trentmkelly/LessWrong-43k huggingface dataset:
Abstraction = Information at a Distance
by johnswentworth — 2020-03-19T00:19:49.189Z

Why is abstraction useful? Why use a high-level model rather than a low-level model?

An example: when I type “4+3” in a python shell, I think of that as adding two numbers, not as a bunch of continuous voltages driving electric fields and current flows in little patches of metal and doped silicon. Why? Because, if I’m thinking about what will show up on my monitor after I type “4+3” and hit enter, then the exact voltages and current flows on the CPU are not relevant. This remains true even if I’m thinking about the voltages driving individual pixels in my monitor - even at a fairly low level, the exact voltages in the arithmetic-logic unit on the CPU aren’t relevant to anything more than a few microns away - except for the high-level information contained in the “numbers” passed in and out.

Another example: if I’m an astronomer predicting the trajectory of the sun, then I’m presumably going to treat other stars as point-masses. At such long distances, the exact mass distribution within the star doesn’t really matter - except for the high-level information contained in the total mass and center-of-mass location.

If I’m running a Markov-Chain Monte Carlo algorithm, then I take sample points fairly far apart in “time”. As long as they’re far enough apart, they’re roughly independent - there isn’t any information from one sample relevant to the next.

If I’m planning a roadtrip from San Francisco to Los Angeles, the details of my route through the Bay Area are irrelevant to planning my route within LA - except for the high-level information contained in my choice of highway for the middle leg of the trip and the rough time I expect to get there.

General point: abstraction, in practice, is about keeping information which is relevant to things “far away”, and throwing out everything else.

Formalization

Let’s start with a bunch of random variables Xi, and some notion of which variables are “nearby”: each variable Xi has a set si of indices of variables considered “nearby” Xi. How si is chosen may vary by application - maybe each Xi is associated with some point in space and/or time, or maybe we’re looking at Markov blankets in a graphical model, or …

We want some high-level summary...
What are good ways of convincing someone to rethink an impossible dream?
by aarongertler — 2020-03-19T00:06:53.991Z

I sometimes get pitches from people who have grandiose plans.

Think of the term "physics crank," but applied to conceptual companies rather than perpetual motion machines. Two prominent examples (but far from the only ones):

A calligrapher and accountant with no entrepreneurial experience who wanted to create an electronic journal that would allow people to "quantify their states of mind," as part of the greatest productivity/creativity enhancement system the world had ever seen. We were friends when I was in college, and this was my first exposure to the type. At first I lumped him in with other friends who were starting companies; he'd drawn up a logo and printed business cards and it all seemed very convincing. But after a few "meetings" that mostly involved scribbling Danielewski-esque charts in a notebook, I realized that all of his research and passion weren't actually going anywhere.
Someone who reached out to me recently about creating a new form of social cryptocurrency that would allow people to collectively vote on corporate actions, to be enforced by threat of boycott. They sent me a 50-page PDF which contained no information on any practical steps to establish or test the currency, but a lot of musings on rising inequality and the will of the people. It was obvious that the document (which included dozens of charts) had taken weeks to produce. The author mentioned having reached out to many people to ask questions about getting attention from funders (I seem to be the only person who responded).

In both cases, a self-educated person with an altruistic mindset got their mind snagged on a project that had no clear way of manifesting in reality, and they couldn't unsnag themselves.

I don't feel any qualms about breaking off situations like this early on with a polite "no," but they still fill me with intense clueyness.

I wish there were a story I could tell these people that would help them understand the way businesses actually work (and, more broadly, the means by which new useful things can actually be introduced to the world). But I've found it difficult to penetrate the mindset of:

"I have a big idea, just like [insert charismatic tech billionaire], and I just need one good break to make it happen. The world is ready for this!"

