If you are a person who finds it difficult to tell "no" to their friends, this one weird trick may save you a lot of time!

Scenario 1

Alice: "Hi Bob! You are a programmer, right?"

Bob: "Hi Alice! Yes, I am."

Alice: "I have this cool idea, but I need someone to help me. I am not good with computers, and I need someone smart whom I could trust, so they wouldn't steal my idea. Would you have a moment to listen to me?"

Alice explains to Bob her idea that would completely change the world. Well, at the least the world of bicycle shopping.

Instead of having many shops for bicycles, there could be one huge e-shop that would collect all the information about bicycles from all the existing shops. The customers would specify what kind of a bike they want (and where they live), and the system would find all bikes that fit the specification, and display them ordered by lowest price, including the price of delivery; then it would redirect them to the specific page of the specific vendor. Customers would love to use this one website, instead of having to visit multiple shops and compare. And the vendors would have to use this shop, because that's where the customers would be. Taking a fraction of a percent from the sales could make Alice (and also Bob, if he helps her) incredibly rich.

Bob is skeptical about it. The project suffers from the obvious chicken-and-egg problem: without vendors already there, the customers will not come (and if they come by accident, they will quickly leave, never to return again); and without customers already there, there is no reason for the vendors to cooperate. There are a few ways how to approach this problem, but the fact that Alice didn't even think about it is a red flag. She also has no idea who are the big players in the world of bicycle selling; and generally she didn't do her homework. But after pointing out all these objections, Alice still remains super enthusiastic about the project. She promises she will take care about everything -- she just cannot write code, and she needs Bob's help for this part.

Bob believes strongly in the division of labor, and that friends should help each other. He considers Alice his friend, and he will likely need some help from her in the future. Fact is, with perfect specification, he could make the webpage in a week or two. But he considers bicycles to be an extremely boring topic, so he wants to spend as little time as possible on this project. Finally, he has an idea:

"Okay, Alice, I will make the website for you. But first I need to know exactly how the page will look like, so that I don't have to keep changing it over and over again. So here is the homework for you -- take a pen and paper, and make a sketch of how exactly the web will look like. All the dialogs, all the buttons. Don't forget logging in and logging out, editing the customer profile, and everything else that is necessary for the website to work as intended. Just look at the papers and imagine that you are the customer: where exactly would you click to register, and to find the bicycle you want? Same for the vendor. And possibly a site administrator. Also give me the list of criteria people will use to find the bike they want. Size, weight, color, radius of wheels, what else? And when you have it all ready, I will make the first version of the website. But until then, I am not writing any code."

Alice leaves, satisfied with the outcome.

This happened a year ago.

No, Alice doesn't have the design ready, yet. Once in a while, when she meets Bob, she smiles at him and apologizes that she didn't have the time to start working on the design. Bob smiles back and says it's okay, he'll wait. Then they change the topic.

Scenario 2

Cyril: "Hi Diana! You speak Spanish, right?"

Diana: "Hi Cyril! Yes, I do."

Cyril: "You know, I think Spanish is the most cool language ever, and I would really love to learn it! Could you please give me some Spanish lessons, once in a while? I totally want to become fluent in Spanish, so I could travel to Spanish-speaking countries and experience their culture and food. Would you please help me?"

Diana is happy that someone takes interest in her favorite hobby. It would be nice to have someone around she could practice Spanish conversation with. The first instinct is to say yes.

But then she remembers (she knows Cyril for some time; they have a lot of friends in common, so they meet quite regularly) that Cyril is always super enthusiastic about something he is totally going to do... but when she meets him next time, he is super enthusiastic about something completely different; and she never heard about him doing anything serious about his previous dreams.

Also, Cyril seems to seriously underestimate how much time does it take to learn a foreign language fluently. Some lessons, once in a while will not do it. He also needs to study on his own. Preferably every day, but twice a week is probably a minimum, if he hopes to speak the language fluently within a year. Diana would be happy to teach someone Spanish, but not if her effort will most likely be wasted.

Diana: "Cyril, there is this great website called Duolingo, where you can learn Spanish online completely free. If you give it about ten minutes every day, maybe after a few months you will be able to speak fluently. And anytime we meet, we can practice the vocabulary you have already learned."

This would be the best option for Diana. No work, and another opportunity to practice. But Cyril insists:

"It's not the same without the live teacher. When I read something from the textbook, I cannot ask additional questions. The words that are taught are often unrelated to the topics I am interested in. I am afraid I will just get stuck with the... whatever was the website that you mentioned."

For Diana this feels like a red flag. Sure, textbooks are not optimal. They contain many words that the student will not use frequently, and will soon forget them. On the other hand, the grammar is always useful; and Diana doesn't want to waste her time explaining the basic grammar that any textbook could explain instead. If Cyril learns the grammar and some basic vocabulary, then she can teach him all the specialized vocabulary he is interested in. But now it feels like Cyril wants to avoid all work. She has to draw a line:

"Cyril, this is the address of the website." She takes his notebook and writes 'www.duolingo.com'. "You register there, choose Spanish, and click on the first lesson. It is interactive, and it will not take you more than ten minutes. If you get stuck there, write here what exactly it was that you didn't understand; I will explain it when we meet. If there is no problem, continue with the second lesson, and so on. When we meet next time, tell me which lessons you have completed, and we will talk about them. Okay?"

Cyril nods reluctantly.

This happened a year ago.

Cyril and Diana have met repeatedly during the year, but Cyril never brought up the topic of Spanish language again.

Scenario 3

Erika: "Filip, would you give me a massage?"

Filip: "Yeah, sure. The lotion is in the next room; bring it to me!"

Erika brings the massage lotion and lies on the bed. Filip massages her back. Then they make out and have sex.

This happened a year ago. Erika and Filip are still a happy couple.

