trentmkelly/LessWrong-43k huggingface dataset:
Does 3-2-1 Voting work using a six point scale?
by TGetschman — 2020-07-07T21:06:32.890Z

Jameson Quinn, wrote, “In 3-2-1 voting, voters rate each candidate “Good”, “OK”, or “Bad”. To find the winner, you first narrow it down to three semifinalists, the candidates with the most “good” ratings. Then, narrow it further to two finalists, the candidates with the fewest “bad” scores. Finally, the winner is the one preferred on more ballots.”

My question is whether or not one can use this same system as an automatic runoff for score voting elections using higher ranges.

If we have a six-point scale (0-5 stars), and each voter scores each candidate from none to five, we can again find the top three scoring candidates. Will the application of the 3-2-1 voting system to the 6-point system result in the same Voter Satisfaction Efficiency as the straight 3-2-1 voting system?

One way of determining the winner of the three candidates would be if we consider the center of the range (2.5) as the “zero-point” and then all numbers higher than it as positive (i.e., #3 = +1) and all those lower as negative (i.e., #2 = -1). We could sum only the negative scores and eliminates the candidate with the most negative score to remove one of the three candidates. Finally, we would find the Condorcet winner of the remaining two. Would that work?
Antitrust-Compliant AI Industry Self-Regulation
by Cullen — 2020-07-07T20:53:37.086Z
Link: https://cullenokeefe.com/blog/antitrust-compliant-ai-industry-self-regulation

Summary:

The touchstone of antitrust compliance is competition. To be legally permissible, any industrial restraint on trade must have sufficient countervailing procompetitive justifications. Usually, anticompetitive horizontal agreements like boycotts (including a refusal to produce certain products) are per se illegal.

The “learned professions,” including engineers, frequently engage in somewhat anticompetitive self-regulation through professional standards. These standards are not exempt from antitrust scrutiny. However, some Supreme Court opinions have nevertheless held that some forms of professional self-regulation that would otherwise receive per se condemnation could receive more preferential antitrust analysis under the “Rule of Reason.” This Rule weighs procompetitive and anticompetitive impacts to determine legality. To receive the rule-of-reason review, such professional self-regulation would need to:

Be promulgated by a professional body;

Not directly affect price or output level; and

Seek to correct some market failure, such as information asymmetry between professionals and their clients.

Professional ethical standards promulgated by a professional body (i.e., comparable to the American Medical Association or American Bar Association) that prohibit members from building unsafe AI could plausibly meet all of these requirements.

This paper does not argue that this would clearly win in court, or that such an agreement would be legal. Nor does it argue that it would survive rule-of-reason review. It merely says that there exists a colorable argument for analyzing such an agreement under the Rule of Reason, rather than a per se rule. Thus, this could be a plausible route to an antitrust-compliant horizontal agreement to not engineer AI unsafely.
Models of Value of Learning
by johnswentworth — 2020-07-07T19:08:31.785Z

I generally subscribe to the

signalling model of higher education

: “education” is mostly about signalling how smart and diligent you are, rather than actually learning useful skills. Under this model, higher education doesn’t produce new human capital, it just makes it more obvious who has it already - it’s essentially marketing.

That said, it still seems like actually learning things does provide at least some value for at least some people - and personally, I think I’ve gained a lot more value than most out of learning things. A lot of this comes from being strategic: choosing what to learn, and how much time to spend on it, in order to maximize value. In order to do that, we need some idea of the mechanism by which learning things can provide value in the first place.

With that in mind, here are five different models for how things we formally learn (i.e. in school/university) can provide value.

Skills

A CS student learns a few programming languages, basic data structures, some aspects of low-level computer architecture and systems design… skills which are directly useful for real-world software development. Obviously not everything one learns in a CS degree is useful in real-world development, but there is a lot of directly-useful knowledge and skills in there.

