trentmkelly/LessWrong-43k huggingface dataset:
Menstrual cycle effects—Clue study summary and commentary
by just_browsing — 2021-03-31T16:28:21.748Z

Two months ago, I posted a question asking how LessWrong users optimize their productivity with respect to their menstrual cycle. User AM helpfully pointed me to a 2021 Nature Human Behavior study Daily, weekly, seasonal, and menstrual cycles in women's mood, behavior and vital signs [1] which provides robust, data-driven answers to some of my questions. The study does so by analyzing 241 million self-reported data points from 3.3 million women who use Clue, a period tracking app.

Main findings

I'm summarizing my interpretation of the paper, which means I will cover the facts I find relevant, skip findings I didn't find interesting, and possibly throw in my own interpretations. Please point out corrections if you have any.

First finding: The menstrual cycle amplitude is often larger than the daily, weekly, and seasonal cycle amplitudes

People experience various cycles. For example, the daily cycle consists of waking up, doing stuff, then going to bed. The weekly cycle often consists of a work week followed by a weekend. The seasonal cycle is a year long and (for example) includes the effects of dark winters in the Northern hemisphere.

Women [2] additionally experience a menstrual cycle—a 21-40 day cycle characterized by a period.

It's common to experience fluctuation in mood across each of these cycles (daily, weekly, seasonal, menstrual). Fluctuation in mood can mean lots of things—two examples are the dimension of happy-sad and the dimension of focused-distracted. Insofar as mood changes are caused by a cycle, one might expect to see roughly periodic data with respect to that cycle. And here it is:

From [1]. This chart shows how the happy-sad dimension is influenced by all four cycles. In particular, I'm guessing the daily cycle is primarily influenced by sunlight, the weekly cycle by weekends, the seasonal cycle by winter [3].

We can see that the menstrual cycle's amplitude is larger than that of the daily, weekly, or seasonal cycle for the happy-sad dimension. This effect was shown to be statistically significant for 6 out of 9 mood dimensions:

From [1], edited to only show mood category. [4] The 6 statistically significant categories are "happy vs sad", "happy vs sensitive", "energized vs exhausted", "motivated vs unmotivated", "supportive vs conflict", "focused vs distracted". The menstrual cycle also has large relative

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Should I advocate for people to not buy Ivermectin as a treatment for COVID-19 in the Philippines for now?
by BrianTan — 2021-03-31T13:59:40.240Z

Hey all, I'm from the Philippines, and there's a huge debate currently in mainstream Philippine media on whether Ivermectin is an effective treatment for COVID-19 or not. And I'm wondering if I should advocate for people to not buy Ivermectin as a treatment for COVID-19 in the Philippines for now. I also want to know if I should be advocating for the Philippine FDA to continue banning its use (or to approve its use). If anyone here has knowledge on Ivermectin, or has spent a bit of time looking at the evidence about it, I'd love to hear your thoughts on it.

For context, "Some doctors in the Philippines and abroad, as well as a number of lawmakers, urged the Department of Health and the Food and Drug Administration (FDA) to hasten the process of approving Ivermectin as a COVID-19 treatment in the country."

The Philippine FDA has not even approved Ivermectin for use by humans, and yet there are local doctors who prescribe it to patients. Some people find ways to procure it (and they may even be unintentionally procuring fake Ivermectin products sold by scammers). And yet, I've heard of some people in my network who are are thinking of buying it or have already bought it for themselves.

Currently, I sense that the majority of Filipinos are skeptical, and they think that there isn't good evidence for Ivermectin as an effective treatment for COVID-19. This statement, released by a group of multiple reputable health associations in the Philippines, does not recommend the use of Ivermectin as a treatment for COVID-19, and they cite what seem to be reputable studies (randomized control trials, though they may be small ~100-person samples) showing that Ivermectin has no statistically significant effect vs. control groups in treating mild or moderate cases of COVID-19. Meanwhile, "the World Health Organization (WHO) Representative to the Philippines Dr. Rabindra Abeyasinghe stressed on Tuesday, March 30, the need for "carefully controlled and planned" clinical trials of the anti-parasitic drug Ivermectin before it could be used to treat COVID-19 in the country."

I want to form better beliefs myself from the available evidence and figure out if I should be telling people in my network to not buy Ivermectin as a treatment for COVID-19. I'm leaning on doing that already, but am open to hearing others' views here on LessWrong.

I also want advice generally on how one can ...
Speculations Concerning the First Free-ish Prediction Market
by mike_hawke — 2021-03-31T03:20:48.379Z

Suppose

Kalshi

launches their cool new prediction marketplace soon, and it attracts a large userbase with high trading volume, and it doesn’t get shut down or strangled with red tape. What then?