Filip's previous relationships didn't work well, in long term. In retrospect, they all followed a similar scenario. At the beginning, everything seemed great. Then at some moment the girl started acting... unreasonably?... asking Filip to do various things for her, and then acting annoyed when Filip did exactly what he was asked to do. This happened more and more frequently, and at some moment she broke up with him. Sometimes she provided explanation for breaking up that Filip was unable to decipher.

Filip has a friend who is a successful salesman. Successful both professionally and with women. When Filip admitted to himself that he is unable to solve the problem on his own, he asked his friend for advice.

"It's because you're a f***ing doormat," said the friend. "The moment a woman asks you to do anything, you immediately jump and do it, like a well-trained puppy. Puppies are cute, but not attractive. Have you ready any of those books I sent you, like, ten years ago? I bet you didn't. Well, it's all there."

Filip sighed: "Look, I'm not trying to become a pick-up artist. Or a salesman. Or anything. No offense, but I'm not like you, personality-wise, I never have been, and I don't want to become your - or anyone else's - copy. Even if it would mean greater success in anything. I prefer to treat other people just like I would want them to treat me. Most people reciprocate nice behavior; and those who don't, well, I avoid them as much as possible. This works well with my friends. It also works with the girls... at the beginning... but then somehow... uhm... Anyway, all your books are about manipulating people, which is ethically unacceptable for me. Isn't there some other way?"

"All human interaction is manipulation; the choice is between doing it right or wrong, acting consciously or driven by your old habits..." started the friend, but then he gave up. "Okay, I see you're not interested. Just let me show you the most obvious mistake you make. You believe that when you are nice to people, they will perceive you as nice, and most of them will reciprocate. And when you act like an asshole, it's the other way round. That's correct, on some level; and in a perfect world this would be the whole truth. But on a different level, people also perceive nice behavior as weakness; especially if you do it habitually, as if you don't have any other option. And being an asshole obviously signals strength: you are not afraid to make other people angry. Also, in long term, people become used to your behavior, good or bad. The nice people don't seem so nice anymore, but they still seem weak. Then, ironicaly, if the person well-known to be nice refuses to do something once, people become really angry, because their expectations were violated. And if the asshole decides to do something nice once, they will praise him, because he surprised them pleasantly. You should be an asshole once in a while, to make people see that you have a choice, so they won't take your niceness for granted. Or if your girlfriend wants something from you, sometimes just say no, even if you could have done it. She will respect you more, and then she will enjoy more the things you do for her."

Filip: "Well, I... probably couldn't do that. I mean, what you say seems to make sense, however much I hate to admit it. But I can't imagine doing it myself, especially to a person I love. It's just... uhm... wrong."

"Then, I guess, the very least you could do is to ask her to do something for you first. Even if it's symbolic, that doesn't matter; human relationships are mostly about role-playing anyway. Don't jump immediately when you are told to; always make her jump first, if only a little. That will demonstrate strength without hurting anyone. Could you do that?"

Filip wasn't sure, but at the next opportunity he tried it, and it worked. And it kept working. Maybe it was all just a coincidence, maybe it was a placebo effect, but Filip doesn't mind. At first it felt kinda artificial, but then it became natural. And later, to his surprise, Filip realized that practicing these symbolic demands actually makes it easier to ask when he really needed something. (In which case sometimes he was asked to do something first, because his girlfriend -- knowingly or not? he never had the courage to ask -- copied the pattern; or maybe she has already known it long before. But he didn't mind that either.)

The lesson is: If you find yourself repeatedly in situations where people ask you to do something for them, but at the end they don't seem to appreciate what you did for them, or don't even care about the thing they asked you to do... and yet you find it difficult to say "no"... ask them to contribute to the project first.

This will help you get rid of the projects they don't care about (including the ones they think they care about in far mode, but do not care about enough to actually work on them in near mode) without being the one who refuses cooperation. Also, the act of asking the other person to contribute, after being asked to do something for them, mitigates the status loss inherent in working for them.

Accounts "The_Lion" and "The_Lion2" are banned now. Here is some background, mostly for the users who weren't here two years ago:

User "Eugine_Nier" was banned for retributive downvoting in July 2014. He keeps returning to the website using new accounts, such as "Azathoth123", "Voiceofra", "The_Lion", and he keeps repeating the behavior that got him banned originally.

The original ban was permanent. It will be enforced on all future known accounts of Eugine. (At random moments, because moderators sometimes feel too tired to play whack-a-mole.) This decision is not open to discussion.

Please note that the moderators of LW are the opposite of trigger-happy. Not counting spam, there is on average less than one account per year banned. I am writing this explicitly, to avoid possible misunderstanding among the new users. Just because you have read about someone being banned, it doesn't mean that you are now at risk.

Most of the time, LW discourse is regulated by the community voting on articles and comments. Stupid or offensive comments get downvoted; you lose some karma, then everyone moves on. In rare cases, moderators may remove specific content that goes against the rules. The account ban is only used in the extreme cases (plus for obvious spam accounts). Specifically, on LW people don't get banned for merely not understanding something or disagreeing with someone.

What does "retributive downvoting" mean? Imagine that in a discussion you write a comment that someone disagrees with. Then in a few hours you will find that your karma has dropped by hundreds of points, because someone went through your entire comment history and downvoted all comments you ever wrote on LW; most of them completely unrelated to the debate that "triggered" the downvoter.

Such behavior is damaging to the debate and the community. Unlike downvoting a specific comment, this kind of mass downvoting isn't used to correct a faux pas, but to drive a person away from the website. It has especially strong impact on new users, who don't know what is going on, so they may mistake it for a reaction of the whole community. But even in experienced users it creates an "ugh field" around certain topics known to invoke the reaction. Thus a single user has achieved disproportional control over the content and the user base of the website. This is not desired, and will be punished by the site owners and the moderators.

