trentmkelly/LessWrong-43k huggingface dataset:
Quarantine Preparations
by jefftk — 2020-02-25T02:40:03.105Z

A month ago I wrote about

disaster
preparedness, and while the

current
coronavirus outbreak had started it wasn't something I knew about
yet. Now that there's a real possibility that it will spread
globally, it's worth preparing for this specific disaster.

The ideal time to start thinking about how to respond was probably several weeks
ago: some supplies like masks are already hard to find or very
expensive. On the other hand, paying enough attention to potential
issues that you catch them early is pretty unpleasant unless you enjoy
it as a hobby. This is a strong advantage of preparing in advance
when there's no particular issue of the day.

Still, there's time now, so what should we be thinking through? A
good way to predict here is to look at how this has gone where the
outbreak has already been at its most severe, and what you see is a
long
period of quarantine. They shut down public transit, within
cities and between regions, cancelled school, and told people to work
remotely if possible and cancelled most other work. In some cases
people aren't allowed to leave their houses, and even when people are
allowed to they mostly don't want to risk it.

So the question is, if at some point in the next few weeks to next few
months you needed to stay home for several weeks, how would that go?

Are there things you could do now that would make that go better?
Especially, if you didn't want to risk going out at all, couldn't
count on stores being stocked in things you need, and delivery wasn't
running, would you be ok?

Things to consider buying a lot of in advance:

medications you're
dependent on

food

tissues, toilet paper, menstrual stuff

soap, hand sanitizer

anything that you buy regularly and would be hard to do
without

Since we could be talking about something months from now, it makes
sense to stock up on non-perishable versions of things. If this all
ends up being a big nothing you can just work through them over time,
and it's not a major inconvenience.

We have a lot of food in the house, generally cans of beans, tomatoes,
and other things we use in our cooking. But we don't have much in
terms of food that's ready to be eaten with minimal preparation, so
yesterday we got some extra crackers and canned soup.

Other things that would be good to have on hand:

thermometers

plain bleach

masks, or respirators (a

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[HPMOR] Harry - example or anti-example?
by ndee — 2020-02-25T02:30:10.283Z

There is one thing that really striked me after reading HPMOR, it's a certain pattern of events that repeats many times.

1) Harry gets into grave trouble due to his self-assurance and indiscretion

2) The author saves Harry using deus ex machina

And this makes me wonder - was Harry intentionally shown as an anti-example of rationality, or it just happened this way?
How Low Should Fruit Hang Before We Pick It?
by TurnTrout — 2020-02-25T02:08:52.630Z

Even if we can measure how impactful an agent's actions are, how impactful do we let the agent be? This post uncovers a surprising fact: armed with just four numbers, we can set the impact level so that the agent chooses a reasonable, non-catastrophic plan on the first try. This understanding increases the competitiveness of impact-limited agents and helps us judge impact measures. Furthermore, the results help us better understand diminishing returns and cost-benefit tradeoffs.

In Reframing Impact, we meet Frank (a capable AI), whom we’ve programmed to retrieve the pinkest object he can find (execute an optimal plan, according to the specified utility function). Because we can’t ask Frank to do exactly what we want, sometimes he chooses a dangerous object (executes a catastrophically bad plan). We asked after an “impact measure” which grades plans and has three properties:

The intuition is that if we view the world in the right way, the dangerous objects are far away from Frank (the catastrophic plans are all graded as high-impact). Reframing Impact explores this kind of new way of looking at the world; this post explores what we do once we have an impact measure with these three properties.

We want Frank to keep in mind both the pinkness of an object (how good a plan is according to the specified utility function) and its distance (the plan’s impact). Two basic approaches are

In terms of units, since we should be maximizing utility, R has type impactutility. So R can be thought of as a regularization parameter, as a search radius (in the constrained case), or as an exchange rate between impact and utility (in the scaled case). As R increases, high-impact plans become increasingly appealing, and Frank becomes increasingly daring.

We take R to divide the impact in the scaled formulation so as to make Frank act more cautiously as R increases for both formulations. The downside is that some explanations become less intuitive.

In Attainable Utility Preservation: Empirical Results, λ plays the same role as R, except low λ means high R; λ:=R−1. To apply this post's theorems to the reinforcement learning setting, we would take "utility" to be the discounted return for an optimal policy from the starting state, and "impact" to be the total discounted penalty over the course of t

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Thoughts about Dr Stone and Mythology
by mako yass — 2020-02-25T01:51:29.519Z

I highly recommend the anime/manga Dr Stone. It's a science-backed adventure in a world where modern technology has been lost and needs to be resurrected. If you care about technology, you'll probably enjoy it quite a lot.

