trentmkelly/LessWrong-43k huggingface dataset:
Contra StatNews: How Long to Herd Immunity?
by AppliedDivinityStudies — 2021-01-20T22:06:43.817Z

Cross post from applieddivinitystudies.com/stat-immunity
Warning: Speculative armchair epidemiology. All emphasis mine.
See also Youyang Gu's projection.

Summary: In an article for Stat, Dr. Zach Nayer misrepresents research, makes indefensibly flawed assumptions, and fumbles basic arithmetic. Per CDC, actual US Covid cases are 4.6x higher than reported, and currently around 2.4x higher. Using improved parameters, our toy model finds that herd immunity may occur in less than 4 months, although neither estimate should be taken too seriously. It all depends on the transmissibility of the new strain, as well as our ability to ramp up vaccine production, distribution and acceptance.

1) Dr. Nayer Misrepresents the Evidence on Monthly Infection Rates

Last month, Dr. Zach Nayer [1] at Stat published an estimate of time to herd immunity, suggesting that without vaccines it may take as long as 55 months.

The model itself is straightforward. Assume we need to hit 75% immunity, then figure out when we'll get there based on existing prevalence and monthly infection rate:

Unfortunately, Nayer's parameters are totally off. Citing a study which found antibody prevalence of 9.3%, Nayer writes:

In late September, a Stanford study estimated that 9.3% of Americans have antibodies against SARS-CoV-2.... If the base prevalence at the end of September --- eight months from the onset of the epidemic in the United States on January 21, 2020 --- was 9.3%, the coronavirus has an infection rate of approximately 1.2% of the population per month.

But take a closer look. Although the study was published in September, it was based on data collected in July. As the authors make explicit:

Our goal was to provide a nationwide estimate of exposure to SARS-CoV-2 during the first wave of COVID-19 in the USA, up to July, 2020

Instead of dividing 9.3% by an eight month range, Nayer should have used the 6 months from January through July. This yields an estimated monthly infection rate of 1.6% rather than 1.2%.

To his credit, Nayer attempts to confirm this result against another source of data, but fumbles the arithmetic. He writes:

one study [estimates] 52.9 million infections in the U.S. from February 27 to September 30, or an infection rate of 1.3% per month.

52.9 million infections is 16% of the US population. Over a 7 month time period, that's a monthly infection rate of 2.3% per month, n...
Recognizing Numbers
by johnswentworth — 2021-01-20T19:50:51.908Z

Warning: this post is moderately technical and very theoretical.

Problem 1: there are five apples on a plate. If you and your friend each eat an apple from the plate, how many apples are left?

Problem 2: what is 5 - 1 - 1?

Word problems are harder than number problems. (Source: every elementary school ever.) This isn’t just true of elementary school students; writing a program which can solve problem 2 is much easier than writing a program which can solve problem 1. Why?

Applying math in the real world requires a recognition/pattern-matching step. We have to notice that some structure in the real world can be represented by mathematical objects, like numbers or functions. In many (most?) cases, that’s the hardest step - figuring out how to translate our own internal world-models into math is more difficult than the math itself.

Unfortunately, the way math is done today often makes the problem harder rather than easier. We usually use definitions and axioms optimized for brevity and elegant proofs, not optimized for ease of recognition in the real world. This post asks: what if our mathematical foundations were optimized from the start for ease of recognition in the hands of AI? In particular, we’ll use the definition of numbers as an example.

Example: Natural Numbers

The usual way mathematicians construct numbers from first principles goes something like this. We have a number 0, and every number x has a “successor” S(x). So, we have numbers 0, S(0), S(S(0)), S(S(S(0))), ... and we usually use the shorthand notation 1 = S(0), 2 = S(S(0)), 3 = S(S(S(0))), etc. A few more axioms guarantee that these numbers are all distinct (i.e. we don’t have S(S(S(0))) = 0) and there aren’t any “extra” numbers (i.e. numbers which can’t be reached by counting up from 0). There’s various different flavors of this construction in different areas - e.g. we can perform the construction starting from set theory, or from lambda calculus, or from category theory - but they all do basically the same thing.

In some sense, this construction seems to capture the essence of the

principle of induction

: if a statement like “x is bamfoozled” is true for x = 0, and “x is bamfoozled” implies “S(x) is bamfoozled” (a.k.a. “x+1 is bamfoozled”), then all numbers are bamfoozled. The successor construction provides the minimum framework needed for the principle of induction to work. From a pattern-recognit...
How much does it cost to design an app?
by kjz — 2021-01-20T17:40:34.476Z

I'm not a programmer, so I have very little idea of how much it costs to, for example, write the code for a website or app. Suppose I wanted to hire someone to design a very simple, secure, and easy to use app - how much should I expect to pay in terms of time and money? To work with a concrete and timely example, suppose I wanted an app that would do the following:

Let people enter the minimal amount of information (or use a series of questions) to help them figure out which phase of Covid vaccination they qualify for in their region (e.g. age, occupation, pre-existing conditions, etc.)
Connect them to the correct website(s) for them to schedule an appointment if they are qualified, or give an estimated timeframe for when people in their phase might be able to get vaccinated.
Allow for notifications, appointment reminders, updates if the rules in their region have been changed and what they need to do instead, etc.