To avoid rules lawyering, there is no exact definition of how much downvoting breaks the rules. The rule of thumb is that you should upvote or downvote each comment based on the value of that specific comment. You shouldn't vote on the comments regardless of their content merely because they were written by a specific user.

Thanks to the reaction to this article and some conversations, I'm convinced that it's worth trying to renovate and restore LW. Eliezer, Nate, and Matt Fallshaw are all on board and have empowered me as an editor to see what we can do about reshaping LW to meet what the community currently needs. This involves a combination of technical changes and social changes, which we'll try to make transparently and non-intrusively.

A few months ago, Vaniver wrote a really long post speculating about potential futures for Less Wrong, with a focus on the idea that the spread of the Less Wrong diaspora has left the site weak and fragmented. I wasn't here for our high water mark, so I don't really have an informed opinion on what has socially changed since then. But a number of complaints are technical, and as an IT person, I thought I had some useful things to say.

I argued at the time that many of the technical challenges of the diaspora were solved problems, and that the solution was NNTP -- an ancient, yet still extant, discussion protocol. I am something of a crank on the subject and didn't expect much of a reception. I was pleasantly surprised by the 18 karma it generated, and tried to write up a full post arguing the point.

I failed. I was trying to write a manifesto, didn't really know how to do it right, and kept running into a vast inferential distance I couldn't seem to cross. I'm a product of a prior age of the Internet, from before the http prefix assumed its imperial crown; I kept wanting to say things that I knew would make no sense to anyone who came of age this millennium. I got bogged down in irrelevant technical minutia about how to implement features X, Y, and Z. Eventually I decided I was attacking the wrong problem; I was thinking about 'how do I promote NNTP', when really I should have been going after 'what would an ideal discussion platform look like and how does NNTP get us there, if it does?'

So I'm going to go after that first, and work on the inferential distance problem, and then I'm going to talk about NNTP, and see where that goes and what could be done better. I still believe it's the closest thing to a good, available technological schelling point, but it's going to take a lot of words to get there from here, and I might change my mind under persuasive argument. We'll see.

Fortunately, this is Less Wrong, and sequences are a thing here. This is the first post in an intended sequence on mechanisms of discussion. I know it's a bit off the beaten track of Less Wrong subject matter. I posit that it's both relevant to our difficulties and probably more useful and/or interesting than most of what comes through these days. I just took the 2016 survey and it has a couple of sections on the effects of the diaspora, so I'm guessing it's on topic for meta purposes if not for site-subject purposes.

Less Than Ideal Discussion

To solve a problem you must first define it. Looking at the LessWrong 2.0 post, I see the following technical problems, at a minimum; I'll edit this with suggestions from comments.

1. Aggregation of posts. Our best authors have formed their own fiefdoms and their work is not terribly visible here. We currently have limited support for this via the sidebar, but that's it.
2. Aggregation of comments. You can see diaspora authors in the sidebar, but you can't comment from here.
3. Aggregation of community. This sounds like a social problem but it isn't. You can start a new blog, but unless you plan on also going out of your way to market it then your chances of starting a discussion boil down to "hope it catches the attention of Yvain or someone else similarly prominent in the community." Non-prominent individuals can theoretically post here; yet this is the place we are decrying as moribund.
4. Incomplete and poor curation. We currently do this via Promoted, badly, and via the diaspora sidebar, also badly.
5. Pitiful interface feature set. This is not so much a Less Wrong-specific problem as a 2010s-internet problem; people who inhabit SSC have probably seen me respond to feature complaints with "they had something that did that in the 90s, but nobody uses it." (my own bugbear is searching for comments by author-plus-content).
6. Changes are hamstrung by the existing architecture, which gets you volunteer reactions like this one.

I see these meta-technical problems:

1. Expertise is scarce. Few people are in a position to technically improve the site, and those that are, have other demands on their time.
2. The Trivial Inconvenience Problem limits the scope of proposed changes to those that are not inconvenient to commenters or authors.
3. Getting cooperation from diaspora authors is a coordination problem. Are we better than average at handling those? I don't know.

Slightly Less Horrible Discussion

"Solving" community maintenance is a hard problem, but to the extent that pieces of it can be solved technologically, the solution might include these ultra-high-level elements:

1. Centralized from the user perspective. A reader should be able to interact with the entire community in one place, and it should be recognizable as a community.
2. Decentralized from the author perspective. Diaspora authors seem to like having their own fiefdoms, and the social problem of "all the best posters went elsewhere" can't be solved without their cooperation. Therefore any technical solution must allow for it.
3. Proper division of labor. Scott Alexander probably should not have to concern himself with user feature requests; that's not his comparative advantage and I'd rather he spend his time inventing moral cosmologies. I suspect he would prefer the same. The same goes for Eliezer Yudkowski or any of our still-writing-elsewhere folks.
4. Really good moderation tools.
5. Easy entrance. New users should be able to join the discussion without a lot of hassle. Old authors that want to return should be able to do so and, preferably, bring their existing content with them.
6. Easy exit. Authors who don't like the way the community is heading should be able to jump ship -- and, crucially, bring their content with them to their new ship. Conveniently. This is essentially what has happened, except old content is hostage here.
7. Separate policy and mechanism within the site architecture. Let this one pass for now if you don't know what it means; it's the first big inferential hurdle I need to cross and I'll be starting soon enough.

As with the previous, I'll update this from the comments if necessary.

Getting There From Here

As I said at the start, I feel on firmer ground talking about technical issues than social ones. But I have to acknowledge one strong social opinion: I believe the greatest factor in Less Wrong's decline is the departure of our best authors for personal blogs. Any plan for revitalization has to provide an improved substitute for a personal blog, because that's where everyone seems to end up going. You need something that looks and behaves like a blog to the author or casual readers, but integrates seamlessly into a community discussion gateway.

