trentmkelly/LessWrong-43k huggingface dataset:
The Skill of Noticing Emotions
by Neel Nanda — 2020-06-04T17:48:28.782Z

The Skill of Noticing Emotions

(Thanks to Eli Tyre and Luke Raskopf for helping teach me the technique. And thanks to Nora Ammann, Fin Moorhouse, Ben Laurense, Daniel Hynk, Nathan Young, Toby Jolly, Michael Ng, James Walsh and Jaime Sevilla for feedback on various drafts!)

(For those familiar with the core idea, you might find it more interesting to skip to the long list of personal examples in the Appendix)

Introduction

I was introduced to a technique called Noticing at my CFAR workshop back in October, to become better aware of my emotions and better at productively reacting to them. I’ve since found this a really powerful technique, and it’s become a pretty key tool for solving problems in my life. My goal in this post is to give my take on Noticing, explain how I actually go about using it, and hopefully convince you to give it a try!

What do I mean by Noticing? I first want to introduce the idea of an emotion or mental state being noticeable: something that feels important and urgent and is immediately promoted to conscious attention. An excellent example of a noticeable experience is hearing my name. When I hear someone say “Neel” it immediately feels important and captures my attention. Even if I’m busy and focused on something, hearing my name can easily break that focus and cause me to change what I’m doing.

Being noticeable breaks down into two parts: something that I’m aware of and something that feels important. For example, I’m often aware that I’m procrastinating, but can’t muster the motivation to actually do anything about it. It’s something I am aware of, but not something that feels important. I like to think of this as spending most of my life on autopilot, focused on the present moment. A noticeable event or emotion will turn off autopilot and engage my conscious mind. Hearing my name does disable autopilot, but if I’m aware that I’m procrastinating and continuing to do so, I’m still on autopilot.

Noticing, then, is a technique to take a specific emotion or mental state[1] and deliberately make it noticeable. This essentially installs automatic triggers to turn off my autopilot. This is extremely useful, because a lot of problems in my life dissolve when I can just be more self-aware at the right times! For example, I’ve had a lot of success ...
Russian x-risks newsletter spring 2020
by avturchin — 2020-06-04T14:27:40.459Z

Coronavirus is rampant in Russia, but the lockdown is easing for political and economic reasons. It was not very strict, in fact: many people were on streets, and infections per day grew 100 times during lockdown in April. The reported infection level peaked at the beginning of May. It declined since in Moscow in 2-3 times, but grew in the regions, so total daily cases are almost not changing in May. There is obvious underreporting of the number of actual cases, but the peak in Moscow seems real: my friends are reporting fewer cases in their facebook.

Voting to extend Putin's rule indefinitely via the new Constitution is scheduled for July 1, 2020, and this could be a political reason to report fewer cases.

A fakenews was officially refuted that coronavirus leaked after the explosion in Novosibirsk Vector biolab in fall 2019 and this information was prohibited for dissemination in social networks.

Vector is working on a vaccine and it is promised soon, maybe in fall 2020, but many people I asked are sceptical of its effectiveness and safety and prefer to wait for FDA approved vaccine.

Russia limited export of some grains.

Moscow is designated as a special area to test AI technology by providing easier access to collected personal data.

Russia declared that it may use nuclear weapons first if its existence is threatened by conventional weapons attack.

The Russian economy is hurt both by coronavirus lockdown and low prices of oil and low demand for natural gas.

Russia joined one more war by sending fighter jets and mercenaries in Libya to participate in the civil war but suffered defeat near Tripoli from Turkish drones.
The Stopped Clock Problem
by  — 2020-06-04T12:07:37.417Z

When a low-probability, high-impact event occurs, and the world “got it wrong”, it is tempting to look for the people who did successfully predict it in advance in order to discover their secret, or at least see what else they’ve predicted. Unfortunately, as Wei Dai discovered recently, this tends to backfire.

It may feel a bit counterintuitive, but this is actually fairly predictable: the math backs it up on some reasonable assumptions. First, let’s assume that the topic required unusual levels of clarity of thought not to be sucked into the prevailing (wrong) consensus: say a mere 0.001% of people accomplished this. These people are worth finding, and listening to.

But we must also note that a good chunk of the population are just pessimists. Let’s say, very conservatively, that 0.01% of people predicted the same disaster just because they always predict the most obvious possible disaster. Suddenly the odds are pretty good that anybody you find who successfully predicted the disaster is a crank. The mere fact that they correctly predicted the disaster becomes evidence only of extreme reasoning, but is insufficient to tell whether that reasoning was extremely good, or extremely bad. And on balance, most of the time, it’s extremely bad.

