what is the point of it all.

As a simple example of how duty can improve well-being, consider this study:

Some [elderly nursing home] residents (the experimental group) were given a plant to take care of, while others (the comparison group) were also given a plant, but were told the staff would take care of it. After three weeks the experimental group had significantly higher wellbeing levels, including sociability, vigour, and self-initiative.[3]

What is duty?

“Many persons have a wrong idea of what constitutes true happiness. It is not attained through self-gratification but through fidelity to a worthy purpose.” ―Helen Keller[4]

The word “duty” derives etymologically from words meaning “due” and “debt” — it suggests something that is owed, an obligation. It usually means that some sort of service is owed (as opposed to some sort of thing, or some cash equivalent, except in some specific usages like “customs duties”). If someone is asked “what is your duty?” they typically respond with ...
Probability theory implies Occam's razor
by Maxwell Peterson — 2020-12-18T07:48:17.030Z

Occam's razor follows from the rules of probability theory. It need not be assumed, or taken as a prior. Looking at what it means to spread probability mass across a larger space, in the absence of any information on how to distribute that probability, shows why.

Spread-out distributions are less accurate distributions

Say I have some single-variable linear model ax+β=r, trying to predict some other single variable r. Finding a good model means finding a good number a that represents much a change in x should affect the value we predict for r. For concreteness, imagine the problem of predicting the weight of a dairy cow, using its height. Then x is the length, and a is how much a change in height changes the weight r (and β isn't important in this post). We may know some things already about a, before we have seen any data, like the smallest and largest values it could reasonably be, a vague guess to its average value, and some idea as to how much it tended to vary. From this information, we can get a prior probability distribution over a. The probability distribution for a single parameter is a 2-dimensional curve. If we don't know much about it, its distribution may be wide:

Unsure about a's true value.

or narrow:

More confident about the value of a.

Doing inference is the process of trying to get from the blue distribution to the pink distribution. When we have less information about a, we'll have more spread-out distributions, and when we have more information, narrower. In particular, the less information we have about a to start with, the more spread out our probability will be, and the more additional information we'll need to get to the same narrower pink curve. A worse situation than the blue would be the following:

Ignorant of the value of a.

which represents us not even having any idea where to center the probability mass around, or knowing anything about how much it tends to vary.

So: getting to pink is desirable because less-spread-out distributions correspond to more accurate predictions. Blue to pink is easier than gray to pink, because blue is a less-spread-out distribution than gray is. And the more spread out your probability, the less you know, and the harder your inference task.

Adding a variable spreads out probability mass

Above...
Genetic magic
by KatjaGrace — 2020-12-18T05:20:13.171Z

23andMe are now willing to guess where one’s ancestors are from at the level of counties. For instance, as well as thinking I have 19% Swedish ancestry, they now guess that it is primarily from Västra Götaland County. Which is in fact where my current Swedish relatives cluster. Their guesses in Ireland center on Cork, with Limerick and Tipperary next door 4th and 8th most likely (of 26 counties), and those two are where the few 17th-19th Century relatives I know about seem to have come from in Ireland, so that also seems pretty good.

Much as I believe all that stuff about one’s body being full of cells that contain genetic code that is shared by one’s relatives, and about historic movement and mixing of populations being low, it’s awesome to actually see someone take a fairly good guess at what part of what country your obscure relatives lived hundreds of years ago by examining your spit.
Thread for making 2019 Review accountability commitments
by Bird Concept — 2020-12-18T05:07:25.533Z

The LessWrong 2019 Review is upon us. We're out of the nomination phase, and starting the review phase. 121 posts have passed into this second stage.

Now we join forces to answer some crucial questions:

Are the claims in these posts true?
Are they important?
Do we want to reify them in the LessWrong canon, as we did with winners of the 2018 Review?

If you've wanted to help in this endeavor, but haven't gotten around to it... then maybe this is for you.