I argue that this can be achieved. I argue that the technical challenges are solvable and the inherent coordination problem is also solvable, provided the people involved still have an interest in solving it.

And I argue that it can be done -- and done better than what we have now -- using technology that has existed since the '90s.

I don't argue that this actually will be achieved in anything like the way I think it ought to be. As mentioned up top, I am a crank, and I have no access whatsoever to anybody with any community pull. My odds of pushing through this agenda are basically nil. But we're all about crazy thought experiments, right?

This topic is something I've wanted to write about for a long time. Since it's not typical Less Wrong fare, I'll take the karma on this post as a referendum on whether the community would like to see it here.

Assuming there's interest, the sequence will look something like this (subject to reorganization as I go along, since I'm pulling this from some lengthy but horribly disorganized notes; in particular I might swap subsequences 2 and 3):

1. Technical Architecture
   1. Your Web Browser Is Not Your Client
   2. Specialized Protocols: or, NNTP and its Bastard Children
   3. Moderation, Personal Gardens, and Public Parks
   4. Content, Presentation, and the Division of Labor
   5. The Proper Placement of User Features
   6. Hard Things that are Suddenly Easy: or, what does client control gain us?
   7. Your Web Browser Is Still Not Your Client (but you don't need to know that)
2. Meta-Technical Conflicts (or, obstacles to adoption)
   1. Never Bet Against Convenience
   2. Conflicting Commenter, Author, and Admin Preferences
   3. Lipstick on the Configuration Pig
   4. Incremental Implementation and the Coordination Problem.
   5. Lowering Barriers to Entry and Exit
3. Technical and Social Interoperability
   1. Benefits and Drawbacks of Standards
   2. Input Formats and Quoting Conventions
   3. Faking Functionality
   4. Why Reddit Makes Me Cry
   5. What NNTP Can't Do
4. Implementation of Nonstandard Features
   1. Some desirable feature #1
   2. Some desirable feature #2
   3. ...etc. This subsequence is only necessary if someone actually wants to try and do what I'm arguing for, which I think unlikely.

(Meta-meta: This post was written in Markdown, converted to HTML for posting using Pandoc, and took around four hours to write. I can often be found lurking on #lesswrong or #slatestarcodex on workday afternoons if anyone wants to discuss it, but I don't promise to answer quickly because, well, workday)

[Edited to add: At +10/92% karma I figure continuing is probably worth it. After reading comments I'm going to try to slim it down a lot from the outline above, though. I still want to hit all those points but they probably don't all need a full post's space. Note that I'm not Scott or Eliezer, I write like I bleed, so what I do post will likely be spaced out]

The Brain Preservation Foundation’s Small Mammalian Brain Prize has been won with fantastic preservation of a whole rabbit brain using a new fixative+slow-vitrification process.

• BPF announcement (21CM’s announcement)
• evaluation

• The process was published as “Aldehyde-stabilized cryopreservation”, McIntyre & Fahy 2015 (mirror)

  We describe here a new cryobiological and neurobiological technique, aldehyde-stabilized cryopreservation (ASC), which demonstrates the relevance and utility of advanced cryopreservation science for the neurobiological research community. ASC is a new brain-banking technique designed to facilitate neuroanatomic research such as connectomics research, and has the unique ability to combine stable long term ice-free sample storage with excellent anatomical resolution. To demonstrate the feasibility of ASC, we perfuse-fixed rabbit and pig brains with a glutaraldehyde-based fixative, then slowly perfused increasing concentrations of ethylene glycol over several hours in a manner similar to techniques used for whole organ cryopreservation. Once 65% w/v ethylene glycol was reached, we vitrified brains at −135 °C for indefinite long-term storage. Vitrified brains were rewarmed and the cryoprotectant removed either by perfusion or gradual diffusion from brain slices. We evaluated ASC-processed brains by electron microscopy of multiple regions across the whole brain and by Focused Ion Beam Milling and Scanning Electron Microscopy (FIB-SEM) imaging of selected brain volumes. Preservation was uniformly excellent: processes were easily traceable and synapses were crisp in both species. Aldehyde-stabilized cryopreservation has many advantages over other brain-banking techniques: chemicals are delivered via perfusion, which enables easy scaling to brains of any size; vitrification ensures that the ultrastructure of the brain will not degrade even over very long storage times; and the cryoprotectant can be removed, yielding a perfusable aldehyde-preserved brain which is suitable for a wide variety of brain assays…We have shown that both rabbit brains (10 g) and pig brains (80 g) can be preserved equally well. We do not anticipate that there will be significant barriers to preserving even larger brains such as bovine, canine, or primate brains using ASC.

  (They had problems with 2 pigs and got 1 pig brain successfully cryopreserved but it wasn’t part of the entry. I’m not sure why: is that because the Large Mammalian Brain Prize is not yet set up?)
• previous discussion: Mikula’s plastination came close but ultimately didn’t seem to preserve the whole brain when applied.
• commentary: Alcor, Robin Hanson, John Smart, Evidence-Based Cryonics, Vice, Pop Sci
  

• donation link

To summarize it, you might say that this is a hybrid of current plastination and vitrification methods, where instead of allowing slow plastination (with unknown decay & loss) or forcing fast cooling (with unknown damage and loss), a staged approach is taking: a fixative is injected into the brain first to immediately lock down all proteins and stop all decay/change, and then it is leisurely cooled down to be vitrified.

