Harley-ml/lesswrong huggingface dataset:
# PhD student mutual line-manager invitation

The greatest challenge of my PhD is the distant deadlines and the lack of immediate structure and accountability. Working on a single project for years with little extrinsic rewards is really hard for me, and most humans. This plagues long solo projects like academic research, but is less common in the normal working world. Rob Wiblin has pointed out that in normal companies weekly meetings with a line manager actually resolve this problem by giving a semiformal context for people to think through the mundane issues of productivity and planning.  I have a link to the podcast below.

I'm seeking another PhD student to try out mutual line managing. We would meet once weekly for half an hour. We would each describe our progress, plan for next week, and discuss emerging problems and strategies. Taking turns sounds easiest, perhaps with timers. I'd prefer someone in the social sciences and in Eastern Standard Time. And the format is super flexible. Message me on EAForum and I'll be in touch.

Inspired by this 80k episode from 27:02 "so I like your idea of a line manager" to 43:23 "let's pivot...". https://podcasts.google.com/feed/aHR0cDovL2ZlZWRzLnNvdW5kY2xvdWQuY29tL3VzZXJzL3NvdW5kY2xvdWQ6dXNlcnM6MTk0MjgyNjgyL3NvdW5kcy5yc3M/episode/NTM5NTVmY2EtNTVjMy0xMWViLTk4ZDctMGUyYTQ3ZjVmMjU5?sa=X&ved=0CA0QkfYCahcKEwi4l86rmLXuAhUAAAAAHQAAAAAQAQ
# Sleep math: red clay blue clay

To me, going to bed often feels more like a tiresome deprivation from life than a welcome rest, or a painless detour through oblivion to morning. When I lack patience for it, I like to think about math puzzles. Other purposeful lines of thought keep me awake or lose me, but math leads me happily into a world of abstraction, from which the trip to dreamland comes naturally.

(It doesn’t always work. Once I was still awake after seemingly solving two Putnam problems, which is about as well as I did in the actual Putnam contest.)

A good puzzle for this purpose should be easy to play with in one’s head. For me, that means it should be amenable to simple visualization, and shouldn’t have the kind of description you have to look at multiple times. A handful of blobs is a great subject matter; an infinite arrangement of algebra is not.

Recently I’ve been going to sleep thinking about the following puzzle. I got several nights of agreeable sleep out of it, but now I think I have a good solution, which I’ll probably post in future.

> Suppose that you have 1 kg of red clay that is 100 degrees and 1 kg of blue clay that is 0 degrees. You can divide and recombine clay freely. If two pieces of clay come into contact, temperature immediately equilibrates—if you put the 1kg of red clay next to 0.5 kg of blue clay, all the clay will immediately become 66 degrees. Other than that the temperature of the clay doesn’t change (i.e. no exchange with air or your hands, no radiation, etc.). Your goal is to end up with all of the blue clay in a single clump that is as hot as possible. How hot can you make it? (Equivalently: how cold can you make the red clay?)

HT Chelsea Voss via Paul Christiano

Clue: it’s more than 50 degrees.
# The Cost of a Sixth Seat

When I posted [about cars](https://www.jefftk.com/p/car-thoughts), [several people](https://www.facebook.com/jefftk/posts/10100208950707942?comment_id=10100208954325692) [suggested](https://www.lesswrong.com/posts/CkXSD4QKSurHCY4oh/car-thoughts?commentId=kftd4yEMNS6m6r9Pp) we might want to get something with more than five seats so we'd have room for friends. This would make a lot of sense, except that it's surprisingly expensive.

The cheapest new (2021) car you can get in the US right now, as far as I can tell, is the 4-seater [Chevy Spark](https://www.chevrolet.com/cars/spark), $13,400 MSRP in its cheapest configuration. The cheapest 5-seater is the [Mitsubishi Mirage](https://www.mitsubishicars.com/mirage/2021), at $14,295. The cheapest 7-seater is the [VW Tiguan](https://www.vw.com/en/models/tiguan.html), at $25,245.

(What about 6-seaters? Sedans seating three in the front and three in the back used to be common, but the last one in the US was the [2013 Chevy Impala](https://en.wikipedia.org/wiki/Chevrolet_Impala#2013).)

Why do you pay $11k (+77%) to seat more than five? Sure, most people don't need a car that big, but there are still a lot of price-sensitive families.

This is not how it used to be: it was once common for sedans to offer a station wagon variant with a third row. They were more expensive, but not by that much. In the US this disappeared in the mid 90s; the 8-seater [Buick Roadmaster Wagon](https://en.wikipedia.org/wiki/Buick_Roadmaster#1991%E2%80%931996) was discontinued in 1996, as was the 7-seater [Toyota Camry Wagon](https://en.wikipedia.org/wiki/Toyota_Camry_(XV10)).

The full-size wagons disappeared because people were switching to minivans, and then later SUVs. Some of this was fashion, but there was also a legal aspect: being technically "light trucks" they had [less strict emissions and fuel economy standards](https://en.wikipedia.org/wiki/Corporate_average_fuel_economy#SUVs_and_Minivans_created_due_to_original_mandate).

I think this explains the cost jump: what was once a relatively minor change to a standard sedan is now a relatively minor change to a standard SUV, but an SUV is a more expensive place to start. For some combination of fashion and regulations, no sedan maker is interested in producing a cheap three-row wagon variant, though there is a small market for fancy European station wagons that seat seven, such as the [Volvo V90](https://www.volvocars.com/us/v/cars/v90) at $52k and the [Mercedes E-class Wagon](https://www.mbusa.com/en/vehicles/class/e-class/wagon) at $67k.

