trentmkelly/LessWrong-43k huggingface dataset:
Soft Skills for Running Meetups for Beginners
by Raemon — 2017-05-06T16:04:29.851Z

Having a vibrant, local Less Wrong or EA community is really valuable, but at least in my experience, it tends to end up falling to the same couple people, and if one of or all of those people get burned out or get interested in another project, the meetup can fall apart. Meanwhile, there are often people who are interested in contributing but feel intimidated by the idea.

In the words of Zvi: You're good enough, you're smart enough and people would like you. (Or more explicitly, "assume you are one level higher than you think you are.")

This is an email I sent to the local NYC mailing list trying to break down some of the soft-skills and rules-of-thumb that I'd acquired over the years, to make running a meetup less intimidating. I acquired these gradually over several years. You don't need all the skills / concepts at once to run a meetup but having at least some of them will help a lot.

These are arranged roughly in order of "How much public speaking or 'interact with strangers' skill they require."

Look for opportunities to help in any fashion

First, if public speaking stuff is intimidating, there're many things you can do that don't require much at all. Some examples:

Sending a email reminder to the group for people to pick a meetup topic each week (otherwise people may forget until the last minute)
Bringing food, or interesting toys to add some fun things to do before or after the official meetup starts.
Helping out with tech (i.e. setting up projectors, printing out things in advance that need printing out in advance)
Take notes during interesting discussions (i.e. start up a google doc, announce that you're taking notes so people can specify if particular things should be off the record, and then post that google doc to whatever mailing list or internet group your community uses to organize)

Running Game Nights

If you're not comfortable giving a presentation or facilitating a conversation (two of the most common types of meetups), a fairly simple meetup is simply to run a game night. Find an interesting board game or card game, pitch it to the group, see if people are interested. (I recommend asking for a firm RSVP for this one, to make sure you have enough people)

Having an explicit activity can take some of the edge off of "talking in public.

Giving a short (3.5 minute) lightning talk

Som...
Nuisance Noise and Competing Access Needs
by Fluttershy — 2017-05-04T22:07:46.780Z

[This post contains discussion of antisemitism].

It's interesting to consider how a utilitarian view informs whether or not it's ok to penalize someone for using particular communication maneuvers. For instance, it seems clear that not giving in to threats which are made conditional on you not expressing your political views is a good idea, not least because it disincentivizes people from issuing such threats by making it seem less likely they'll work in the future.

A longer example: nuisance noise and antisemitic "nuisance noise"

Another similar example is that of nuisance noise: some people are particularly bothered by it, and others aren't, so the results of the utilitarian calculation of whether it's a net positive to penalize someone for making nuisance noise when they've told you they won't stop doing so voluntarily depends on the weighted preferences of the particular parties involved. Also, certain kinds of nuisance noise will upset people more than other kinds of noise; loud music might be less disturbing than radio propaganda asserting that there's a wide body of scientific evidence that the presence of certain unflattering physical and personal characteristics can be used to determine that someone is Jewish.
Certain behaviors can add to the effect that nuisance noise has; if our neighbor, who is regularly broadcasting antisemitic radio propaganda, has an established habit of befriending Jewish people, inviting them over to his house, and then beginning to talk with them about all the scientific evidence there is that large, downward-bent noses are an unflattering but sure sign of Judaism, that's more harmful than broadcasting antisemitic radio propaganda alone.
My view is that libertarianism has some very good points, and that being able to do things without having to deal with red tape prevents *a bunch* of failure modes. I would further say that the claim that "socially penalizing the loud antisemitic radio broadcast-player isn't warranted because he's doing his own thing in his own house" isn't something I'd choose to include under the umbrella of libertarianism. Imposing costs on others and claiming that you shouldn't be penalized for deliberately doing so doesn't really count as acting in the spirit of libertarianism; if anything, it provides supporting data to people who are interested in showing that libertarianism doesn't work.

Trickier situatio...
AI arms race
by Stuart_Armstrong — 2017-05-04T10:59:22.298Z

Racing to the Precipice: a Model of Artificial Intelligence Development

by Stuart Armstrong, Nick Bostrom, and Carl Shulman

This paper presents a simple model of an AI arms race, where several development teams race to build the first AI. Under the assumption that the first AI will be very powerful and transformative, each team is incentivised to finish first – by skimping on safety precautions if need be. This paper presents the Nash equilibrium of this process, where each team takes the correct amount of safety precautions in the arms race. Having extra development teams and extra enmity between teams can increase the danger of an AI-disaster, especially if risk taking is more important than skill in developing the AI. Surprisingly, information also increases the risks: the more teams know about each others’ capabilities (and about their own), the more the danger increases.
Thoughts on civilization collapse
by Stuart_Armstrong — 2017-05-04T10:41:57.943Z

Epistemic status: an idea I believe moderately strongly, based on extensive reading but not rigorous analysis.

