trentmkelly/LessWrong-43k huggingface dataset:
Objective Dog Ratings: The Shiba Inu
by Callmesalticidae — 2020-09-18T00:12:11.328Z

The Shiba Inu

Shibas are a spitz dog, which, if you remember from the original Dog Ratings post, is my projected winner for the All-Dog All-Stars Contest, but that doesn't mean that this spitz will take the trophy home.

Let's take a look:

First, it must be noted that shibas have the curly tail which is most distinctive of domesticated animals, which is a Good Thing. Props to you, noble shibas.

Behaviorally, shibas are very clean animals. They clean themselves, they're very easy to housebreak, and...well, they will track mud into the house and not wipe their feet off first, but they're still dogs. You can only expect so much from them. They're clever, but the jury is out on just how much so.

Shibas do not bark very much, instead preferring to emit terrible, nearly goat-like noise called a "shiba scream," which has been described as "bloodcurdling" and like the "screams of the damned." The name sounds like an anime attack move, and I wholeheartedly approve.

I could not find much in the way of terrible diseases, which is a little surprising given their history. Shibas were extensively crossbred with other dogs in the 19th century and, between crossbreeding, distemper outbreaks, and WWII food shortages, only three "bloodlines" or "variations" of shiba still remained in the post-war era. It's hard for me to get details on this, so I can only assume that the gene pools were relatively large and healthy or the breeders did a good job, because, as I say, they aren't a tragedy of inbreeding.

This is, admittedly, a little bit conjecture, but I imagine that their healthiness has something to do with the story of their preservation as a breed. Shibas were bred to do a job (hunting and flushing out small game and, on occasion, boar), and hunters were among those who tried to preserve them.

Anyway, the point is, they are pretty healthy dogs, and that's pretty important here at Objective Dog Ratings. Shibas aren't exceptionally intelligent, but there's something awful and awesome about their unique vocal talents, so I'm going to award them a very tentative, by-the-tip-of-their-nose, three stars. A forward-thinking, politically progressive wolf would not be ashamed to know a shiba, though they might well be a little unsettled.

Rating: ★★★ out of &#...
Gems from the Wiki: Do The Math, Then Burn The Math and Go With Your Gut
by habryka — 2020-09-17T22:41:24.097Z
Link: https://www.lesswrong.com/tag/do-the-math-then-burn-the-math-and-go-with-your-gut

During the LessWrong 1.0 Wiki Import we (the LessWrong team) discovered a number of great articles that most of the LessWrong team hadn't read before. Since we expect many others to also not have have read these, we are creating a series of the best posts from the Wiki to help give those hidden gems some more time to shine.

The original wiki article was fully written by riceissa, who I've added as a coauthor to this post. Thank you for your work on the wiki!

"Do the math, then burn the math and go with your gut"1 is a procedure for decision-making that has been described by Eliezer Yudkowsky. The basic procedure is to go through the process of assigning numbers and probabilities that are relevant to some decision ("do the math") and then to throw away this calculation and instead make the final decision with one's gut feelings ("burn the math and go with your gut"). The purpose of the first step is to force oneself to think through all the details of the decision and to spot inconsistencies.

History

In July 2008, Eliezer Yudkowsky wrote the blog post "When (Not) To Use Probabilities", which discusses the situations under which it is a bad idea to verbally assign probabilities. Specifically, the post claims that while theoretical arguments in favor of using probabilities (such as Dutch book and coherence arguments) always apply, humans have evolved algorithms for reasoning under uncertainty that don't involve verbally assigning probabilities (such as using "gut feelings"), which in practice often perform better than actually assigning probabilities. In other words, the post argues in favor of using humans' non-verbal/built-in forms of reasoning under uncertainty even if this makes humans incoherent/subject to Dutch books, because forcing humans to articulate probabilities would actually lead to worse outcomes. The post also contains the quote "there are benefits from trying to translate your gut feelings of uncertainty into verbal probabilities. It may help you spot problems like the conjunction fallacy. It may help you spot internal inconsistencies – though it may not show you any way to remedy them."2

In October 2011, LessWrong user bentarm gave an outline of the procedure in a comment in the context of the Amanda Knox case. The steps were: "(1) write down a list of all of the relevant facts on either side of the argument. (2) assign numerical weights to each of...
Let the AI teach you how to flirt
by DirectedEvolution — 2020-09-17T19:04:05.632Z

"It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates" is a fascinating approach to the study of flirtation. It uses a machine learning model to parse speed-dating data and detect whether the participants were flirting. Here's a sci-hub link. I found three key insights in the paper.

First of all, people basically assume that others share their own intentions. If they were flirting, they assume their partner was too. They're quite bad at guessing whether their partner was flirting, but they do a bit better than chance.

