trentmkelly/LessWrong-43k huggingface dataset:
COVID-19's Household Secondary Attack Rate Is Unknown
by jimrandomh — 2020-03-16T23:19:47.117Z

For group houses, one of the most important factors when deciding how to relate to COVID-19 is the question: If one of my housemates gets infected, how likely am I to also get infected? This is known as the household secondary attack rate, and it determines how much you need to worry about your housemates' level of precaution (as compared to your own), and how much within-house social distancing is necessary.

Household secondary attack rate is context dependent; it could mean one of several things, which I will illustrate with two scenarios:

Scenario 1: A member of your household is returning from a trip to Wuhan, which you have heard is high risk. You react… however you react to that. You might find a way to be out of the house for awhile, or prepare an isolated room they can lock themself in. If they're your spouse, you might decide that isolation isn't worth it. Some time not long after they become symptomatic, they check into a hospital, where they remain until after their infectious period is over.
Scenario 2: You live in a group house. You all have separate rooms, but share a kitchen and living room. One of your housemates is infected during a trip to the grocery store. Some time later they become symptomatic, and you react… however you react to that. You might or might not have somewhere to move to, or somewhere to move them to, but they can't check into a hospital because they're all full. You might or might not have been exposed to

presymptomatic transmission

. If neither of you has the ability to move, you'll be in the same house until after they've recovered.

Right now there are two studies which purport to measure the household secondary attack rate:

Active Monitoring of Persons Exposed to Patients with Confirmed COVID-19 — United States, January–February 2020

Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts

I'll refer to these as the CDC report and the Shenzhen study, respectively. These are the only studies I have been able to find which make any quantitative claim about COVID-19's secondary attack rate, and all other claims I've found have traced back to either of these two. The CDC study finds a household secondary attack rate of 10%; the Shenzhen study finds a household secondary attack rate of 15%.

When I started writing this post, I thought I'd be focusing on the difference between

...
AI Alignment Podcast: On Lethal Autonomous Weapons with Paul Scharre
by Palus Astra — 2020-03-16T23:00:17.664Z

Most relevant to AI alignment, and a pertinent question to focus on for interested readers/listeners is: if we are are unable to establish a governance mechanism as a global community on the concept that we should not let AI make the decision to kill humans, then what effects will this have on and can we still deal with more subtle short-term alignment considerations and long-term AI x-risk?

Podcast page and audio: https://futureoflife.org/2020/03/16/on-lethal-autonomous-weapons-with-paul-scharre/

Transcript:

Lucas Perry: Welcome to the AI Alignment Podcast. I’m Lucas Perry. Today’s conversation is with Paul Scharre and explores the issue of lethal autonomous weapons. And so just what is the relation of lethal autonomous weapons and the related policy and governance issues to AI alignment and long-term AI risk? Well there’s a key question to keep in mind throughout this entire conversation and it’s that: if we cannot establish a governance mechanism as a global community on the concept that we should not let AI make the decision to kill, then how can we deal with more subtle near term issues and eventual long term safety issues about AI systems? This question is aimed at exploring the idea that autonomous weapons and their related governance represent a possibly critical first step on the international cooperation and coordination of global AI issues. If we’re committed to developing beneficial AI and eventually beneficial AGI then how important is this first step in AI governance and what precedents and foundations will it lay for future AI efforts and issues? So it’s this perspective that I suggest keeping in mind throughout the conversation. And many thanks to FLI’s Emilia Javorsky for much help on developing the questions for this podcast.

Paul Scharre is a Senior Fellow and Director of the Technology and National Security Program at the Center for a New American Security. He is the award-winning author of

Army of None: Autonomous Weapons and the Future of War

, which won the 2019 Colby Award and was named one of Bill Gates’ top five books of 2018.