I wonder whether there's an opening for a three-row station wagon variant of a standard sedan starting at ~$19k?

*Comment via: [facebook](https://www.facebook.com/jefftk/posts/10100209208656012)*
# Quarantine variety

Among people sheltering from covid, I think there is a common thought that being stuck in your home for a year begets a certain sameyness, that it will be nice to be done with.

It’s interesting to me to remember that big chunk of the variety that is missing in life comes from regular encounters with other people, and their mind-blowing tendencies to do and think differently to me, and jump to different conclusions, and not even know what I’m talking about when I mention the most basic of basic assumptions.

And to remember that many of those people are stuck in similar houses, similarly wishing for variety, but being somewhat tired of a whole different set of behaviors and thoughts and framings and assumptions.

Which means that the variety is not fully out of safe reach in the way that, say, a big lick-a-stranger party might be. At least some of it is just informationally inaccessible, like finding the correct answer to a hard math problem. If I could somehow spend a day living like a person stuck in their house across the street lives, I would see all kinds of new things. My home itself—especially with its connection to the internet and Amazon—is capable of vastly more variety than I typically see.
# [Link] Whittlestone et al., The Societal Implications of Deep Reinforcement Learning

While much of the information in this paper will already be familiar to many here, I still want to highlight this paper. It's a really good, clearly articulated summary of some of the key challenges facing real-world applications of advanced AI algorithms, applied to DRL in particular. The focus on DRL will make this paper particularly appealing and accessible to ML engineers and to relevant policy makers. To me it kind of feels like an updated version of the Concrete Problems in AI Safety paper, although with a bit less technical detail and with the inclusion of some discussion related more to policy and ethics. This makes the paper important if only as a reference for talking to ML engineers or policy makers who are not necessarily familiar with safety issues and/or who are not particularly concerned about longer-term issues.
# TASP Ep 3 - Optimal Policies Tend to Seek Power

Welcome to the Technical AI Safety Podcast, the show where I interview computer scientists about their papers. This month I covered [Optimal Policies Tend to Seek Power](https://arxiv.org/abs/1912.01683v6), which is closely related to [Seeking Power is Often Robustly Instrumental in MDPs](https://www.lesswrong.com/posts/6DuJxY8X45Sco4bS2/seeking-power-is-often-robustly-instrumental-in-mdps) which is a part of the [Reframing Impact](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW) sequence and was recently a part of the [2019 review](https://www.lesswrong.com/posts/kdGSTBj3NA2Go3XaE/2019-review-voting-results).

The point of the show is to make papers more parsable, the interview features a detailed walkthrough padded on either side by discussion of where the work came from and where it's going. 

I had a lot of fun doing this month's episode, a tricky paper to wrap my head around but very rewarding. Do let me know if you have trouble finding it on your favorite podcast app, thanks! 

## Show notes: 

With Alex Turner

[Feedback form](https://forms.gle/4YFCJ83seNwsoLnH6)

[Request an episode](https://forms.gle/AA3J7SeDsmADLkgK9)

# Optimal Policies Tend to Seek Power

by Alexander Matt Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli

Abstract: 
> Some researchers have speculated that capable reinforcement learning agents are often incentivized to seek resources and power in pursuit of their objectives. While seeking power in order to optimize a misspecified objective, agents might be incentivized to behave in undesirable ways, including rationally preventing deactivation and correction. Others have voiced skepticism: human power-seeking instincts seem idiosyncratic, and these urges need not be present in reinforcement learning agents. We formalize a notion of power within the context of Markov decision processes. With respect to a class of neutral reward function distributions, we provide sufficient conditions for when optimal policies tend to seek power over the environment.

[What Counts as Defection?](https://www.lesswrong.com/posts/8LEPDY36jBYpijrSw/what-counts-as-defection)

[Non-Obstruction](https://www.lesswrong.com/posts/Xts5wm3akbemk4pDa/non-obstruction-a-simple-concept-motivating-corrigibility)
# Tom Chivers, author of "The AI Does Not Hate You", is running an AMA on the EA Forum

Tom's book on the rationalist community was generally well-received by community members. I enjoyed the book, and have continued to enjoy following Tom's work on [COVID and many other things](https://unherd.com/author/tom-chivers/). Among science journalists, I find him to be an unusually clear writer, with good taste when it comes to [choosing which topics are worth covering](https://www.lesswrong.com/posts/6vcxuRHzeM99jYcYd/privileging-the-question).

Tom is [running an AMA on the EA Forum](https://forum.effectivealtruism.org/posts/Q6LY8Tmw6BFuat2BX/ama-tom-chivers-science-writer-science-editor-at-unherd). I think LessWrongers are likely to ask good questions, so I'd love to see you join in! 

(Given the community's recent conflict with certain journalists, this is an interesting chance to ask questions about journalism to someone who is... better at that job.)
# Are dogs bad?

This is my response to [Trapped Priors As A Basic Problem Of Rationality](https://astralcodexten.substack.com/p/trapped-priors-as-a-basic-problem) by Scott Alexander.

I felt that the discussion in the post and comments lack some references to lessons learned from Yudkowsky's [Causal Diagrams and Causal Models](https://www.lesswrong.com/posts/hzuSDMx7pd2uxFc5w/causal-diagrams-and-causal-models) and [Fake Causality](https://www.lesswrong.com/posts/RgkqLqkg8vLhsYpfh/fake-causality) and [One Argument Against An Army](https://www.lesswrong.com/posts/WN73eiLQkuDtSC8Ag/one-argument-against-an-army) which is that you can't bounce back and forth between verdict and assumptions double-counting the evidence in self-reinforcing loop.

