trentmkelly/LessWrong-43k huggingface dataset:
Power Buys You Distance From The Crime
by Elizabeth — 2019-08-02T20:50:01.106Z

Introduction

Taxes are typically meant to be proportional to money (or negative externalities, but that's not what I'm focusing on). But one thing money buys you is flexibility, which can be used to avoid taxes. Because of this, taxes aimed at the wealthy tend to end up hitting the well-off-or-rich-but-not-truly-wealthy harder, and tax cuts aimed at the poor end up helping the middle class. Examples (feel free to stop reading these when you get the idea, this is just the analogy section of the essay):

Computer programmers typically have the option to work remotely in a low-tax state; teachers need to be where the classroom is.
Estate taxes tend to hit families with single large assets (like a business) harder than those with diverse investments (who can simply sell assets to pay for taxes), who are hit harder than those with enough wealth to create trust funds.
Executives can choose to receive stock (which is taxed more favorably) instead of cash to the exact percentage they desire. Well paid employees are offered stock, but the amount will not be tailored to their needs. Lower level employees either are not offered this, or are not in a position to take advantage of it.
The legal distinction between a business (whose expenses are tax deductible) and a hobby (deductions not allowed) is based on whether the activity nets you income (there are complications and you can sometimes prove a money loser is a business, but this is a good rule of thumb). Small business owners (e.g. lawyers) can fold their occasionally-revenue-generating hobby (e.g. photography) into their real business, enabling tax deductions for their hobby.
IRAs, 401ks, HSAs, and FSAs all lock your money up for a time or purpose, in exchange for lower or delayed taxes. You can only take advantage of them if you’re sure you won’t need the money for another purpose sooner.
More examples here.

Note that most of these are perfectly legal and the rest are borderline. But we're still not getting the result we want, of taxes being proportional to income.

When we assess moral blame for a situation, we typically want it to be roughly in proportion to much power a person has to change said situation. But just like money can be used to evade taxes, power can be used to avoid blame. This results in a distorted blame-distribution apparatus which assigns the least blame to the person most able to change the situat...
Very different, very adequate outcomes
by Stuart_Armstrong — 2019-08-02T20:31:00.751Z

Let Up be the utility function that - somehow - expresses your preferences[1]. Let Uh be the utility function expresses your hedonistic pleasure.

Now imagine an AI is programmed to maximise U(q)=qUp+(1−q)Uh. If we vary q in the range of 5% to 95%, then we will get very different outcomes. At 5%, we will generally be hedonically satisfied, and our preferences will be followed if they don't cause us to be unhappy. At 95%, we will accomplish any preference that doesn't cause us huge amounts of misery.

It's clear that, extrapolated over the whole future of the universe, these could lead to very different outcomes[2]. But - and this is the crucial point - none of these outcomes are really that bad. None of them are the disasters that could happen if we picked a random utility U. So, for all their differences, they reside in the same nebulous category of "yeah, that's an ok outcome." Of course, we would have preferences as to where q lies exactly, but few of us would risk the survival of the universe to yank q around within that range.

What happens when we push q towards the edges? Pushing q towards 0 seems a clear disaster: we're happy, but none of our preferences are respected; we basically don't matter as agents interacting with the universe any more. Pushing q towards 1 might be a disaster: we could end up always miserable, even as our preferences are fully followed. The only thing protecting us from that fate is the fact that our preferences include hedonistic pleasure; but this might not be the case in all circumstances. So moving q to the edges is risky in the way that moving around in the middle is not.

In my research agenda, I talk about adequate outcomes, given a choice of parameters, or acceptable approximations. I mean these terms in the sense of the example above: the outcomes may vary tremendously from one another, given the parameters or the approximation. Nevertheless, all the outcomes avoid disasters and are clearly better than maximising a random utility function.

This being a somewhat naive form of preference utilitarianism, along the lines of "if the human choose it, then its ok". In particular, you can end up in equilibriums where you are miserable, but unwilling to choose not to be (see for example, some forms of depression). ↩︎

This fails to be true if preference and hedonism can be maximised independently; eg if we could take an effective h

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Rethinking Batch Normalization
by Matthew Barnett — 2019-08-02T20:21:16.124Z

Yesterday we saw a glimpse into the inner workings of batch normalization, a popular technique in the field of deep learning. Given that the effectiveness of batch normalization has been demonstrated beyond any reasonable doubt, it may come as a surprise that researchers don't really know how it works. At the very least, we sure didn't know how it worked when the idea was first proposed.

One might first consider that last statement to be unlikely. In the last post I outlined a relatively simple theoretical framework for explaining the success of batch normalization. The idea is that batch normalization reduces the internal covariate shift (ICS) of layers in a network. In turn, we have a neural network that is more stable, and robust to large learning rates, and allows much quicker training.