Unfortunately, the problem here is not just that the good predictors are buried in a mountain of random others; it’s that the good predictors are buried in a mountain of extremely poor predictors. The result is that the mean prediction of that group is going to be noticeably worse than the prevailing consensus on most questions, not better.

Obviously the 0.001% and 0.01% numbers above are made up; I spent some time looking for real statistics and couldn't find anything useful; this article claims roughly 1% of Americans are "preppers", which might be a good indication, except it provides no source and could equally well just be the lizardman constant. Regardless, my point relies mainly on the second group being an order of magnitude or more larger than the first, which seems (to me) fairly intuitively likely to be true. If anybody has real statistics to prove or disprove this, they would be much appreciated.
A Practical Guide to Conflict Resolution: Introduction
by  — 2020-06-04T01:03:26.576Z

This is the first post in a sequence aiming to create a practical guide to resolving interpersonal conflict (with a light focus on workplace conflicts). It draws on a lot of rationalist ideas without having any prerequisite readings, and with an attempt at accessible language; I hope this ends up equally useful to everyone, whether or not you consider yourself part of the "rationalsphere".

The ideas behind this sequence started as some quick notes for a colleague at work, based on a long whiteboard conversation we had. Eventually I wrote it up properly as a massive 6000-word personal blog post. Finally after some prompting, I'm polishing it a bit further, splitting it up into usable chunks, and sharing it here.

Conflict, and Conflict Resolution

We encounter conflict every day. Perhaps you’re having an unavoidable Thanksgiving-dinner conversation about politics, or maybe you’re chatting with your neighbour when you realize that you have very different views on a recent change by the local sports team. For me, as for many people, a lot of these conflicts tend to arise at work: dealing with unreasonable customers, unreasonable coworkers, or unreasonable managers is just part of the job. Whether your work is blue-collar, white-collar, retail, or even raising chickens, conflict happens whenever two people want or believe different things, and that isn’t exactly rare.

With so many conflicts in our day-to-day lives, resolving them becomes an important life skill. Typically, we do this using communication; it’s thankfully rare that minor conflicts devolve into violence. And yet, communicating well is one of the hardest parts of modern life. Technology has created a number of new ways to communicate our ideas, those ideas grow more complex every day, we spend less and less time face to face, and partisan political bias seems to be driving us further and further apart. Even so, communication is still our main approach for resolving most of the conflicts we encounter.

Given its importance, it shouldn’t be surprising that conflict resolution is a topic already rich in conventional wisdom, academic studies, and self-help books; practically speaking I don’t have much that is new to contribute. However, at the heart of many great innovations is the combination of multiple ideas in different fields, and that’s what ...
A History Of Universalist Greed
by namespace — 2020-06-04T00:22:50.647Z
Link: https://www.thelastrationalist.com/a-history-of-universalist-greed.html

[Special thanks to Ratheka Stormbjorne and Shiloh Miyazaki for doing some of the research for this essay. All opinions expressed are my own however.]

In my essay on Fuzzies and Saddies I wrote about four components that were necessary to implement "Eliezer's version of extropy":

1. High future shock. This is necessary to realize that there are solutions to the problems we have, and anything really worth fighting for. That it's not all hopeless, there are glorious things within our reach.

2. A love for the world and its inhabitants, the belief that death is Bad, a fully developed secular moral system. New Atheism is toxic nonsense because skepticism is toxic nonsense. The skeptic focuses only on downside risk, EY-style rationality is an improvement because it considers opportunity cost. It's not enough to not-lose in rationality, you need to capture the foregone upside.

3. Sanity. You need to have a very clear view of the world, and be very well in tune with yourself, have a strong well constructed (i.e., not full of ad-hoc garbage) identity, good epistemics, etc.

4. Agency. You need to be well versed in the practical methods of piloting yourself to actually do things. Building habits, not giving up at the first setback, strength, etc.

I currently believe that the path to this starts with agency, because without that there is no motivation to put in the work to get these other things. Agency is hard for people because the default implementation of human motivation is a satisficer. You get hungry, you eat and are no longer hungry; until the equilibrium is disrupted and you become hungry again. People rarely act like maximizers, getting as much 'good' as they can from a situation. Perhaps part of the magic of money is that it reliably gets people to act like maximizers, people are willing to keep pursuing money no matter how much they have. People are not willing (nor able) to keep eating food after their hunger has been sated. A maximizing agent by contrast implicitly wants everything, the infamous paperclip maximizer will, if given the power to do so, consume every available resource in the universe to make more paperclips. Any full-scope agent will begin to look like a maximizer, regardless of their morality. Maximizing and agency are not synonyms, but maximizing and Agency are.