Also, I posit that you have to be careful to distinguish separate concepts of "prior on this particular encounter going badly" and "prior on the fraction of the encounters which will go badly" etc. Scott's post seems to hide both under the same phrase "prior that dogs are terrifying". 

I've made a colorful charts showing the story of three encounters with dogs, which illustrate how I believe one should update their prior (and meta-prior) and what can go badly if you let your final verdicts influence them instead:

[https://docs.google.com/spreadsheets/d/1vQfK-g0zAMEcXwbbPbAWWMTpHprE1jpf6bmwDjtJkHo/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1vQfK-g0zAMEcXwbbPbAWWMTpHprE1jpf6bmwDjtJkHo/edit?usp=sharing)

One important feature of the epistemology algorithm I propose above, is that you can admit you were wrong about your past judgments: after exposure to many friendly puppies and updating your prior on "fraction of good dogs" up sufficiently, you may reevaluate your previous verdicts ("see them in a new light") and decide that perhaps they were also good dogs. And there is no risk of this causing a loop in the reasoning, because there are no edges from "verdicts" to "the prior" in this algorithm.

There are many open questions, though. Like for example: should one also update the operational definition of word "bad" over time - in my example, initially we assume that "bad dog" will make us feel threatened 2/3 of the time, and this remains fixed throughout the lifetime. But perhaps this should also update, adding one more layer of Bayes computations. (Perhaps this is exactly the part which is "broken" in people suffering from phobia - perhaps they have a different value than 2/3, or let it drift too much or too little w.r.t. rest of us?). I don't know how to do it, without risking that "bad" and "good" will lose their meaning over time. The 1/3 vs 2/3 split might also change for a different reason: perhaps over time we learn something about our sensors' reliability, their mapping to reality, etc. This might introduce another layer of Bayes. I'd like to know how a full-fledged, correct, Bayesian, mental model of this "simple" issue of "Are dogs bad?" should really look like.
# Parameter vs Synapse?

In terms of AI timelines, the biggest question I haven't seen addressed is the computational equivalence of a synapse vs a parameter in modern neural nets. This seems like a very important input for any prediction on when we will have human-level AI.

Moravec's estimates of a retina vs the then-current edge-detection methods are sort of worthless under the assumption that AI will be built using modern learning methods, because feature engineered code is plausibly much more compute efficient than learned policies on tasks that are comprehensible to human programmers, and Moravec compared the retina with the former.

To pump this intuition, if we assume that 1 synapse == 1 parameter, then Moravec's estimates are more than 6 orders of magnitude too low. The size of the largest models we are able to train, on computers that are more powerful than Moravec predicted are needed for AGI, is at most 10 billion parameters, which is about as many synapses as the retina has. Though, it would be more accurate to say we are memory bandwidth constrained, as we train neural networks at much higher temporal resolution than human brains.

A very interesting question, then, is how many parameters does a modern learned model need to have plausibly similar capabilities to a human retina. This seems hard to investigate but not impossible. If we look at the first few layers of a conv net, for example, they seem to be doing the sort of edge detection that the retina does.

I think this would be a high-leverage thing to investigate as both the parameter > synapse and parameter < synapse are not implausible at first glance. Some people think the brain is doing something mysterious and better than backpropagation. Some people think it is just doing a worse, Rube Goldberg approximation of backpropagation. Artificial spiking neural networks perform really poorly compared standard stuff, which may imply paramater > synapse, etc.

If we assume AGI will come from scaling up current methods (rather than, as Moravec predicted, very efficient programs that imitate the aggregate function of thousand-neuron assemblies) then this question is very pertinent to any prediction. Is anyone here aware of any work on this?
# [recurrent] Focusmate: cryonics and lifelogging

Event link: https://meet.google.com/ufp-fnqt-efn

Arrive at 11:00, 12:00, or 13:00 AST, and stay for as long as you want (there will be 3x 1-hour focusmate session)

Send me your Gmail address to contact@matiroy.com to be invited to the recurrent Google Calendar event.

After the successful [Focusmate: Cryonics sign up](https://www.lesswrong.com/events/PEA45j9eHpcKnnwmp/focusmate-cryonics-sign-up) event, I decided to make a weekly event where people could work on their cryonics or [lifelogging as life extension](https://www.facebook.com/groups/LifeloggingAsLifeExtension/) projects.

Focusmate is an event format where we take a few minutes to share what we'll work on, and then all work at the same time, but without talking; and at the end we report back on the progress we made. It can help get things done. 

I particularly invite people that what to make progress on signing up for cryonics:
- deciding whether we want to sign up for cryonics
- making a plan for when to sign up for cryonics (if not now)
- making progress towards being signed up for cryonics

I've helped multiple people with that process, and I will be there to answer people's questions by text. Why by text?
- because I will document the answers one the Biostasis Wiki
- to avoid distracting others

Note: This event is lifelogging-friendly (ie. participants are welcomed to record it, but for their personal use only).

x-post: https://www.facebook.com/events/1588598061334430/
# Kelly Betting Discussion

This Sunday join an informal discussion with Abram Demski, Lsuser, SimonM and Bunthut on the subject of Kelly Betting.

The Kelly Criterion is a formula for bet-sizing that leads (almost) surely to higher wealth compared to any other strategy in the long run.

Last month, on LessWrong, we had a number of posts exploring the concept of Kelly Betting. It seemed useful to have a live conversation where people could dig deeper into the concept, and discuss their open questions.