We may have a dramatically wrong idea of civilization collapse, mainly inspired by movies that obsess over dramatic tales of individual heroism.

Traditional view:

In a collapse, anarchy will break out, and it will be a war of all against all or small groups against small groups. Individual weaponry (including heavy weapons) and basic food production will become paramount; traditional political skills, not so much. Government collapse is long term. Towns and cities will suffer more than the countryside. The best course of action is to have a cache of weapons and food, and to run for the hills.

Alternative view:

In a collapse, people will cling to their identified tribe for protection. Large groups will have no difficulty suppressing or taking over individuals and small groups within their areas of influence. Individual weaponry may be important (given less of a police force), but heavy weaponry will be almost irrelevant as no small group will survive alone. Food production will be controlled by the large groups. Though the formal "government" may fall, and countries may splinter into more local groups, government will continue under the control of warlords, tribal elders, or local variants. Cities, with their large and varied-skill workforce, will suffer less than the countryside. The best course of action is to have a stash of minor luxury goods (solar-powered calculators, comic books, pornography, batteries, antiseptics) and to make contacts with those likely to become powerful after a collapse (army officers, police chiefs, religious leaders, influential families).

Possible sources to back up this alternative view:

The book Sapiens argues that governments and markets are the ultimate enablers of individualism, with extended-family-based tribalism as the "natural" state of humanity.

The history of Somalia demonstrates that laws and enforcement continue even after a government collapse, by going back to more traditional structures.

During China's period of anarchy, large groups remained powerful: the nationalists, the communists, the Japanese invaders. The other sections of the country were generally under the control of local warlords.

Rational Wiki argues that examples of collapse go against the individualism narrative.
Meetup : Washington, D.C.: Regression to the Mean
by RobinZ — 2017-05-04T02:09:49.058Z

Discussion article for the meetup : Washington, D.C.: Regression to the Mean

WHEN:
07 May 2017 03:30:00PM (-0400)

WHERE:
Donald W. Reynolds Center for American Art and Portraiture

This week, we will be meeting in the courtyard to discuss the statistical concept of regression to the mean - the tendency of outliers in one test to fall closer to average on repeat tests - and some examples thereof.

Upcoming meetups:

May 14: Survivorship Bias

May 21: Autism

The AI Risk/Safety meetup has been postponed to accommodate the schedule of the meetup regular presenting it. - RZ

Discussion article for the meetup : Washington, D.C.: Regression to the Mean
Meetup : Chicago Applied Rationality Training
by mingyuan — 2017-05-04T01:29:58.465Z

Discussion article for the meetup : Chicago Applied Rationality Training

WHEN:
07 May 2017 01:00:00PM (-0500)

WHERE:
Harper Memorial Library Room 148, 1116 E 59th St, Chicago, IL 60637

The Chicago rationality group meets every Sunday from 1-3 PM in Room 148 of Harper Memorial Library. Though we meet on the University of Chicago campus, anyone is welcome to attend.

Now that we've covered all the core applied rationality techniques, I'll be teaching some miscellaneous other techniques; the exact content will depend on what people are interested in. This will be the second-to-last session of applied rationality training that I teach, so come through!

In case you missed the previous meetings, this video is endorsed as a good introduction to the techniques I've taught so far.

If you're interested in rationality-related events in the Chicago area, request to be added to our Google Group and I'll approve you!

Discussion article for the meetup : Chicago Applied Rationality Training
Intertheoretic utility comparison: outcomes, strategies and utilities
by Stuart_Armstrong — 2017-05-03T08:37:09.000Z

This post seeks to clarify the question of what utility functions should be defined over, in the situation where an agent has uncertainty over their own utility function.

Outcomes or worlds

The most intuitively natural thing to define utility functions over is outcomes or worlds. The problem with this approach is that it results in an excessive amount of utility functions.

For instance, imagine the agent has two actions a1 and a2. The action a1 will map straight to world w1, while a2 will map to a complicated probability distribution over a large set of worlds W2. The agent gets no further action.

If we see the set of worlds as W={w1}∪W2, then we have a vast amount of possible utility functions. Whereas in practice only two parts of the utility function matters: its value on w1, and its expected value on W2.

An agent that knows those values (technically, that knows the ordering of those values) for a single utility function, can reach a decision. Therefore it would seem perverse to have things change when the agent starts to have uncertainty over its utility.

