trentmkelly/LessWrong-43k huggingface dataset:
How to Know Yourself
by regkith — 2021-05-09T21:06:04.311Z

Cross-posted from https://regkith.substack.com/p/how-to-know-yourself

“To thine own self be true.”

While not the best father figure, at least Polonius got this one right.

Or did he? It’s definitely appealing, but what does it mean to know yourself? You might think you know yourself, no doubt believing that you’re an upstanding person. You even have proof, listing out all the good things you’ve done. Fair enough. But what about all the things you’ve done which you shouldn’t have? Is that, you? What about the good things you could’ve done, but didn’t. Is that you?

The truth is they’re all you. Life is like a Rorschach, and it’s easy to sculpt whatever image we want out of ourselves. But that’s all it is, a convenient compression, and a significantly lossy one at that. Never forget to take someone, especially yourself, “all in all.” Certainly society would benefit from more compartmentalization. For similar reasons it’s trivial to convince yourself that you know something you don’t, whether about your personal identity or the world at large. And because you don’t just how easy it is to trick yourself, you won’t do anything about it. Worst of all, when you know so little about yourself, what can you really know about others?

“Man is by nature a social animal.”

The default solution to the above problems hasn’t changed since Aristotle's time: talk to people.

But maybe you don’t have access to the right people to talk to. Maybe the right people don’t even exist. Maybe, you’re even worried about being judged, and regardless consensus has its own issues.

What else can you do? Well, you’re a person. So one thing you can do is talk to yourself, or even a rubber duck. Yet I wouldn’t make talking to yourself a habit, especially if you want to avoid the mental asylum.

Or…you can write. If you accept that we orient ourselves in the world by talking to others, for which talking to yourself is a perfectly valid substitute, it’s only a short jump to believing that Writing is Thinking. The best part? After writing something you don’t need to come back to it, something Eisenhower knew when he said “Plans are worthless, but planning is everything.” Perhaps writing could’ve saved Ophelia’s sanity from the unfolding tragedy around her.

Sun Tzu recognized the importance of knowing oneself too. Source

Even so, I don’t completely stand by the Writing Is Thinking community. The movement tends to...
Peekskill Lyme Incidence
by jefftk — 2021-05-09T19:50:05.931Z

Let's say you're

considering
moving your nonprofit out of the Bay Area, and are considering
somewhere in the Hudson Valley, perhaps near Peekskill NY. Looking at
CDC maps it seems like

Lyme is
something to consider:

The dots are close enough together in the Northeast, however, that this
map implies more uniformity than there actually is. Several years ago
I made a map, coloring each
county by Lyme incidence 1992-2011, and the Hudson Valley
is really pretty bad:

What do things look like with more recent data? I went back to the CDC site
and downloaded their public use dataset. It has annual cases by
county, 2000-2018. Peekskill is in the far North-West corner of
Westchester County, surrounded by Putnam, Orange, and Rockland. Even
though it is in Westchester, so much of the Westchester population is
farther South that my guess is Putnam is probably a better proxy?

Taking population data from the census, here's the annual rate for
each county:

Putting these percentages in perspective, a 0.1% annual rate
represents 1 in 1,000 people getting it each year, and if you stayed
for 20 years you'd roughly have a 1 in
50 (2%) chance.

Mostly this makes me sad that no one has decided to manufacture the FDA-approved Lyme
vaccine LYMErix, whose patent expired in 2014.

(I would like to update my map with this new data, but there are
artifacts I need to look into first.)
Academia as Company Hierarchy
by Virgil Kurkjian — 2021-05-09T17:29:47.842Z

In The Gervais Principle, Venkatesh Rao argues that the show The Office "is not a random series of cynical gags aimed at momentarily alleviating the existential despair of low-level grunts. It is a fully realized theory of management that falsifies 83.8% of the business section of the bookstore."

In this post, I argue that viewing academia through this lens can be equally revealing. But first, we need to discuss the lens itself.