This is exciting progress because the new method may wind up preserving better than either of the parent methods, but also because it gives much greater visibility into the end-results: the aldehyde-vitrified brains can be easily scanned with electron microscopes and the results seen in high detail, showing fantastic preservation of structure, unlike regular vitrification where the scans leave opaque how good the preservation was. This opacity is one reason that as Mike Darwin has pointed out at length on his blog and jkaufman has also noted that we cannot be confident in how well ALCOR or CI’s vitrification works - because if it didn’t, we have little way of knowing.

EDIT: BPF’s founder Ken Hayworth (Reddit account) has posted a piece, arguing that ALCOR & CI cannot be trusted to do procedures well and that future work should be done via rigorous clinical trials and only then rolled out. “Opinion: The prize win is a vindication of the idea of cryonics, not of unaccountable cryonics service organizations”

…“Should cryonics service organizations immediately start offering this new ASC procedure to their ‘patients’?” My personal answer (speaking for myself, not on behalf of the BPF) has been a steadfast NO. It should be remembered that these same cryonics service organizations have been offering a different procedure for years. A procedure that was not able to demonstrate, to even my minimal expectations, preservation of the brain’s neural circuitry. This result, I must say, surprised and disappointed me personally, leading me to give up my membership in one such organization and to become extremely skeptical of all since. Again, I stress, current cryonics procedures were NOT able to meet our challenge EVEN UNDER IDEAL LABORATORY CONDITIONS despite being offered to paying customers for years[1]. Should we really expect that these same organizations can now be trusted to further develop and properly implement such a new, independently-invented technique for use under non-ideal conditions?

Let’s step back for a moment. A single, independently-researched, scientific publication has come out that demonstrates a method of structural brain preservation (ASC) compatible with long-term cryogenic storage in animal models (rabbit and pig) under ideal laboratory conditions (i.e. a healthy living animal immediately being perfused with fixative). Should this one paper instantly open the floodgates to human application? Under untested real-world conditions where the ‘patient’ is either terminally ill or already declared legally dead? Should it be performed by unlicensed persons, in unaccountable organizations, operating outside of the traditional medical establishment with its checks and balances designed to ensure high standards of quality and ethics? To me, the clear answer is NO. If this was a new drug for cancer therapy, or a new type of heart surgery, many additional steps would be expected before even clinical trials could start. Why should our expectations be any lower for this?

The fact that the ASC procedure has won the brain preservation prize should rightly be seen as a vindication of the central idea of cryonics –the brain’s delicate circuitry underlying memory and personality CAN in fact be preserved indefinitely, potentially serving as a lifesaving bridge to future revival technologies. But, this milestone should certainly not be interpreted as a vindication of the very different cryonics procedures that are practiced on human patients today. And it should not be seen as a mandate for more of the same but with an aldehyde stabilization step casually tacked on. …

I looked at my old post Zombies! Zombies? and it seemed to have some extraneous content.  This is a redacted and slightly rewritten version.

Recently, someone on the Facebook page asked if anyone had used rationality to target anxieties.  I have, so I thought I’d share my LessWrong-inspired strategies.  This is my first post, so feedback and formatting help are welcome.  

First things first: the techniques developed by this community are not a panacea for mental illness.  They are way more effective than chance and other tactics at reducing normal bias, and I think many mental illnesses are simply cognitive biases that are extreme enough to get noticed.  In other words, getting a probability question about cancer systematically wrong does not disrupt my life enough to make the error obvious.  When I believe (irrationally) that I will get fired because I asked for help at work, my life is disrupted.  I become non-functional, and the error is clear.

Second: the best way to attack anxiety is to do the things that make your anxieties go away.  That might seem too obvious to state, but I’ve definitely been caught in an “analysis loop,” where I stay up all night reading self-help guides only to find myself non-functional in the morning because I didn’t sleep.  If you find that attacking an anxiety with Bayesian updating is like chopping down the Washington monument with a spoon, but getting a full night’s sleep makes the monument disappear completely, consider the sleep.  Likewise for techniques that have little to no scientific evidence, but are a good placebo.  A placebo effect is still an effect.

Finally, like all advice, this comes with Implicit Step Zero:  “Have enough executive function to give this a try.”  If you find yourself in an analysis loop, you may not yet have enough executive function to try any of the advice you read.  The advice for functioning better is not always identical to the advice for functioning at all.  If there’s interest in an “improving your executive function” post, I’ll write one eventually.  It will be late, because my executive function is not impeccable.

Simple updating is my personal favorite for attacking specific anxieties.  A general sense of impending doom is a very tricky target and does not respond well to reality.  If you can narrow it down to a particular belief, however, you can amass evidence against it. 

Returning to my example about work: I alieved that I would get fired if I asked for help or missed a day due to illness.  The distinction between believe and alieve is an incredibly useful tool that I immediately integrated when I heard of it.  Learning to make beliefs pay rent is much easier than making harmful aliefs go away.  The tactics are similar: do experiments, make predictions, throw evidence at the situation until you get closer to reality.  Update accordingly.  

The first thing I do is identify the situation and why it’s dysfunctional.  The alief that I’ll get fired for asking for help is not actually articulated when it manifests as an anxiety.  Ask me in the middle of a panic attack, and I still won’t articulate that I am afraid of getting fired.  So I take the anxiety all the way through to its implication.  The algorithm is something like this:

1.       Notice sense of doom
2.       Notice my avoidance behaviors (not opening my email, walking away from my desk)
3.       Ask “What am I afraid of?”
4.       Answer (it's probably silly)
5.       Ask “What do I think will happen?”
6.       Make a prediction about what will happen (usually the prediction is implausible, which is why we want it to go away in the first place)

In the “asking for help” scenario, the answer to “what do I think will happen” is implausible.  It’s extremely unlikely that I’ll get fired for it!  This helps take the gravitas out of the anxiety, but it does not make it go away.*  After (6), it’s usually easy to do an experiment.  If I ask my coworkers for help, will I get fired?  The only way to know is to try. 