The Gospel Of Univers

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Is a near-term, self-sustaining Mars colony impossible?
by ESRogs — 2020-06-03T22:43:08.501Z

Some argue that Elon Musk's plan to build a self-sustaining colony on Mars is effectively impossible for the foreseeable future without discontinuous tech progress. (See e.g. Jess Riedel's claim here, which Robin Hanson agrees with.)
Is there a simple explanation for why this might be so? I'd be especially interested in Fermi estimates of key bottleneck resources.
Status-Regulating Emotions
by lsusr — 2020-06-03T22:32:03.082Z

Elizer Yudkowsky wrote an interesting comment on What Universal Human Experiences Are You Missing Without Realizing It?

It was the gelling of the HPMOR hatedom which caused me to finally realize that I was blind, possibly I-don’t-have-that-sense blind, to the ordinary status-regulation emotions that, yes, in retrospect, many other people have, and that evolutionary psychology would logically lead us to expect exists.

…It was only afterward that I looked back and realized that nobody ever hates Hermione, or Harry, on account of either of them acting like they have more status than someone else has already mentally assigned. Characters in HPMOR may dislike people who are ahead of them, or envy people who have things they want, but “you don’t have a license to be important” is not a thing that anyone in HPMOR, hero or villain or NPC, ever feels.

For though I have known many a negative emotion in my life, yea I have known bitterness, frustration, despair, resentment, and a few times even envy and a sense that someone else has something that I deserve more, I have never felt the status-regulation-slapdown emotion. I needed to deduce its abstract existence via evolutionary psychology once HPMOR finally provoked enough data. I still have no idea what it feels like.

Are these a real thing? To find out, I asked my friend Justin Ith. Justin Ith is a master of social finesse. Asking him if status-regulation emotion is real is like asking Leonardo da Vinci if red and green are different colors.

Justin: "Are you asking if status-regulation emotion is a real thing people feel?"

Me: "Yes."

Justin: "If so, then absolutely yes."

Justin: "Lets say there are 9 followers and 1 leader in a group. If one of the followers started bossing the other followers around, it's frustrating because that person is acting out of their status."

Justin: "Even more clearly, lets say 9 soldiers and 1 general. If one of the soldiers starts commanding the others without any authority bestowed on them by the general, the other soldiers would react in a pissed way because they don't have to listen to them."

It bothers me when someone is disruptive. It bothers me when someone attempts to selfishly seize power. I can be jealous of people in positions of authority. But someone "acting above their station" has never bothered me. I barely comprehend the idea.

I to treat waiters and laborers with absolute

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Focus: you are allowed to be bad at accomplishing your goals
by adamShimi — 2020-06-03T21:04:29.151Z

When asked about what it means for a system to be goal-directed, one common answer draws on some version of Dennett’s intentional stance: a goal-directed system is a system such that modeling it as having a goal provides accurate and efficient predictions about its behavior. I agree up to that point. But then, some people follow up by saying that the prediction is that the system will accomplish its goal. For example, it makes sense to model AlphaGo as goal-directed towards winning at Go, because it will eventually win. And taking the intentional stance allows me to predict that.

But what if I make AlphaGo play against AlphaZero, which is strictly better at Go? Then AlphaGo will consistently lose. Does it mean that it’s no longer goal-directed towards winning?

What feels wrong to me is the implicit link drawn between goal-directedness and competence. A bad Go player will usually lose, but it doesn’t seem any less goal-directed to me than a stronger one that consistently wins.

Competence is thus not the whole story. It might be useful to compute goal-directedness; reaching some lower-bound of competency might even be a necessary condition for goal-directedness (play badly enough and it becomes debatable whether you're even trying to win). But when forcing together the two, I feel like something important is lost.

To solve this problem, I propose a new metric of goal-directedness, focus: how much is the system trying to accomplish a certain goal. Focus is not the whole story about being goal-directed, but I think computing the focus of a system for some goal (details in the next paragraph) gives useful information about its goal-directedness.

Given a system S (as a function from states or histories to actions) and a goal G (as a set of states), here are the steps to compute the focus of S towards G.