And this was the standard story in the field for years, until a few researchers decided to actually investigate it.

Here, I hope to convince you that the theory really is wrong. While I'm fully prepared to make additional epistemic shifts on this question in the future, I also fully expect to never shift my opinion back.

When I first read the original batch normalization paper, I felt like I really understood the hypothesis. It felt simple enough, was reasonably descriptive, and intuitive. But I didn't get a perfect visual of what was going on — I sort of hand-waved the step where ICS contributed to an unstable gradient step. Instead I, like the paper, argued by analogy, that since controlling for covariate shifts were known for decades to help training, a technique to reduce internal covaraite shift is thus a natural extension of this concept.

It turned out this theory wasn't even a little bit right. It's not that covariate shifts aren't important at all, but that the entire idea is based on a false premise.

Or at least, that's the impression I got while reading Shibani Santurkar et al.'s How Does Batch Normalization Help Optimization? Whereas the original batch normalization paper gave me a sense of "I kinda sorta see how this works," this paper completely shattered my intuitions. It wasn't just the weight of the empirical evidence, or the theoretical underpinning they present; instead what won me over was the surgical precision of their rebuttal. They saw how to formalize the theory of improvement via ICS reduc...
Permissions in Governance
by sarahconstantin — 2019-08-02T19:50:00.592Z

Compliance Costs

The burden of a rule can be separated into (at least) two components.

First, there’s the direct opportunity cost of not being allowed to do the things the rule forbids. (We can include here the penalties for violating the rule.)

Second, there’s the “cost of compliance”, the effort spent on finding out what is permitted vs. forbidden and demonstrating that you are only doing the permitted things.

Separating these is useful. You can, at least in principle, aim to reduce the compliance costs of a rule without making it less stringent.

For instance, you could aim to simplify the documentation requirements for environmental impact assessments, without relaxing standards for pollution or safety. “Streamlining” or “simplifying” regulations aims to reduce compliance costs, without necessarily lowering standards or softening penalties.

If your goal in making a rule is to avoid or reduce some unwanted behavior — for instance, to reduce the amount of toxic pollution people and animals are exposed to — then shifting up or down your pollution standards is a zero-sum tradeoff between your environmental goals and the convenience of polluters.

Reducing the costs of compliance, on the other hand, is positive-sum: it saves money for developers, without increasing pollution levels. Everybody wins. Where possible, you’d intuitively think rulemakers would always want to do this.

Of course, this assumes an idealized world where the only goal of a prohibition is to reduce the total amount of prohibited behavior.

You might want compliance costs to be high if you’re using the rule, not to reduce incidence of the forbidden behavior, but to produce distinctions between people — i.e. to separate the extremely committed from the casual, so you can reward them relative to others. Costly signals are good if you’re playing a competitive zero-sum game; they induce variance because not everyone is able or willing to pay the cost.

For instance, some theories of sexual selection (such as the handicap principle) argue that we evolved traits which are not beneficial in themselves but are sensitive indicators of whether or not we have other fitness-enhancing traits. E.g. a peacock’s tail is so heavy and showy that only the strongest and healthiest and best-fed birds can afford to maintain it. The tail magnifies variance, making it easier for peahens to distinguish otherwise small variat...
Open problems in human rationality: guesses
by romeostevensit — 2019-08-02T18:16:18.342Z

A couple months back Raemon wrote this excellent question to which Scott Alexander shared his ongoing list. I think it would be great to have people try to give their current guesses for a lot of these. My guesses in a comment below. My intuition is that value is created in four ways from this:

1. Discovery of things you didn't realize you believed in the process of writing the answer.

2. Generation of cruxes if people give you feedback/alternatives for answers.

3. Realization that your guess isn't even wrong, but fundamentally wasn't built from building blocks that can, in principle, be rearranged to form a correct answer.

4. Help in coordination as people get a sense of what others believe about navigating this space. Seeing cognitive diversity on fundamental questions has helped me in this area.
How to evaluate neglectedness and tractability of aging research
by emanuele ascani — 2019-08-02T10:33:40.647Z

This is the fourth post of a series in which I'm trying to build a framework to evaluate aging research. Previous posts:

1. A general framework for evaluating aging research. Part 1: reasoning with Longevity Escape Velocity

2. Aging research and population ethics

3. Impact of aging research besides LEV

Summary

In the first section, I propose a method to scout for impactful and neglected aging research. This method is comprised of two steps: Identifying what is necessary to achieve LEV and, among what is necessary, identifying what is most neglected. This differs from the Open Philanthropy Project's approach of ignoring impact derived from Longevity Escape Velocity. A preliminary evaluation, made through including all research on the hallmarks and using lifespan.io's Rejuvenation Roadmap to identify neglected projects, leads us to identify genomic instability, telomere attrition, epigenetic alterations, deregulated nutrient sensing, loss of proteostasis, and mitochondrial dysfunction as neglected and important areas of research. Other neglected research must be scouted in other equally important areas that have already been identified by Open Philanthropy, such as improving delivery methods and developing better biomarkers.

