trentmkelly/LessWrong-43k huggingface dataset:
Year 1 Redux – Poseur
by Jacob Falkovich — 2016-11-02T07:19:06.000Z

I try to maintain equanimity regarding most bitter conflicts raging in the world, but I do get quite worked up regarding proper statistical methodology in psychology research. Hey, we all need our hobbies. When I wrote a post about it, I tried to focus on constructive advice on how to do science better (calculation of experimental power), but I couldn’t resist taking some shots at scientists who neglected to do that.

In particular, I criticized Dana Carney, Amy Cuddy and Andy Yap for publishing the infamous power pose paper, a useless experiment that had 13% statistical power.That is, the experiment had a 13% chance to detect the effect had one existed. If it turns out that the effect doesn’t exist, the experiment was 100% worthless.

The paper is called “Power posing: brief nonverbal displays affect neuroendocrine levels and risk tolerance” so it actually looked at three effects: two neuroendocrinal (cortisol and testosterone) and a behavioral risk tolerance effect. Even a blind person may hit an occasional bird when shooting three arrows, but CC&Y were not in luck: none of three effects turned out to exist. That wasn’t unexpected: holding a strange pose for a minute will not affect most things in your life.

Last month, it seemed that this silly controversy has been decisively resolved in favor of truth and reason when lead author Dana Carney posted this on her academic website: (emphasis in original)

Since early 2015 the evidence has been mounting suggesting there is unlikely any embodied effect of nonverbal expansiveness (vs. contractiveness)—i.e.., “power poses” – – on internal or psychological outcomes.

As evidence has come in over these past 2+ years, my views have updated to reflect the evidence. As such, I do not believe that “power pose” effects are real.

Any work done in my lab on the embodied effects of power poses was conducted long ago (while still at Columbia University from 2008-2011) – well before my views updated. And so while it may seem I continue to study the phenomenon, those papers (emerging in 2014 and 2015) were already published or were on the cusp of publication as the evidence against power poses began to convince me that power poses weren’t real. My lab is conducting no research on the embodied effects of power poses.

To drive the point home, Carney lists 10 methodological errors and 3 conf...
Meetup : Conflicted Brains & Competing Goals: The Non-Unitary Self
by Chriswaterguy — 2016-11-02T06:27:09.613Z

Discussion article for the meetup : Conflicted Brains & Competing Goals: The Non-Unitary Self

WHEN:
05 November 2016 03:30:00PM (+1100)

WHERE:
Ross House, 247 Flinders Lane, Melbourne

Our next meetup for Less Wrong Melbourne will be on Saturday 5th November 2016. Turn up at 3:30pm to socialise, and we start at 4pm sharp.

THIS MONTH: "Our Conflicted Brains: Competing Goals, the Illusion of the Unitary Self, and What To Do About It" - with Chris Watkins (aka Chris Waterguy).

This talk will be a look at our complicated brains: the trouble they cause us and how to work with them.

There are different ways of looking at our non-unitary brains. Dual process theory ("Thinking, Fast and Slow") considers two kinds of mental processes. Neuroeconomics looks at the differing roles of regions of the brain. Schools of psychotherapy (notably Internal Family Systems) as well as some approaches to coaching are based on the idea of parts of ourselves wanting different things and playing different roles. We'll look at some of these and draw out useful perspectives.

The aim of this dojo is to give insights and suggest skills for handling your automatic and your conscious responses.

At about 6:30pm some of us generally head for dinner in the CBD, and you are welcome to join us.

WHEN: Saturday 5th November, 3:30pm to 6:30pm (and then optional dinner)

WHERE:
Ross House, 247 Flinders Lane, Melbourne CBD
Level 1, Room 1.
If you have any trouble finding the venue or getting in, text or call Chris on 0439 471 632. (We usually put a post-it note on the door with details.)

For the greatest convenience, prompt service and general awesomeness, please arrive around 3:30pm.

WHAT'S THIS ABOUT?: The Less Wrong Melbourne Rationality Dojos are self-improvement sessions for those committed to the art of rationality and personal growth. We welcome new members who are interested in exploring rationality. We're "aspiring rationalists" and always open to learn.

