trentmkelly/LessWrong-43k huggingface dataset:
Maximal Ventilation
by jefftk — 2020-07-11T01:20:02.171Z

At this point, one of the things I'm most concerned about with
covid-19 is breathing air that has been

recently
breathed by other people. Six foot distancing would be pretty good
if it quickly settled out of the air, but it's looking more like it
can stay suspended in the air for extended periods.

The ideal is outdoors. Even on a relatively calm day, the amount of
air flow outdoors is enormous compared to what you might get inside. A
gentle 1mph breeze clears an 90-ft diameter circle in a minute. An
aggressive level of ventilation indoors is about a quarter of that,
and most places get far less. So the more we can move outside, the
better: I think it's foolish that we have restaurants open for indoor
dining when outdoor dining gets you most of the same benefit with much
less shared air. Especially when considering that eating means people
will have their masks off while talking. Similarly, while people
definitely need to be able to buy food, moving grocery stores to a
model of offering only outdoor pickup would be safer for both the
customers and the employees then letting the public in.

The previous two summers my kids did an outdoor summer program in a
park, where the only indoor component was bathrooms. It didn't run on
rainy days, which would make it hard to plan around, but it's much
more socialization for the level of risk than something
indoors. Prompted by tuberculosis risk, 100 years ago, occasionally students would
have
school outdoors, even in the winter:

When things can't be moved outdoors, it's still possible to
dramatically increase the level of ventilation indoors. Dance
organizers and people who cool their
houses using fans are
familiar with this: put a lot of fans in windows, use some fans inside
to stir up the air, and think through the path the air will take. The
best case is, one side of a room has fans blowing out each window,
with cowls, and the other side of the room has a set fans blowing
in. For example, if you have a 900 sqft classroom with four 20-in box
fans, you are changing over the air in the room
approximately once a minute.

This doesn't work well in hard rain, and you're not going to be able
to have the place much warmer or cooler than the outside, but it would
still help a lot. Since you're inside, you could give people electric
blankets in the winter.

I don't see reopening guidelines talking about...
Mod Note: Tagging Activity is Not Private
by Ruby — 2020-07-11T00:56:10.267Z

We don’t yet have UI for it, so this isn’t obvious (hence the announcement), but it seemed good to clearly communicate that if you tag a post or vote on the tag relevance of a tag-post combination, this information may be viewed by other users and/or the mod team (after we built the relevant UI).

Tagging activity is unlike normal (karma-related) voting, for which the LessWrong team is fully committed to maintaining privacy for.

Karma voting expresses a judgment of quality and/or approval, and it makes sense that for users to vote honestly, they need to feel that their votes will not be scrutinized or judged by others. In contrast, tagging activity – both tagging and tag voting – is an act of content creation for the community. In the same way we want to know the authors of posts and comments, we want to know the “authors” of the tags and tag pages that arise from tagging activity.

Knowledge of the content creators for tagging helps us both in positive ways, i.e. giving credit to those who make valuable contributions, and also in “negative” ways by making it easy to catch abuse/vandalism of the system. And by making it clear who is tagging in what way, it also becomes easier to have discussions about which tags apply where, which are good tags, etc.

A step beyond all this is that possibly the tagging feature will be extended into a full-fledged wiki feature. Obviously, wiki activity (creating and modifying wiki pages) needs to be linked to whoever is making changes. That makes it naturally the case that the tagging system (almost a proto-wiki) needs to link activity to user accounts.

All of the above makes it seem correct for tagging activity to not be private, unlike regular karma-related voting. We will soon launch the expanded UI for tagging that contains a history for each tag which will more clearly display what information is getting shared and how.

Sorry for not having been clearer about this earlier. If you have any concerns about this, don’t hesitate to reach out. Also if you made any activity in the last few weeks that you really don't want to be public, feel free to ping us and we can make sure that those stay private (DM, Intercom, or team@lesswrong.com are all good for reaching out about this.)
Cost/Benefit analysis of School Closures in the US
by  — 2020-07-10T23:35:47.865Z

TLDR: Covid 19 school closures may do more harm than good, because years of schooling affects overall life expectancy and quality of life for millions of children.