General model: formal learning provides practical value by teaching how to use a useful tool or perform a useful task. Some examples:

Generic communication skills like legal/scientific/business writing or public speaking
Useful ways to frame problems, like thinking about risk and discounted expected value of potential business projects
When and how to use a physical tool, like a pipette or atomic force microscope
Mathematical/algorithmic tools, like techniques for solving ODEs/PDEs, statistical tests, or data structures
When and how to apply mathematical/algorithmic tools to real-world problems, e.g. turning a physical system into equations in physics or engineering
Most vocational education

This sort of thing is how I think most people imagine learning will provide value. We learn to use some technique which we will directly apply to valuable real-world problems. Keep it in mind as a baseline against which to compare the models below.

Gears

A doctor studies the physiology of the kidney - they gain a

gears-level model

of kidney function. Hopefully, this will later allow them to recognize/predict a wide variety

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Breaking Questions Down
by Elizabeth — 2020-07-07T18:10:03.009Z

Previously I talked about discovering that my basic unit of inquiry should be questions, not books. But what I didn’t talk about was how to generate those questions, and how to separate good questions from bad. That’s because I don’t know yet; my own process is mysterious and implicit to me. But I can give a few examples.

For any given question, your goal is to disambiguate it into smaller questions that, if an oracle gave you the answers to all of them, would allow you to answer the original question. Best case scenario, you repeat this process and hit bedrock, an empirical question for which you can find accurate data. You feed that answer into the parent question, and eventually it bubbles up to answering your original question.

That does not always happen. Sometimes the question is one of values, not facts. Sometimes sufficient accurate information is not available, and you’re forced to use a range- an uncertainty that will bubble up through parent answers. But just having the questions will clarify your thoughts and allow you to move more of your attention to the most important things.

Here are a few examples. First, a reconstructed mind map of my process that led to several covid+economics posts. In the interests of being as informative as possible, this one is kind of stylized and uses developments I didn’t have at the time I actually did the research.

If you’re curious about the results of this, the regular recession post is here and the oil crisis post is here.

Second, a map I created but have not yet researched, on the cost/benefit profile of a dental cleaning while covid is present.

Aside: Do people prefer the horizontal or vertical displays? Vertical would be my preference, but Whimsical does weird things with spacing so the tree ends up with a huge width either way.

Honestly this post isn’t really done; I have a lot more to figure out when it comes to how to create good questions. But I wanted to have something out before I published v0.1 of my Grand List of Steps, so here we are.

Many thanks to Rosie Campbell for inspiration and discussion on this idea.
The Equation of Knowledge
by Lê Nguyên Hoang — 2020-07-07T16:09:10.367Z

My book The Equation of Knowledge has just been published at CRC Press, and I'm guessing that it may be of interest to readers of LessWrong. The book aims to be a somewhat accessible and very complete introduction to Bayesianism. No prior knowledge is needed; though some sections require an important familiarity with mathematics and computer science. The book has been designed so that these sections can be skipped without hindering the reading.

The aim of the book is to (1) highlight the most compelling arguments, theorems and empirical evidence in favor of Bayesianism, (2) present numerous applications in a very wide variety of domains, and (3) discuss solutions for pragmatic Bayesianism with limited computational resources. Please find here a promotional 5-minute video of the book.

In this post, I will briefly sketch the outline of the book. Just like the book, I'll divide the post in four sections.

Pure Bayesianism

The first section of the book is a gentle introduction to pure Bayesianism, which is defined as obeying strictly to the laws of probability theory. The key equation is evidently Bayes rule, which I like to write as follows:

P[T|D]=P[D|T]P[T]P[D|T]P[T]+∑A≠TP[D|A]P[A].

This equation says that the critical variable is P[T|D], that is, the credence of theory T given data D. Computing this is arguably the end goal of Bayes rule. Bayes rule thus does not quite aim to distinguish truth from falsehood; it rather motivates us to assign quantitative measures of reliability to different theories, given observed data. It suggests that we should replace questions like "is T true?" by "how credible is T?" (or perhaps even by "how much should I trust the predictions of theory T?"). I argue in the book that this is a great way to improve the quality of many debates.