Secondly, the machine learning model was about 70% accurate in detecting flirtation. It's much better than the speed date participants themselves, despite having far less information to draw upon and the fact that the authors used a more forgiving standard of success for people's detection rates than for the detection rates of the machine learning model.

Thirdly, storytelling and conversations about friends seem to be the strongest signals of flirtation. Talking about the mundane details of student life (this was on a college campus) were the strongest signals of non-flirtation.

Finally, men and women have quite different approaches to flirtation:

Men who say they are flirting ask more questions, and use more you and we. They laugh more, and use more sexual, anger [hate/hated, hell, ridiculous, stupid, kill, screwed, blame, sucks, mad, bother, shit], and negative [bad, weird, hate, crazy, problem*, difficult, tough, awkward, boring, wrong, sad, worry] emotional words. Prosodically they speak faster, with higher pitch, but quieter (lower intensity min). Features of the alter (the woman) that helped our system detect men who say they are flirting include the woman’s laughing, sexual words [love, passion, virgin, sex, screw] or swear words, talking more, and having a higher f0 (max).

Women who say they are flirting have a much expanded pitch range (lower pitch min, higher pitch max), laugh more, use more I and well, use repair questions [Wait, Excuse me] but not other kinds of questions, use more sexual terms, use far less appreciations [Wow, That’s true, Oh, great] and backchannels [Uh-huh., Yeah., Right., Oh, okay.], and use fewer, longer turns, with more words in general. Features of the alter (the man) that helped our system detect women who say they are flirting include the male use of you, questions, and faster and quiete

...
Covid 9/17: It’s Worse
by Zvi — 2020-09-17T15:10:01.649Z

Last week we learned there is plausibly a simple, cheap and easy way out of this entire mess. All we have to do is take our Vitamin D. In case it needed to be said, no. We are not taking our Vitamin D. There’s definitely some voices out there pushing it, including the nation’s top podcaster Joe Rogan, but I don’t see any signs of progress.

Instead, as school restarts, the outside gets colder and pandemic fatigue sets in, people’s precautions are proving insufficient to the task. This week showed that we have taken a clear step backwards across the country.

I see three ways for things not to get steadily worse for a while. Either a vaccine arrives, which is unlikely, something else new (that we see little sign of) arrives to change behavior for the better, or this week was a blip. It’s only one week of data, it follows labor day, and it is wise not to move too quickly to extrapolation. The effect size seems too large, though, and too distributed among outcomes, to be coincidence.

In terms of news, it was a quiet week. There was some bluster, but little substance.

Let’s run the numbers. They’re not good.

Positive Test Counts

Date	WEST	MIDWEST	SOUTH	NORTHEAST
July 23-July 29	110219	67903	240667	26008
July 30-Aug 5	91002	64462	212945	23784
Aug 6-Aug 12	93042	61931	188486	21569
Aug 13-Aug 19	80887	63384	156998	20857
Aug 20-Aug 26	67545	66540	132322	18707
Aug 7-Sep 2	55000	75401	127414	21056
Sep 3-Sep 9	47273	72439	106408	21926
Sep 10-Sep 16	45050	75264	115812	23755

This doesn’t look too bad on its own. Whether it’s good, bad or very bad news depends on whether testing is improving. If testing were still ramping up, it could easily count as good news, even with the worrying reversal in the South. Unfortunately, that’s not what happened. Testing actually declined this week to 4.63 million tests, the lowest value since the first week of July.

Alas, the positive test percentages:

Date	USA tests	Positive %	NY tests	Positive %	Cumulative Positives
July 16-July 22	5,456,168	8.6%	454,995	1.1%	1.20%
July 17-July 29	5,746,056	7.9%	452,889	1.0%	1.34%
July 30-Aug 5	5,107,739	7.8%	484,245	1.0%	1.46%
Aug 6-Aug 12	5,121,011	7.3%	506,524	0.9%	1.58%
Aug 13-Aug 19	5,293,536	6.2%	548,421	0.8%	1.68%
Aug 20-Aug 26	4,785,056	6.0%	553,369	0.7%	1.77%
Aug 27-Sep 2	5,042,113	5.5%	611,721	0.8%	1.85%
Sep 3-Sep 9	4,850,253	5.3%	552,624	0.9%	1.93%
Sep 10-Sep 16	4,632,005	5.8%	559,463	0.9%	2.01%

This was very surprising to me. It would not have been too surprising to see ...
Making the Monte Hall problem weirder but obvious
by dynomight — 2020-09-17T12:10:23.472Z
Link: https://dyno-might.github.io/2020/09/17/making-the-monty-hall-problem-weirder-but-obvious/

The Monty Hall problem is famously unintuitive. This post starts with an extreme version where the solution is blindingly obvious. We then go through a series of small changes. It will be clear that these don’t affect the solution.
Noticing Wasted Motion
by lynettebye — 2020-09-17T02:30:28.273Z

This post is about a practical skill you need to effectively minimize wasted motion - noticing. Noticing is a key skill for changing behavior in general, because it allows you to respond deliberately instead of automatically. Yet in my experience, it is one of the more difficult skills to learn. Hopefully the following will give you a toehold.