Rao develops this theory of management around the comic Company Hierarchy by Hugh MacLeod:

Hugh MacLeod’s Company Hierarchy

The theory begins by dividing people in an organization into three categories: Sociopaths at the top, Clueless middle managers, and the average workers as Losers. For brevity we will sometimes call this system the SCL hierarchy.

Because these category names were chosen for a gag comic, they aren't great matches for the groups they describe, and can even be a little confusing. Losers aren't losers in the normal sense, they are losers economically — they have struck a bad bargain where they labor for a paycheck. They don't have equity. This bargain can be perfectly rational; it's low reward but it's also low risk. Someone without great natural talents or large amounts of capital may be smart not to take these risks, and in these cases being an economic Loser is often the right call. Most of the characters in The Office are Losers, essentially everyone who isn't in management.

Sociopaths may or may not be literal sociopaths — they are like clinical sociopaths in that they are willing to take risks in the service of rewards, and that they are willing to bend social rules to do so. This too is often rational, for people who are willing to take risks and have the ability or capital to do so. It may or may not be admirable. Bending ethical rules is often bad, but bending rules like "don't question authority" or "don't have original ideas" is often good. In The Office, executives like David Wallace are Sociopaths.

Clueless might be the most appropriate of the three terms. These are people who are clueless enough that they can be easily manipulated to serve the purposes of the organization. In The Office, Michael Scott is the flagship Clueless.

The three categories can be understood as a developmental trajectory, but curiously the development is not the same as the company hierarchy.

Clueless are underdeveloped, and a...
[Event] Weekly Alignment Research Coffee Time (05/10)
by adamShimi — 2021-05-09T11:05:30.875Z

Just like every Monday now, researchers in AI Alignment are invited for a coffee time, to talk about their research and what they're into.

Here is the link.

And here is the everytimezone time.

Small change for this second edition: the link to the walled garden now only works for AF members. Anyone who wants to come but isn't an AF member needs to go by me. I'll broadly apply the following criteria for admission:

If working in a AI Alignment lab or funded for independent research, automatic admission
If recommended by AF member, automatic admission
Otherwise, to my discretion

I prefer to not allow people who might have been interesting but who I'm not sure will not derail the conversation, because this is supposed to be the place where AI Alignment researchers can talk about their current research without having to explain everything.

See you then!
Social host liability
by CraigMichael — 2021-05-09T07:24:32.844Z

I'd like to talk about social host liability. If, as you read that term, you parsed it as 'social' as in social media, 'host' as in webhost, and 'liability' as in if a social media webhost does something negligent that causes harm to come to third parties that they should be held legally liable... then you don't know what social host liability is. You do, however, have an intuitive grasp of the point I'd like to make in this article.

Social host liability means that if you are serving alcohol at a party, and you serve one of your guests to a point of obvious intoxication, and that guest leaves and gets in a car accident or otherwise hurts someone where the proximate cause is determined to be intoxication, then you're legally liable. Dram shop laws are similar but apply to commercial establishments (e.g. bars) rather than private citizens hosting parties.

I mention this because we have a clear precedent in another context where we stop people from irresponsible overuse of a cognition-impairing addictive substance. In some cases we go as far to hold businesses and private citizens legally liable if they allow a person to continue to irresponsibly use that substance.

While I've seen various criticisms of social host liability and dram shop laws, it's never along the lines of "we're violating people's First Amendment rights by prohibiting them freedom of expression through drunk driving."

What is Twitter?

Twitter in 2021 is one of several businesses that have successfully developed a scalable way to surreptitiously give hundreds of millions of people a behavioral addiction and monetize it… oh and all the while making them believe they’re virtuously practicing free speech.

A more accessible answer to this question comes from an Irish podcaster, Blindboy (quote starts around 1:07:00).

Twitter is a video game that people don't know they're playing. Because the thing with Twitter is you're always engaged in an act of performance... Twitter encourages people to create a characterized version of themselves and to preform this character, like a role playing game. Twitter is a giant massively multiplayer online role playing game–text based–where it rewards hostility... You get rewarded on Twitter for having really good complaints. If you can think of a really good complaint on Twitter, you'll get lots of points in the form of retweets and likes. But the thing is, i

...
Implementing the Second Virtue: Feel the Emotions Which Fit the Facts
by LukeOnline — 2021-05-09T05:47:31.587Z

Yudkowsky's Second Virtue of Rationality is "Relinquishment". You'd expect this to be about relinquishing mistaken beliefs. But most of the description of the virtue actually focuses on feelings.