Bayes rule then goes on telling us how to compute the credence of a theory given empirical data. Importantly, on the right hand side, we have the term P[T] which measures the credence of the theory prior to the observation of data D. This is critical. A theory which was extremely unlikely before we knew D will likely remain unlikely even given D, unless D is overwhelmingly compelling. This corresponds to Carl Sagan's phrase "extraordinary claims require extraordinary evidence" (which was analyzed mathematically by Laplace back in 1814!)...
Translate music intonation into words using color semantics (as a means of communication)
by Alexey Smirnov — 2020-07-07T15:26:47.937Z

Scientists from the University of California, Berkeley, found that music and color are connected through an intermediate link - emotions
news: news.berkeley.edu/2013/05/16/musiccolors/
paper: www.pnas.org/content/early/2013/05/10/1212562110.abstract

So why not apply in this case color semantics, which has existed since 1949 in the form of the Max Luscher's eight-color test, which converts a sequence of colors into a sequence of words?

"Max Lüscher believed that sensory perception of color is objective and universally shared by all, but that color preferences are subjective, and that this distinction allows subjective states to be objectively measured by using test colors. Lüscher believed that because the color selections are guided in an unconscious manner, they reveal the person as they really are, not as they perceive themselves or would like to be perceived"

So, if you sync your mood with the mood of the music, you can determine the mood of the music using the color test
The sequence of passing the test is simple:
1. Which of the eight colors seems the most pleasing?
2. Which of the remaining colors seems the most pleasing? (until all the color cards go to the result)
You can find the test version here: www.colorquiz.com
Here: en.testometrika.com/personality-and-temper/color-personality-test/
Here you can find the test and simpliest MVP of music recommendation system how it can be created (MUSLI stands for Musica Lingua or Music Lingo): musli.icu
Since the interpretation text can be translated into any language in the world, this makes music a universal language. Thus music as a means of communication and coordination can become a means against Moloch

Jim Carrey about music: www.youtube.com/watch?v=xaoCB0vkMT8

Also listen to three songs from Paul McCartney's Flaming Pie album:

1. The Song We Were Singing www.youtube.com/watch?v=Q4qObxQL4x0

2. The World Tonight www.youtube.com/watch?v=irt1OH_NNFQ

3. Great Day www.youtube.com/watch?v=2pTpCaV1zzw

And so here are some examples of matching the first pair of colors of the test and the songs:

Your Desired Objective
"Wants to sweep aside the things that stand in his way, to follow his impulses and be involved in special or exciting happenings. In this way he hopes to deaden the intensity of his conflicts, but his impulsive behavior leads him to take risks":
1. The Clash - London Calling www...
Spoiler-Free Review: Horizon Zero Dawn
by Zvi — 2020-07-07T13:20:00.894Z

Previously: Spoiler-Free Review: Witcher 3: Wild Hunt (plus a Spoilerific section), Spoiler-Free Review: Assassin’s Creed Odyssey

This review of a giant open world game is being written on July 7. That’s three days after the review of Witcher 3 was posted.

You can guess it’s not going to be a ringing endorsement.

Horizon Zero Dawn seems to be a Tier 4 game. I spent most of my time either frustrated, pissed off, or waiting for something interesting to happen. There were periods of satisfying combat, and some potentially interesting aspects of the world building, but my lord on reflection was that a bad experience.

I say seems to be because when one quits this early in a game, missing most of what it has to offer, and lots of others love it, one presumes one is likely missing something. I encourage those who told me I should choose this game next – it got 40%+ of a 4-way Twitter poll, and an endorsement in a comment – to explain why it’s secretly good.

Here I am going to talk about why it seems terrible.

This isn’t a ‘I criticize because I love’ post. This is a ‘I criticize because other people love and I can’t figure out why’ post.

Jump To Your Death, No Chance to Save

In general, if you fall, you die.

The game has lots of points early in the game where you have to jump in exactly the right place. If you jump elsewhere, you fall, and you die.

The game resets, often forcing a lot of doing things over. At least the load times were quick. If the load times had been slower I would have quit very quickly.

You see, you can only save at a campfire, or when the game chooses to save for you.