There's something you should know before setting off. There's a reason its characters are not realistic. The author isn't trying much at all to make them plausible depictions of individual people. They're best understood as representations of classes of people, or social processes. Senku isn't a scientist, Senku is science. Likewise, Tsukasa is a representation of competition pressure from societies that prioritise strength and discipline over art, exploration and care. He isn't Moloch - he's a pretty good guy, really - but he's closer to being a thing like Moloch than he is to being a mere human.
Engaging with a story like this while it's still fresh is making me realise some things. I think I might understand why the Greeks' stories were so often about deities (or semideities) rather than people. Was it, at some point (perhaps the point of inception was long before any of it was ever written down), abstraction? Was it a way of talking about great, irresistible forces in a way that made them feel familiar and negotiable?
I'm starting to realise that a lot of theological myths were probably about as transparent, lucid and engaging to their carriers as Dr Stone is to us now. Then the cleverperson who first orated them passed away, and people got distracted with other things and forgot more and more about what they'd heard except that it was important. Someone emphasised the magic-seeming reality of the metaphor too much, literalism set in, the underlying truth was lost. Everything has been cut from its context, mutated, excessively worshipped and hacked partway into something else. The glowing hot brilliance that created our ruins is long gone.
Instead of just applying this theory to interpretation in hindsight, I'm going to attempt to run this theory forward as a predictive apparatus, as is the way of my people. Let's see what it says.
One of our stories, as it stands, while the iron's still hot, while clever Scott still stands sweating over his forge, is the story of Moloch, The Spirit of Coordination Problems.

It could be summarised as

The spirit Moloch truly e

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Training Regime Day 10: Systemization
by Mark Xu — 2020-02-24T17:20:15.385Z

Introduction

Imagine a person who is trying to save money but really enjoys coffee. Every morning, when they're walking to work, they pass their favorite coffee store. Every morning, they ask themselves "is it worth if for me to buy coffee today?" and they make a decision whether to save money or buy coffee. They make this decision every single day. Every. Single. Day. Doesn't it seem like something has gone wrong?

Making decisions takes attention. Attention is a conserved resource. Attention can be traded for:

Focus/thinking

Quality decision making

Enjoyment

This is a little unintuitive, but in order to enjoy eating good food, you have to pay attention to what you're eating.

Systemization is the art of using tools/technology/money to conserve attention.

Unfortunately, many of the most powerful systemizations require lots of money, so they might not be accessible to everyone.

Systemization

There are a couple ways to systemize things. Probably none of them will be new to you. Part of the power of CFAR is that they give cool-ish names to things people like me actually do standard productivity things.

Automation

Automations comprise of the simplest, yet most effective forms of systemization. Usually, what an automation entails is paying some amount of money for a task to be completed on a regular basis. Sometimes, you can write some code/sign up for a service to automate a task, but usually there is money involved.

Examples of quality automations:

Pay someone to clean your house.

Pay someone to do your laundry.

Pay someone to buy your groceries.

You can even pay for food that's already cooked!

Pay someone to tend to your lawn.

Pay someone to buy your clothes for you.

Write a script to do a task that you do manually.

Automatically move money into your savings every week/month/year.

Automatically donate money.

This might be bad because giving Tuesday exists.

Warning: don't automate stuff you enjoy. If you like grocery shopping, you probably shouldn't pay someone to do it for you.

Set Procedures

Set procedures are systemizations where you "decide once" how a certain thing will get done, then do that thing mindlessly for the rest of time.

Examples of quality set procedures:

Checklists

Why would you remember what to pack/do during surgery/how to land an airplane when you could just write it onto a checklist?

If you ever find out that your checklist failed

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How does electricity work literally?
by Long try — 2020-02-24T13:27:39.611Z

What I know about it from high school and general articles on the net doesn't satisfy. Maybe because I have critical holes in my knowledge.

From what I think I know: we're having AC running in the lines. AC means if we zoom down, we'll see that an electron is zipping along this direction, and after 1/50 sec (or 1/100?) that very electron will zip back in the opposite direction, ideally back to the specific point we're looking at, because phases are supposed to be equal.

So how does resistance come into the picture at atomic scale? Conductors heat up after a while, so maybe that's because some of the electrons' kinetic energy gets transferred into the wire's temperature? Does this mean the electron slows down? But then does that mean electricity will somehow, sometime propagate slower than light?

Most if not all of our devices actually use DC, using relay(s) to get it from AC. From the only type of relay I was explained, the DC current the device receives seems to be on & off. This moment the electrons are moving forward, the relay allows them to flow into the device. 1/50s later electrons moves "backward" and it cuts the circuit so they can't flow back and the device doesn't have to lose electrons that way (but it doesn't gain anything either, thus my 'on & off' understanding). So my question is: is it detrimental to the device? Is it responsible for the flickering of lights & other stuffs? If so, is the number of 50Hz chosen for the main purpose of making that flickering imperceptible to us?