…That’s actually not true, of course.  A sense of my environment, my coworkers, and my general competence at work should be enough.  But if it was, we wouldn’t be here, would we?

So I perform the experiment.  And I wait.  When I receive a reply of any sort, even if it’s negative, I make a tick mark on a sheet of paper.  I label it “didn’t get fired.”  Because again, even if it’s negative, I didn’t get fired. 

This takes a lot of tick marks.  Cutting down the Washington monument with a spoon, remember?

The tick marks don’t have to be physical.  I prefer it, because it makes the “updating” process visual.  I’ve tried making a mental note and it’s not nearly as effective.  Play around with it, though.  If you’re anything like me, you have a lot of anxieties to experiment with. 

Usually, the anxiety starts to dissipate after obtaining several tick marks.  Ideally, one iteration of experiments should solve the problem.  But we aren’t ideal; we’re mentally ill.  Depending on the severity of the anxiety, you may need someone to remind you that doom will not occur.  I occasionally panic when I have to return to work after taking a sick day.  I ask my husband to remind me that I won’t get fired.  I ask him to remind me that he’ll still love me if I do get fired.  If this sounds childish, it’s because it is.  Again: we’re mentally ill.  Even if you aren’t, however, assigning value judgements to essentially harmless coping mechanisms does not make sense.  Childish-but-helpful is much better than mature-and-harmful, if you have to choose.

I still have tiny ugh fields around my anxiety triggers.  They don’t really go away.  It’s more like learning not to hit someone you’re angry at.  You notice the impulse, accept it, and move on.  Hopefully, your harmful alief starves to death.

If you perform your experiment and doom does occur, it might not be you.  If you can’t ask your boss for help, it might be your boss.  If you disagree with your spouse and they scream at you for an hour, it might be your spouse.  This isn’t an excuse to blame your problems on the world, but abusive situations can be sneaky.  Ask some trusted friends for a sanity check, if you’re performing experiments and getting doom as a result.  This is designed for situations where your alief is obviously silly.  Where you know it’s silly, and need to throw evidence at your brain to internalize it.  It’s fine to be afraid of genuinely scary things; if you really are in an abusive work environment, maybe you shouldn’t ask for help (and start looking for another job instead). 

*using this technique for several months occasionally stops the anxiety immediately after step 6.  

This is a follow-up to last year's report. Here, I will talk about my successes and failures using Spaced Repetition Software (SRS) in the classroom for a second year. The year's not over yet, but I have reasons for reporting early that should become clear in a subsequent post. A third post will then follow, and together these will constitute a small sequence exploring classroom SRS and the adjacent ideas that bubble up when I think deeply about teaching.

Summary

I experienced net negative progress this year in my efforts to improve classroom instruction via spaced repetition software. While this is mostly attributable to shifts in my personal priorities, I have also identified a number of additional failure modes for classroom SRS, as well as additional shortcomings of Anki for this use case. My experiences also showcase some fundamental challenges to teaching-in-general that SRS depressingly spotlights without being any less susceptible to. Regardless, I am more bullish than ever about the potential for classroom SRS, and will lay out a detailed vision for what it can be in the next post.

Previously: 2011; 2012-2013; 2013-2014; 2014-2015

"When I was one-and-twenty / I heard a wise man say, / 'Give crowns and pounds and guineas / But not your heart away; / Give pearls away and rubies / But keep your fancy free.' / But I was one-and-twenty, / No use to talk to me."

My past year of completed writings, sorted by topic:

Genetics:

• Embryo selection for intelligence cost-benefit analysis
  • meta-analysis of intelligence GCTAs, limits set by measurement error, current polygenic scores, possible gains with current IVF procedures, the benefits of selection on multiple complex traits, the possible annual value in the USA of selection & value of larger GWASes, societal consequences of various embryo selection scenarios, embryo count versus polygenic scores as limiting factors, comparison with iterated embryo selection, limits to total gains from iterated embryo selection etc.
• Wikipedia article on Genome-wide complex trait analysis (GCTA)

AI:

• Computational Complexity vs the Singularity
• Adding metadata to an RNN for mimicking individual author style
• Armstrong’s AI control problem: Reinforce.js demo

Biology:

• vitamin D & metformin for life-extension: cost-benefit analysis

Statistics:

• Candy Japan new packaging decision analysis
• “The Power of Twins: Revisiting Student’s Scottish Milk Experiment Example”
• Genius Revisited: Critiquing the Value of High IQ Elementary Schools
• Inferring mean ethnic IQs from very high IQ samples like TIP/SMPY

Cryptography:

• shell scripts for easy cryptographic timestamping

Misc:

• Newton’s comet apocalypse system

gwern.net itself has remained largely stable (some CSS fixes and image size changes); I continue to use Patreon and send out my newsletters.

2016 LessWrong Diaspora Survey Analysis

Overview

• Results and Dataset
• Meta
• Demographics
• LessWrong Usage and Experience
• LessWrong Criticism and Successorship
• Diaspora Community Analysis (You are here)
• Mental Health Section
• Basilisk Section/Analysis
• Blogs and Media analysis
• Politics
• Calibration Question And Probability Question Analysis
• Charity And Effective Altruism Analysis

Introduction

Before it was the LessWrong survey, the 2016 survey was a small project I was working on as market research for a website I'm creating called FortForecast. As I was discussing the idea with others, particularly Eliot he made the suggestion that since he's doing LW 2.0 and I'm doing a site that targets the LessWrong demographic, why don't I go ahead and do the LessWrong Survey? Because of that, this years survey had a lot of questions oriented around what you would want to see in a successor to LessWrong and what you think is wrong with the site.

LessWrong Usage and Experience

How Did You Find LessWrong?