What finally caused the rage-quit was when I spent ten minutes replaying a quest sequence to where I jumped and died trying to follow the game’s narrative instructions, used a walk-through video to see what I was supposed to do, jumped slightly wrong trying to trigger something, and died again. I mean, I can’t take it. My life is too short and I have enough things to rage about as it is.

These three dimensional games need to decide if they want to be platform games or not. If they want to be platform games, then do a very, very good job of it and make it fair and interesting and reasonable, like Assassin’s Creed: Odyssey did. If not, then stop giving us these terrible ‘guess which places you are supposed to jump’ ‘puzzles.’ They are the actual worst.

Resource and Inventory Management and Gat

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Dynamic inconsistency of the inaction and initial state baseline
by Stuart_Armstrong — 2020-07-07T12:02:29.338Z

Vika has been posting about various baseline choices for impact measure.

In this post, I'll argue that the stepwise inaction baseline is dynamically inconsistent/time-inconsistent. Informally, what this means is that an agent will have different preferences from its future self.

Losses from time-inconsistency

Why is time-inconsistency bad? It's because it allows money-pump situations: the environment can extract free reward from the agent, to no advantage to that agent. Or, put more formally:

An agent A is time-inconsistent between times t and t′>t, if at time t it would pay a positive amount of reward to constrain its possible choices at time t′.

Outside of anthropics and game theory, we expect our agent to be time-consistent.

Time inconsistency example

Consider the following example:

The robot can move in all four directions - N, E, S, W - and can also take the noop operation, ∅. The discount rate is γ<1.

It gets a reward of r>0 for standing on the blue button for the first time. Using attainable utility preservation, the penalty function is defined by the auxiliary set R; here, this just consists of the reward function that gives p>0 for standing on the red button for the first time.

Therefore if the robot moves from a point n steps away from the red button, to one m steps away, it gets a penalty[1] of p|γn−γm| - the difference between the expected red-button rewards for an optimiser in both positions.

Two paths

It's pretty clear there are two potentially optimal paths the robot can take: going straight to the blue button (higher reward, but higher penalty), or taking the long way round (lower reward, but lower penalty):

Fortunately, when summing up the penalties, you sum terms like …p|γn−1−γn|+p|γn−γn+1|…, so a lot of the terms cancel.

Thus for the short route, the reward is r⋅γ8 (distance of eight to the blue button) and the penalty is 2p(γ3−γ7) (closest to the red button: 3 squares, furthest: 7 squares).

For the long route, the reward is r⋅γ46 (distance forty-six to the blue button) and the penalty is 2p(γ7−γ30) (closest to the red button: 7 squares, furthest: 30 squares).

Take the third option

Now suppose that the robot, as a free action, could block itself from choosing S, for the next n>30 turns. When choosing that action, it gets a one-time penalty[2] of p(γ7−γn+3); and now it can walk directly to the blue button without incurri

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[Reference request] Can Love be Explained?
by Alexander Gietelink Oldenziel — 2020-07-07T10:09:17.508Z

Proximity, physical attraction, happenstance, personality, wealth, health, values, shared experiences, individual preferences.

What factors primarily determine falling in love with someone?

I'm looking for reliable scientific sources quantitifying the relative contribution of these factors for short - and long-term couplings.
What is the scientific status of 'Muscle Memory'?
by Alexander Gietelink Oldenziel — 2020-07-07T09:57:12.311Z

When you stop exercising, muscles quickly wither away. Yet many people claim that the muscles 'remember' some of the previous exercise and it is much easier to regain some of the muscle.

Some story about myonuclei and satelite cells

What is some reliable and scientific source on this phenomenon? How strong is the effect?
DARPA Digital Tutor: Four Months to Total Technical Expertise?
by SebastianG  — 2020-07-06T23:34:21.089Z

DARPA spent a few million dollars around 2009 to create the world’s best digital tutoring system for IT workers in the Navy. I am going to explain their results, the system itself, possible limitations, and where to go from here.