We'll be meeting in the Walled Garden at noon PM this Sunday:

[https://www.lesswrong.com/walledGardenPortal/?code=6AdT&event=kelly-betting](https://www.lesswrong.com/walledGardenPortal/?code=6AdT&event=kelly-betting)
# How I come up with ideas

I desire many things. I invent ideas to satisfy my desires. Whenever I learn something new, I compare the new fact against my database of desires. If my new fact could be used to satisfy a desire then I get an idea.

Some desires are universal among humans.
- I want to be rich without putting in much effort.
- I want lots of people to think I'm attractive.
- I want a space station.
- I want an electric car that costs $100.
- I want a time machine, an invisibility cloak and a magic wand.
- I want to live for a long time in good health.
- I want to make my friends happy.
- I want an ethical source of tasty protein.
- I want to save and/or destroy the world.

Common desires are hard to satisfy creatively because too many people are competing to satisfy them. The possibility space of solution tends to have been picked clean. Either common desires are already satisfied or common desires are irreducibly hard to satisfy.

# Cultivating Weird Desires

The trick to coming up ideas is to cultivate **weird** desires. Weird desires often arise from **caring too much** about [**things that don't matter**](http://paulgraham.com/genius.html) (yet).
- I want my keyboard inputs to be as efficient *as humanly possible*. (Solutions: Vim, Spacemacs.)
- I want to learn foreign languages as efficiently *as humanly possible*. Anki isn't good enough. (Solution: write my own ML-powered flashcard software.)
- I run LineageOS to evade adware, bloat and root protections on my Android phone.

Another way to cultivate weird desires is to be stingy with your money.
- I write meta-ML software to automate architecture search because I need machine learning software but I do not want to hire lots of data scientists.
- I lived in a hallway for two years to save money in college. I lived in a kitchen cupboard to save money in China. I like capsule hotels.
- I use use AWS Lambda to avoid paying for more expensive EC2 instances. I use S3 to avoid the minimum price of a traditional RDS.

[![](https://www.lsusr.com/comics/s2.png)](https://www.lsusr.com/0.html)

Weird desires motivate weird behaviors in a self-sustaining chain reaction.
- I wrote a bash script that uses `xrandr` to adjust my screen resolution to the resolution of  my drawing tablet because my drawing tablet's drivers for Linux did not map correctly to i3.
- I do data science in Hy because my meta-ML software requires custom macros for its UI.
- I am reading Tae Kim's *Guide to Learning Japanese* because I want to better understand the poetry of Zen Master Ryokan.

# Announcement

Justin Ith and I will discuss ["How do you come up with ideas?" via Clubhouse](https://www.joinclubhouse.com/event/MRl7Dg3A) @lsusr at 7pm Pacific Time on March 15th, 2021. (Note: Clubhouse runs only on iPhone.)
# Rigorous political science?

Voting theory seems pretty easy to Google, find interesting reference material on, etc. But I'm currently interested in going a bit beyond voting theory, to broader questions of governance. For example, what are the real advantages of parliamentary systems vs presidential systems? What elements of constitutions lead to actual good governance? 
Questions like these are less clear-cut and more highly politicized, so I expect it to be much more difficult to filter out BS. Wikipedia is full of good historical information, but articles on specific government styles tend to only contain vague statements of advantages and disadvantages, rather than rigorous assessment.

Are there good subfields of political science that would offer powerful predictive models, or at least mathematically rigorous analysis?

Are there terms I should Google? Areas with good textbooks? Other book recommendations? Specific papers?
# "Beliefs" vs. "Notions"

The term "belief" is used very differently in Bayesian ML and everyday life.

**1)** In Bayesian ML, is refers to a distribution over entire world models.  
**2)** In everyday life, it refers to something more like a single proposition.

I propose the term "notion" for technical use to describe something like (2).
# Strong Evidence is Common

*Portions of this are taken directly from [Three Things I've Learned About Bayes' Rule](https://markxu.com/Three-Bayes-Learnings).*

One time, someone asked me what my name was. I said, “Mark Xu.” Afterward, they probably believed my name was “Mark Xu.” I’m guessing they would have happily accepted a bet at 20:1 odds that my driver’s license would say “Mark Xu” on it.

The prior odds that someone’s name is “Mark Xu” are generously 1:1,000,000. Posterior odds of 20:1 implies that the odds ratio of me saying “Mark Xu” is 20,000,000:1, or roughly 24 bits of evidence. That’s **a lot** of evidence.

Seeing a Wikipedia page say “X is the capital of Y” is tremendous evidence that X is the capital of Y. Someone telling you “I can juggle” is massive evidence that they can juggle. Putting an expression into Mathematica and getting Z is enormous evidence that the expression evaluates to Z. Vast odds ratios lurk behind many encounters.

One implication of the Efficient Market Hypothesis (EMH) is that is it difficult to make money on the stock market. Generously, maybe only the top 1% of traders will be profitable. How difficult is it to get into the top 1% of traders? To be 50% sure you're in the top 1%, you only need 200:1 evidence. This seemingly large odds ratio might be easy to get.

On average, people are overconfident, but [12% aren't](https://srconstantin.wordpress.com/2014/06/09/do-rationalists-exist/). It only takes 50:1 evidence to conclude you are much less overconfident than average. An hour or so of [calibration training](https://www.guidedtrack.com/programs/icg4cze/embed/auth?source=https%3A%2F%2Fprograms.clearerthinking.org%2Fcalibrate_your_judgment.html) and the resulting calibration plots might be enough.