Mr. Scharre worked in the Office of the Secretary of Defense (OSD) where he played a leading role in establishing policies on unmanned and autonomous systems and emerging weapons technologies. Mr. Scharre led the DoD working group that drafted DoD D...
Kevin Simler: Outbreak
by habryka — 2020-03-16T22:50:37.994Z
Link: https://meltingasphalt.com/interactive/outbreak/

Kevin Simler released another blogpost, this time on disease spread modeling. Excerpt:

Harry Stevens at The Washington Post recently published a very elegant simulation of how a disease like COVID-19 spreads. If you haven't already, I highly recommend checking it out.

Today I want to follow up with something I've been working on: playable simulations of a disease outbreak. "Playable" means you'll get to tweak parameters (like transmission and mortality rates) and watch how the epidemic unfolds.

By the end of this article, I hope you'll have a better understanding — perhaps better intuition — for what it takes to contain this thing. But first!...

AN IMPORTANT WARNING:

This is not an attempt to model COVID-19.

What follows is a simplified model of a disease process. The goal is to learn how epidemics unfold in general.

WARNING #2: I'm not an epidemiologist! I defer to infectious disease experts (and so should you). I have almost certainly made mistakes in this article, but I'll correct them as quickly as I can. If you see any problems, please get in touch.

Alright?

Let's do this.
March 14/15th: Daily Coronavirus link updates
by habryka — 2020-03-16T22:24:11.637Z

As part of the LessWrong Coronavirus Link Database, I am publishing a daily update post 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 we (Elizabeth, Ben and I) added over the weekend, by topic.

Guides/FAQs/Intros

Coronavirus: Why you must act now (medium post)

Summary and call to action, one of the best summaries I've found, focuses more on policy-interventions than on individual actions, but is still good at giving you an overview

UpToDate: Coronavirus Overview

UpToDate very frequently has the best overviews over many crucial medical topics. Geared towards a more professional medical audience.

Letter to loved ones asking to take CV seriously

Well written, compassionate explanation of why this is worse than what we've seen before and need to take this seriously. A few pointers on where to get started

Early February 80,000 hours episode on Coronavirus (1h 18min)

Long episode on coronavirus. Includes all the basic information, and discussion of big-picture implications

Spread & Prevention

Masks help prevent infection in schoolchildren

:

In a group of 10,524 Japanese schoolchildren, masks and vaccination decreased the chance of catching influenza, gargling and handwashing increased the chance

Metareview: facemasks at mass gatherings

A collection of 25 studies with 12710 participants was examined. Out of 13 studies on respiratory illness, 4 showed statistically significant effectiveness, 1 showed statistically significant ineffectiveness, the rest produced no statistically significant results

“Flattening the Curve” is a deadly delusion

At current medical capacity, we'd need to flatten the curve for 10 years in order to get everyone full treatment

Summary of papers arguing that many/most infections spread from people before they show symptoms

A readable summary of some papers about how quickly people transmit the disease and how quickly the symptoms show. Argues that perhaps the majority of people transmit it before they show symptoms.

Timothy Gowers: Herd immunity is very costly, and we can't get all the things we ant

Twitter thread on costs of UK's herd immunity strategy, argues that we have to give up at least one of: (1) herd immunity (2) non-overwhelmed hospitals (3) get life back to normal before next winter

Paper: derives CV prevalence estimates from data on unexplained flu-

...
Positive Feedback -> Optimization?
by johnswentworth — 2020-03-16T18:48:52.297Z

When I imagine the beginnings of life on earth, I imagine a handful of molecules which just-so-happen to catalyze reactions which produce more of those same molecules. The more of molecule X there is, the more molecule X is produced. The chemical kinetic equations contain a positive feedback loop (aka instability).

There might also be other molecules which catalyze their own production. If Y catalyzes its own production more efficiently than X, then we eventually expect to see more Y than X. Still at the level of chemical kinetics, we’d say that the equations contain multiple positive feedback loops, and the feedback loop for Y has a faster doubling time than that for X.

We haven’t used the word “fitness” here at all. We’re quite literally talking about eigenvalues of a matrix (i.e. the Jacobian of the kinetic equations of the chemical system) - those are what determine the relevant doubling times, at least close to ambient steady-state concentrations. As we move away from ambient concentrations, the math will get a bit more complicated, but the qualitative idea remains: we’re talking about positive feedback loops, without any explicit mention of fitness or optimization.

