trentmkelly/LessWrong-43k huggingface dataset:
Humans are the universal economic bottleneck
by Dach — 2021-10-18T08:32:25.616Z

There's this idea in computer science wherein the maximum theoretical speedup that can be acquired with an arbitrary number of processors is related to the percentage of the program which can be parallelized. If we have two segments of code that take the same amount of time to execute with one CPU core in which the first segment can't be parallelized at all and the second segment is perfectly parallelizable, we can only run the program twice as fast, no matter how many CPU cores we have.

There's a similar idea in economics. It seems like the most powerful and civilizationally relevant feature controlling the medium to long term change in the price of goods is the extent to which the production of that good can be decoupled from the expenditure of man-hours. Some economic activity isn't "parallelizable" using current technology- we can't practically make that activity much faster without building powerful substitutes for humans, technology which is (for now) mostly out of our reach...

For example, it turns out that moving stuff over land is not easy to decouple from human labor without self-driving cars. There are two methods of overland transportation worth noting here: Cars and Trains.

Due to the nature of our road infrastructure, there's a pretty clear upper bound on how efficient car-based transportation of goods can get. There are legal limits on the allowed speed and size of vehicles, so without self-driving tech, we can't change how many man-hours need to be spent per cubic meter per kilometer.

Train-based transportation has its own problems which limit its ability to dominate overland transportation. Namely, our train transportation network is incomplete in that goods must still be ferried to their final destination by cars, so overland transportation is only some % "parallelizable".

Using this we can predict that overseas transportation would be really efficient compared to overland transportation. I suspect that Nautical miles per cargo container per hour per person can be pushed extremely high using current technology, laws, port and canal infrastructure, etc.

And indeed this is true:

Be warned that overland freight for even short distances can often be almost as much as ocean freight for thousands of miles (I recently paid $2000 for a 20′ container to be shipped from Shanghai to Los Angeles and then $1100 for it to be shipped 30 miles from Los Ange

...
Book Review: The Ethics of What We Eat
by Jonah_O — 2021-10-18T08:16:49.682Z

[Epistemic status: The authors of this book make many factual claims that I'm not equipped to conclusively verify. Much of the publicly available information on the food industry comes from agribusinesses themselves or from activists who bitterly oppose them. In this review I've tried to summarize the authors' claims as they've presented them, with the occasional corroborating link, but as a layman I can't offer a much more complex perspective on these topics beyond what I learned from this book. The value judgments expressed in this review are my attempt to capture the authors' point of view, except where otherwise noted. I've absorbed many convincing arguments against factory farming from Effective Altruists over the years though, and as of this writing I've drastically cut back my meat consumption because of it.]

The Ethics of What We Eat: Why Our Food Choices Matter was published in 2006 and written by Peter Singer and Jim Mason. You’ve probably already heard of Singer, due to his enormous influence on the ideas and practices of Effective Altruism. (If not, his 1971 essay "Famine, Affluence, and Morality" is a good intro to his moral viewpoint.)

Jim Mason, meanwhile, is an author and attorney whose prior work focuses primarily on animal advocacy. The Ethics of What We Eat is actually the second collaboration between the pair; the co-authors connected in the ‘70s, after Singer published Animal Liberation, and in 1980 published Animal Factories, an exposé on the abuses of industrial farming.

In The Ethics of What We Eat, however, their aims and research are somewhat more sprawling in scope. While animal treatment and factory farming remain a major focus of the book, M&S also address the job conditions of food production workers, as well as the merits and demerits of specific subjects like organic farming, GMOs, Fair Trade certification, and "eat local" movements.

Mason and Singer present their research as a case study of three American families and their day-to-day food choices, each representing a certain set of popular American diets. All of the families are real people who cooperated with the authors’ research for several months; each main section of the book starts with a short segment introducing us to the family members and their life before diving into a farm-to-table examination of where their food comes from. (The authors mention that ~20 families...
Quick Look: Altitude and Child Development
by Elizabeth — 2021-10-18T01:20:02.081Z

A client came to me to investigate the effect of high altitude on child development and has given me permission to share the results. This post bears the usual marks of preliminary client work: I focused on the aspects of the question they cared about the most, not necessarily my favorite or the most important in general. The investigation stops when the client no longer wants to pay for more, not when I’ve achieved a particular level of certainty I’m satisfied with. Etc. In this particular case they were satisfied with the answer after only a few hours, and I did not pursue beyond that.

That out of the way: I investigated the impact of altitude on childhood outcomes, focusing on cognition. I ultimately focused mostly on effects visible at birth, because birth weight is such a hard to manipulate piece of data. What I found in < 3 hours of research is that altitude has an effect on birth weight that is very noticeable statistically, although the material impact is likely to be very small unless you are living in the Andes.