Been here since it was started in the Overcoming Bias days: 171 8.3%
Referred by a link: 275 13.4%
HPMOR: 542 26.4%
Overcoming Bias: 80 3.9%
Referred by a friend: 265 12.9%
Referred by a search engine: 131 6.4%
Referred by other fiction: 14 0.7%
Slate Star Codex: 241 11.7%
Reddit: 55 2.7%
Common Sense Atheism: 19 0.9%
Hacker News: 47 2.3%
Gwern: 22 1.1%
Other: 191 9.308%

How do you use Less Wrong?

I lurk, but never registered an account: 1120 54.4%
I've registered an account, but never posted: 270 13.1%
I've posted a comment, but never a top-level post: 417 20.3%
I've posted in Discussion, but not Main: 179 8.7%
I've posted in Main: 72 3.5%

[54.4% lurkers.]

How often do you comment on LessWrong?

I have commented more than once a week for the past year.: 24 1.2%
I have commented more than once a month for the past year but less than once a week.: 63 3.1%
I have commented but less than once a month for the past year.: 225 11.1%
I have not commented this year.: 1718 84.6%

[You could probably snarkily title this one "LW usage in one statistic". It's a pretty damning portrait of the sites vitality. A whopping 84.6% of people have not commented this year a single time.]

How Long Since You Last Posted On LessWrong?

I wrote one today.: 12 0.637%
Within the last three days.: 13 0.69%
Within the last week.: 22 1.168%
Within the last month.: 58 3.079%
Within the last three months.: 75 3.981%
Within the last six months.: 68 3.609%
Within the last year.: 84 4.459%
Within the last five years.: 295 15.658%
Longer than five years.: 15 0.796%
I've never posted on LW.: 1242 65.924%

[Supermajority of people have never commented on LW, 5.574% have within the last month.]

About how much of the Sequences have you read?

Never knew they existed until this moment: 215 10.3%
Knew they existed, but never looked at them: 101 4.8%
Some, but less than 25% : 442 21.2%
About 25%: 260 12.5%
About 50%: 283 13.6%
About 75%: 298 14.3%
All or almost all: 487 23.3%

[10.3% of people taking the survey have never heard of the sequences. 36.3% have not read a quarter of them.]

Do you attend Less Wrong meetups?

Yes, regularly: 157 7.5%
Yes, once or a few times: 406 19.5%
No: 1518 72.9%

[However the in-person community seems to be non-dead.]

Is physical interaction with the Less Wrong community otherwise a part of your everyday life, for example do you live with other Less Wrongers, or you are close friends and frequently go out with them?

Yes, all the time: 158 7.6%
Yes, sometimes: 258 12.5%
No: 1652 79.9%

About the same number say they hang out with LWers 'all the time' as say they go to meetups. I wonder if people just double counted themselves here. Or they may go to meetups and have other interactions with LWers outside of that. Or it could be a coincidence and these are different demographics. Let's find out.

P(Community part of daily life | Meetups) = 40%

Significant overlap, but definitely not exclusive overlap. I'll go ahead and chalk this one up up to coincidence.

Have you ever been in a romantic relationship with someone you met through the Less Wrong community?

Yes: 129 6.2%
I didn't meet them through the community but they're part of the community now: 102 4.9%
No: 1851 88.9%

LessWrong Usage Differences Between 2016 and 2014 Surveys

How do you use Less Wrong?

I lurk, but never registered an account: +19.300% 1125 54.400%
I've registered an account, but never posted: -1.600% 271 13.100%
I've posted a comment, but never a top-level post: -7.600% 419 20.300%
I've posted in Discussion, but not Main: -5.100% 179 8.700%
I've posted in Main: -3.300% 73 3.500%

About how much of the sequences have you read?

Never knew they existed until this moment: +3.300% 217 10.400%
Knew they existed, but never looked at them: +2.100% 103 4.900%
Some, but less than 25%: +3.100% 442 21.100%
About 25%: +0.400% 260 12.400%
About 50%: -0.400% 284 13.500%
About 75%: -1.800% 299 14.300%
All or almost all: -5.000% 491 23.400%

Do you attend Less Wrong meetups?

Yes, regularly: -2.500% 160 7.700%
Yes, once or a few times: -2.100% 407 19.500%
No: +7.100% 1524 72.900%

Is physical interaction with the Less Wrong community otherwise a part of your everyday life, for example do you live with other Less Wrongers, or you are close friends and frequently go out with them?

Yes, all the time: +0.200% 161 7.700%
Yes, sometimes: -0.300% 258 12.400%
No: +2.400% 1659 79.800%

Have you ever been in a romantic relationship with someone you met through the Less Wrong community?

Yes: +0.800% 132 6.300%
I didn't meet them through the community but they're part of the community now: -0.400% 102 4.900%
No: +1.600% 1858 88.800%

Write Ins

In a bit of a silly oversight I forgot to ask survey participants what was good about the community, so the following is going to be a pretty one sided picture. Below are the complete write ins respondents submitted

Issues With LessWrong At It's Peak

Philosophical Issues With LessWrong At It's Peak[Part One]
Philosophical Issues With LessWrong At It's Peak[Part Two]
Community Issues With LessWrong At It's Peak[Part One]
Community Issues With LessWrong At It's Peak[Part Two]

Issues With LessWrong Now

Philosophical Issues With LessWrong Now[Part One]
Philosophical Issues With LessWrong Now[Part Two]
Community Issues With LessWrong Now[Part One]
Community Issues With LessWrong Now[Part Two]

Peak Philosophy Issue Tallies

Well, those are certainly some results. Top answers are:

Narrow Scholarship: 20
Arrogance: 16
Too Much Theory: 14
Lack of Rigor: 14
Misfocused: 13
Nothing: 13
Reinvention (reinvents the wheel too much): 10
Personality Cult: 10