In the second section, I take Open Philanthropy's and Aubrey de Grey's stance about considering interventions targeting "aging in general", such as caloric restriction or metformin, as having low tractability and impact. What remains after this first skimming is translational research focusing on the hallmarks, basic non-translational research, and enabling research, such as developing new tools and delivery methods. When prioritizing inside these areas, tractability should be considered only after having first considered neglectedness while trying to maximize scope by looking at research that is necessary for reaching LEV. Otherwise, a relatively small gain in tractability would sacrifice an extreme amount of impact and neglectedness. This is true because the hardest problems in the field are often the most neglected, but if they are necessary for achieving LEV, they will be solved later, and by accelerating them, we can impact the expected date of LEV the most.

A method for evaluating neglectedness

In its medium investigation on the cause area of aging, the Open Philanthropy Project identified some scientific a...
[Site Update] Weekly/Monthly/Yearly on All Posts
by Raemon — 2019-08-02T00:39:54.461Z

Last week, our friends over at the EA Forum coded a new feature for the /allPosts page – in addition to the daily view, you can now view posts by weekly, monthly and yearly. (Thanks JP!)

This is most exciting when you also set the sorting to "top karma", making it an easy way to catch up on the most important posts you missed.

For convenience, sorted by top-karma, here are links to:

The Weekly View
The Monthly View
The Yearly View

Have fun perusing posts you haven't read yet, or may have forgotten about, and perhaps catching up on newer comments on some of the top discussions. :)
Why Subagents?
by johnswentworth — 2019-08-01T22:17:26.415Z

The justification for modelling real-world systems as “agents” - i.e. choosing actions to maximize some utility function - usually rests on

various coherence theorems

. They say things like “either the system’s behavior maximizes some utility function, or it is throwing away resources” or “either the system’s behavior maximizes some utility function, or it can be exploited” or things like that. Different theorems use slightly different assumptions and prove slightly different things, e.g. deterministic vs probabilistic utility function, unique vs non-unique utility function, whether the agent can ignore a possible action, etc.

One theme in these theorems is how they handle “incomplete preferences”: situations where an agent does not prefer one world-state over another. For instance, imagine an agent which prefers pepperoni over mushroom pizza when it has pepperoni, but mushroom over pepperoni when it has mushroom; it’s simply never willing to trade in either direction. There’s nothing inherently “wrong” with this; the agent is not necessarily executing a dominated strategy, cannot necessarily be exploited, or any of the other bad things we associate with inconsistent preferences. But the preferences can’t be described by a utility function over pizza toppings.

In this post, we’ll see that these kinds of preferences are very naturally described using subagents. In particular, when preferences are allowed to be path-dependent, subagents are important for representing consistent preferences. This gives a theoretical grounding for multi-agent models of human cognition.

Preference Representation and Weak Utility

Let’s expand our pizza example. We’ll consider an agent who:

Prefers pepperoni, mushroom, or both over plain cheese pizza
Prefers both over pepperoni or mushroom alone
Does not have a stable preference between mushroom and pepperoni - they prefer whichever they currently have

We can represent this using a directed graph:

The arrows show preference: our agent prefers B over A if (and only if) there is a directed path from A to B along the arrows. There is no path from pepperoni to mushroom or from mushroom to pepperoni, so the agent has no preference between them. In this case, we’re interpreting “no preference” as “agent prefers to keep whatever they have already”. Note that this is NOT the same as “the agent is indifferent”, in which case the agent is willing to s...
Sleeping Beauty: Is Disagreement While Sharing All Information Possible in Anthropic Problems?
by dadadarren — 2019-08-01T20:10:46.445Z

Preamble

This post discusses why any halfer position in the Sleeping Beauty Problem would lead to disagreements between two agents sharing all information. This issue has not been much discussed except by @Katja Grace and John Pittard. Furthermore I would explain why these seemingly absurd disagreements are actually valid. This post is another attempt by me trying to get attention to the important difference between reasoning as the first person versus reasoning as an impartial observer in anthropic problems.

The Disagreement

To show any halfer position would lead to disagreements between two communicating agents consider this problem:

Bring a Friend: You and one of your friend are participating in a cloning experiment. After you fall asleep the experimenter would toss a fair coin. If it lands Heads nothing happens. If it lands Tails you would be cloned and the clone would be put into an identical room. The cloning process is highly accurate such that it retains the memory to a level of fidelity that is humanly indistinguishable. As a result, next morning after waking up there is no way to tell if you are physically the original or the clone. Your friend wouldn’t be cloned in any case. The next morning she would choose one of the two rooms to enter. Suppose your friend enters your room. How should she reason the probability of Heads? How should you reason?