It is a truth universally acknowledged that a single nerd having read Ender’s Game must be in want of the Fantasy Game. The great draw of the Fantasy Game is that the game changes with the player and reflects the needs of the learner growing dynamically with him/her. This dream of the student is best realized in the world of tutoring, which while not as fun, is known to be very, very effective. Individualized instruction can make students jump to the 98 percentile compared to non tutored students. DARPA poked at this idea with their Digital Tutor trying to answer this question: How close to the expertise and knowledge base of well-experienced IT experts can we get new recruits in 16 weeks using a digital tutoring system?

I will say the results upfront, but before I do, I want to do two things. First pause to note the audacity of the project. Some project manager thought, “I bet we can design a system for training that is as good as 5 years on the job experience.” This is astoundingly ambitious. I love it! Second a few caveats. Caveat 1) Don’t be confused. Technical training is not the same as education. The goals in education are not merely to learn some technical skills like reading, writing, and arithmetic. Getting any system to usefully measure things like inculturation, citizenship, moral uprightness, and social mores is not yet something any system can do, let alone a digital system. Caveat 2) Online classes have notoriously high attrition rates, drop rates, and no shows. Caveat 3) Going in we should not expect the digital tutor to be as good as a human tutor. A human tutor likely can catch nuances that a digital tutor, no matter how good cannot. Caveat 4) Language processing technology, chat bots, and AI systems are significantly better in 2020 than they were 2009, so we should be forgiving if the DARPA IT program is not as good as it would be if the experiment were rerun today.

All these caveats, I think should give us a reason to adjust our mental score of the Digital Tutor a few clicks upward and give it some credit. However, this charitable read of the Digital...
Short (2.5 min) intro to Effective Altruism for busy people
by dominicq — 2020-07-06T19:53:46.140Z

https://www.youtube.com/watch?v=9vehER01Fdg

Recently I read this article about a study that tested how effective various purely philosophical arguments affected donations. I thought that Singer's and Lindauer's argument was really well-put, so, inspired by them, I wanted to make a short introduction that you can send to people that might want to know about effective altruism, or donate somewhere, but just don't have the time to delve deeper into the subject-matter.

Essentially, I tried to just keep their original argument, but to present it in video form.
AI Benefits Post 3: Direct and Indirect Approaches to AI Benefits
by Cullen — 2020-07-06T18:48:02.363Z

This is a post in a series on "AI Benefits." It is cross-posted from my personal blog. For other entries in this series, navigate to the AI Benefits Blog Series Index page.

This post is also discussed on the Effective Altruism Forum.

For comments on this series, I am thankful to Katya Klinova, Max Ghenis, Avital Balwit, Joel Becker, Anton Korinek, and others. Errors are my own.

If you are an expert in a relevant area and would like to help me further explore this topic, please contact me.

Direct and Indirect Approaches to AI Benefits

I have found it useful to distinguish between two high-level approaches to producing AI Benefits: direct and indirect. The direct approach, which dominates current discussions of AI for Good, is to apply AI technologies to some problem. This is a natural way to think of creating AI Benefits: to try to use the AI itself to do something beneficial.

However, AI companies should resist becoming hammers to whom every social problem looks like a nail. Some social problems are not yet, and may never be, best addressed through the application of AI. Other resources, particularly money, are perhaps more useful in these contexts. Thus, in some circumstances, an AI developer might do the most good by maximizing its income (perhaps subject to some ethical side-constraints) and donating the surplus to an organization better-positioned to turn spare resources into good outcomes. This is the indirect approach to AI Benefits.

Actors, including actors aiming to be beneficial, only have finite resources. Therefore, there will often be a tradeoff between pursuing direct and indirect benefits, especially when Benefits are not profit-maximizing (which by hypothesis they are not for the sake of this blog series). A company that uses spare resources (compute, employee time, etc.) to build a socially beneficial AI application presumably could have also used those resources to derive a profit through its normal course of business.

The beneficial return on resources allocated directly versus indirectly will probably vary considerably between organizations. For example, a company working in algorithmic trading might not be able to directly solve many neglected social problems with its software, but could probably easily donate a chunk of its profits to some charity helping the poor. Conversely, an NLP startup working on a translation for a language spoken prima

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