Asking about how they plan to test their idea sometimes helps a bit, but what I really want is a message that serves as a ...
[UPDATED] COVID-19 cabin secondary attack rates on Diamond Princess
by Bucky — 2020-03-18T22:36:06.099Z

Update 19/03/20: Inspired by johnswentworth's comment, I implemented a multinomial distribution on the 4-berth cabin result. Taking this additional information into account the model shows reduced likelihood of secondary attack rates of >0.9.

Introduction

Jimrandomh recently showed how we have no real idea about the household secondary attack rates of COVID-19.

The Diamond Princess data showed that the proportion of passengers infected with COVID-19 increased with cabin occupancy.

It occurred to me that this data could be used to infer the cabin secondary attack rates.

Data

I eyeballed the data in figure 2 in the report linked above.

There were 6 COVID-19 cases in single passenger cabins which looks like ~8% infection rate so there were ~75 passengers in single cabins.

For double cabins the numbers are 485/2425 = 20%.

For triple cabins 27/129 = 21%.

For 4-berth 18/60 = 30%.

(all numbers are per person, rather than per cabin)

These numbers add up to 2,689 total passengers which is slightly more than 2,646 actually included but this is close as eyeballing is likely to get me.

Method

I implemented a model with 2 variables:

1. The background rate of infection without sharing a cabin (just from being on the ship).

2. An additional rate of infection for each infected person an individual shared a cabin with.

Given those two variables I was able to create predicted infection rates for each size of cabin by calculating the probability of the number of initial cases in a cabin (before secondary attack) and then the probability of each result after applying secondary attacks.

I created 2 models, one where I only included secondary attack and another where the victim of the secondary attack could in turn cause a tertiary attack on any remaining healthy members of the cabin. Tertiary attack may not have been possible (or somewhat suppressed) by the quarantine and/or other factors.

Importantly the secondary attack rate as used by me here is “probability of contracting COVID-19 for each person in the cabin who had COVID-19”. So if you live with 2 infected people then you have a higher probability of contracting than if you just lived with 1. In 4-berth cabins having even one person infected gives a high probability of at least one of the remaining people being infected at which point the other 2 have a higher chance (when allowing for tertiary attack).

Even with a re...
March 17th: Daily Coronavirus Links
by habryka — 2020-03-18T20:55:45.372Z

As part of the LessWrong Coronavirus Link Database, Ben, Elizabeth and I are publishing daily update posts with all the new links we are adding each day that we ranked a 3 or above in our importance rankings. Here are all the top links that we added yesterday (March 17th), by topic.

Aggregators

Notion board aggregating mostly project-related news

The goal of COVIDbase is to link people who have skills to relevant projects and to inspire new ideas.

Dashboards

Wolfram's tools and data for C19 visualization

Collection of Wolfram resources relating to C19, including data visualization and mining tools and datasets to run them on

DIY

Discussion: are cloth masks useful?

Do masks made from typical clothes do enough to be worth the convenience? Hrothgar concludes they do

Guides/FAQs/Intros

Intro to basic science of C19 and treatment/prevention

Gentle guide to the basic science of C19, diagnostics, the course of the disease, and types of treatment one might receive

Medical System

Small RCT shows promise for chloroquine, w/ or w/o azithromycin

(FRENCH) N=24 study showed patients recovering several days faster with chloroquine, and even faster with azithromycin

Characteristics of 140 Wuhan patients

Fever (91.7%), cough (75.0%), fatigue (75.0%), and gastrointestinal symptoms (39.6%) were the most common clinical manifestations, hypertension (30.0%) and diabetes mellitus (12.1%) were the most common comorbidities. Drug hypersensitivity (11.4%) and urticaria (1.4%) were self-reported by several patients. Asthma or other allergic diseases were not reported by any of the patients. Chronic obstructive pulmonary disease (COPD, 1.4%) patients and current smokers (1.4%) were rare. `

Paper: study of C19 protease for possible drug targets

Scientists testesd C19 M-pro protease to determine its preferences, using a large number of both natural and unnatural amino acides.