For the friend this is not an anthropic problem. So her answer shouldn’t be controversial. If the coin landed Heads she has 50% chance of seeing an occupied room. While if the coin landed Tails both room would be occupied. Therefore seeing me in the room is evidence favouring Tails. She would update the probability of Heads to 1/3.

From my (the participant’s) perspective this is a classical anthropic problem just like Sleeping Beauty. There are two camps. Halfers would say the probability of Heads is 1/2. Reason being I knew I would find myself in this situation. Therefore I haven’t gained any new information about the coin toss. The probability must remain unchanged. The other camp says the probability of Heads should actually be 1/3. Most thirders argue I have gained the new information that I exist which is evidence favouring more copies of the participants exist. Therefore the probability of Tails shall increase from the prior.

Both camps should agree that seeing the friend (or not) would not change their

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Understanding Batch Normalization
by Matthew Barnett — 2019-08-01T17:56:12.660Z

Batch normalization is a technique which has been successfully applied to neural networks ever since it was introduced in 2015. Empirically, it decreases training time and helps maintain the stability of deep neural networks. For that reason practitioners have adopted the technique as part of the standard toolbox.

However, while the performance boosts produced by using the method are indisputable, the underlying reason why batch normalization works has generated some controversy.

In this post, I will explore batch normalization and will outline the steps of how to apply it to an artificial neural network. I will cover what researchers initially suspected were the reasons why the method works. Tomorrow's post will investigate new research which calls these old hypotheses into question.

To put it in just a few sentences, batch normalization is a transformation that we can apply at each layer of a neural network. It involves normalizing the input of a layer by dividing the layer input by the activation standard deviations and subtracting the activation mean. After batch normalization is applied it is recommended to apply an additional transformation to the layer with learned parameters which allow the neural network to learn useful representations of the input. All of these steps are then incorporated into the backpropagation algorithm.

The mechanics of batch normalization can be better understood with an example. Here, I have illustrated a simple feed-forward neural network. Our goal is to apply batch normalization to the hidden layer, indicated in green.

Let the vector h stand for the input to the hidden layer. The input h is processed in the layer by applying an activation function element-wise to the vector computed from the previous layer. Let H stand for a mini-batch of activations for the hidden layer, each row corresponding to one example in the mini-batch.

What batch normalization does is subtract the activation unit mean value from each input to the hidden layer, and divides this expression by the activation unit standard deviation. For a single unit, we replace hi with

h′i=(hi−μi)/σi

where μi is the mean input value for hi across the mini-batches. In symbolic form, in order to calculate μi we compute

μi=1m∑jHj,:

similarly, we calculate σi by computing

σi=√δ+1m∑i(H−μ)2i

The above expression is the standard deviation for the input to the ith activati...
The Internet: Burning Questions
by Hazard — 2019-08-01T14:46:17.164Z

(cross-posted from my personal blog)

I'm about to start a learning project and I'm paying extra close attention, "What feels most interesting?" rather than merely, "What am I supposed to know?" When I stopped to think about it, there's plenty of very specific (and vague) things that I'm curious about regarding how the Internet and the network stack operate. Below is my lightly edited brainstorm of "What's confusing and interesting about computer networking?"

Another way of framing this is that I find myself more easily bored when being told "Here's how a system works" compared to when I go, "How the hell would I build this? None of my pieces seem to fit.... maybe if I...."

If you know anything about networking and would enjoy giving me answers/hints/nudges on any of these questions, go ahead!

How does anything find where it's going?

What local knowledge does any given router have, and what search algorithim does it use to eventually end up in the right place?

What (if any) are the guarantees of this (or other) algos?

How often do you not find the node you're looking for?
Is it deterministic and/or predictable? i.e will a request always follow the same path?
Is there an average number of "hops" till success?

Is there anything where after making an initial request, and a path to the end node needs to be found, that path is then cached for use in ongoing exchanges? Is the original search process efficient enough that you don't really get any gains from that? Does IP even allow for specifying a specific path?

How the hell are IP addresses and DNS records regulated?

My current understanding is there's some central committee that dolls out IP addresses based on geographic considerations.

So IP only needs to be unique. It doesn't matter what IP you have, so it seems like the main job here is just to avoid collisions.
I'm guessing there storing of "where does a given IP address live?" is stored in a distributed way across many routers across the whole internet.

What do you have to do to be a DNS service provider? It seems like they all have to coordinate on not letting multiple people get domain names, and they also are in change of maintaining the mapping between URLs and IPs.

How does my computer access/interact with the DNS records?

Is it a bootstr

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