This piece is cross-posted on my blog here.

In my last post, I talked about the cost of wasted motion - the lost opportunity to go straight for the goal and make the world a bit better. If you’re staring into the dark world of the cost of wasted motion, don’t despair. Minimizing wasted motion is a skill and you can train that skill.

But before you can do that, you need to learn to notice wasted motion. This is hard. First, it’s hard to remember to pay attention, especially if you’re already doing something else. Second, it’s hard because -- deep down -- you might not want to.

It feels ughy to start working, because that project is big and hard. The immediate

pain

of starting work looms large in the hyperbolic-discounting agents we call brains. Maybe you're anxious that you’ll look bad if you try and fail. So you just...let your mind slip away to easier tasks. You may not even notice you’re doing it.

But, if you’re paying attention, there’s a little catch there, in your mind. When you stop and think, you know you're procrastinating a little bit. You know you're wasting motion toward your goal. You want to complete your goal, but you also want to avoid dealing with those bad feelings. And that internal conflict makes you feel guilty or frustrated or anxious.

But that little catch is small and easily ignored. If you haven't thought about it before, you may not even have realized it's there. Maybe that guilt is so small you never really notice it. And maybe it's grown so large that you feel overwhelmed and ashamed (link to measuring progress).

Learn to notice that bit of frustration at how you’re doing the task, that ugh feeling around a certain plan.

If you’re not sure where to start, it might help to read more about

noticing

. A therapy-style

before-after record

can also be helpful for identifying emotions, what issues trigger that emotion, and reliable ways to respond.

Persona...
Artificial Intelligence: A Modern Approach (4th edition) on the Alignment Problem
by Zack_M_Davis — 2020-09-17T02:23:58.869Z
Link: http://aima.cs.berkeley.edu/

Previously: AGI and Friendly AI in the dominant AI textbook (2011), Stuart Russell: AI value alignment problem must be an "intrinsic part" of the field's mainstream agenda (2014)

The 4th edition of Artificial Intelligence: A Modern Approach came out this year. While the 3rd edition published in 2009 mentions the Singularity and existential risk, it's notable how much the 4th edition gives the alignment problem front-and-center attention as part of the introductory material (speaking in the authorial voice, not just "I.J. Good (1965) says this, Yudkowsky (2008) says that, Omohundro (2008) says this" as part of a survey of what various scholars have said). Two excerpts—

1.1.5 Beneficial machines

The standard model has been a useful guide for AI research since its inception, but it is probably not the right model in the long run. The reason is that the standard model assumes that we will supply a fully specified objective to the machine.

For an artificially defined task such as chess or shortest-path computation, the task comes with an objective built in—so the standard model is applicable. As we move into the real world, however, it becomes more and more difficult to specify the objective completely and correctly. For example, in designing a self-driving car, one might think that the objective is to reach the destination safely. But driving along any road incurs a risk of injury due to other errant drivers, equipment failure, and so on; thus, a strict goal of safety requires staying in the garage. There is a tradeoff between making progress towards the destination and incurring a risk of injury. How should this tradeoff be made? Furthermore, to what extent can we allow the car to take actions that would annoy other drivers? How much should the car moderate its acceleration, steering, and braking to avoid shaking up the passenger? These kinds of questions are difficult to answer a priori. They are particularly problematic in the general area of human–robot interaction, of which the self-driving car is one example.

The problem of achieving agreement between our true preferences and the objective we put into the machine is called the value alignment problem: the values or objectives put into the machine must be aligned with those of the human. If we are developing an AI system in the lab or in a simulator—as has been the case for most of the field's history—there i

...
What are examples of simpler universes that have been described in order to explain a concept from our more complex universe?
by Mati_Roy — 2020-09-17T01:31:10.367Z

Sometimes there's a concept that can be difficult to understand when entangle with everything else that needs to be understood about our physics.

If you isolate that concept in a simpler universe, it makes it easier to explain how the concept works.

What are such examples?

(I feel like I asked a similar question somewhere at some point, but can't find it)
Sunday September 20, 12:00PM (PT) — talks by Eric Rogstad, Daniel Kokotajlo and more
by habryka — 2020-09-17T00:27:47.735Z

This Sunday at 12pm (PT), we're running another session of "lightning talks" by curated LessWrong authors (see here for previous weeks' transcripts).