But it sure seems like there should be a higher level of abstraction, at which the positive feedback loops for X and Y are competing, Y eventually wins out due to higher fitness, and there’s some meaningful sense in which the system is optimizing for fitness.

More generally, whenever there’s a dynamical system containing multiple instabilities (i.e. positive feedback loops), it seems like there should be a canonical way to interpret that system as multiple competing subsystems, under selection, optimizing for some kind of fitness function. I’d like a way to take a dynamical system containing positive feedback loops, and say both (a) what the competing subsystems are, and (b) what fitness function it’s implicitly maximizing.

Something like this would likely be useful in a number of areas:

Alignment: notice implicit optimization by looking for dynamic instabilities (e.g.

instabilities in imperfect search

).
Agent foundations: formulate “agents” as self-reinforcing feedback loops in dynamical systems. Tying effective self-reinforcement to world-models would probably be a key piece (e.g. along these lines).
Biology: gener

...
Why such low detected rates of COVID-19 in children?
by Bucky — 2020-03-16T16:52:02.508Z

In China 0.9% of COVID-19 cases were in people <10. In the Chinese population 11.9% of people are <10.

In Italy its 0.5% and 8.4%.

In South Korea it's 1% and 8.4%.

10-19 year olds are also very underrepresented although less severely so.

This seems odd.

I've seen suggestions that this is down to younger people not being symptomatic and hence being less likely to be tested.

Contra this, these countries have implemented ALOT of tests (China: unknown but apparently can do >1.5M/week, S. Korea: 275k, Italy: 125k). I can imagine children being tested less, but this much less? If there are 97 negative tests in 100 positive tests (as in South Korea), I'm struggling to believe that there are hundreds of children out there who are asymptomatic COVID-19 carriers whom the testing agencies are just missing.

On Diamond Princess, with 100% testing, 33% of <20s who contracted Coronavirus were symptomatic, albeit with a small sample (2/6, in under 10s it was 0/1) which isn't hugely far off the average of 49%. 20-29 year olds had a larger sample and symptomatics were 89%. It would be strange to see such a dramatic change and I am no less confused than before.

So, what gives?
Could you save lives in your community by buying oxygen concentrators from Alibaba?
by abergal — 2020-03-16T08:58:31.381Z

Alibaba (and Amazon) sell lots of "portable oxygen concentrators" in bulk for $200 - $300 a piece, e.g this one. These seem to differ from medical-grade oxygen concentrators (which you need a prescription to buy) in that they deliver less oxygen at less purity, i.e. medical-grade oxygen concentrators can provide >5L/min of oxygen at > 85% purity, whereas one of these goes from 1L/min at 92% to 5L/min at 30%.

Here's a case for why buying one or more could be life-saving, which I would love LessWrong's opinion on. I think any of these points could be quite wrong.

Some sources I've consulted suggest that something like ~7% of infected patients must go to the hospital, are put on low-flow (<5L/min) ventilation, and then recover.

The source above suggests that this could be a life-or-death difference.
Seems plausible that one Alibaba oxygen concentrator would be enough for some patients.
In a pinch, seems like you could put several of these together with some tubing to make a 5L / min concentrator at high purity.

Ventilators are much more limiting than oxygen, but if hospitals become absurdly overwhelmed, it seems plausible that even oxygen access will be limiting.
These are produced in China, which has (for now) enough health-care capacity to deal with its patients, so you may not be taking away medical supplies from people who use them otherwise. I worry that they will be used by more legitimate institutions in the future, but maybe they can just be donated to those institutions if that seems to be the case. Alibaba also lists these as "Hot New March 2020" products, suggesting they might be increasing supply to meet demand.