This is my very first post here, so if I apologize if I say something that has been said many times before or that does not seem to belong on this forum. That said, here goes...

Coronavirus school closures and the move to online schooling has resulted in a very significant decrease in the quality of education in the US. In person schooling provides a lot of important resources, especially for low income students, that cannot be easily accessed in a poor home environment. Online education also makes it much easier to cheat, and this provides students with less incentive to learn. Additionally, since many parents work full-time, this also means that students have limited adult supervision throughout the day. Thus, I'm going to assume for the purposes of this argument, that online schooling is roughly equivalent to no schooling at all.

There have been many studies showing that additional years of schooling correlate with longer life expectancy, higher income, and lower incarceration rates. Now obviously, you might worry about a correlation/causation problem with that kind of statement. However, there have also been several twin studies that help eliminate this bias. These studies follow identical twins who grow up in the same home but have different levels of educational attainment. The results of theses studies still show a statistically significant difference between the incomes of twins who have different educational status. Now, these studies are not perfect, but there have been several of them and they tend to get similar results. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3359051/ Thus, I think it's fair to conclude that years of schooling, particularly at the high school level, have a positive impact on income. There are no twin studies that I know of regarding education and life expectancy specifically in the US, but I think that if education impacts income, it likely impacts life expectancy in a statistically significant way. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3359051/

Another thing to note is that the impact of education, and higher quality education is often most significant for low income children. https://www.nber.org/papers/w20847#:~:text=Event%2Dstudy%20and%20instr...
Your Prioritization is Underspecified
by romeostevensit — 2020-07-10T20:48:29.654Z

If you were really convinced that the next task on your to do list was the very best thing to advance your goals you'd feel a lot more interested in it. Some part of you believes this to be the case and other parts very obviously don't. Thus, uncertainty about how to prioritize. My impression is that various systems for prioritization get much of their power from addressing some core ambiguity and that people thus self-sort into those systems based on whether that ambiguity was a key bottleneck for them. This post isn't about outlining another set of antidotes but merely mapping out some (not necessarily all) various kinds of ambiguity.

Short term goals have nice legible feedback loops with yummy rewards. Long term goals face an uphill battle, but potentially give higher effort:reward ratios if successfully fought. Presumably if our throughput rate were high enough and our incoming tasks low enough we wouldn't need to rearrange our queue and a simple FIFO scheme would do. In practice there are always lots of opportunities rolling in, and this pileup only gets worse the better you get at prioritizing as people put more opportunities on your plate as you get more done. So we're stuck with prioritizing. Let's sketch some key fronts.

Two types of ambiguity: risk and uncertainty

When I use the term ambiguity in this post, I'll be referring to both risk and uncertainty as potential roadblocks. Risk is within model ambiguity. Uncertainty is outside of model ambiguity. If I ask you to bet on a coin flip, you'll model the odds as 50:50 and your downside risk will be that 50 percent chance of loss. That model doesn't include things like 'the person I'm making a bet with pulls out a gun and shoots me while the coin is in the air.' That broader context within which the risk model is situated deals with uncertainty, including the uncertainty over whether or not your model is correct (weighted coins). Most of the other categories could be further broken down along the risk/uncertainty dimension, but that is left as run-time optimization in the interests of brevity.

Between-task ambiguity

There are a few ways that we are already prioritizing, and confusion about which one would be best in a given situation can serve as a roadblock.

In First-Due prioritization we simply do whatever will have the nearest deadline.
In Lo

...
Was a PhD necessary to solve outstanding math problems?
by DirectedEvolution — 2020-07-10T18:43:17.342Z

My composition teacher in college told me that in some pottery schools, the teacher holds up your pot, examines it, comments on it, and then smashes it on the floor. They do this for your first 100 pots.

In that spirit, this post's epistemic status is SMASH THIS POT.

Previous post: Was a terminal degree ~necessary for inventing Boyle's desiderata?

This is my second post investigating whether a terminal degree is practically ~necessary for groundbreaking scientific work of the 20th century.

Mathematics seems like a great field for outsiders to accomplish groundbreaking work. In contrast to other fields, many of its open problems can be precisely articulated well in advance. It requires no expensive equipment beyond computing power, and a proof is a proof is a proof.