Link to Database
My Functor is Rich!
by adamShimi — 2020-03-18T18:58:39.002Z

This is the first post in a sequence of write-up about my study of advanced Haskell concepts.
As mentioned in the
intro to this sequence,
this post does not pretend to be a tutorial, merely an exploration
of the subject with many explanations.

Intro

My first couple of stops on the road to Haskell proficiency
are the most important typeclasses. This includes the infamous
Monad, the unknown Applicative, and the subject of this post: Functor.
But before detailing the latter, let's refresh our definition of a
Haskell typeclass.

What's a typeclass?

One of Haskell's selling points is its strong type system. We might
even say the focus on functional purity stems from the want for
powerful type systems. Now, using strong types in a naive way creates
massive redundancy: each function would have to be implemented for all the
different types it works on. Of course, there will be some implementation to
be done -- but the less the better.

Here, genericity comes to the rescue. We use generic types instead of concrete ones
to write functions only once -- for the generic type. But this
brings another set of problems: with completely generic types, we can't do
anything. That's because a type tells us what we can do with its values: you can
ask if an int is even or odd, but that doesn't make sense for a bool or a float.
For example, a function of type

f :: a -> a

can only be the identity function. Why? Because I don't know anything about
values of type a. Notably, I don't know how to build one. So when I need
to return a value of type a, and I got one in input, my only option is
to give it back.

Typeclasses are a clean way to constrain genericity, so that we can implement
non trivial functions on generic types. Formally, a typeclass is defined
by a set of functions:

class OurTypeclass a where
f1 :: a -> [a]
f2 :: a -> a -> Bool

An instance of this typeclass for a concrete type is an implementation of these
functions.

instance OurTypeclass Bool where
f1 x = [x]
f2 x y = x

Then this instance gives us the ability to call f1 and f2 on values
of type Bool. Which means we can define a generic function on any type with
an instance of OurTypeclass, and use f1 and f2 in this function.

f :: (OurTypeclass a) => a -> a -> [a]
f x y = filter (f2 y) (f1 x)

The more types with instances of OurTypeclass, the most useful
the abstraction becomes.

Another aspect of

...
Welcome to the Haskell Jungle
by adamShimi — 2020-03-18T18:58:18.083Z

In my first year of engineering school (basically last year of undergraduate school in France), I became a fixture of the computer programming club. It was a weird nerd den, but there was always something to learn there. And not too late after I started skipping classes, a friend there talked to me about Haskell. I don't remember how much I understood of the advantages and issues of pure functional programming; but I remember I liked the language, its elegance, and also its deepness of abstraction. The math kid in me liked a programming language where each new idea required a paper to express.

Flash-forward five years, and I am not a master of Haskell. I know many of the concepts at a shallow level and I toyed with it a bit, sure. Being in a formal methods research team also ensured that I learned many ideas inherent to functional programming and the like. But for Haskell itself, I never really plunged into its deepness. Never found the time.

Until today. Or a few weeks ago, to be more precise. Because as part of my reorientation to AI safety research, I now have an incentive to learn more of Haskell: MIRI wants Haskell programmers. Even if that's not exactly the job I'm aiming at, Buck Schlegeris, one of the recruiters at MIRI, confirmed in a call that it was one of the criteria MIRI used for hiring. And since I wanted to dig deeper into it for years now, that's an awesome excuse.

Which brings me to the point of this post: explaining what I'm going to do in this sequence. My goal is to learn the deep parts of Haskell, in as much detail as I need to be a good Haskell programmer. This means I want to eventually be able to:

Write and read idiomatic Haskell code
Optimize Haskell code
Understand basically any Haskell code

Ideally, I would write a tutorial on every thing I study. But I want to learn fast, and writing tutorials is extremely time consuming. So I will instead post write-up of my explorations of a topic. These will generally include explanations, though without the promise of a complete narrative. Also, I will assume in the reader a general knowledge of functional programming, just not of Haskell specifics.