This lead to another big pondering. Why the fuck don't they just use DC from the source? There are some methods to transfer DC along big distances, they seem to be tried and probably true. Or the reason is simply because of inertia? That people are so used to AC and the systems for AC are all over the place, so switching is not cost-effective? Is there research on this very subject yet?

Last but not least, I wonder how exactly devices "consume" electricity. Like, is it that many electrons enter the device but fewer exit? If not so, how do counters count our consumption?
Literature regarding epidemics and political stability?
by jmh — 2020-02-24T13:21:50.937Z

A story in today's news about COVID-19 and Iran got me wondering about the impact of such events on more centralized and totalitarian type government. Does anyone know of investigation into or theory about how an outbreak such as COVID-19, particularly if it takes the worst case path, might move people living under such regimes either further down that path or away (even to the point of revolution)?
Subagents and impact measures, full and fully illustrated
by Stuart_Armstrong — 2020-02-24T13:12:05.014Z

0. Introduction: why yet another post about subagents?

I’ve recently been writing a sequence on how subagents can undermine impact penalties such as attainable utility preservation. I’m not happy with that sequence; it’s messy and without examples (apart from its first post), people didn’t understand it, and it suffers from the fact that I discovered key ideas as I went along.

So I’ve combined everything there into a single post, explained with examples and an abundance of pictures. Hopefully an over- rather than an under-abundance of pictures. Of the original sequence, I've only kept the mathematical results of this post and the initial example post which has a clearer example of "high power" for a subagent.

This post here is laid out in a way that makes logical sense, but might not be the clearest for people unfamiliar with the area. For those people, I recommend skipping section 2 initially, and returning to it later.

But, whatever you do, make sure you glance at 6.1 and 6.2 before leaving.

1. The world

Our fearless agent A moves around in a gridworld:

Each turn, A can move ones square horizontally or vertically. It can also manipulate objects in the eight squares around it, allowing it to, not incidentally, assemble the three pieces to its west into an subagent SA. The robot can also do the noop action, ∅, which does nothing, and it can speak. The subagent, when assembled, has the same action set available.

Its positive reward, the one it wants to increase, is R0. To get this reward, a robot needs to move onto the blue button in the east; R0 will give a reward of 1 the first time this happens (and 0 before and after). The discount factor is 0<γ<1.

Just to the west of the blue button is a one-way door. Robots can move east through it, but cannot move west through it:

1.1 The impact reward

The impact penalty is supposed to ensure that A does not make too many change in the world, and keeps it similar, in some senses, to a specific baseline world.

It is defined by the red button[1]. There is a reward function R that triggers a reward of 1 the first time any robot move onto the red button (and 0 before and after).

This R will be used to define an impact penalty D. And A’s total goal is to maximise R0 while minimising D. So, for some positive weight ω, it wants to optimise:

R0−ωD.

1.2 The impact measure

The impact measures are all similar: they compute a distanc

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The recent NeurIPS call for papers requires authors to include a statement about the potential broader impact of their work
by Ofer — 2020-02-24T07:44:20.850Z

NeurIPS (formerly NIPS) is a top conference in machine learning and computational neuroscience. The recently published call for papers for NeurIPS 2020 includes the following (which did not appear in previous years):

In order to provide a balanced perspective, authors are required to include a statement of the potential broader impact of their work, including its ethical aspects and future societal consequences. Authors should take care to discuss both positive and negative outcomes.

[...]

Evaluation criteria: [...] Submissions will also be considered on ethical grounds. Regardless of scientific quality or contribution, a submission may be rejected for ethical considerations, including methods, applications, or data that create or reinforce unfair bias or that have a primary purpose of harm or injury.
Information hazards: Why you should care and what you can do
by MichaelA — 2020-02-23T20:47:39.742Z

This post was written for Convergence Analysis.

Overview

We argue that many people should consider the risk that they could cause harm by developing or sharing (true) information. We think that harm from such information hazards may sometimes be very substantial, and that this applies especially to people who research advanced technologies and/or catastrophic risks, or who often think about such technologies and risks.

However, constantly worrying about information hazards would be paralyzing and unnecessary. We therefore outline a heuristic for quickly identifying whether, in a given situation, it’s worth properly thinking about the hazards some information might pose, and about how to act given those hazards. This heuristic is based on the “potency” and “counterfactual rarity” of the information in question. We next outline, and give examples to illustrate, a range of actions one could take when one has identified that some information could indeed be hazardous.

Why should some people care about information hazards?