Unlike awards like the Nobel Prize or Fields Medal, and unlike grants, a simple list of open problems established in advance seems immune to credentialism. It's a form of pre-registration of what problems are considered important. Wikipedia has a list of 81 open problems solved since 1995. ~146 mathematicians were involved in solving them (note: I didn't check for different people with the same last name). I'm going to randomly choose 30 mathematicians, and determine whether they got a PhD on or prior to the year of their discovery.

The categories will be No PhD, Partial PhD, PhD, evaluated in the year they solved the problem. In my Boyle's desiderata post, 2/15 (13%) of the inventors had no PhD. I'd expect mathematics to exceed that percentage.

Results:

Robert Connelly: PhD Anand Natarajan: PhD Mattman: PhD Croot: PhD Mineyev: PhD Taylor: PhD Antoine Song: Partial PhD Vladimir Voevodsky: PhD Ngô Bảo Châu: PhD Haas: PhD Andreas Rosenschon: PhD Paul Seymour: PhD (D. Phil) Oliver Kullmann: PhD Shestakov: PhD Merel: PhD Lu: PhD Knight: PhD Grigori Perelman: PhD Haiman: PhD Ken Ono: PhD Ben J. Green: PhD Demaine: PhD Jacob Lurie: PhD Harada: PhD McIntosh: PhD Naber: PhD Adam Parusinski: PhD Atiyah: PhD Benny Sudakov: PhD John F. R. Duncan: PhD

Contrary to my expectation, all of these mathematicians had a PhD except Antoine Song, the only partial PhD. He finished his PhD the year after his work on Yau's conjecture.

So either:

a) This list is not in fact an unedited list of important mathematical conjectures and who solved them, but instead a list retroactively edited by Wikipedia editors to select for the ...
Talk: Key Issues In Near-Term AI Safety Research
by Aryeh Englander — 2020-07-10T18:36:12.462Z

I gave a talk for the Foresight Institute yesterday, followed by a talk from Dan Elton (NIH) on explainable AI and a panel discussion that included Robert Kirk and Richard Mallah.

While AI Safety researchers are often pretty well aware of related work in the computer science and machine learning fields, I keep finding that many people are not aware of a lot of very related work that is taking place in an entirely different research community. This other community is variously referred to as Assured Autonomy, Testing Evaluation Verification & Validation (TEV&V), or Safety Engineering (as they relate to AI-enabled systems, of course). As I discuss in the talk, this is a much larger and more established research community than AI Safety, but unfortunately until recently there was very little acknowledgement by the Assured Autonomy community of closely associated work by the AI Safety community, and vice versa.

Recently organizations such as CSER and FLI have been doing a lot of great work helping to connect these two communities with jointly-sponsored workshops at major AI conferences - some of you may have attended those. But I still think it would be useful if more people in both communities were a bit more aware of the work of the other community. This talk represents my attempt at a short intro to that.

Video (my presentation is from 2:28 to 19:00)

Short version of slide deck (the one I used in the presentation)

Longer version
When is evolutionary psychology useful?
by snog toddgrass — 2020-07-10T18:22:36.483Z

I am currently improving my courtship skills and am reading Mate by Geoffrey Miller. The book relies heavily on evolutionary psychology, which I was previously critical of.

My earlier perspective was that because we know little about the human evolutionary environment, evo psych is full of "fully general arguments". I can assert lots of attributes to the evo environ to support any position, so I can support no position.

But I do not have a good source of priors for what women find sexy. Evo psych is offering priors and most of them so far match observational data. Was my previous view of Evo psych wrong, and if so how?
A space of proposals for building safe advanced AI
by Richard_Ngo — 2020-07-10T16:58:33.566Z

I liked

Evan’s post on 11 proposals for safe AGI

. However, I was a little confused about why he chose these specific proposals; it feels like we could generate many more by stitching together the different components he identifies, such as different types of amplification and different types of robustness tools. So I’m going to take a shot at describing a set of dimensions of variation which capture the key differences between these proposals, and thereby describe an underlying space of possible approaches to safety.

Firstly I’ll quickly outline the proposals.