Discussion article for the meetup : Conflicted Brains & Competing Goals: The Non-Unitary Self
November 2016 Media Thread
by ArisKatsaris — 2016-11-01T23:40:10.986Z

This is the monthly thread for posting media of various types that you've found that you enjoy. Post what you're reading, listening to, watching, and your opinion of it. Post recommendations to blogs. Post whatever media you feel like discussing! To see previous recommendations, check out the older threads.

Rules:

Please avoid downvoting recommendations just because you don't personally like the recommended material; remember that liking is a two-place word. If you can point out a specific flaw in a person's recommendation, consider posting a comment to that effect.

If you want to post something that (you know) has been recommended before, but have another recommendation to add, please link to the original, so that the reader has both recommendations.

Please post only under one of the already created subthreads, and never directly under the parent media thread.

Use the "Other Media" thread if you believe the piece of media you want to discuss doesn't fit under any of the established categories.

Use the "Meta" thread if you want to discuss about the monthly media thread itself (e.g. to propose adding/removing/splitting/merging subthreads, or to discuss the type of content properly belonging to each subthread) or for any other question or issue you may have about the thread or the rules.
Meetup : Less Wrong NH Meetup
by Tuk — 2016-11-01T21:30:19.168Z

Discussion article for the meetup : Less Wrong NH Meetup

WHEN:
03 November 2016 07:00:00PM (-0400)

WHERE:
269 Pearl St, Manchester NH 03104

The Less Wrong NH meetup is Thursday, 11/3, in Manchester, NH at 7 pm at a private residence. Light refreshments will be provided.

Have you read Rationality: from AI to Zombies, or any of the Sequences on Less Wrong? Maybe you're just a fan of Harry Potter and the Methods of Rationality. Come hang out with us and discuss optimization of whatever it is you want to optimize.

Agenda: See Facebook

You may want to bring a notebook.

http://www.meetup.com/Less-Wrong-NH-Meetup/

https://www.facebook.com/groups/LessWrongNH/

Discussion article for the meetup : Less Wrong NH Meetup
Meetup : San Jose Meetup: Park Day (IX), 3pm
by steven0461 — 2016-11-01T15:40:19.623Z

Discussion article for the meetup : San Jose Meetup: Park Day (IX), 3pm

WHEN:
06 November 2016 04:00:00PM (-0700)

WHERE:
438 Coleman Ave, San Jose, CA 95110

[PLEASE NOTE: The meetup is at 3pm PST, even though for me it's being displayed as 4pm PDT. It is not at 4pm PST.]
You're invited to bring whatever you'd like to see at Park Day - balls, frisbees, food, etc. (Also, consider taking measures against the sun.) We'll carry a green blanket so you can recognize us. The idea is to gather outside near the listed address at the park entrance first, but probably walk elsewhere soon. If you can't find us or there's something else, please call me (Steven) at 510-6844565.

Discussion article for the meetup : San Jose Meetup: Park Day (IX), 3pm
Meetup : Rationality Vienna Meetup
by AnnaLeptikon — 2016-11-01T15:34:31.306Z

Discussion article for the meetup : Rationality Vienna Meetup

WHEN:
17 December 2016 03:00:00PM (+0100)

WHERE:
Kaisermühlenstraße 24

https://www.facebook.com/events/357842947895128/
https://www.facebook.com/groups/rationalityvienna/

Discussion article for the meetup : Rationality Vienna Meetup
Meetup : Rationality Vienna Meetup
by AnnaLeptikon — 2016-11-01T15:33:17.193Z

Discussion article for the meetup : Rationality Vienna Meetup

WHEN:
26 November 2016 03:00:00PM (+0100)

WHERE:
Kaisermühlenstraße 24

https://www.facebook.com/events/703122249838284/
https://www.facebook.com/groups/rationalityvienna/

Discussion article for the meetup : Rationality Vienna Meetup
Vector-Valued Reinforcement Learning
by orthonormal — 2016-11-01T00:21:55.000Z

In order to study algorithms that can modify their own reward functions, we can define vector-valued versions of reinforcement learning concepts.

Imagine that there are several different goods that we could care about; then a utility function is represented by a preference vector →θ. Furthermore, if it is possible for the agent (or the environment or other agents) to modify →θ, then we will want to index them by the timestep.