Speculations

Replication markets will mitigate some of the bias afflicting scientific research.
Market prices will become an additional source of bias afflicting scientific research.
There will be sets of questions that can sketch out causal models. The market will give predictions not only on what will happen but why.
Philanthropists will subsidize markets of public interest unless and until governments do.
There will be great demand for trustworthy reports of ground truths. Some kind of payment scheme will be worked out that pays fees to trustworthy reporters. Costly signals of honesty will be developed and popularized.

"A survey of 100K residents showed [some result]. Survey integrity was established by [some standardized proof of neutrality or costly signal of honesty]".

There will be market estimates on how long we have until the next respiratory pandemic, the next natural disaster, the next large military conflict, and so on.
The userbase will contain some amount of institutional investment even though individual bets are capped at $25K. There will be professional traders whose daily workflow is similar to that of existing professional stock traders.
Futurism will be more disciplined. Conversations about technological forecasting will always involve some discussion of contract prices. "Majority self-driving traffic within the next 20 years, huh? Well the markets have that at X cents."
The $25K cap will sometimes be low enough for price manipulation by special interests, so makers of important policy will be hesitant to use decision markets.

Questions

How reasonable is the initial assumption--that Kalshi does not shut down or get hamstrung with red tape?
How much extra complication will there be in betting about inflation? Betting on the value of the very currency with which you are betting seems possibly kinda tricky.
Direct questions about assassinations will be banned of course. But how hard will it be to arrange bets across multiple innocent markets in order to indirectly bet on banned markets? Are there other ways that perverse incentives can leak into otherwise regular usage?
How much will insurance bets cause market prices to diverge from notional market probabilities? I

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A use for Classical AI -- Expert Systems
by Glpusna — 2021-03-31T02:37:23.484Z

Two questions from different perspectives. Why do colleges teach calculus, physics, and chemistry? Why don't they just tell students to "read the book, answer the questions and solve the problems? When done, we will test you." There are a lot of issues that involve money, perceived prestige, etc., but one of the main reasons colleges can't do that is that most text books are not written for self learners. You have to work hard to learn without the guidance of an instructor/tutor.

Today, we can use YouTube and other videos to help. But, consider what it would be like to have an expert system on each topic that is designed to help you learn? If you surf the net, you can follow links that you find interesting. Similarly, if you were learning and ran into difficulty, you could follow a link that would help you learn that topic. The current Covid virus has put a lot more demand on self-learning and has demonstrated how ineffective it is. Expert systems could be designed as study aids and as games to make learning easier and sometimes fun.

As more people get replaced by robots and automation, there will be a need to educate them in new fields. AI could help with that education. The traditional educational alternative is very costly. An additional alternative is to have people just wallow in ignorance while the world passes them by -- there was a Star Trek episode about a race of people who said: "We are smart" and who traveled through space acquiring technology that they could use but didn't understand. Our replaced workers could meet the same fate. A better alternative is to create educational expert systems to help them learn. I am currently developing an expert system in chemistry. There is an old saying: "If you want to learn something, teach it." I don't have anyone to teach, but I can learn a great deal by constructing my own system. In doing so, I have experienced first hand how poor some text books are. Two of my objectives are to solve each problem correctly and to explain, at any level of detail, what theories, factors, equations, etc. are involved. I have delayed indefinitely answering questions until an adequate NLP system exists. I choose Chemistry because we all know that "Chemists have all the solutions."

Any expert systems that have been or will be developed can be used as building blocks for more complex systems. Each such base syst...
Goldfish Reading
by DirectedEvolution — 2021-03-31T01:04:04.375Z

Math is like constructing a Lego set on a picnic table outside in the middle of a thunderstorm. I grope blindly in the pouring rain for the first piece, and finally put it in place, but by the time I’ve found the second piece and move to connect it to the first piece, the first piece has blown away and is nowhere to be found, and the instructions are sopping wet, and the picnic table has just been carried away by a tornado. - Scott Alexander, The Lottery of Fascinations

Introduction

It's not hard to remember a simple sentence in isolation.

The spacing effect is arguably the most replicable and robust finding from experimental psychology.

But just a little more information can make reading much more challenging.

In these studies, memory is typically tested by presenting learners with lists of words on two learning schedules, massed and spaced. Massed learning schedules present participants with learning events in immediate succession (i.e., one right after the other). In contrast, spaced learning schedules distribute learning events across time (i.e., separated by an operationally defined amount of time).

Even this is enough to give me that wall of text reaction. My eyes don't "latch on" to the first word in the paragraph and proceed from left to right. Instead, they stare at the middle of the paragraph, while I silently ponder whether to make myself read it. Or else they zip back and forth, grabbing a word or phrase here and there ("memory," "participants," "immediate succession"), while my brain forms this inchoate judgment of whether or not to commit to actually reading it.