What would make sense is to have utilities defined over expected outcomes -- ie over {w1} and W2 -- but that definition doesn't seem to work in more general situations.

Histories

Model the agent as interacting n times with the world. It gets an observation, then follows that by an action until it takes its last action an. Call the set of n alternating observations and actions a history, and let H be the set of histories. We can also define partial histories, sequences h=o1a1o2a2…om of length m<n.

Note this inverts the usual order, by starting with observations and following with actions. This is because the history ends with an action: since the agent can't affect anything more after than, subsequent observations are meaningless. Partial histories, though, end with observations, not actions (this is for subsequent ease of use).

Any utility function u can be seen as function from H to R, by mapping the history hn∈H to u(hn), the expected utility of the history.

So a more natural class would be to define utilities as being over H, the set of possible histories. Note that actions are included in histories, and histories with the same observations but different actions can have different utility values. This is because, eg, the observation of someone saying 'You succeed in your task!' will have different impli

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Intertheoretic utility comparison: simple theory
by Stuart_Armstrong — 2017-05-03T08:36:59.000Z

There's been a lot of work on how to reach agreement between people with different preferences or values. In practice, reaching agreement can be tricky, because of issues of extortion/trade and how the negotiations actually play out.

To put those issues aside, let's consider a much simpler case: where a single agent is uncertain about their own utility function. Then there is no issue of extortion, because the agent's opponent is simply itself.

This type of comparison is called intertheoretic, rather than interpersonal.

A question of scale

It would seem that if the agent believed with probability p that it followed utility u, and 1−p that it followed utility v, then it should simply follow utility w=(p)u+(1−p)v.

But this is problematic, because u and v are only defined up to positive affine transformations. Translations are not a problem: sending u to u+c sends w to w+pc. But scalings are: sending u to ru does not usually send w to any scaled version of w.

So if we identify [u] as the equivalence class of utilities equivalent to u, then we can write w=(p)u+(1−p)v, but it's not meaningful to write [w]=(p)[u]+(1−p)[v].

From clarity, we'll call things like u (which map worlds to real values) utility functions, while [u] will be called utility classes.

The setup

This is work done in collaboration with Toby Ord, Owen Cotton-Barratt, and Will MacAskill. We had some slightly different emphases during that process. In this post, I'll present my preferred version, while adding the more general approach at the end.

We will need the structure described in this post:

#. A finite set S of deterministic strategies the agent can take.
#. A set U of utility classes the agent might follow.
#. A distribution p over U, reflecting the agent's uncertainty over its own utility functions.
#. Let Up⊂U be the subset to which p assigns a non-zero weight. We'll assume p puts no weight on trivial, constant utility functions.

We'll assume here that p never gets updated, that the agent never sees any evidence that changes its values. The issue of updating p is analysed in the sections on reward learning agent.

We'll be assuming that there is some function f that takes in S and p and outputs a single utility class f(S,p)∈U reflecting the agent's values.

Basic axioms

Relevant data: If the utility classes [u] and [v] have the same values on all of S, then they are interchangeable from f's perspect

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Equilibria in adversarial supervised learning
by RyanCarey — 2017-05-03T08:14:45.000Z

Note: This describes an idea of Jessica Taylor's.

Introduction

An important aim in safe AI development is to make systems whose submodules analyze how they might go wrong. One model for this in academic machine learning research is training on adversarial examples.

In order to better understand the adversarial training process, we outline a specific adversarial learning model and consider what kind of equilibrium state it might possess.

Setup

Consider a general adversarial learning scenario described by the tuple (X,Y,W,g,f,ℓ) where:

X is a set of examples

Y is a set of the labels for these examples

W⊂Rn is a non-empty, compact, and convex set of hypotheses that can be chosen by the learner

g:X↦ΔY is a continuous mapping that nature uses to probabilistically assign ground truth labels to each example (ΔY represents a probability distribution over Y, ΔY:={p:Y↦[0,∞):∫y∈Yp(y)dy=1})

f:W↦ΔX is a continuous function that adversarially maps a hypothesis to some probability distribution over examples, allocating more probability mass to examples that will give the hypothesis difficulty.

ℓ:X×Y×W↦R, is a general loss function that appraises the performance of some hypothesis on some labeled example. It is convex in its third argument.

The learner's aim is to choose a hypothesis w∈W that achieves low expected loss ℓ(x,y,w), given that the labeled example is sampled from an adversarial distribution, x∼f(w),y∼g(x).