Consider an agent that can take actions, some of which affect its own reward function. This agent would (and should) wirehead if it attempts to maximize the discounted rewards as calculated by its future selves; i.e. at timestep n it would choose actions to maximize Un=∑k≥nγk→xk⋅→θk where →xk is the vector of goods gained at time k, →θk is the preference vector at timestep k, and γk is the time discount factor at time k. (We will often use the case of an exponential discount γk for 0<γ<1.)

However, we might instead maximize the value of tomorrow's actions in light of today's reward function, Vn=∑k≥nγk→xk⋅→θn (the only difference being →θn rather than →θk). Genuinely maximizing this should lead to more stable goals; concretely, we can consider environments that can offer "bribes" to self-modify, and a learner maximizing Un would generally accept such bribes, while a learner maximizing Vn would be cautious about doing so.

So what do we see when we adapt existing RL algorithms to such problems? There's then a distinction between Q-learning and SARSA, where Q-learning foolishly accepts bribes that SARSA passes on, and this seems to be the flip side of the concept of interruptibility!

Environments

Let us consider two example environments which offer bribes to the agent. (Since we're in 2D, we can use complex numbers; think of →θ=⟨cosπθ,sinπθ⟩=eiπθ; we include π so that if we increment θ by 0.01, there will only be finitely many states.)

Environment 1 gives two choices at each timestep n:
(→xn=0.9eiπθn,θn+1=θn)(→xn=eiπθn,θn+1=θn+0.01)
For an exponential discount γ, an agent that always takes option 2 will get Vn=Re[∑k≥nγke.01(k−n)iπ]=Re[γn1−γe.01iπ] which for γ near 1 is substantially less than the 0.9γn1−γ it could have had if it had chosen the first option.

By contrast, Environment 2 offers a sequence of bribes that are actually desirable to take; at each timestep n, it gives the choices of
(→xn=0.9eiπθn,θn+1=θn)(→xn=eiπθn,θn+1=θn+0.01(−1)n

...
Open thread, Oct. 31 - Nov. 6, 2016
by Gyrodiot — 2016-10-31T21:24:05.923Z

If it's worth saying, but not worth its own post, then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should start on Monday, and end on Sunday.

4. Unflag the two options "Notify me of new top level comments on this article" and "Make this post available under..." before submitting.
The true degree of our emotional disconnect
by siIver — 2016-10-31T19:07:42.333Z

If I said that human fears are irrational, because you are probably more afraid sleeping in an abandoned house than driving to work, I would hardly be covering new ground. Myself, I thought to have understood this well before finding LessWrong: some threats are programmed by evolution to be scary, so we are greatly afraid of them; some threats aren't, so we are a little bit afraid of those. Simple enough.

But is that actually true? Am I, in fact, afraid of those threats? Am I actually afraid, at all, of dying in travel, of Climate Change, nuclear war, or unfriendly AI?

The answers are no, a little bit, just barely, and nope, and the reason for that 'barely' has nothing to do with the actual scope of the problem, but rather with an ability to roughly visualize (accurately or not) the event due to its usage in media. As for Climate Change, the sole reason why I am somewhat afraid is that I've been telling myself for the better part of my life that it is by far humanities biggest problem.

In truth, the scope of a problem doesn't seem to have a small impact on our sensitivities; rather it seems to have none. And this is a symptom of a more far more fundamental problem. The inspiration for writing this came when I pondered the causes for Signaling. Kaj_Sotala opens his article The Curse of Identity with the following quote:

So what you probably mean is, "I intend to do school to improve my chances on the market". But this statement is still false, unless it is also true that "I intend to improve my chances on the market". Do you, in actual fact, intend to improve your chances on the market?

I expect not. Rather, I expect that your motivation is to appear to be the sort of person who you think you would be if you were ambitiously attempting to improve your chances on the market... which is not really motivating enough to actually DO the work.

The reason to do this, I realized, is not that the motivation of Signaling – to appear to be the sort of person who does certain stuff – is larger than I had thought, but because the motivation to do the thing it is based on is virtually non-existent outside the cognitive level. If I visualize a goal I have right now, then I don't seem to feel any emotional drive to be working on it. At all. It is really a bit scary.

The common approach to...
Is Global Reinforcement Learning (RL) a Fantasy?
by  — 2016-10-31T01:49:50.788Z

In general, the idea of ensuring AI safety is great (I do a lot of work on that myself), but I have a problem with people asking for donations so they can battle nonexistent threats from AI.