It's similar to the experience of navigating a crowd at a festival where many bands are playing, speakers are talking, and vendors are hawking their wares, and deciding whether or not to pay attention to whatever's in front of me.

But if I do manage to start actually reading, it gets more challenging. Even a few dense sentences can be enough to overload my very limited, goldfish-like working memory.

For a while, I looked into memory competitions. The world record for memorizing digits in an hour is currently 4620 digits, and the record for memorizing words in 15 minutes is 335 random words. These admirable memorizers use all kinds of specialized visual-memory tricks, which you can read about in Moonwalking With Einstein. But these tricks don't seem too promising for vastly more complex real-worl...
Hardware is already ready for the singularity. Algorithm knowledge is the only barrier.
by Andrew Vlahos — 2021-03-30T22:48:18.207Z

How close are we to a singularity? Well, computers started being able to think faster than us in the 1990s, (neurons have a processing speed of 200 Hz), and are now many orders of magnitude faster.

"But wait!" you reasonably object, "that's only one aspect of computer power! What about hard drive storage, and how does working memory compare to RAM?"

I'm not sure how much memory the human brain can hold. Scientific American says it can hold 2.5 petabytes, and this figure seems to be the most heavily cited among pop science articles, but they don't really explain their numbers. AI Impacts, which does seem to be much more mathematically rigorous in their articles and usually shows their mathematical process, claims that "Most computational neuroscientists tend to estimate human storage capacity somewhere between 10 terabytes and 100 terabytes" (about 1/10th of a petabyte). Humans are very unlikely to have the most efficient possible algorithm, of course, and it's possible that 1 terabyte is more than enough for a perfectly-optimized general intelligence. But still, let's assume for the sake of argument that the highest estimate of 2.5 petabytes is correct and is the absolute minimum needed for a human-level or higher intelligence. How does that compare to the current state of computers?

The internet has been estimated to contain hundreds of exabytes of data... in 2007! A 2009 article said that Google alone had about an exabyte of data. In 2013 Randall Munroe (of xkcd) estimated that Google had 10 exabytes. If the whole internet is added up in 2020, the estimates range in the double digits of zettabytes. Each exabyte is a thousand petabytes and each zettabyte is a thousand exabytes. This memory is unavailable and used for other purposes, but if any big company knew how to program a superintelligence it's easy to see that they'd be able to build a "mere" 2.5 petabyte server building. Some botnets also probably contain this amount of hardware already. Memory would need to be distributed in some way, but there are already some methods to do that used for things like archives and youtube. Computers clearly have enough physical memory for a superhuman intellegence.

What about RAM? This is so different from human working memory that a direct comparison can't really be made. Humans can analyze a very complicated ongoing event based on vision and sound in ...
Optimization, speculations on the X and only X problem.
by Donald Hobson — 2021-03-30T21:38:01.889Z

Epistemic status: Speculative. Rambling stream of ideas.

Recomended semiprerequisite https://www.lesswrong.com/posts/DkcdXsP56g9kXyBdq/coherence-arguments-imply-a-force-for-goal-directed-behavior

How do we tell if an arbitrary system is an optimizer or not?

Well "optimiser" is a set of hypothesis. We can take an AIXItl like structure, and use the universal prior over utility functions. The other hypothesis set is the default solomonov prior. All computer programs weighted by complexity.

Note that each hypothesis set contains a shrunken copy of the other. Within the space of all computer programs, some of those programs will be optimisers. Within the space of all possible optimizers, some will have the goal of making their output match the output of an arbitrary computer program. Thus the odds ratio in either direction is bounded. We call this the odds ratio approach.

The intuition is that this odds ratio provides a good measure if a program is optimizing.

(There is another measure of optimization, namely the weighted average utility over all Turing machine environments. We call this the weighted reward approach)

I will discuss a few programs, and how much optimization each formalism thinks they display.

Trivial constant programms

While True:
print(0)

and

While True:
print(1)

Under the odds ratio approach, both these programs are on the not optimizer end of the scale and right next to each other.

Under the weighted reward approach, the answer is much more sensitive on choice of universal Turing machine. An environment that just gives reward for each 1 outputted is very simple. It might even account for say 5% (random number) of weight across all computable environments. If the environment that just rewards 0's isn't given a correspondingly high weight, then according to the weighted reward framework, one of these programs can be much more optimizing than the other.

Conditional AIXI

if input.read(100)==long_random_string:
behave_like_AIXI()
else:
while True:
print(0)

and

if input.read(100)!=long_random_string:
behave_like_AIXI()
else:
while True:
print(0)

In the odds ratio formulation, both of these are pretty optimizerry, and almost identical. In either formalization they need to take the complexity of describing the long random string. In the optimizer formulation, they need to take the complexity of constantly ...
A command-line grammar of graphics
by philh — 2021-03-30T20:30:03.071Z

Sometimes I'm at my command prompt and I want to draw a graph.