Relinquish the emotion which rests upon a mistaken belief, and seek to feel fully that emotion which fits the facts. If the iron approaches your face, and you believe it is hot, and it is cool, the Way opposes your fear. If the iron approaches your face, and you believe it is cool, and it is hot, the Way opposes your calm. Evaluate your beliefs first and then arrive at your emotions. Let yourself say: “If the iron is hot, I desire to believe it is hot, and if it is cool, I desire to believe it is cool.”

What is not written, but seems easily deduced, is: "If the iron that approaches my face is hot, I desire to feel fear. If the iron is cool, I desire to feel calm." But where are the limits of this desire? Is it only applicable to face-approaching iron? Of course not.

Most people claim that receiving a million dollars would dramatically improve their lives. They would feel very happy for a very long time after receiving such a gift. The things they currently possess are worth only a small fraction of such a massive sum, and growing their net worth a hundredfold seems life-altering.

I regularly ask these people how much money they'd like to receive for one or multiple of their limbs. Would you trade both arms for a million dollars? Would you want to be quadriplegic and blind for five million? Nearly everyone answers no. A healthy body is so much more valuable than such a sum of money.

And then they go right back to saying that they would be so much happier if they received a million dollars.

Of course, such a position could be rationally defended. A million dollars in cash allows people to do a lot of things that can't be done with just a functioning leg. But I don't think the position above can be defended as rationally "feeling the emotions which fit the facts". Because that's not how our emotions work.

We suffer from hedonic adaptation. We are not made for static enjoyment. Think about driving a car, especially one with manual transmission. It's highly complicated and takes quite a lot of effort to learn. But after years of practice, many people drive miles and miles, executing all kinds of complicated techniques, without a second of con...
The Nuclear Energy Alignment Problem
by lsusr — 2021-05-09T03:50:32.726Z

Berlin, 1938.

"Have you ever thought about the Anthropic principle? Chances are about half the people who have ever lived will die before us and half after. The human population has been growing at an exponential rate. There is probably a disaster waiting just around the corner," said Schumann.

"I bet it's nuclear energy," said Heisenberg.

"There is no way a single machine could destroy the world," said Schumann.

"Shut up and multiply. The Earth has a mass of M⊕=6×1024 kilograms and a radius of R⊕=6×106 meters. Let us treat the Earth as a uniform sphere. The gravitational potential energy is E⊕=3GM2⊕5R⊕=2×1032 joules. The total energy necessary to destroy the Earth is thus 2×1032 joules," said Heisenberg.

Heisenberg wrote E=mc2 on a sheet of paper.

m=Ec2=2×1032 joules(3×108ms)2=2×1015 kilograms

"How much is that?" said Schumann.

"All the carbon in the terrestrial biosphere," said Heisenberg, "Or half the carbon in all of the Earth's coal."

"No nation on Earth has the industrial capacity to dump that much mass into a single machine. We are safe," said Schumann.

"Not if there is a chain reaction. Suppose a nitrogen atom releases two neutrons when it fissions. It could ignite all the nitrogen in the atmosphere," said Heisenberg.

"Is there more nitrogen in the atmosphere than carbon in the biosphere?" said Schumann.

"It doesn't matter. If we destroy the atmosphere then humanity is doomed whether or not the planet itself technically survives," said Heisenberg.

"We had better invent a theory of ethics to guide the use of nuclear energy. Otherwise someone might accidentally build a bomb," said Schumann.

"What does ethics have to do with anything? Keeping the energy contained is a purely technical problem," said Heisenberg.

"We need a theory of ethics so we can coordinate with the Allied powers. It does us no good to hold the moral high ground if the United States accidentally destroys the world," said Schumann.