Children gestated at higher altitudes have lower birth weights

This seems to be generally supported by studies which are unusually rigorous for the field of fetal development. Even better, it’s supported in both South America (where higher altitudes correlate with lower income and lower density, and I suspect very different child-rearing practices) and Colorado (where the income relationship reverses and while I’m sure childhoods still differ somewhat, I suspect less so). The relationship also holds in Austria, which I know less about culturally but did produce the nicest graph.

This is a big deal because until you reach truly ridiculous numbers, higher birth weight is correlated with every good thing, although there’s reason to believe a loss due to high altitude is less bad than a loss caused by most other causes, which I’ll discuss later.

[Also for any of you wondering if this is caused by a decrease in gestation time: good question, the answer appears to be no.]

Children raised at higher altitudes do worse on developmental tests

There is a fair amount of data supporting this, and some even attempt to control for things like familiar wealth, prematurity, etc. I’m not convinced. The effects are modest, I expect families living at very high altitudes (typically rural) to be different in many ways from lower altitudes ...
Your Time Might Be More Valuable Than You Think
by Mark Xu — 2021-10-18T00:55:03.380Z
Link: https://markxu.com/value-time

Summary

People often seem to implicitly value their time at the amount they can convert hours to dollars given their current skills.

However, the value of saving the marginal hour today is to increase the total number of one's working hours by one, resulting in a new hour at the end of one's career, not a new hour at their current skill level.

This suggests that people who expect their time to become valuable in the future must think their time is approximately just as valuable now, because saving time now gets them to the point where their time is valuable faster and gives them more of such time.

This analysis is complicated by various temporal dependencies (e.g. time discounting) that push the value of the current hour up or down compared to the value of the marginal hour at the end of one's career.

Under such a view, finding promising young altruists and speeding up their careers represents a significant value add.

Intro

Many people in my social circles have an amount they "value their time." Roughly speaking, if someone values their time at $50/hr, they should be willing to pay $50 to save an hour of time, or be paid $50 to do work that has negligible non-monetary value. Knowing this value can provide a simple decision rule for deciding which opportunities to trade money for time it's efficient for you to take. I will argue that a naive perspective on time evaluations generally results in an underestimate. This analysis suggests that altruistic actors with large amounts of money giving or lending money to young, resource-poor altruists might produce large amounts of altruistic good per dollar. I will analyze the situation mostly in terms of wages as expressed in dollars; however, readers might want to substitute "altruistic impact" instead. I will begin by analyzing a simplified situation, adding more nuance later.

The value of your time is the value of the marginal hour at the end of your career

If I currently have a job that lets me convert one hour of time into $50 dollars, then it's clear that I should take all time-saving opportunities at less than $50 dollars. (Note that this doesn't mean that I should pay $50 to save an hour of furniture assembly. Furniture assembly might be enjoyable, teach me valuable skills, etc.) However, this assumes that the benefits I receive from my job are entirely monetary. For most jobs, this will not be the case. If ...
Applied Mathematical Logic For The Practicing Researcher
by Jan — 2021-10-17T20:28:10.211Z
Link: https://universalprior.substack.com/p/applied-mathematical-logic-for-the

Asking for a friend[1]: what happened to Richard Hamming's social status after he started asking those pointed questions about the importance of research questions and individual career decisions? Was he, like, actually banished from the lunch table?

Technically, I am modeling for a living

A couple of months ago I've started asking my colleagues during lunch what their definition of a "model" is. This question is important: our job consists of building, evaluating, and comparing models. I am not hoping for an Aristotelean list of necessary & sufficient conditions, but it still appears like a good idea to "survey the land". Also, admittedly, lunch can get a bit boring without challenging questions.

An abstract drawing of a computational model. CGD generated.

I got a range of responses:

"a description of a phenomenon from which you can reason (= a description you can manipulate to tell more about the phenomenon than you would have been able to tell without it)"

"It should be something like a representation of the modelled system without representing it completely. Perhaps most importantly that it preserves the causal relationships between the system elements without completely mapping these elements?"

"an abstraction of reality"

I also ran into this adage again and again (attributed to a different person every time):

"All models are false, but some are useful."

Along similar lines, there is a quote from the influential computational neuroscientist Larry Abbott:

"the term 'realistic' model is a sociological rather than a scientific term."

Alright, survey done, lunch is over. Back to...