Eat that frog

Sometimes engaging with LessWrong is pure fun. Good opinions just keep coming to you. You ride a wave of excitement.

Other times, however, you need to roll up your sleeves and Eat That Frog.

In an inspirational haze, we spent the wild night of 2019 producing a ton of content. Now we have woken up the morning after. With the dry lucidity of hindsight, time has come to atone for our sins. Or celebrate our victories.

One of the most beautiful things about LessWrong is our joint vision of a better way to do Science on the internet. We know that Facebook, Twitter, and the journal system are utterly broken. We know that, truly, so much more is possible.

But, well, if we want good things to happen to us, we need to put in the work.

All those things that seemed a bit schlepy, and like maybe-it-would-be-good-if-someone-but-not-me-did-them-some-day?

Operationalising fuzzy claims
Fact-checking and doing literature reviews
Putting in that effort to go "no, but actually, does this stuff hold up? Have I really turned over every stone in this argument, and not just spent time playing in the exciting corners?"
Making spreadsheets and Guesstimates
Making other models and simulations
Running model validations, sensitivity analyses
Doing replications
Running MTurk surveys
Formalising and checking proof steps
Spending long, meandering, 10h conversation with a burning curiosity to really know whether this essay is actually true, and refusing to accept a "maybe"
...and more!

Now is the time.

How to participate

Write a comment and commit to what you want to accomplish this Review season. When the end date comes, I will follow up on your comment to ask you how it went. It is expected you'll reply with a brief report (can be as short as a sentence).

I'll probably celebrate things that seem like victories, but I don't plan to give particular negative reinforcement otherwise. I trust that the ac...
Adapting to Means Testing
by jefftk — 2020-12-18T03:30:05.793Z

There are some things that we want everyone to have regardless of
ability to pay, but providing them to everyone would be expensive. So
we means test. A

fancy
college may agree to meet 100% of demonstrated financial need,
which means they examine your finances and charge you the sticker
price or the most they think you can afford, whichever is less. This
makes a lot of sense, at least initially, but then people start
adjusting their lives in response to incentives.

Take Medicaid and long-term care. Medicaid will cover nursing homes, but
only if you have exhausted your savings first. To discourage people
from transferring their money to relatives to intentionally become
destitute, Medicaid has complex
asset transfer rules. Still, they only look back five years,
which means that with planning this is still quite practical and many
people do it.

As the amount of money involved starts to get large enough, it makes sense
for people to plan farther in advance and do stranger things to
qualify. Here are two that I think are quite uncommon now,
but if current rules persist I expect to see more of:

Intentionally destitute grandparents planning to go on Medicaid
adopting their college-ready grandchildren. A few years ago I wrote a
silly
article on getting married to avoid having parents considered in
college financial aid, and people pointed out in the comments that for
the schools I was discussing marriage is not sufficient: CSS Profile still
cares about parental income and assets. Adoption, however, is
different: once you are adopted by your grandparents they are now your
only legal parents. If they die, you're an orphan. Looking into
adoption rules, it seems to me that adoption by grandparents generally
just requires everyone involved to be OK with it.

Affordable housing is typically structured with income limits:
you are only eligible if you make below some percentage (30%, 60%,
90%, etc) of the area median income. Today there are not very many
affordable units, but if programs like this were expanded as much as
proponents advocate
then it could make sense for people to intentionally keep incomes low
until accepted. There are also some places that let you intentionally
opt an existing unit into affordable status, which is especially
open to misuse. (more)

If you start trying to figure out how to make a system that is robust...
Do I have to trust past experience because past experience tells me that?
by znajd — 2020-12-18T02:43:43.195Z

If the only reason to use past experience (memories/knowledge) for making decisions as to what to do, is because that experience shows me it worked most of the time and that it's the only thing I have for that, then does it really mean I have to use it for making decisions?

And if yes, is there any other way of saying this, other than "I have to trust past experience because past experience tells me that"?