Finally, what will I study? Inspired by the following two study plans, I have this list for starters:

Common Typeclasses, like Functor and Monad
Concurrency
Laziness
Functional Data Structures (a la Okasaki)
Optimization of functional

...
How can we protect economies during massive public health crises?
by kithpendragon — 2020-03-18T18:56:21.933Z

Without the economic knock-on effects, telling people to all close up shop and stay home during a massive health crisis would be a no-brainer. Unfortunately, the risk of a nasty economic shock seems to be weighing heavily in the decisions of our leadership here in the USA at every level of government.

We already have certain economic safeties installed from previous shocks, like the safeguards that stop trade in the stock market under certain conditions so we don't easily get a repeat of the crash of 1929. Or the foundations and laws that are designed to swoop into action after a hurricane. Seems like the COVID-19 crisis is providing us with information that could help us build new safeties.

Health crises are different from (other) natural disasters because infrastructure is generally not affected. The two are similar because each can cause people become unable or less able to participate in driving the economy. In both, economy can resume once the crisis is over, but a natural disaster tends to have a much smaller footprint and longer economic reboot time than a health crisis might.

The U.S. federal government is too slow (by design) and apparently quite unprepared to react effectively to a disaster on the scale of COVID-19. Even if they were faster, handing over control of large portions of the economy to the federal government always leaves a bad taste and could easily have consequences just as dire as doing nothing.

I tend to think of the economy as an information entity driven by the movement of value among various actors like individuals, companies and corporations, states and so on up to world-scale organizations. Since actors on all levels are affected in a health crisis, it makes sense for actors on all levels to have access to safety mechanisms that protect various interests.

What kinds of systems could we put in place on each of these levels to allow the relevant slices of economy to go on hold for at least a few weeks as needed? To put it another way: What would we need to do to install economic pause buttons at various scales, and to minimize the effects of any given entity actually using those buttons?
Does the 14-month vaccine safety test make sense for COVID-19?
by orthonormal — 2020-03-18T18:41:24.582Z

I was first wondering why, if we keep hearing about teams rapidly generating vaccines for COVID-19, the common wisdom is that it will take 18 months to start vaccinating at a large scale.

Turns out that the scaling up takes a few months, but the real blocker is the Phase 1 trial, which requires monitoring patient health for 14 months after vaccination.

Doesn't it seem like the cost-benefit analysis changes a bit if we're in the midst of a pandemic? Wouldn't it be worth cutting it down to e.g. 3 months before at least vaccinating the highest-risk populations? Is anyone official even thinking about this?
[AN #91]: Concepts, implementations, problems, and a benchmark for impact measurement
by Rohin Shah — 2020-03-18T17:10:02.205Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

HIGHLIGHTS

Reframing Impact - Part 2 (Alex Turner) (summarized by Rohin): In part 1 (AN #68) of this sequence, we saw that an event is impactful if it changes our ability to get what we want. This part takes this understanding and applies it to AI alignment.

In the real world, there are many events that cause objective negative impacts: they reduce your ability to pursue nearly any goal. An asteroid impact that destroys the Earth is going to be pretty bad for you, whether you want to promote human flourishing or to make paperclips. Conversely, there are many plans that produce objective positive impacts: for many potential goals, it's probably a good idea to earn a bunch of money, or to learn a lot about the world, or to command a perfectly loyal army. This is particularly exacerbated when the environment contains multiple agents: for goals that benefit from having more resources, it is objectively bad for you if a different agent seizes your resources, and objectively good for you if you seize other agents' resources.

Based on this intuitive (but certainly not ironclad) argument, we get the Catastrophic Convergence Conjecture (CCC): "Unaligned goals tend to have catastrophe-inducing optimal policies because of power-seeking incentives".

Let's now consider a conceptual version of Attainable Utility Preservation (AUP) (AN #25): the agent optimizes a primary (possibly unaligned) goal, but is penalized for changing its "power" (in the intuitive sense). Intuitively, such an agent no longer has power-seeking incentives, and so (by the contrapositive of the CCC) it will not have a catastrophe-inducing optimal policy -- exactly what we want! This conceptual version of AUP also avoids thorny problems such as ontology identification and butterfly effects, because the agent need only reason about its own beliefs, rather than having to reason directly about the external world.