I define a reward function over states R valued 1 at states in and 0 at all other states.
Then I define Pol be the set of all policies that can be generated by Reinforcement Learning (RL) on R. I’ll go into details about Pol below, but the most important part here is that it isn't limited to optimal policies; I also consider policies of RL with “few resources”. Basically all policies at intermediary steps of the RL training are in Pol.
Lastly, I pick a distance between policies. If the two policies are deterministic, a H

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Effective children education
by kotrfa — 2020-06-03T20:35:52.743Z

I am trying to find out what are the most cost-effective ways of (early) education. I have a 4-year-old daughter and that gives me about ~2 more years to figure this out and I am trying to put together as much material as I can. Given the age of my daughter, I’d like to “solve” something like K-12 for now, but I guess some things may be applicable at any age.

I am familiar with Bryan Caplan's main theses formulated in the Case Against Education or Robin Hanson's Elephant in the Brain arguing that education is mostly about signalling and stuff. I therefore partly understand what's wrong and I am now trying to make my findings actionable and unsurprisingly, it seems pretty hard. I also do realize that there is very little research indicating that specific parental choices have much long term impact.

The important attributes of the ideal solution:

comply to rationalists mindset - understanding and applying basic CFAR-like style of reasoning or at least knowing and applying a 5-year-old version of how beliefs work, bugs-are-for-solving idea
follow scientific/experimental approach (I know it's overlapping), not being afraid of finding the right explore vs. exploit balance
the educators should grasp at least basics about how to learn and teach effectively, such as Learning how to learn related concepts or even as little as this post
not to waste the time of my kid (like on signalling, or studying things just to forget them after "exam"), generally trying to reduce the stupid parts about the current education system as much as possible
make students excited about the real world (I love Joy in the merely real)
convey what it is like to be a human (like, emotions exist and you can learn about yourself from them or deal with them)
convey important concepts about the world (for example altruism, cost-effective analysis, probability, or even markets or signalling)
teaching students things that are actually useful on the job market
not-USA-specific - we are living in Central Europe. It's of course still good to point to things that work well somewhere as an inspiration for replication locally.

I do realize it’s a lot to ask, but I don't aim for perfection. I believe the current system is so badly broken and inadequate (seems that almost everyone agrees on this fact) that even with a little effort invested we can get better outcomes after a few months of preparation.

For example

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Spoiler-Free Review: CrossCells
by Zvi — 2020-06-03T18:30:00.873Z

I played through the entire game this morning, so figured I’d leave a quick review.

CrossCells is a Tier 3 game. It does its job, which is provide logic puzzles in the vein of Sudoku, without being actual Sudoku. There are 50 of them, which together should take you a few hours. I found those hours pleasant. If this seems worth the money and time to you, go for it. Otherwise, you’re not missing much.

That is all.
[AN #102]: Meta learning by GPT-3, and a list of full proposals for AI alignment
by Rohin Shah — 2020-06-03T17:20:02.221Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

SECTIONS

HIGHLIGHTS

TECHNICAL AI ALIGNMENT

MISCELLANEOUS (ALIGNMENT)

OTHER PROGRESS IN AI

REINFORCEMENT LEARNING

DEEP LEARNING

HIERARCHICAL RL

HIGHLIGHTS

Language Models are Few-Shot Learners (Tom B. Brown et al) (summarized by Rohin): The biggest GPT-2 model (AN #46) had 1.5 billion parameters, and since its release people have trained language models with up to 17 billion parameters. This paper reports GPT-3 results, where the largest model has 175 billion parameters, a 10x increase over the previous largest language model. To get the obvious out of the way, it sets a new state of the art (SOTA) on zero-shot language modeling (evaluated only on Penn Tree Bank, as other evaluation sets were accidentally a part of their training set).

The primary focus of the paper is on analyzing the few-shot learning capabilities of GPT-3. In few-shot learning, after an initial training phase, at test time models are presented with a small number of examples of a new task, and then must execute that task for new inputs. Such problems are usually solved using meta-learning or finetuning, e.g. at test time MAML takes a few gradient steps on the new examples to produce a model finetuned for the test task. In contrast, the key hypothesis with GPT-3 is that language is so diverse, that doing well on it already requires adaptation to the input, and so the learned language model will already be a meta-learner. This implies that they can simply "prime" the model with examples of a task they care about, and the model can learn what task is supposed to be performed, and then perform that task well.

For example, consider the task of generating a sentence using a newly made-up word whose meaning has been explained. In one notable example, the prompt for GPT-3 is:

A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is:

We were traveling in Africa and we saw these very cute whatpus.

To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:

Given this prompt,...