Note that I'm not suggesting that such an app would make much of a dent in the logistical nightmare that is our healthcare and political systems. I'm using it as a hopefully easy to understand example of the level of complexity I'm thinking about.

As a follow up, how much additional cost would be needed to process payments through an app? (Again, not thinking of this in terms of the vaccine example app, but asking about apps in general).

And finally, if I did want to eventually hire someone to write an app, what would be the best way to go about it? Would people in this community be willing to help?
Covid: The Question of Immunity From Infection
by Zvi — 2021-01-20T15:50:01.101Z

Over and over and over again, I’ve been told we should expect immunity from infection to fade Real Soon Now, or that immunity isn’t that strong.

With several recent papers and the inevitable media misinterpretations of them, it’s time to take a close look at the findings.

This was originally part of the 1/21 update, but I’ve split it off so that it can be linked back to as needed, and to avoid cluttering up the weekly update.

Note that this post is not looking at any new strains that might provide immune escape. It’s studying infections during a period when such strains were not a substantial issue. This is distinct from concerns about strains with immune escape characteristics.

First up is this paper:

From this, of course, media headlines were things like “immunity only lasts five months,” but let’s ignore that and keep looking at the data, and see what the study actually says.

RESULTS SECTION:

Bottom line infection rates:

Finally:

I’ll pause here before I read the discussion section.

What I am seeing is that in probable infections, meaning infections that were serious enough and real enough to get confirmed, we see a 99% reduction, a large enough reduction that error in the original antibody/PCR tests might well account for either or both of the remaining two (2) cases.

Even looking at only symptomatic infections, we still get a 95% reduction.

Whereas if we only look at ‘there was a test that came back positive on people getting periodically tested, but without requiring any symptoms or verification’ we only get an 83% reduction.

Naturally, the public-facing articles all seem to quote the 83%, and ignore the 95% and 99%.

I’d also note that they nowhere attempt to control for the two most obvious differences between the two samples, which are:

The antibody positive sample knows they are antibody positive, and thus likely took fewer precautions across the board than they would have otherwise.
The antibody positive sample are the people who got infected the first time.

If immunity conferred zero protection, what do you think the risk ratio would have been? Three?

(And again, they also take something presented after five months of follow-up, and report it as ‘immunity lasts five months’ because journalism.)

They start off with this in the discussion, which seems maximally skeptical (e.g. ‘at least 75%’...
Infra-Bayesianism Unwrapped
by adamShimi — 2021-01-20T13:35:03.656Z

Introduction

Infra-Bayesianism

is a recent theoretical framework in AI Alignment, coming from Vanessa Kosoy and Diffractor (Alexander Appel). It provides the groundwork for a learning theory of RL in the non-realizable case (when the hypothesis is not included in the hypothesis space), which ensures that updates don’t throw away useful information, and which also satisfies important decision-theoretic properties playing a role in

Newcomb-like problems

.

Unfortunately, this sequence of posts is really dense, and uses a lot of advanced maths in a very “textbook” approach; it’s thus hard to understand fully. This comes not from the lack of intuition (Diffractor and Vanessa are both very good at providing intuitions for every idea), but from the sheer complexity of the theory, as well as implicit (or quickly mentioned) links with previous research.

Thus my goal in this post is to give enough details for connecting the intuitions provided and the actual results, as well as the place of Infra-Bayesianism within the literature. I will not explain every proof and every result (if only because I’m not sure of my understanding of all of them). But I hope by the end of this post, you have a clearer map of Infra-Bayesianism, one good enough to dig into the posts themselves.

This post is splitted into three section

Section 1 explores the context of Infra-Bayesianism, the problem it attempts to solve, and some of the relevant literature. You can see it as unwrapping

this section

in Introduction to

The Infra-Bayesianism Sequence

.
Section 2 gives a bird’s-eye view of Infra-Bayesianism; a map to navigate through the sequence.
Section 3 follows one path through this map: the one focused on decision-theoretic properties for Newcomb-like problems.

Reading advice: The reader is king/queen, but I still believe that there are two main ways to read this post to take something out of it:

Read it quickly, in one go to get a general idea of Infra-Bayesianism and a very high-level map.
Read it in detail, for a time between 2 hours and a whole afternoon, to get every detail explained here. If you do so, I really believe that you’ll have a quite detailed map of Infra-Bayesianism, enough to explore the sequence by yourself without getting lost in the mathematical jungle.