Rohin’s overview

of them is a good place to start - he categorises them as:

7 proposals of the form “recursive outer alignment technique” plus “robustness technique”.

The recursive outer alignment technique is either debate, recursive reward modelling, or amplification.
The robustness technique is either transparency tools,

relaxed adversarial training

, or intermittent oversight by a competent supervisor.

2 proposals of the form “non-recursive outer alignment technique” plus “robustness technique”.

The outer alignment technique is either

reinforcement learning in a multiagent environment

, or narrow reward learning.

2 other proposals: Microscope AI; STEM AI.

More specifically, we can describe the four core recursive outer alignment techniques as variants of iterated amplification, as follows: let Amp(M) be the procedure of a human answering questions with access to model M. Then we iteratively train M* (the next version of M) by:

Imitative amplification: train M* to imitate Amp(M).
Approval-based amplification: train M* on an approval signal specified by Amp(M).
Recursive reward modelling: train M* on a reward function specified by Amp(M).
Debate: train M* to win debates against Amp(M).

Here are six axes of variation which I claim underlie Evan’s proposals. Each proposal is more or less:

Supervised
Structured
Adversarial
Language-based
Interpretability-dependent
Environment-dependent

In more detail:

Supervised: this axis measures how much the proposal relies on high-quality supervision by a (possibly amplified) human. Imitative amplification places the heaviest burden on the supervisor, since they need to identify a good action at each timestep. Approval-based amplification requires the supervisor to recognise good actions at each tim

...
Mesa-Optimizers vs “Steered Optimizers”
by Steven Byrnes — 2020-07-10T16:49:26.917Z

Abstract:

The paper

Risks from learned optimization

introduced the term "inner alignment" in the context of a specific class of scenarios, namely a "base optimizer" which searches over a space of “inner” algorithms. If the inner algorithm is an optimizer, it's called a "mesa-optimizer", and if its objective differs from the base algorithm's, it's called an "inner alignment" problem. In this post I want to plead for us to also keep in mind a different class of scenarios, which I'll call "Steered Optimizers", and which also has an "inner alignment" problem. The inner alignment problem for mesa-optimizers is directly analogous to the inner alignment problem for steered optimizers, but the specific failure modes and risk factors and solutions are all somewhat different. I’ll argue that it's at least comparably likely for our future AGIs to be steered optimizers rather than mesa-optimizers. So again, we should keep both scenarios in mind.

Introduction

I recently wrote

a post about brain algorithms with "inner alignment" in the title

, but I was talking about something kinda different than in the famous

Risks from Learned Optimization paper

that I was implicitly referring to. I didn't directly explain why I felt entitled to use the term “inner alignment” for this different situation, but I think it's worth going into, especially because it’s a more general approach to making AGI that goes beyond brain-inspired approaches.

(Terminology note: Following “Risks From Learned Optimization”, I will use the term "optimizer" in this post to mean an algorithm which uses foresight / planning to search over possible actions in pursuit of a particular goal, a.k.a. a

"selection"-type optimizer

. I want humans to count as “optimizers”, so I will also allow “optimizers” to sometimes choose actions for other reasons, and to maybe have inconsistent, context-dependent goals, as long as they at least sometimes use foresight / planning to pursue goals.)

Let's start with two scenarios in which we might create highly intelligent AI "optimizers":

1. Search-over-algorithms scenario: (this is the one from

Risks from Learned Optimization

). Here, you have a "base optimizer" which searches over a space of possible algorithms for an algorithm which performs very well according to a "base objective". For example, the base optimizer might be doing gradient descent on the weights of an RNN (large enough ...
Born as the seventh month dies ...
by Rudi C — 2020-07-10T15:07:32.434Z

Epistemic status: Mathematical reasoning by an amateur, but I feel confident that it’s mostly correct.

Update: There is a Wikipedia page on this with lots of details and other similar problems with different answers. This paper referenced there seems a good summary.

The problem

I was reading The Equation of Knowledge, and it starts with this little cute problem:

Suppose a dad has two kids. At least one of them is a boy born on Tuesday. What's the odds of his sibling being a boy?