Running through Bayes’ Rule explicitly might produce a bias towards middling values. Extraordinary claims require extraordinary evidence, but extraordinary evidence might be more common than you think.
# Apollo Creed problems and Clubber Lang problems -- framing, and how to address the latter?

If you've seen the flims Rocky I, II and III you'll be familiar with Apollo Creed and Clubber Lang, but will give a quick summary for those who haven't (spoilers ahead!). In Rocky I, Rocky (the character) is an underdog who is fighting the champion Apollo Creed. He eventually loses to Creed, but preforms so well in the fight that his status as a boxer rises and his given a re-match against Creed in Rocky II that he wins.

Rocky III (the film) begins with Rocky (the character) losing embarrassingly to Clubber Lang who he was favored to defeat. Rocky (the character) spends the rest of the film trying to overcome the psychological consequences of losing to Clubber Lang and getting his mind back in to a place where he can face Clubber Lang again and win. 

I find in my own life when I'm starting something fresh and I'm interested, it's like an Apollo Creed problem. I could "win" or "lose" but so long as a I preform well, the conditions of my life improves and eventually can overcome whatever it is.

But when I've already failed at something once and then need to get back in it, there's a psychological wall that's difficult for me to overcome -- this would be like a Clubber Lang problem. Like I have more of an unconscious resistance to working on those problems. I really have to force myself to work on them, and if manage to work on them progress goes very slowly.  My mind just wants to focus on like anything else it possibly can. It feels icky and demoralizing to work on them, and it's hard for me to sort out exactly why. 

I would like to be better and solving Clubber Lang problems. So... two questions... 

(1) Is there a better way to frame this issue? I feel like it should have been studied before and I don't have the keywords to go looking around for what has been written about it so far. 

(2) If you (the reader) are or aren't aware of answers to (1), do you have insight on to this issue either from your own research or personal experience?
# Blue is Arbitrary

When I was 13, the coolest thing in the world was Seto Kaiba summoning a Blue-Eyes White Dragon.

![](https://s3-us-west-2.amazonaws.com/www.lsusr.com/lesswrong/blue-eyes/summoning-blue-eyes.gif)

The name "Blue-Eyes White Dragon" is a translation of the Japanese "青眼の白龍".
- "龍" means "dragon".
- "白" means "white".
- "の" turns the previous word into an adjective.
- "眼" means "eyes".
- ["青"](https://www.lesswrong.com/posts/SQwTttZA8hznDwZ4N/the-history-of-color-in-chinese#Blue_Green) has no English equivalent.

The word "青" heralds back to a time before the adoption of green (緑) in the Heian period. I speculate that before the Heian period the primary colors used to be white, black, red, gold and 青. "青" is usually translated as "a color spanning blue or green". But to define "青" in terms of blue and is to contort it into a foreign ontology. We don't define "blue" as "a color spanning голубой and синий". 

[![](https://www.lsusr.com/comics/paintriarchy.png)](https://www.lsusr.com/4.html)

To us, "blue" is a singular color with an ideal RGB value around `0x0000ff`.

![](https://s3-us-west-2.amazonaws.com/www.lsusr.com/lesswrong/blue-eyes/blue-square.png)

If you asked a pre-Heian Japanese peasant to imagine the color 青 then ze wouldn't imagine the modern colors blue and green side-by-side. Ze would picture a single color. We can't look up how a pre-Heian Japanese person pictured 青 but we *can* look up what 青 looks like in modern Japanese by searching for it on Google.

![](https://s3-us-west-2.amazonaws.com/www.lsusr.com/lesswrong/blue-eyes/ao-screenshot.png)

For comparison, here are the corresponding English results for "blue".

![](https://s3-us-west-2.amazonaws.com/www.lsusr.com/lesswrong/blue-eyes/blue-screenshot.png)

At first glance, these colors appear similar—and they are. But, conceptually, they are derived from different things. The first (top left) image of Japanese 青 is lighter than the equivalent image of English blue. 青 is the color of the sky as seen between clouds or reflected by the ocean. "Blue" is the color of indigo dye and blue jeans. That's why we have to specify the ["sky blue"](https://blog.xkcd.com/2010/05/03/color-survey-results/) variant in English. "Blue" is the color of blue pigment.

What about the Chinese 青 from which the Japanese 青 is derived?

![](https://s3-us-west-2.amazonaws.com/www.lsusr.com/lesswrong/blue-eyes/qingse-screenshot.png)

The Chinese results for "青" are closer to turquoise than to blue AND they include images of sky and water like the Japanese results. The *things* associated with the color 青 are more similar between Japanese and Chinese even though the absolute value of the colors are more similar between Japanese and English. 青 is the color of nature and plants. Blue is the color of the [future](https://www.youtube.com/watch?v=P7nq-r7QWW0) and [pants](https://www.levistrauss.com/).

I hypothesize the Japanese 青 used to be the same as the Chinese 青 but the Japanese ontology of colors drifted towards the European concept of blue due to greater cultural influences from the West.
# AstraZeneca COVID Vaccine and blood clots

Any thoughts on the reality of the new reports about the potential that the vaccine exposes receivers to a risk of blood clot problems?

I did a quick Google Scholar search but nothing seemed to apply just looking at the title listing in the summary page. The stories I've seen reported in the press seem weak on any actual details about how that vaccine might produce the problem while it seems unheard of with the other vaccines. Incident rates seem really small so it makes me wonder. Are we really getting the real story here or is this a case of some chicken little worries about the sky falling?