In search of tighter concepts

No! I'm not satisfied. What do you mean it's a sociological term? What do you mean they are false? Can a model have a truth value? If a model is a "representation" / "abstraction" / "description" then what exactly is a "representation" / "abstraction" / "description"? This is not some idle philosophical nitpicking, this question is immediately important. As a reviewer, I have to judge whether a model is good (enough). As a researcher, I want to build a good model. I'm not going to devote my career to building models if I don't have a really good idea of what a model is.

I hope you can tell from my extensive use of italicized words that this is a topic I am rather passionate about. If the question of a good model is a sociological question then it's subject to t...
How much should you update on a COVID test result?
by mayleaf — 2021-10-17T19:49:42.001Z

This is a writeup of COVID test accuracies that I put together for my own interest, and shared with friends and housemates to help us reason about COVID risk. Some of these friends suggested that I post this to LessWrong. I am not a statistician or an expert in medical research.

Background

We often hear that some kinds of COVID tests are more accurate than others — PCR tests are more accurate than rapid antigen tests, and rapid antigen tests are more accurate if you have symptoms than if you don't. A test's accuracy is often presented as two separate terms: sensitivity (what proportion of diseased patients the test accurately identifies as diseased) and specificity (what proportion of healthy people the test accurately identifies as healthy). But it's not obvious how to practically interpret those numbers: if you test negative, what does that mean about your odds of having COVID?

This writeup attempts to answer to the question, "how much more (or less) likely am I to have COVID given a positive (or negative) test result?" In particular, this is an attempt to calculate the Bayes factor for different types of COVID test results.

The Bayes factor is a number that tells you how much to update your prior odds of an event (in this case, your initial guess at how likely someone is to have COVID) given some piece of new evidence (in this case, a test result). It's calculated based on the test's sensitivity and specificity. If a test has a Bayes factor of 10x for a positive test result, and you test positive, then you should multiply your initial estimated odds of having COVID by 10x. If the same test has a Bayes factor of 0.3x for a negative test result, and you test negative, then you should update your prior odds of having COVID by 0.3x.

Using Bayes factors

(For an excellent explanation of Bayes factors and the motivation behind using them to interpret medical tests, I highly recommend this 3Blue1Brown video, which inspired this post.)

There's a well-known anecdote where doctors in a statistics seminar were asked how they would interpret a positive cancer test result for a routine mammogram taken by an asymptomatic patient. They were told that the test has a sensitivity of 90% (10% false negative rate), a specificity of 91% (9% false positive rate), and that the base rate of cancer for the patient's age and sex is 1%. Famously, nearly half of doctors incorrectly answered...
Feature Suggestion: one way anonymity
by Yair Halberstadt — 2021-10-17T17:54:09.182Z

Sometimes there's something I want to write, where I would be perfectly happy for whoever happens to read it on Less Wrong and is interested in who the author is to check and see it's me, but I don't want the post to appear in search results for my name.

I also don't want anyone who opens my profile on Less Wrong to see the post.

There could be various reasons for this - perhaps I'm about to claim that tabs are better than spaces and I don't want potential employers to see such heresy if they search for me. Perhaps I'm asking for suggestions for my wife's birthday present and don't want her to find out what it is.

My suggestion is to allow posting semi-anonymously. The name that would appear on such posts is "anonymous" but the name would be a link to the author's user profile. The post wouldn't appear on the users profile, but any votes would change the users karma as usual.

Perhaps it should only be possible for a user with sufficient karma to click on the link to the user profile, so that some bad actor can't scrape all semi-anonymous posts and publish the author's names in a searchable format, or at least if they do it should be straightforward to detect and block that user.
[Prediction] We are in an Algorithmic Overhang, Part 2
by lsusr — 2021-10-17T07:48:20.305Z

In [Prediction] We are in an Algorithmic Overhang I made technical predictions without much explanation. In this post I explain my reasoning. This prediction is contingent on there not being a WWIII or equivalent disaster disrupting semiconductor fabrication.

I wouldn't be surprised if an AI takes over the world in my lifetime. The idea makes me uncomfortable. I question my own sanity. At first I think "no way could the world change that quickly". Then I remember that technology is advancing exponentially. The world is changing faster than ever has before and the pace is accelerating.

Superintelligence is possible. The laws of physics demand it. If superintelligence is possible then it is inevitable. Why hasn't we built one yet? There are four[1] candidate limitations:

Data. We lack sufficient training data.

Hardware. We lack the ability to push atoms around.

Software. The core algorithms are too complicated for human beings to code.

Theoretical. We're missing one or more major technical insights.

We're not limited by data

There is more data available on the Internet than in the genetic code of a human being plus the life experience of a single human being.

We're not (yet) limited by hardware

This is controversial but I believe throwing more hardware at existing algorithms won't bring them to human level.