Generalizing the problem:

P(2 boys | 1Bn := At least one boy with an independent characteristic (named N here) that has the probability 1/n)

The original problem can now be seen as an instance of P(2 Boys | 1B7).

Simple Bayesian solution

I started solving this with a simple application of Bayes:

P(1Bn | 2 boys) = P(First child being a boy having N | 2 boys) + P(Second child being a boy having N | 2 boys) - P(Both children being boys having N | 2 boys) = 1/n + 1/n - 1/(n^2)

P(1Bn) = P(First child being a boy having N) + P(Second child being a boy having N) - P(Both children being boys having N) = (1/2)(1/n) + (1/2)(1/n) - ((1/2)(1/n))^2 = 1/n + 1/4(n^2)

BayesFactor(2 boys | 1Bn) = P(1Bn | 2 boys)/P(1Bn) = (8n - 4)/(4n - 1)

P(2 boys | 1Bn) = BayesFactor(2 boys | 1Bn) * P(2 boys)=((8n - 4)/(4n - 1)) * (1/4) = (2n - 1)/(4n - 1)

We have:

P(2 boys | 1B1 == At least one boy) = 1/3

P(2 boys | 1B7 == At least one boy born on Sunday) = 13/27

lim{n -> +Inf}[P(2 boys | 1Bn == At least one boy born exactly x seconds after the big bang)] = 2n/4n = 1/2

So ... I am somewhat confused. It's intuitively obvious that having two boys creates more opportunity for specific independent phenomena to happen. But, at first blush, my intuition was firmly suggesting that I throwaway the additional information as useless, and only careful thinking lead me to the (hopefully) correct answer. I also can't quite think of any practical examples for this epistemic error. Your thoughts appreciated.

Generalizing more

Repeating the same analysis, but generalizing the probability of "being a boy" to 1/k,

BayesFactor(2 boys | P(boy)=1/k, 1Bn) = (2n(k^2) - (k^2))/(2nk - 1)

lim{n -> +Inf}[P(2 Boys | P(boy)=1/k, 1Bn)] = 1/k

Generalizing to random variables

Suppose we have two independent, identically distributed variables X1 and X2, and another two i.i.d variables Z1 and Z2. All of these variables are mutually independ

...
Frankenstein Delenda Est
by  — 2020-07-10T14:38:04.763Z

[Not fiction, but still art. Cross-posted from Grand, Unified, Crazy.]

I.

I am terrified by the idea that one day, I will look back on my life, and realize that I helped create a monster. That my actions and my decisions pushed humanity a little further along the path to suffering and ruin. I step back sometimes from the gears of the technology I am creating, from the web of ideas I am promoting, and from the vision of the future that I am chasing, and I wonder if any of it is good.

Of course I play only the tiniest of roles in the world, and there will be no great reckoning for me. I am a drop in the ocean of the many, many others who are also trying to build the future. But still, I am here. I push, and the levers of the world move, however fractionally. Gears turn. Webs are spun. If I push in a different direction, then the future will be different. I must believe that my actions have meaning, because otherwise they have nothing at all.

No, I do not doubt my ability to shape the future; I doubt my ability to choose it well. The world is dense with opportunity, and we sit at the controls of a society with immense potential and awful power. We have at our disposal a library full of blueprints, each one claiming to be better than the last. I would scream, but in this library I simply whisper, to the blueprints: how do you know? How do you know that the future you propose has been authored by The Goddess of Everything Else, and is not another tendril of Moloch sneaking into our world?

Many people claim to know, to have ascended the mountain and to be pronouncing upon their return the commandments of the one true future: There is a way. Where we are going today, that is not the way. But there is a way. Believe in the way.

I hear these people speak and I am overcome with doubt. I think of butterflies, who flap their wings and create Brownian motion, as unfathomable as any hurricane. I think of fungi, whose simple mushrooms can hide a thousand acres of interwoven root. I think of the human brain, a few pounds of soggy meat whose spark eludes us. The weight of complexity is crushing, and any claim to understanding must be counterbalanced by the collected humility of a thousand generations of ignorance.

And on this complexity, we build our civilization. Synthesizing bold new chemicals, organizing the world’s information, and shaping the future through a patchwork ...