An equilibrium in expert selection

One plausible approach for the learner is to consider hypotheses that perform well on some adversarial distribution f(w). The hypotheses that perform best on a some fixed distribution f(w) are given by the set-valued function:

h(w)=argminw′∈WEx∼f(w),y∼g(x)[ℓ(x,y,w′)]

The difficulty with choosing a hypothesis is that the adversarial distribution is not fixed, but rather it varies according to which hypothesis is chosen. One response is to choose some hypothesis w∗ that is located at a fixed point, such that w∗∈h(w∗). There is always at least one such fixed point, w∗, and this can be shown by Kakutani's fixed-point theorem.

To apply Kakutani's fixed point theorem, we need to show that: i) the domain of h is compact, convex and nonempty, ii) h is convex-valued, and iii) h is compact-valued and upper hemicontinuous.

First, by its definition, W, the domain of h, is non-empty, closed, and compact. Second, h is convex-valued because it

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How I'd Introduce LessWrong to an Outsider
by Adam Zerner — 2017-05-03T04:32:21.396Z

Note/edit: I'm imagining explaining this to a friend or family member who is at least somewhat charitable and trusting of my judgement. I am not imagining simply putting this on the About page. I should have made this clear from the beginning - my bad. However, I do believe that some (but not all) of the design decisions would be effective on something like the About page as well.

There's this guy named Eliezer Yudkowsky. He's really, really smart. He founded MIRI, wrote a popular fanfic of Harry Potter that centers around rationality, and has a particularly strong background in AI, probability theory, and decision theory. There's another guy named Robin Hanson. Hanson is an economics professor at George Mason, and has a background in physics, AI and statistics. He's also really, really smart.

Yudkowsky and Hanson started a blog called Overcoming Bias in November of 2006. They blogged about rationality. Later on, Yudkowsky left Overcoming Bias and started his own blog - LessWrong.

What is rationality? Well, for starters, it's incredibly interdisciplinary. It involves academic fields like probability theory, decision theory, logic, evolutionary psychology, cognitive biases, lots of philosophy, and AI. The goal of rationality is to help you be right about the things you believe. In other words, the goal of rationality is to be wrong less often. To be LessWrong.

Weird? Useful?

LessWrong may seem fringe-y and cult-y, but the teachings are usually things that aren't controversial at all. Again, rationality teaches you things like probability theory and evolutionary psychology. Things that academics all agree on. Things that academics have studied pretty thoroughly. Sometimes the findings haven't made it to mainstream culture yet, but they're almost always things that the experts all agree on and consider to be pretty obvious. These aren't some weird nerds cooped up in their parents basement preaching crazy ideas they came up with. These are early adopters who are taking things that have already been discovered, bringing them together, and showing us how the findings could help us be wrong less frequently.

Rationalists tend to be a little "weird" though. And they tend to believe a lot of "weird" things. A lot of science-fiction-y things. They believe we're going to blend with robots and become tr...
Acting on your intended preferences - What does that look like in practice? (critical introspective questions)
by Elo — 2017-05-03T00:51:58.834Z

Part 1: Exploration-Exploitation
Part 2: Bargaining Trade-offs to your brain.
. Part 2a: Empirical time management
Part 3: The time that you have
. Part 3a: A purpose finding exercise
Part 4: What does that look like in practice?
. . Part 4a: Lost purposes – Doing what’s easy or what’s important
. . Part 4b.1 In support of yak shaving
. . Part 4b.2 Yak shaving 2
. Part 4c Filter on the way in, Filter on the way out…
. . Part 4d.1 Scientific method
. . Part 4d.2 Quantified self
Part 5: Call to action

It’s all good and well to know that you should be doing good work, deep work, hard work, and work that you really value. That time is really really running out, and that sometimes you have to wrangle your brain to get it to consider the world-problem space in the right terms. That is that you have to be making actions that are the right trade-offs between actions you want to do and the other actions you want to do. But how do you do that? How do you keep at it?

I suggest by first doing them – making a start at it, then constantly check that you are still doing the highly valuable actions. How do you do that?

I suggest critical questions. In your running consciousness you want to install critical questions.

Is this task the most important task right now?

If you have ever heard of an eisenhower matrix, this is a very powerful organisation tool that got a mention in both Getting Things done and The seven habits of highly effective people. An eisenhower matrix is a punnet square that straddles the question of importance and urgency.

Important
Not important

Urgent
Do now
Schedule

Not urgent
Delegate
Don’t do

Knowing this table, and the suggested responses to each type of task is interesting but it doesn’t teach us to feel it in System 1.

Is this conversation valuable?

If you are in a conversation, you should check if it’s giving anyone anything good. You don’t need to check in any other way other than thinking about it briefly. But this can save you from many kinds of failure modes. (Future post – what happens if your check in returns, “no good is coming from this conversation...