Even if this is a good idea, I'm not sure if it's cost-effective over other life-saving interventions, but it seems plausible that you could convince someone who wouldn't otherwise give money to buy them (e.g., they could buy one if they were particularly concerned for a family member and then donate it as necessary).
What are some exercises for building/generating intuitions about key disagreements in AI alignment?
by riceissa — 2020-03-16T07:41:58.775Z

I am interested in having my own opinion about more of the key disagreements within the AI alignment field, such as whether there is a basin of attraction for corrigibility, whether there is a theory of rationality that is sufficiently precise to build hierarchies of abstraction, and to what extent there will be a competence gap.

In "Is That Your True Rejection?", Eliezer Yudkowsky wrote:

I suspect that, in general, if two rationalists set out to resolve a disagreement that persisted past the first exchange, they should expect to find that the true sources of the disagreement are either hard to communicate, or hard to expose. E.g.:

Uncommon, but well-supported, scientific knowledge or math;

Long inferential distances;

Hard-to-verbalize intuitions, perhaps stemming from specific visualizations;

Zeitgeists inherited from a profession (that may have good reason for it);

Patterns perceptually recognized from experience;

Sheer habits of thought;

Emotional commitments to believing in a particular outcome;

Fear that a past mistake could be disproved;

Deep self-deception for the sake of pride or other personal benefits.

I am assuming that something like this is happening in the key disagreements in AI alignment. The last three bullet points are somewhat uncharitable to proponents of a particular view, and also seem less likely to me. Summarizing the first six bullet points, I want to say something like: some combination of "innate intuitions" and "life experiences" led e.g. Eliezer and Paul Christiano to arrive at different opinions. I want to go through a useful subset of the "life experiences" part, so that I can share some of the same intuitions.

To that end, my question is something like: What fields should I learn? What textbooks/textbook chapters/papers/articles should I read? What historical examples (from history of AI/ML or from the world at large) should I spend time thinking about? (The more specific the resource, the better.) What intuitions should I expect to build by going through this resource? In the question title I am using the word "exercise" pretty broadly.

If you believe one just needs to be born with one set of intuitions rather than another, and that there are no resources I can consume to refine my intuitions, then my question is instead more like: How can I better introspect so as to find out which side I am on :)?

Some ideas I am aware of:

...
How Do You Convince Your Parents To Prep? To Quarantine?
by Elizabeth — 2020-03-16T03:42:06.061Z

A number of my friends are having difficulty conveying the seriousness of COVID-19 to their parents, or getting them to take action instead of panic. What have you found useful in convincing your parents to prep and stay home, and for bonus points, convincing their gatherings to close up?

I'm actually not a great case for answering this- my dad was born 30% prepper. But I think I could have saved a statistical life had I convinced him to convince his church to close a week earlier. And he might have been able to do that sooner had he known the technical solutions available to hold virtual church. So in addition to rousing essays and threats, please consider what tools would make parents' quarantined lives better.
Coronavirus Justified Practical Advice Summary
by Elizabeth — 2020-03-15T22:25:17.492Z

Two weeks ago Ben Pace and I asked for practical advice on what to do about coronavirus, with the requirement that advice be justified in some way- ideally full blown models informed by empirical data, but at a minimum, an explanation of why it was helpful. The resulting thread had wonderful advice but makes for, uh, inefficient reading, at best. This post is an attempt to take the best of the Justified Practical Advice Thread and present it in a clear, easy-to-digest format.

It’s important to note that neither the original post nor this one are attempting to give comprehensive advice. If you’re looking for that, I suggest checking out the Guide/FAQs/Intros tab on the LessWrong Coronavirus Link Database. This is for advice that fell through the cracks.

Though I will briefly mention the standard advice. From what I can tell, the most important (and luckily widely publicized advice) is the following:

Wash your hands a lot, and wash them to a hospital standard (spend 20 seconds and follow these instructions).
Socially isolate yourself, and don't be around other people unless really necessary. Avoid groups of people, especially large gatherings. Really don't go to bars, clubs, restaurants or other places that lots of people pass through (like public transportation).
If you are sick, use a mask. They are probably also effective at preventing the disease when you are not sick, but most of the western world currently has massively limited supply, so leave the masks to health workers and sick people.