For the first hour, we will have a series of lightning talks each lasting about 5 minutes followed by discussion. The talks will be short and focus on presenting one core idea well, rather than rushing through a lot of content.
From 1PM to 2PM, we'll have a hangout in breakout rooms. If you are not interested in the talks, feel free to just show up for this part (or the other way around).
We want to give top LessWrong writers an interesting space to discuss their ideas, and have more fruitful collaboration between users. Think of it like a cross between an academic colloquium and some friends chatting by a whiteboard.

If you're a curated author and interested in giving a 5-min talk at a future event, which will then be transcribed and edited, sign up here.

Speakers

Eric Rogstad: The AI Does Not Care about Your Atoms Either
Daniel Kokotajlo: Why GWP is a bad metric for thinking about timelines and takeoff speeds

Details

When? Sunday September 20, 12:00PM (PT)

Where?

https://us02web.zoom.us/j/84108675070
Rationality for Kids?
by goose000 — 2020-09-16T22:58:21.300Z

UPDATE 11NOV:

I came up with a game to use as an icebreaker. And I'd love ideas for future variations. It's a combination of Credence Calibration, 20 Questions, and Taboo. The children are trying to determine which of three possible states exist on the card which I have face down (for my first iteration, the possibilities will be "Cat", "Rat", and "Dog"). Every kid gets 30 poker chips to allocate to each of the three possibilities. Kids will then take turns asking a yes or no question, but before each Q, I roll a six sided die. If it comes up six, all chips placed on a wrong answer are turned in, otherwise, they ask their question, I answer with something on a scale of "Never" to "Always", and they are permitted to reallocate their chips. But there is a catch: they are not permitted to use certain words (i.e. cat, dog, rat, meow, bark, pet, etc.) in their questions.
The point is to find tests which can serve as evidence between the possibilities and recognize how confidence should change according to evidence.

Would be interested in other possible states for future iterations

END UPDATE

So I really appreciate the lessons I've learned from "Rationality", but I wish I had learned them earlier in life. We are now homeschooling my kids, and I want to volunteer to teach my kids plus others who are interested lessons about thinking rationally.

Does anyone have recommendations on how to put together a curriculum which gets at the core ideas of rationality, but is oriented towards young kids? Some criteria:

Children will likely range from 7-11, meaning they should be simple concepts and require very little prior knowledge and only the simplest math.

Lessons should be interactive.

Lessons should include TRUE experiments (not just doing fun stuff with chemicals).

Lessons should be fun and appealing enough that parents will want to sign their kids up.

Any other suggestions on the course (wording that will be appealing without sounding too "nerdy" or alarming to the conservative types who usually homeschool) are welcome.

UPDATE: the Inflection Point Curriculum appears to be the middle school version of what I am looking to do: https://drive.google.com/file/d/1tcUJXRlZXeKjAWeU9Y37FcPKv3lj6PsX/view?usp=sharing

I currently envision the course as a combination of game type exercises like Credence Cal...
What Does "Signalling" Mean?
by abramdemski — 2020-09-16T21:19:00.968Z

[Epistemic status: shortly after writing this post, I thought I'd regret it. It was not rated very highly by LW karma, and on outside view, it's the sort of rant I often don't endorse later on. But re-reading it after a few weeks, I think it holds up. I still endorse everything I've written here, with the exception of my suggestion that "virtue signalling" is an appropriate replacement for what most people mean by "signalling".]

I still feel a strong empathy for the post You Can't Signal to Rubes, which called out LessWrong for using the word "signalling" incorrectly. That post got heavily, and rightly, downvoted because it also got the definition wrong. :( But it had a point!

At the time of writing, the current definition of signalling on the LessWrong tag is:

Signaling is behavior whose main purpose is to demonstrate to others that you possess some desirable trait. For example, a bird performing an impressive mating display signals that it is healthy and has good genes.

I'm not even sure I should correct it, because this does seem to summarize the LessWrong consensus on what signalling means. But we already have a term for signalling desirable properties about yourself: virtue signalling! Maybe you'll object that "virtue signalling" isn't quite right. Ok. But, could you find another word? I would prefer for "signalling" to point to the subject of signalling theory, which I understand to be the game theory of communication (often focusing on evolutionary game theory).

Scott Alexander's What Is Signaling, Really? seems to get most things right:

In conclusion, a signal is a method of conveying information among not-necessarily-trustworthy parties by performing an action which is more likely or less costly if the information is true than if it is not true. Because signals are often costly, they can sometimes lead to a depressing waste of resources, but in other cases they may be the only way to believably convey important information.

Although all of his examples are about signalling self-properties, he never stipulates that, instead always using the more general conveying-information definition. He also avoids the signalling is automatically bad pitfall. Instead, he explains that signalling is often unfortunately costly, but is nonetheless a very useful tool.

However, reading it, I'm not sure whether he means to contrast signalling with "mere assertion", or whether h...