I also wonder, given a prior post on the correlation implying some causality, if that thought should not shift one's views towards not really getting the whole story. If so, just what "the story" might actually be.
# Nitric Oxide Spray... a cure for COVID19??

According to [https://www.biospace.com/article/releases/uk-clinical-trial-confirms-sanotize-s-breakthrough-treatment-for-covid-19/](https://www.biospace.com/article/releases/uk-clinical-trial-confirms-sanotize-s-breakthrough-treatment-for-covid-19/)  

> Patients with a self-administered nasal spray application found to have reduced SARS-CoV-2 log viral load by more than 95% in infected participants within 24 hours of treatment, and by more than 99% in 72 hours.

Update: the link to the company's site: https://sanotize.com/covid-19/

This sounds too good to be true, but says it's been RCT'd. What are the odds that by using a nasal spray for 3 days once you get positive test results, or even once you have symptoms would protect you from complications?
# Wild Reading

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/0b79f1b5297472375b8f077e08f3746c852701d971bc39ce.jpg)

When I was in therapy a few years ago, my therapist introduced me to a technique she called *Wild Mind*. The idea is simple: just let yourself do what you want from moment to moment. Follow your impulses. If you feel like cleaning, start cleaning. If, halfway through, you get peckish, eat whatever food occurs to you to eat. Take a walk while you're eating that apple, and if you want, stop and look at the scenery when you notice something striking.

*Wild Reading* introduces one constraint. Follow your impulses, but while staring at the text you need to read. If you have a dense textbook, perhaps you'll idly flip through the pages. Maybe you'll glance at the headers, or skim a sentence or two here and there. Stare at the figures. Count the paragraphs. Maybe you'll find yourself drawn in to read the sentences themselves occasionally. Whatever you do, follow your impulses, no matter how shallow or lazy they seem, as long as you're staring at the text while you're doing it.

I'm just starting to experiment with it, and I like it. But I see this as a hypothesis, one that might be worth experimenting with for several hours on a single text to see how it works for you?
# Sharing a Car

Last week [I posted](https://www.jefftk.com/p/car-thoughts) about how we were thinking of getting a car, but at least for now we've decided instead to share a car with one of our [housemates](https://www.jefftk.com/p/shared-house-setup). They have a Honda Fit, and while it is important to them to have a car, they don't use it very often.

We're planning on dividing costs in proportion to mileage, though if that ends up being too much hassle we may shift to just splitting things 50-50. This includes repairs, gas, and damage to the car that is not the driver's fault. Damage that is the driver's fault will be covered by the driver. We'll pay half the cost of car insurance or the cost of adding us, which ever is more.

The Bluebook value is [$5.9k](https://www.kbb.com/honda/fit/2013/hatchback-4d/?condition=good&intent=trade-in-sell&mileage=89942&modalview=false&options=6520169%7ctrue&pricetype=private-party&vehicleid=378827) and we're going to pay them half that up front. This compensates them for the opportunity cost of having some of their money invested in the car. At some point, when we're no longer sharing a car, they'll return half the remaining value. This is not quite right: if we put more miles on the car we should be getting slightly less back, but it's pretty close. If the car travels 30k miles over the next three years it will be worth [$4.4k](https://www.kbb.com/honda/fit/2010/hatchback-4d/?condition=good&intent=trade-in-sell&mileage=119942&pricetype=private-party&vehicleid=261553), while if it travels 40k miles it will be worth [$4.2k](https://www.kbb.com/honda/fit/2010/hatchback-4d/?condition=good&intent=trade-in-sell&mileage=129942&pricetype=private-party&vehicleid=261553), a difference of just $0.02/mile.

There is also some hassle associated with having the car. When it needs service, we'll take turns bringing it in. Our housemate prefers to leave it on the street, despite having to move it twice a month for street cleaning, and will plan to continue doing that. If we're returning the car within two days of street cleaning, we'll make sure to park it in a street-cleaning-safe spot. Our housemate will still be responsible for moving it for street cleaning if no one has used it in a while. If we choose to park the car in to avoid street cleaning, we'll be responsible for freeing it after.

For managing usage, we'll keep a shared calendar, defaulting to first come first served. If we want to use the car at the same time we'll talk and figure out what makes sense. If no one is currently using the car and it is not reserved, it's fine to use.

We'll keep our carseats buckled in, though if our housemate needs the backseat they are free to remove them or move them to the trunk. We'll make sure we have an extra set so we'll still be able to travel by car in an emergency if our housemate is out.

If this doesn't work, or stops working, we can go ahead with getting our own car, but it seems worth trying as an intermediate step.
# AstraZeneca vaccine shows no protection against Covid-19 variant from Africa

A recently published study has found that the AstraZeneca vaccine does not protect against the "African variant" of nCoV-19, which is a worrying outcome, since the paper believes that we probably won't be able to manufacture a specifically targeted new vaccine for the rest of 2021.

> ### **CONCLUSIONS**
> 
> A two-dose regimen of the ChAdOx1 nCoV-19 vaccine did not show protection against mild-to-moderate Covid-19 due to the B.1.351 variant.

[https://www.nejm.org/doi/10.1056/NEJMoa2102214#.YFJIkr3ju7k.twitter](https://www.nejm.org/doi/10.1056/NEJMoa2102214#.YFJIkr3ju7k.twitter)
# "Objective vs Social Reality" vs "Simulacra 1/3"

Mild rant:

a) I think "Simulacrum Levels 1 - 4" are terrible names and we should be looking to replace them, generally.

b) usually, there's not really a good reason to bring "simulacrum" into it. The concept of simulacrum levels makes sense when you're talking about how how relations-to-reality change over time, or how people relate to it in different ways. But I don't think it makes sense to bring it up every time the distinction between physical/objective-level reality and social reality. Social Reality is a perfectly good name that I think is at least relatively transparent about what it means. 