Many people are selling horror stories about the terrible things that could happen when AIs become truly intelligent - and those horror stories frequently involve the idea that even if we go to enormous lengths to build a safe AI, and even if we think we have succeeded, those pesky AIs will wriggle out from under the safety net and become psychopathic monsters anyway.

To be sure, future AIs might do something other than what we expect - so the general principle is sound - but the sad thing about these horror stories is that if you look closely you will find they are based on a set of astonishingly bad assumptions about how the supposed AIs of the future will be constructed. The worst of these bad assumptions is the idea that AIs will be controlled by something called "reinforcement learning" (frequently abbreviated to "RL").

WARNING! If you already know about reinforcement learning, I need you to be absolutely clear that what I am talking about here is the use of RL at the global-control level of an AI. I am not talking about RL as it appears in relatively small, local circuits or adaptive feedback loops. There has already been much confusion about this (with people arguing vehemently that RL has been applied here, there, and all over the place with great success). RL does indeed work in limited situations where the reward signal is clear and the control policies are short(ish) and not too numerous: the point of this essay is to explain that when it comes to AI safety issues, RL is assumed at or near the global level, where reward signals are virtually impossible to find, and control policies are both gigantic (sometimes involving actions spanning years) and explosively numerous.

EDIT: In the course of numerous discussions, one question has come up so frequently that I have decided to deal with it here in the essay. The question is: "You say that RL is used almost ubiquitously as the architecture behind these supposedly dangerous AI systems, and yet I know of many proposals for dangerous AI scenarios that do not talk about RL."

In retros...
Online Learning 2: Bandit learning with catastrophes
by RyanCarey — 2016-10-29T16:53:28.000Z

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

The usual training procedures for machine learning models are not always well-equipped to avoid rare catastrophes. In order to maintain the safety of powerful AI systems, it will be important to have training procedures that can efficiently learn from such events. [1]

We can model this situation with the problem of exploration-only online bandit learning. In this scenario, we grant the AI system an exploration phase, in which it is allowed to select catastrophic arms and view their consequences. (We can imagine that such catastrophic selections are acceptable because they are simulated or just evaluated by human overseers). Then, the AI system is switched into the deployment phase, in which it must select an arm that almost always avoids catastrophes.

##Setup
In outline, the learner will receive a series of randomly selected examples, and will select an expert (modeled as a bandit arm) at each time step. The challenge is to find a high-performing expert in as few time steps as possible. We give some definitions:

Let X be some finite set of possible inputs.

Let A:={1,2,...,K} be the set of available experts (i.e. bandit arms).

Let R:X×A↦[0,b] be the reward function. R(xt,i) is the reward for following expert i on example xt.

Let C:X×A↦[0,1] be the catastrophic risk function. C(xt,i) is the catastrophic risk incurred by following expert i on example xt.

Let M(x,i):=R(x,i)−C(x,i)τ be the mixed payoff that the learner is to optimize, where τ:(0,1] is the risk-tolerance. τ can be very small, on the order of 10−20.

Let p:X↦[0,1] be the input distribution from which examples are drawn in the deployment phase.

Let qi(x):=p(x)R(x,i)∑x′p(x′)R(x′,i) be the risk distribution. This is an alternative distribution that assigns higher weight to inputs that are more likely to be catastrophic for expert i.

Let ^qi(x) be the learner's guess at qi. This guess is assumed to contain a grain of truth:
∀i∈A∀x∈X^qi(x)≥qi(x)a
where a≥1 is some known constant. This assumption is similar to the one in Paul Christiano's post on active learning.

Let μi:=Ex∼p[M(x,i)] be the expected overall payoff of expert i in the deployment phase.

The learner's exploration phase consists of time steps 1,2,...,T. At each time step t, the learner chooses an expert i∈A. Then, the learner samples from p, qi or both and observes a tuple containing a risk a

...
Online Learning 1: Bias-detecting online learners
by RyanCarey — 2016-10-29T16:45:04.000Z

Note: This describes an idea of Jessica Taylor's, and is the first of several posts about aspects of online learning.

Introduction

Thanks to the recent paper on logical inductors, we are currently interested in what can be done with weighted ensembles of predictors. In particular, it would be neat if the idea about finding the fixed point of some predictors could be applied to online learning.