Problem: I don't know gnuplot. Also, there's a couple things about it that bug me, and make me not enthusiastic about learning it.

One is that it seems not really designed for that purpose. It implements a whole language, and the way to use it for one-off commands is just to write a short script and put it in quotes.

The other is its whole paradigm. At some point in the distant past I discovered ggplot2, and since then I've been basically convinced that the "grammar of graphics" paradigm is the One True Way to do graphs, and everything else seems substandard. No offense, gnuplot, it's just… you're trying to be a graphing library, and I want you to be a graphing library that also adheres to my abstract philosophical notions of what a graphing library should be.

If you're not familiar with the grammar of graphics, I'd summarize it as: you build up a graph out of individual components. If you want a scatter plot, you use the "draw points" component. If you want a line graph, you use the "draw line segments" component. If you want a line graph with the points emphasized, you use both of those components. Want to add a bar chart on top of that too? Easy, just add the "draw bars" component. Want a smoothed curve with confidence intervals? There's a "smooth this data" component, and some clever (but customizable) system that feeds the output of that into the "draw a line graph" and "draw a ribbon" components. Here's a gallery of things it can do

So, rather than adapt myself to the world, I've tried to adapt the world to myself.

There's a python implementation of the paradigm, called plotnine.1 (It has its own gallery.) And now I've written a command-line interface to plotnine.

It's not as powerful as it plausibly could be. But it's pretty powerful2, and if I stop developing now I might find it fully satisfies my needs in future. For example, I took a dataset of covid cases-per-capita timeseries for multiple countries. Then both of these graphs came from the same input file, only manipulated by grep to restrict to twelve countries:

(The second one isn't a type of graph that needs to be implemented specifically. It's just a combination of the components "draw points", "draw line segments" and "draw text".)

Now admittedly, I had to use a pretty awful hack to get that second one to work, and it wouldn't shock...
How many micromorts do you get per UV-index-hour?
by David Scott Krueger (formerly: capybaralet) — 2021-03-30T17:23:26.566Z

I'm trying to get a good estimate of the risks associated with sun exposure.

I'm assuming things scale linearly (the UV index units, which need a name ("UVees"?) are linear).

This may not be a good assumption, but it seems somewhat likely that it would be within the range I most care about (significant exposure, but not enough to get burnt -- if I'd get burnt, I'll certainly cover up or wear sunscreen).
I know sun exposure is a major source of skin cancer risk, but I'm not sure how much of that is coming from people who have tons of exposure, and how much risk a moderate level of exposure actually brings.
Many methods of causal inference try to identify a "safe" subset of variation
by tailcalled — 2021-03-30T14:36:10.496Z

(crossposted from my substack)

I have been thinking a lot about causality lately, and as a result I’ve come up with a common way to think of many different methods of causal inference that often seem to be used in science. This is probably not very novel, but I asked around whether it had a standard term, and I couldn’t find any, so I decided to write it up. I’ve personally found this model helpful, as I think it sheds light on what the limitations of these methods are, and so I thought I would write about it.

Roughly speaking, many methods of causal inference try to identify a “safe” subset of variation. That is, when trying to examine the causal effect of X on Y, they look in the variation of X to see how it relates to variation of Y. But this is possibly biased due to common causes or reverse causality (Y influencing X); so therefore to limit this problem, they find some way of partitioning X into a “safe” sort of variation that is not subject to these problems, and an “unsafe” sort that may be subject to them. Then they examine the correlation between this safe variation and Y.

The model abstracts over naive regression or multiple regression, longitudinal studies, instrumental variables, co-twin controls (Turkheimer’s “quasicausality”), and even randomized controlled trials. So to understand the common model, it is worth going through these methods of causal inference first.

Vanilla methods for causal inference

I don’t know who is going to read this blog, but I assume that most of my readers will be familiar with regression and experiments. Still it might be worth taking a recap:

Regression is the most naive method of causal inference; you’ve got two variables X and Y, and then you assume that any association between X and Y is due to a causal effect of X on Y. With regression, it is then straightforward to use data to see how much X correlates with Y and use this as the causal estimate. This is invalid if Y affects X, or if there is some common factor Z which affects both X and Y, which there usually is; hence the phrase “correlation is not causation”.[1]

To give an example, in order to figure out the effect of a medical treatment, you could look at differences between the people who get the treatment and the people who don’t get the treatment on various outcomes, such as the disease it’s supposed to treat, potential side-effects like mortality, and similar. T...