This mild rant brought to you by [this comment which was one-straw-too-many for me](https://www.lesswrong.com/posts/Js34Ez9nrDeJCTYQL/politics-is-way-too-meta?commentId=yFRQWxX89fYreb5q7#comments).
# What are fun little puzzles / games / exercises to learn interesting concepts?

Things in the category of the 2-4-6 puzzle where you need to guess the rule and can try as many examples as you want to figure out the puzzle, and it teaches 'gung jr ner cebar gb pbasvezngvba ovnf' (https://rot13.com/).

Please describe the puzzle / game / exercise and its solution / strategy / take-away separately in order to avoid spoiling it.

Am particularly interested in cognitive biases, but don't want to restrict the answer-space to those.
# Book Club: Software Design for Flexibility

Gerry Sussman's new book on how to build adaptive systems just came out. It's called "Software Design for Flexibility: How to Avoid Programming Yourself into a Corner" and it covers similar ground to Sussman's [advanced symbolic programming class](https://groups.csail.mit.edu/mac/users/gjs/6.945/red-tape.pdf) which I liked a lot. The class itself is a kind of follow-up to [SICP](https://en.wikipedia.org/wiki/Structure_and_Interpretation_of_Computer_Programs).

I'm reading the book over the next 8 weeks. If you want to read it together, and share notes and toy implementations, add your name to [this doc](https://docs.google.com/document/d/1vs9ep6A9VLvtn6QLMalYgxQSMXvfPdZA0vdVT6POoJs/edit).

Excerpt:

> Our goal in this book is to investigate how to construct computational systems so that they can be easily adapted to changing requirements. One should not have to modify a working program. One should be able to add to it to implement new functionality or to adjust old functions for new requirements. We call this *additive programming*. We explore techniques to add functionality to an existing program without breaking it.
# "You and Your Research" – Hamming Watch/Discuss Party

This Sunday join a watching party for Richard Hamming's famous talk "You and Your Research" then discuss it with other LessWrongers.

Richard Hamming was a researcher at Bell Labs, who worked beside people like Claude Shannon and Richard Feynman. In this talk he gives advice, based on observing these people for many decades, on how not to merely do good work, but Great Work, the sort that Nobel Prizes commemorate.

It is a 45-minute talk that has been influential on many LessWrongers.

We're meeting in Walled Garden at noon PM (PDT) this Sunday:

[http://garden.lesswrong.com?code=OOkD&event=hamming-watch-party](http://garden.lesswrong.com/?code=OOkD&event=hamming-watch-party&fbclid=IwAR18ukIfkHulFuZknVuPhq_wgAd44iZe2oaDzSF6EfFpxE_uFydWrppuQjo)
# Babble Challenge: 50 Ways to Overcome Impostor Syndrome

Here we go again. [Time to become stronger.](https://www.lesswrong.com/posts/DoLQN5ryZ9XkZjq5h/tsuyoku-naritai-i-want-to-become-stronger)

This week’s challenge: 

Imagine you are a bright young PhD student with interesting articles to write.  You have picked your journals, developed the thesis, and are hard at work dratfting publications.

But there's one problem. You have impostor syndrome. You often feel like your ideas are bad, or that you are unqualified, or should only write about one very narrow area. When you examine each doubt, they are clearly unfounded. Impostor syndrome slows down your progress during day long mood swings.

Give me 50 ideas to solve impostor syndrome!

## Rules

Rules from JacobJacob's [babble challenges](https://www.lesswrong.com/posts/pDkZitYsJAwf8mHKJ/babble-challenge-50-ways-of-sending-something-to-the-moon)

- 50 answers please
- Post answers inside [spoiler tags](https://www.lesswrong.com/posts/2rWKkWuPrgTMpLRbp/lesswrong-faq#How_do_I_insert_spoiler_protections_)
- Not all you ideas have to work ("feel better" or "go for a walk" are great answers)
- When your stuck, say something stupid.

> If you spend 5 min agonising over not having anything to say, you’re doing it wrong. You’re being too critical. Just lower your standards and say something, anything. Soon enough you’ll be back on track. 

## Go get them tiger!

I will post my attempt at 5:30 EST today
# What are the best resources to point people who are skeptical of getting vaccinated for COVID-19 to?

There are some people who won't be convinced no matter what but on the other hand there are people who might be conviced to vaccinate against COVID-19 who are currently not planning to be vaccinated. What are the best online resources to point non-rationalists towards to convince them that getting vaccinated is a good idea?
# Vows & Declaration

*This is the eighth and final section of the wedding ceremony of Ruby & Miranda. See the* [*Sequence introduction*](https://www.lesswrong.com/posts/v7eXLKzBN3q4TiGXn) *for more info.*

* * *

**IMAGE 7 PROJECTED ON PLANETARIUM**

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/46aeb4b36f5985eafb38a35f0530504866e90b6712ab9fa1.jpeg)

**Brienne:** We’ve spoken of the darkness, and of our hope to overcome it. We’ve spoken of the light that will come when we do, and of our commitment to become stronger. We’ve spoken of the community that acts together toward this goal, of the purpose of a new partnership within it, and of why those partners have chosen each other.

I now ask them to proclaim their bond, crystallizing their promises before us, beneath the stars.