"Please be serious. There is no way the United States could build a working nuclear machine. Their whole society is run by Jews," said Heisenberg.

"Britain or France might pull it off," said Schumann.

"Do you remember what happened the last time we tried to coordinate with Britain and France?" said Heisenberg.

"Oh right," said Schumann.

"Fortunately, nuclear energy is sixty years away and it always will be," said Heisenberg.

"Not anymore," said Schumann. He had a propo...
Review and Comparison: Onto-Cartography & Promise Theory
by Josephine — 2021-05-09T00:42:00.280Z

Introduction

This is my first post here, although I've lurked for five or six years. I came up with this idea when I noticed similarities in these two books and reread them - both tackle some flavor of practical ontology, but from different angles. The first is primarily a philosophy text and the other's the basis for CFEngine, a "configuration management system" for computer networks. One works with words, the other theorems - one has agents, the other machines. I think there's a lot of overlap in how these ontologies behave and what concepts we can draw from them, so here's a hybrid casual review and attempt to extract the marrow.

Onto-Cartography: An Ontology of Machines and Media

This book is part of a current trend in philosophy to take objects just as seriously as subjects (OOO, or object-oriented ontology). One of the better motivations for this trend is global warming and existential risk more broadly - the idea that some objects are "hyperobjects" too multifarious or widely-distributed for people to deal with them directly. This book tries to come at this sort of problem from another angle, by maintaining materialism while making everything an agent no matter how non-agentic it looks.

The "machines" of the title are just things that perform functions, in the mathematical sense - to be worth discussing you've got to be changing inputs into outputs. Any machine in this sense is also made of tinier machines doing things to each other. (This also includes abstract concepts, which are taken as different from concrete ones only in that they're "multiply-instantiated" in the world.) The exceptions here being "dormant objects" that don't affect things any longer, like a book that no one alive has read, and "dark objects" that exist theoretically but have no effect on anything yet, like a planet outside of our light cone. In the opposite direction some objects are "bright" or "rogue" and interact a lot with others or have massive influence, like the Sun does for Earth or clocks do for the structure of our days.

The book's proposal is that we can tinker with these machines by understanding the smaller machines they're made of, their levels of "gravity" for other machines and what sorts of relations they have with each other. When you find a situation defined heavily by one machine, you can introduce alternatives that are brighter and restructure everything, ...
MIRI location optimization (and related topics) discussion
by Rob Bensinger — 2021-05-08T23:12:02.476Z

Added June 28: Blake Borgeson comments below,

Update: After a lot more discussion and thinking things over, MIRI has decided against relocating away from the Bay Area, unless a new impetus comes along to move some time in the future.

Mainly, ongoing/propagating strategic updates are behind this decision. We're more uncertain about our strategy overall following our 2020 strategy update (https://intelligence.org/2020/12/21/2020-updates-and-strategy/#3), so we’d rather not pay a big up-front move cost based on a prediction about what we expect MIRI to look like 2 or 5 years from now. Related to the same strategic updates, over the next 12 months or so we plan to experiment with placing less of an emphasis on staff working from our Berkeley base, and expect some staff to live and work from other places.

Separately, in the background, considerations against our recent favorite places became stronger:

Bellingham is far away from big cities, and it seemed increasingly like MIRI would be more isolated there than we wanted. Also, we became more concerned about low light levels during winter months.
The area around Peekskill has some of the highest Lyme disease incidence in the US, and it seems possible this is a high enough risk of disability to cause us to want to avoid it. We were still investigating this when we ended our location search.

Who am I? I’m a MIRI board member, and was in recent months leading/coordinating this MIRI relocation decision process, which is now complete.

Thanks for all the feedback in these comments, in emails, and on Facebook. I (and probably others) read all of it I could find, and it was important input for us.

Original post:

MIRI is moving (with high probability)!

We haven’t finalized a location yet, but there’s a good chance we’ll make our decision in the next six weeks. I want to solicit:

Feedback on our current top location candidates.
Ideas for other places that might fit our criteria.