I don't think we're limited by our ability to write software

I suspect that the core learning algorithm of human beings could be written in a handful of scientific papers comparable to the length and complexity of Einstein's Annus Mirabilis. I can't prove this. It's just gut instinct. If I'm wrong and the core learning algorithm(s) of human beings is too complicated to write in a handful of scientific papers then superintelligence will not be built by 2121.

Porting a mathematical algorithm to a digital computer is straightforward. Individual inputs like snake detector circuits can be learned by existing machine learning algorithms and fed into the core learning algorithm.

We are definitely limited theoretically

We don't know how mammalian brains work.

I don't think there's a big difference of fundamental architecture between human brains and e.g. mouse brains. Humans do have specialized brain regions for language like Broca's area but I expect language comprehension would be easy to solve if we had an artificial mouse brain running on a computer.

Figuring ou...
Lies, Damn Lies, and Fabricated Options
by Duncan Sabien (Inactive) — 2021-10-17T02:47:24.909Z

This is an essay about one of those "once you see it, you will see it everywhere" phenomena. It is a psychological and interpersonal dynamic roughly as common, and almost as destructive, as motte-and-bailey, and at least in my own personal experience it's been quite valuable to have it reified, so that I can quickly recognize the commonality between what I had previously thought of as completely unrelated situations.

The original quote referenced in the title is "There are three kinds of lies: lies, damned lies, and statistics."

Background 1: Gyroscopes

Gyroscopes are weird.

Except they're not. They're quite normal and mundane and straightforward. The weirdness of gyroscopes is a map-territory confusion—gyroscopes seem weird because my map is poorly made, and predicts that they will do something other than their normal, mundane, straightforward thing.

In large part, this is because I don't have the consequences of physical law engraved deeply enough into my soul that they make intuitive sense.

I can imagine a world that looks exactly like the world around me, in every way, except that in this imagined world, gyroscopes don't have any of their strange black-magic properties. It feels coherent to me. It feels like a world that could possibly exist.

"Everything's the same, except gyroscopes do nothing special." Sure, why not.

But in fact, this world is deeply, deeply incoherent. It is Not Possible with capital letters. And a physicist with sufficiently sharp intuitions would know this—would be able to see the implications of a world where gyroscopes "don't do anything weird," and tell me all of the ways in which reality falls apart.

The seeming coherence of the imaginary world where gyroscopes don't balance and don't precess and don't resist certain kinds of motion is a product of my own ignorance, and of the looseness with which I am tracking how different facts fit together, and what the consequences of those facts are. It's like a toddler thinking that they can eat their slice of cake, and still have that very same slice of cake available to eat again the next morning.

Background 2: H2O and XYZ

In the book Labyrinths of Reason, author William Poundstone delves into various thought experiments (like Searle's Chinese Room) to see whether they're actually coherent or not.

In one such exploration, he discusses the idea of a Tw...
Explaining Capitalism Harder
by jefftk — 2021-10-17T02:40:07.488Z

A friend recently shared a sharing of a screenshot of a reblogging of
a reblogging of this tumblr post:

Pro-Capitalist's defense of capitalism is just explaining how it
works, and then when you say "yes I know, I just think it shouldn't be
like that" they explain it to you again but angrier this time

—strawberry-crocodile

I really like this perspective, even as someone relatively
pro-capitalism, because I think it captures something that often goes
wrong in these discussions.

The strongest argument in favor of capitalism is that in practice it
works for most things, better than the other systems we've tried. Not
because it was designed to work, but because that's just how it falls
together. When someone points at a piece of the system that seems
unfair or wasteful and says "I just think it shouldn't be like that," stopping
it's going to have effects elsewhere in the system, often negative
ones. And so pro-capitalism folks often respond by trying to explain
capitalism harder: what role is the thing you want to change filling?
When people propose removing something without engaging with how it
ties in to the rest of the system, it is natural to assume they don't
know about its function and try to explain.

As in the opening quote, however, people don't want more explanation
of the workings of the status quo. Instead, I think a better response
is to think about what you expect would go wrong, and ask if they
would expect that. Perhaps they don't, and you can try and figure out
where specifically your expectations diverge. Perhaps they do, and
they think it's worth it. Perhaps they have additional proposals which
work together. Whichever way the conversation goes, I think it
probably is more productive?

(Overall my perspective is that while things are much worse than they
could be, they're also much better than they have ever been. I
really don't want us to break the system that keeps improving our
ability to turn time and stuff into what people need. At the same
time, to the extent that we can do it without breaking this cycle of
improvement, I'd like to see far more redistribution of wealth. In my
own life this looks like giving.)

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