The rest of this post will summarise the interesting ideas that went beyond these basic recommendations. But if you haven't done (or at least seriously considered doing) all of the above, I would recommend you prioritise the things listed above.

Many of the below recommendations involve buying things. When possible I’ve included a link to a particular amazon page. This is not a strong recommendation for that particular product: it’s an attempt to lower activation energy for acting on a recommendation. If you have reason to believe a particular model is better than what I linked to, please comment and I’ll update if appropriate. Speaking of which, if you think of anything else I missed or got wrong, please comment with that too.

That said, here is the top advice from the Coronavirus Justified Practical Advice thread.

Cover your h

...
Forecasting an 80% chance of an effective anti COVID-19 drug (probably Remdesivir)
by DirectedEvolution — 2020-03-15T19:21:31.187Z

I'm gathering info on anti-COVID-19 drug efforts. Here is what I've compiled so far. I've combined the number of trials in various phases with big data on expected success rates for drugs in those phases to forecast an 80% probability that at least one drug currently in phase 1-3 testing will be approved. Because trial efforts are unusual, I've used the following categories in my model:

0: pre-trial (not factored into probability of success)

1: new drug in early testing

2: approved drug being tested against COVID-19

3: official phase 3 trial

This is just the product of a morning's research, so if anybody finds better information, I'd appreciate it if you'd link a source so I can include it.

Remdesivir is the drug we'll know about soonest - one expert is quoted as saying in a couple months, so around May 2020. Other analysts say they're optimistic that production can be scaled up quickly. It treats the pneumonia of severe cases, so it might cut down on mortality. Other treatments are also being tested.

The CDC projects that "In the coming months, most of the U.S. population will be exposed to this virus." If the virus grows exponentially, the relative timing of a Remdesivir rollout and a COVID-19 explosion could matter hugely in terms of mortality.

Remdesivir has been administered via compassionate use permission already for at least 15 patients who weren't part of the trial (which is recruiting volunteers now). So although we won't know its effectiveness for a while, if it is effective, it can potentially begin saving lives immediately via compassionate use permission.
Frivolous speculation about the long-term effects of coronavirus
by skybrian — 2020-03-15T19:12:39.444Z

This is just for fun as a diversion from more serious news and thinking. I would be interested in hearing what people on LessWrong have to say about cultural / economic changes. Let's assume we (well, the survivors) get through this but it takes long enough to result in cultural changes, and extrapolate in a science fiction sort of way. What would be some changes that persist after it's over? I think there could be lots of interesting side effects.

A trivial one might be that mask usage becomes more popular outside Asia. I'm a bit surprised people aren't making their own masks already. Mask fashion? (Update: this happened.) Veils? More use of gloves?

Increased comfort and skill with video chat. More working from home, more online education, video doctor's appointments become routine. (Of course this was already well under way.) Even more e-sports. More tiny desk concerts?

A resurgence of car culture? There's nothing like a car for enforcing social distance even in traffic. More drive-ins at restaurants, and also more comfort and use of curbside pickup. Contactless payment becomes routine. Maybe people will want their own driverless cars just for sanitary reasons? One downside: maybe less use of public transportation because crowds are scary, although getting into mask fashion might be more practical.

More delivery of course.

Probably less emphasis on recycling and more on safety and cleanliness, and safe disposal.

Prepping could go mainstream. Hopefully we won't be too silly about it. In urban areas, maybe you'd buy an option on a disaster kit so you don't need to store it locally or worry about refreshing it. Also, tracking the location of supplies you have an option on, so you know they are actually there and can be delivered in a day.

Outdoor and home exercise become more popular.

Increased use of copper in architecture for buttons, doorknobs, railings, etc? At least in richer areas. Copper keycaps? Copper mice? Though, when placed next to electronics, you'd want to make sure it's well-grounded.

Vote by mail everywhere.

Any other ideas?