Thanks to Vanessa and Diffractor for going above and beyond in answering all my questions and providing feedback on this post. Than...
On fundamental solitude
by KatjaGrace — 2021-01-20T10:10:16.983Z

A quote from Aldous Huxley that has stuck in my mind more than perhaps any other over the years:

“We live together, we act on, and react to, one another; but always and in all circumstances we are by ourselves. The martyrs go hand in hand into the arena; they are crucified alone. Embraced, the lovers desperately try to fuse their insulated ecstasies into a single self-transcendence; in vain. By its very nature every embodied spirit is doomed to suffer and enjoy in solitude. Sensations, feelings, insights, fancies—all these are private and, except through symbols and at second hand, incommunicable. We can pool information about experiences, but never the experiences themselves. From family to nation, every human group is a society of island universes.”

I used to be fairly troubled by this kind of thought. These days I’m more inclined to think of memories of myself, my own writing from yesterday, my sense of a person in my arms, words vibrating my inner ears as light bounces between someone’s eyes and mine, words reaching me across the internet from a stranger, barely understandable lines from thousand year old pages, as more of a piece—physical communications between scattered consciousness. All interpreted with more or less insight and confidence and detail and sense of being an ‘experience’ and not just ‘information’, depending on the quality and nature of the message. But my ‘imagining’ of your mental state, and my ‘knowing’ of my own are both guesses. The sense that they are different is a pragmatic, superficial, quantitative one, not the symptom of a deep metaphysical separation.
#3: Choosing a cryonics provider
by mingyuan — 2021-01-20T01:47:36.839Z

This is post 3 of 10 in my sequence on how to sign up for cryonics.

There are a fair few companies working in or adjacent to the cryonics space, but so far as I can tell*, the most commonly considered ones are:

Alcor

near Phoenix
The

Cryonics Institute

(CI) near Detroit (whole-body only)

KrioRus

in Moscow

I only seriously considered Alcor and CI, which is the case for most people I know, since KrioRus is 6000 miles away from us.

Edit 9/2022: I've heard good things about Tomorrow Biostasis, based in Germany and serving all of Europe. Given that most Europeans probably currently can't / don't want to deal with Moscow, this seems like a good alternative to look into.

Honestly, I chose Alcor long before I started the signup process – basically because my friends had chosen it, and I figured they'd done so for good reason. But in the interest of information-sharing (which is the whole reason I'm writing this sequence, after all), I decided to dig into the Alcor vs CI question. I came out the other side more confident in my choice of Alcor.

Process

The first thing I did was to look back into ancient history, to

this 2012 LessWrong question

about Alcor vs CI. While the comments did raise a lot of important considerations and were a helpful starting point, the primary thing I found out was.... that there's a lot of catfighting in the cryonics community. Here's hoping this post attracts more constructive feedback and fewer diatribes.

Notably, I did not reach out to either Alcor or CI directly, instead working only from publicly available information. While I'm told that people at both organizations are very helpful and happy to answer questions, I think it's illuminating to see what they share publicly. Also, honestly, I just don't like talking to people and didn't want to devote that much time and energy to looking into minutiae, when the broader picture was already pretty clear to me. People who have insider information are welcome to set me straight when I raise questions or reveal gaps in my knowledge.

One more note

While people argue a lot about which organization is better for them at the individual level, I think it’s good to acknowledge that all cryonics organizations are part of the same ecosystem that's pushing research forward and aiming at long-term human flourishing. In this capacity, cryonics organizations all complement one another. People seem to get ...
For Better Commenting, Avoid PONDS
by DirectedEvolution — 2021-01-20T01:11:36.860Z

"All around the bog still sprawls, from out the drear lake come soulless thoughts and drift into the hearts of the people, and they are one with their surroundings."— Alan Garner, The Weirdstone of Brisingamen.

If our blogging is to be more than shouting into the void, we have to write good comments. But that's hard to do. Too much perfectionism, enforced positivity, criticism, status smackdowns, or vagueness in our commenting standards can all be problematic.

I'm using a framework that I think has been helpful. It's simple enough.

Avoid PONDS.

P = Prickly
O = Opaque
N = Nitpicky
D = Disengaged
S = Shallow

Let me define each term.

Prickly means that your comment has a chilly, disapproving, mean, or unappreciative tone. It could hurt feelings, and make somebody feel dumb for opening their virtual mouth.

Opaque means that your comment makes assertions without backing them up. You're saying stuff without giving any reason at all for it. This can also include some types of lazy "questions" that are really meant as cheap shots. Even giving a partial reason or motivation for your comment or question means that it is not opaque.