***Ruby and Miranda recite their vows in unison.***

* * *

**I vow to love you, always and forever. To make your joy my joy, your sorrow my sorrow. To keep your flourishing foremost among my concerns. To stand by you in vigor and frailty, in abundance and famine.  To accept you when you are strong and when you are weak, when you prevail and when you falter. To care for you, especially when you forget to care for yourself.**

**I vow to be proud of you, to respect you, and to always remind you how dear you are to me. To listen to you and to understand you; to give you my attention when you request it, and silence when you request that. To share my thoughts with you and to cultivate our closeness.**

**I vow to become worthy of your love and dedication. To never ask of you more than you are able, but also never less. To help you grow, so that you can help me, so that I can help you, ad infinitum. **

**I vow always to be faithful to our values, visions, and goals. **

**I vow to be your spouse, your companion, your closest friend. **

* * *

**Brienne:** You’ve chosen rings of meteorite as symbols of your dedication to fulfill your promises to each other, and to fulfill the promise of the night sky. Exchange these symbols now.

***Miranda gives Ruby ring.** (Kenzi gives it to her.)*

***Ruby gives Miranda ring.** (Brayden gives it to him.)*

**Brienne:** I would like all of you here to repeat after me.

**"Ruben Michael Bloom and Miranda Irene Dixon-Luinenburg,"**

(guests repeat)

\*pause\*

**"We now pronounce you husband and wife."**

\*pause\*

**Brienne:** You may embrace.

\*applause\*

**PLAY SONG 3: FUTURE - REAL AND TRUE BY MILEY CYRUS**
# [event] Cryonics icebreaker

Get to know other cryonics enthusiasts at the icebreaker event happening on Saturday April 10! Afterward, we will move to The Cryosphere, a Discord server for discussion about cryonics. The goals of this event are to connect cryonics enthusiasts and to promote The Cryosphere, where everyone is welcome to chat, ask questions, and collaborate on projects! If you are interested in this topic but not sure if you'd fit in at the event, then you are especially encouraged to attend! Anyone interested in cryonics is welcome. 

Please sign up on: https://www.eventbrite.com/e/cryonics-icebreaker-tickets-147414616085

The link to the event will be sent through the Eventbrite mailing list before the event. We will use the Icebreaker Video platform for the event (https://icebreaker.video/). You need a Google / Gmail account to login on the platform. No knowledge of the platform is needed, simply click the link to the event when it starts.

Link to The Cryosphere https://discord.gg/FezPKfDuYn

Starting time:
- 10:40 am Pacific Time
- 1:40 pm Eastern Time
- 8:40 pm Moscow Time

Schedule (Eastern Time):
- 13:40 Arrival
- 14:00 Welcome/intro
- 14:10 1:1 icebreakers
- 14:25 Group activity
- 14:40 Q&A
- 14:50 Intro to Discord server; goals of the server, creating new collaborative projects and working on them there
- 15:00 Wrap up and move to Discord

Organizers: Eric Magro, Nikki Olson, Mati Roy
# Six-Door Cars

While at this point [my family has decided](https://www.jefftk.com/p/sharing-a-car) what we want to do about cars, I'm still thinking about why the [market for moderately larger cars](https://www.jefftk.com/p/the-cost-of-a-sixth-seat) is so awkward.

One issue with three-row vehicles is that the third row is hard to get into. If that row is rear-facing, like in a traditional station wagon, you get in through the rear door. If it is front facing, you either remove one of the seats from the middle row, or have a way to slide or fold them out of the way. This is awkward for the same reason that the rear seats in a two door car are awkward, and there we fix it by adding a second row of doors. Why don't we see six door cars?

Car manufacturers have made some:

[![](https://www.jefftk.com/6-door-mercedes-w124-taxi-cyprus.jpg)](https://www.jefftk.com/6-door-mercedes-w124-taxi-cyprus-big.jpg)  
*Mercedes-Benz [w124](https://en.wikipedia.org/wiki/Mercedes-Benz_W124) six-door, [source](https://www.flickr.com/photos/andreboeni/48730624418)*

[![](https://www.jefftk.com/checker-aerobus-ad.jpg)](https://www.jefftk.com/checker-aerobus-ad-big.jpg)  
*Checker [Aerobus](https://en.wikipedia.org/wiki/Checker_Aerobus), also produced with eight doors*

You also see aftermarket conversions:

[![](https://www.jefftk.com/six-door-buick-roadmaster-1992.jpg)](https://www.jefftk.com/six-door-buick-roadmaster-1992-big.jpg)

[![](https://www.jefftk.com/six-door-f350.jpg)](https://www.jefftk.com/six-door-f350-big.jpg)

[![](https://www.jefftk.com/six-door-eagle-coach-2018-Cadillac-XTS.jpg)](https://www.jefftk.com/six-door-eagle-coach-2018-Cadillac-XTS-big.jpg)

[![](https://www.jefftk.com/six-door-prius.jpg)](https://www.jefftk.com/six-door-prius-big.jpg)

These are mostly very long cars, but it seems to me like if a manufacturer wanted to make a six door with a more restrained length they could. For example, a Honda Fit is 161" and a typical rear seat pitch is 35", so a hypothetical six-door version would be something like 196". This is long, but still well within the standard range: it's 6" shorter than the Toyota Sienna and 8" shorter than the Honda Odyssey or Chrysler Pacifica.

Overall, it looks like this is something that can be done, has been done, and hasn't been popular. Maybe I'm weird in liking the six door look?

*Comment via: [facebook](https://www.facebook.com/jefftk/posts/10100210605885952)*