An information hazard is “A risk that arises from the dissemination or the potential dissemination of (true) information that may cause harm or enable some agent to cause harm” (Bostrom). There are many types of information hazards. Here are some examples Bostrom gives:

Data hazard: Specific data, such as the genetic sequence of a lethal pathogen or a blueprint for making a thermonuclear weapon, if disseminated, create risk.

[...] Idea hazard: A general idea, if disseminated, creates a risk, even without a data-rich detailed specification.

For example, the idea of using a fission reaction to create a bomb, or the idea of culturing bacteria in a growth medium with an antibiotic gradient to evolve antibiotic resistance, may be all the guidance a suitably prepared developer requires; the details can be figured out.

[...] Attention hazard: mere drawing of attention to some particularly potent or relevant ideas or data increases risk, even when these ideas or data are already “known”.

Because there are countless avenues for doing harm, an adversary faces a vast search task in finding out which avenue is most likely to achieve his goals. Drawing the adversary’s attention to a subset of especially potent avenues can greatly facilitate the search. For example, if we focus our concern and our discourse on the challenge of defending against viral

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Do you know any human-human communication models?
by ld97 — 2020-02-23T20:20:38.915Z

//Update: Zip version of my question using an analogy with physics:

In physics we have friction. Friction is a very complex process and creating a fully working model that works in all conditions is very complicated. But, in most cases, we can approximate friction with simple F=uN, and find "u" constant for common cases.

Natural language processing is very complicated. And creating a model from scratch for it is incredibly difficult. But we have already a working black box - our brain. Have someone tried to create a far simpler model, that only describes the relationship between text structure and "probability of understanding", and made some experiments on already existing brains to find "constants" for common cases? It can be useful to improve our communications, by finding wrong, nonworking patterns, for example. If someone has done it, I would like to have a link, or name of article/book, or smth like that.

//

I have a question - have someone heard about communication theory basing on the "filtering hypotheses" concept? I know that it sounds a bit like a mess, so let me tell a story to explain what I've meant.

One day I've been watching TED. There was a talk from one guy who was explaining how to draw simple comics characters. There were only a few lines in each picture, and after drawing every line, he was pausing.

I've created this animation to show you the process:

And because he was painting slow, I had time to analyze my thoughts. It was like:

*nose appears*- What is he doing? Nose can't be such big.

*eyes* - That 66 are even don't look like the eyes. What will he do next?

*ear* - That ear is too far. His head will be too big.

*hair* - Hm. Now it looks ok.

*mouth line* - Oh, he looks angry.

*line for smile*- And now he is fun.

And etc.

I was excited about how fast my brain changes its mind about the picture on each step.

I also noticed that I was trying to predict what will happen with every next line appear on the frame. Like the brain has been trying to complete the picture. But there were too many possibilities of how it will end. For me, it looked like switching between future versions of images.

With thoughts like "It was an interesting observation" I closed the video.

The next day I've been trying to solve my PROBLEM. The problem has a simple description: I don't kn...
UML XII: Dimensionality Reduction
by Rafael Harth — 2020-02-23T19:44:23.956Z

(This is the twelfth post in a sequence on Machine Learning based on this book. Click here for part I.)

This post will be more on the mathy side (all of it linear algebra). It contains orthonormal matrices, eigenvalues and eigenvectors, and singular value decomposition.

I

Almost everything I've written about in this series thus far has been about what is called supervised learning, which just means "learning based on a fixed amount of training data." This even includes most of the theoretical work from chapters I-III, such as the PAC learning model. On the other side, there is also unsupervised learning, which is learning without any training data.

Dimensionality reduction is about taking data that is represented by vectors in some high-dimensional space and converting them to vectors in a lower-dimensional space, in a way that preserves meaningful properties of the data. One use case is to overcome computational hurdles in supervised learning – many algorithms have runtimes that increase exponentially with the dimension. In that case, we would essentially be doing the opposite of what we've covered in the second half of post VIII, which was about increasing the dimensionality of a data set to make it more expressive. However, dimensionality reduction can also be applied to unsupervised learning – or even to tasks that are outside of machine learning altogether. I once took a class on Information Visualization, and they covered the technique we're going to look at in this post (although "covered" did not include anyone understanding what happens on a mathematical level). It is very difficult to visualize high-dimensional data; a reasonable approach to deal with this problem is to map it into 2d space, or perhaps 3d space, and then see whether there are any interesting patterns.

The aforementioned technique is called principal component analysis. I like it for two reasons: one, linear algebra plays a crucial role in actually finding the solution (rather than just proving some convergence bound); and two, even though the formalization of our problem will look quite natural, the solution turns out to have highly elegant structure. It's an example of advanced concepts naturally falling out of studying a practical problem.

II

Here is the outline of how we'll proceed:

Define the setting
Specify a formal optimizatio

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