I’m also interested in a more general location-optimizing discussion. What are your general thoughts on where you’d like to live, and have they changed any since the hub conversations Claire began in

September

and

November

? If a new rationality community hub sprang up at any of these locations, would you be tempted to join? Is there a different place you’d prefer (either personally, or for the community)?

Anything from 'statements of perso...
Android Video with External Microphone
by jefftk — 2021-05-08T22:30:05.778Z

My smartphone is my best camera, but its microphone is not nearly as
good. When recording video where I care about the audio (

ex), I've
normally

recorded audio and
video separately and then combined them. This works, but is a
hassle. Can we record them together?

An Android phone (likely iPhone too, but I don't know those) can
record audio from any standard ("class compliant") USB audio
interface. This may bring up images of spending hundreds of dollars
on professional audio equipment, but if you only need a single channel
and don't need a preamp (or you already have mixing equipment) you can
use a "USB soundcard":

This is a Sabrent USB-C
adapter one I got for $7.30 shipped.

While it's supposed
to be possible to use an external microphone with the built-in
camera, I couldn't get it to work. Instead, I used Open
Camera:

This is "Settings > Video settings > Audio source > External
mic (if present)", and worked without issue. Here's an example:

Videography courtesy of Lily

I previously made a lot of
recordings with the microphone on the camera just because it was
so much more convenient than combining audio and video from two
sources, but I think I'm done with that.
Prediction and Calibration - Part 1
by Jan Christian Refsgaard — 2021-05-08T19:48:16.847Z
Link: https://www.badprior.com/blog/bayesian-prediction-and-calibration-1/

Scott Alexander is a darling of the Bayesian rationalist community, he has a lot
more epistemic humility than most, despite being an impressively well-calibrated predictor.

In this series we will try to achieve 2 things:

(this post) We try to understand what a likelihood function is, and use it
to evaluate predictions

(next post) We Make a
Bayesian calibration model, and get an uncertainty estimate over our calibration.

The likelihood function

Let's first look at Bayes Theorem

p(θ∣y)=p(y∣θ)p(θ)p(y)

In common parlance, the 4 parts of Bayes Theorem are called:

posterior=likelihood×priordata

What we want is our posterior, the probability of some model parameters (often
θ) given some data (y). We construct a model with two things, a
prior function which describes what we believe before seeing the data, and a
likelihood function (p(y∣θ)) which given a model (θ, drawn from the
prior) scores the data.

The simplest and most relevant likelihood function is the Bernoulli

p(y|θ)=θy(1−θ)1−y

Here y is 1 when our prediction turns out to be correct and is 0 otherwise.
And θ represents our model. Our model for now is just 'what Scott
predicted.'

As an example, let's take a prediction of θ=0.6. If the prediction turns
out to be true (y=1), then the Bernoulli likelihood function is equal to 0.6:

p(y=1|θ=0.6)=θy(1−θ)1−y=0.61(1−0.6)1−1=0.61×0.40=0.6

And if the prediction turned out wrong (y=0), then:

p(y=0|θ=0.6)=θy(1−θ)1−y=0.60(1−0.6)1−0=0.60×0.41=0.4

The likelihood function says that there was a 40% chance you were wrong.
Which is the same a predicting not θ with 40%.

If a person makes 3 predictions θ=[0.6,0.6,0.7] and the outcomes
were y=[1,0,1], then the likelihood of all 3 observations is simply the
product of the 3 Bernoulli likelihoods:

p(y∣θ)=3∏i=1p(yi∣θi)=0.6×(1−0.6)×0.7=0.168

Better predictions will have higher numbers.

It can be useful to divide by the null predictor to compare against random performance:

p(y∣θ=0.5)=N∏i=1p(yi∣θi=0.5)=0.5N

So the likelihood of the 3 above predictions are 0.1680.53≈1.34
times more likely than random. Making this person slightly better than random.

How good a predictor is Scott

Because Scott has made a lot of predictions, and because we will later implement
a 'calibration' model of Scott, let's try to compare the likelihood of his 2019
predictions with the null model which predicts everything with 50% (which
implicitly mean that it also predicts it doe...