Nitpicky means that your comment is expressing criticism of one particular part of an argument, without stating whether or how this local disagreement informs your view of the original argument as a whole. Even saying "this is just a local nitpick; I don't know if it means much for the argument as a whole" is enough to make your comment not a nitpick.

Disengaged means that your comment doesn't give the impression that you'd be likely to carry the conversation further if you received a response. It's a "drive-by commenting."

Shallow means that you didn't read the entire original post or comment thread to which you're responding.

Each category is meant to be a very low bar. Express even mild warmth, underlying reasoning, attempt at synthesis, display of engagement, or depth of consideration -- not even all five, just one! -- and it's not PONDS. This term is only for the very worst comments.

Comments that are even slightly better than PONDS are totally acceptable. This is a way of speaking and responding that gives almost total freedom for Socratic grilling, while creating some minimal self-enforced safeguards to promote good conversation.

I've build a habit of checking my own comments to ensure they're not PONDS. It's not that hard to improve a comment to do ...
Deutsch and Yudkowsky on scientific explanation
by Richard_Ngo — 2021-01-20T01:00:04.235Z

Science aims to come up with good theories about the world - but what makes a theory good? The standard view is that the key traits are predictive accuracy and simplicity. Deutsch focuses instead on the concepts of explanation and understanding: a good theory is an explanation which enhances our understanding of the world. This is already a substantive claim, because various schools of instrumentalism have been fairly influential in the philosophy of science. I do think that this perspective has a lot of potential, and later in this essay explore some ways to extend it. First, though, I discuss a few of Deutsch's arguments which I don't think succeed, in particular when compared to the bayesian rationalist position defended by Yudkowsky.
To start, Deutsch says that good explanations are “hard to vary”, because every part of the explanation is playing a role. But this seems very similar to the standard criterion of simplicity. Deutsch rejects simplicity as a criterion because he claims that theories like “The gods did it” are simple. Yet I’m persuaded by

Yudkowsky’s argument

that a version of “The gods did it” theory which could actually predict a given set of data would essentially need to encode all that data, making it very complex. I’m not sold on Yudkowsky’s definition of simplicity in terms of Kolmogorov complexity (for reasons I’ll explain later on) but re-encoding a lot of data should give rise to a complex hypothesis by any reasonable definition. So it seems most parsimonious to interpret the “hard to vary” criterion as an implication of the simplicity criterion.
Secondly, Deutsch says that good explanations aren’t just predictive, but rather tell us about the underlying mechanisms which generate those predictions. As an illustration, he argues that even if we can predict the outcome of a magic trick, what we really want to know is how the trick works. But this argument doesn’t help very much in adjudicating between scientific theories - in practice, it’s often valuable to accept purely predictive theories as stepping-stones to more complete theories. For example, Newton’s inverse square law of gravity was a great theory despite not attempting to explain why gravity worked that way; instead it paved the way for future theories which did so (and which also made better predictions). If Deutsch is just arguing that eventually science should aim to identif...
The Teleological Mechanism
by Gordon Seidoh Worley — 2021-01-19T23:58:54.496Z

I just wrote this up as a comment, but I think it deserves to be a top level post because it's an important idea. Additionally, this formulation is crisp enough that folks should be able to usefully engage with it.

In this seminal cybernetics essay a way of thinking about the concept we might variously call care, concern, telos, or purpose is laid out. This is relevant both to thinking about goal-directed behavior in AI and other non-human systems and to thinking about why humans do things.

I reference this concept a lot, but I've not (yet) had a good reference post to link about it. Usually I default to pointing at something about Heidegger's Sorge (tr. "care" or "concern"), but Heidegger is notoriously hard to read and lots of people don't like him. Also there's not a detailed argument for why care is so important, so I find myself trying to make the case all the time. Hopefully this will put an end to that.

So in that essay, first, they consider systems that have observable behavior, i.e. systems that take inputs and produce outputs. Such systems can be either active, in that the system itself is the source of energy that produces the outputs, or passive, in that some outside source supplies the energy to power the mechanism. Compare an active plant or animal to something passive like a rock that only changes when heated by an outside source, though obviously whether or not something is active or passive depends a lot on where you draw the boundaries of its inside vs. its outside (e.g. is a plant passive because it gets its energy from the sun, or active because it uses stored energy to perform its behaviors?).

Active behavior is subdivided into two classes: purposeful and purposeless. They say that purposeful behavior is that which can be interpreted as directed to attaining a goal and purposeless behavior does not. They spend some time in the paper defending the idea of purposefulness and their vague definition of it. I'd instead propose we think of these terms differently. I prefer to think of purposeful behavior as that which creates a reduction in entropy within the system and its outputs and purposeless behavior does not. This doesn't quite line up with how they think about it, though, so I'm open to arguments that entropy is not the useful place to draw the line here.

They then go on to divide purposeful behavior into teleological and non-teleological b...