Rohin's opinion: This was my favorite part of the sequence, as it explains the conceptual case for AUP clearly and concisely. I especially liked the CCC: I be...
Request for Comments on Online LessWrong/SSC Meetup--Rump Session
by JoshuaFox — 2020-03-18T16:51:57.249Z

EDIT: We're doing it!

See here for meetup announcement.

--------------

Anyone interested in an online LessWrong/SlateStarCodex meetup?

We'd start with personal introductions for some sociability, and then do a Rump Session, where anyone can step up and give a four minute talk; then, if you want to keep talking, the audience votes on whether to go another three minutes. Talks can be spontaneous rather than prepared, and on any subject that you'd like: Science topics, poetry readings, or anything else.

At LessWrong Israel, we've found this to be a lot of fun--no risk of being bored or boring, as talks are so short.

As it would be online, people from anywhere could join as timezones allow.

If you think you'd like such a thing, please upvote and/or comment.
Can we use Variolation to deal with the Coronavirus?
by FactorialCode — 2020-03-18T14:40:35.090Z

Credit for the original idea comes from this thread: https://www.reddit.com/r/slatestarcodex/comments/fk5au8/corona_variolation/

The idea is to use variolation to innoculate young people and build up herd immunity to the virus. Perhaps with an infected scratch to the shin.

I imagine this would be completely politically infeasible, but ignoring that, can anyone weigh in on whether this would work or not? I thought I read somewhere that getting infected with the virus through the eyes was far less lethal than getting infected through the lungs, but I can't find the source ATM.
Ubiquitous Far-Ultraviolet Light Could Control the Spread of Covid-19 and Other Pandemics
by avturchin — 2020-03-18T12:44:42.756Z

Roko Mijic, Alexey Turchin

Epistemic status: Many different uncertainties here, but the idea has some good evidence in favor of it and a high potential payoff.

Tl;dr: We should urgently investigate putting special human-safe Far-UVC lamps all over our built environment to ‘kill’ virus particles whilst they are in the air, thereby vastly reducing covid-19 spread.

Inspired by: https://www.nature.com/articles/s41598-018-21058-w

One of the most promising and neglected ideas for combating the spread of covid-19 is the use of ubiquitous ultraviolet light in our built environment (trains, offices, hospitals, etc). Ultraviolet light is already being used as a disinfecting agent across the world; it goes by the acronym UVGI - “Ultraviolet germicidal irradiation”. The energetic photons of UVC light break chemical bonds in DNA and kill/inactivate both viruses and bacteria.

Ultraviolet light on earth exists on a spectrum between 200nm and 400nm. Light above 400nm is blue visible light. Light below 200nm is called “vacuum UV” because it is strongly absorbed by the oxygen in ordinary air and therefore cannot exist except in a vacuum or some other non-air medium. Within the 200-400nm range we have UVA, UVB and UVC, and at the short-wave edge of the UVC band we have “Far-UVC”, from roughly 200nm–220 nm.

Safety considerations

Human beings are also vulnerable to UV radiation. It causes skin cancer and serious eye damage.

However, recent research suggests that the Far-UVC band is actually safe for human skin because it cannot penetrate through the thin layer of dead skin cells on the surface of our skin.

This means that it might be possible to mount a long-term response to covid and other pathogens by constantly illuminating our built environment with light from specifically the Far-UVC band. If the Far-UVC light is indeed safe for humans, the Far-UVC could be on at all times and could destroy or deactivate viral particles before they can spread from person to person.

Why hasn’t this already been considered by relevant authorities? Far-UVC appears in a literature review by WHO, but it is not currently being acted upon as the amount of evidence in favor of safety and efficacy is small.

There is some uncertainty about whether Ozone generation by this band (200nm-220nm) would be problematic. Ozone is no...