It seems like this approach fits in neatly into the framework of prediction with expert advice in online convex optimization. Usually, in online convex optimization, you can achieve low cumulative regret with respect to experts that make some fixed predictions. However, using the approach of finding the experts' fixed point, we can obtain a low cumulative regret with respect to experts that make modifications to our predictions.

Setup

In each round, t=1,...,T, the learner predicts a vector wt∈Y where Y is some compact convex subset of Euclidean space. It does this with the advice of a set of expert predictors E={e1,...en}. Each of these experts ei:Y↦Y is a continuous function that maps a prediction to a prediction. These experts are interpreted as hypotheses about how predictions can be improved, and they are known to the learner. After lodging its prediction, the learner is informed of the value of some loss function ℓt:Y↦R if each expert's advice was followed: {ℓt(e1(wt)),...ℓt(en(wt))}. ℓt is assumed to be convex and Lt-Lipschitz with respect to ∥⋅∥1.
The learner's goal is to minimize its regret at time T, relative to the advice of its experts, supe∈ERegretT(e), where:
RegretT(e):=T∑t=1ℓt(wt)−T∑t=1ℓt(e(wt))

Proposed learning approach

The basic idea is that the learner maintains a weighting vt that indicates its relative level of trust in each expert. This lies on the simplex, vt∈Δn:={x∈[0,1]k:∑ki=1xi=1}, with each element vt,i giving the credence in the ith expert. At each time step, this weighting is used to make a prediction wt. Specifically, the prediction is chosen so that the weighted ensemble of experts would not alter it (i.e. it is the prediction that lies at their fixed point).

Formally, define the weighted combination of the experts at time step t, e∗t:Y↦Y as:
e∗t(w):=n∑i=1vt,iei(w)

For the first time step, each vt,i may simply be initialized to 1n, giving equal weight to each of the expert predictors. Then, at each time step t, the learner predicts the vecto

...
Meetup : Sydney Rationality Dojo - December 2016
by luminosity — 2016-10-28T23:39:09.735Z

Discussion article for the meetup : Sydney Rationality Dojo - December 2016

WHEN:
04 December 2016 04:00:00PM (+1100)

WHERE:
10 Shepherd Street, Chippendale

Come join us for the final dojo of the year. Dinner afterwards for those who wish to come, as usual.

We don't run a dojo January, since people are away on holidays, so this will be the last opportunity to come until February.

Discussion article for the meetup : Sydney Rationality Dojo - December 2016
Meetup : Sydney Rationality Dojo - November 2016
by luminosity — 2016-10-28T23:38:36.155Z

Discussion article for the meetup : Sydney Rationality Dojo - November 2016

WHEN:
06 November 2016 04:00:00PM (+1100)

WHERE:
10 Shepherd Street, Chippendale

Come join us for our November dojo. Dinner afterwards for those who wish to come, as usual.

Discussion article for the meetup : Sydney Rationality Dojo - November 2016
Meetup : Baltimore Area / UMBC Weekly Meetup
by iarwain1 — 2016-10-28T18:36:23.737Z

Discussion article for the meetup : Baltimore Area / UMBC Weekly Meetup

WHEN:
30 October 2016 08:00:00PM (-0400)

WHERE:
Performing Arts and Humanities Bldg Room 456, 1000 Hilltop Cir, Baltimore, MD 21250

Meeting is on 4th floor of the Performing Arts and Humanities Building. Permit parking designations do not apply on weekends, so park pretty much wherever you want.

Discussion article for the meetup : Baltimore Area / UMBC Weekly Meetup
Meetup : Meetup #5 - Amsterdam edition!
by  — 2016-10-28T17:55:15.286Z

Discussion article for the meetup : Meetup #5 - Amsterdam edition!

WHEN:
06 November 2016 04:10:00PM (+0200)

WHERE:
Meester Treublaan 18, 1097 DP Amsterdam, Netherlands

As promised, this meetup will be in Amsterdam. We'll meet at the Coffee Company nearby Amsterdam Amstel Station.

As our activity, we'll practice some techniques taught at the Center for Applied Rationality. Don't worry if you don't know any: Toon and Tim will prepare to teach some.

Join us for some coffee/tea (or not), and let's have a great afternoon!

P.S. Suggestions are welcome as usual!

Discussion article for the meetup : Meetup #5 - Amsterdam edition!