So condensing a bit: Pay more attention to mainstream scholarship and ideas, try to do better about intellectual rigor, be more practical and focus on results, be more humble. (Labeled Dataset)

Peak Community Issue Tallies

Top Answers:

Unwelcoming: 12
Too Much Theory: 12
Groupthink: 11
Not Enough Community Infrastructure: 10
Too Contrarian: 10
Insufficiently Indexed: 9
Cultish: 9

Again condensing a bit: Work on being less intimidating/aggressive/etc to newcomers, spend less time on navel gazing and more time on actually doing things and collecting data, work on getting the structures in place that will onboard people into the community, stop being so nitpicky and argumentative, spend more time on getting content indexed in a form where people can actually find it, be more accepting of outside viewpoints and remember that you're probably more likely to be wrong than you think. (Labeled Dataset)

One last note before we finish up, these tallies are a very rough executive summary. The tagging process basically involves trying to fit points into clusters and is prone to inaccuracy through laziness, adding another category being undesirable, square-peg into round-hole fitting, and my personal political biases. So take these with a grain of salt, if you really want to know what people wrote in my advice would be to read through the write in sets I have above in HTML format. If you want to evaluate for yourself how well I tagged things you can see the labeled datasets above.

I won't bother tallying the "issues now" sections, all you really need to know is that it's basically the same as the first sections except with lots more "It's dead." comments and from eyeballing it a higher proportion of people arguing that LessWrong has been taken over by the left/social justice and complaints about effective altruism. (I infer that the complaints about being taken over by the left are mostly referring to effective altruism.)

Traits Respondents Would Like To See In A Successor Community

Philosophically

Attention Paid To Outside Sources
More: 1042 70.933%
Same: 414 28.182%
Less: 13 0.885%

Self Improvement Focus
More: 754 50.706%
Same: 598 40.215%
Less: 135 9.079%

AI Focus
More: 184 12.611%
Same: 821 56.271%
Less: 454 31.117%

Political
More: 330 22.837%
Same: 770 53.287%
Less: 345 23.875%

Academic/Formal
More: 455 31.885%
Same: 803 56.272%
Less: 169 11.843%

In summary, people want a site that will engage with outside ideas, acknowledge where it borrows from, focus on practical self improvement, less on AI and AI risk, and tighten its academic rigor. They could go either way on politics but the epistemic direction is clear.

Community

Intense Environment
More: 254 19.644%
Same: 830 64.192%
Less: 209 16.164%

Focused On 'Real World' Action
More: 739 53.824%
Same: 563 41.005%
Less: 71 5.171%

Experts
More: 749 55.605%
Same: 575 42.687%
Less: 23 1.707%

Data Driven/Testing Of Ideas
More: 1107 78.344%
Same: 291 20.594%
Less: 15 1.062%

Social
More: 583 43.507%
Same: 682 50.896%
Less: 75 5.597%

This largely backs up what I said about the previous results. People want a more practical, more active, more social and more empirical LessWrong with outside expertise and ideas brought into the fold. They could go either way on it being more intense but the epistemic trend is still clear.

Write Ins

Diaspora Communities

So where did the party go? We got twice as many respondents this year as last when we opened up the survey to the diaspora, which means that the LW community is alive and kicking it's just not on LessWrong.

LessWrong
Yes: 353 11.498%
No: 1597 52.02%

LessWrong Meetups
Yes: 215 7.003%
No: 1735 56.515%

LessWrong Facebook Group
Yes: 171 5.57%
No: 1779 57.948%

LessWrong Slack
Yes: 55 1.792%
No: 1895 61.726%

SlateStarCodex
Yes: 832 27.101%
No: 1118 36.417%

[SlateStarCodex by far has the highest proportion of active LessWrong users, over twice that of LessWrong itself, and more than LessWrong and Tumblr combined.]

Rationalist Tumblr
Yes: 350 11.401%
No: 1600 52.117%

[I'm actually surprised that Tumblr doesn't just beat LessWrong itself outright, They're only a tenth of a percentage point behind though, and if current trends continue I suspect that by 2017 Tumblr will have a large lead over the main LW site.]

Rationalist Facebook
Yes: 150 4.886%
No: 1800 58.632%

[Eliezer Yudkowsky currently resides here.]

Rationalist Twitter
Yes: 59 1.922%
No: 1891 61.596%

Effective Altruism Hub
Yes: 98 3.192%
No: 1852 60.326%

FortForecast
Yes: 4 0.13%
No: 1946 63.388%

[I included this as a 'troll' option to catch people who just check every box. Relatively few people seem to have done that, but having the option here lets me know one way or the other.]

Good Judgement(TM) Open
Yes: 29 0.945%
No: 1921 62.573%

PredictionBook
Yes: 59 1.922%
No: 1891 61.596%

Omnilibrium
Yes: 8 0.261%
No: 1942 63.257%

Hacker News
Yes: 252 8.208%
No: 1698 55.309%

#lesswrong on freenode
Yes: 76 2.476%
No: 1874 61.042%

#slatestarcodex on freenode
Yes: 36 1.173%
No: 1914 62.345%

#hplusroadmap on freenode
Yes: 4 0.13%
No: 1946 63.388%

#chapelperilous on freenode
Yes: 10 0.326%
No: 1940 63.192%

[Since people keep asking me, this is a postrational channel.]

/r/rational
Yes: 274 8.925%
No: 1676 54.593%

/r/HPMOR
Yes: 230 7.492%
No: 1720 56.026%

[Given that the story is long over, this is pretty impressive. I'd have expected it to be dead by now.]

/r/SlateStarCodex
Yes: 244 7.948%
No: 1706 55.57%

One or more private 'rationalist' groups
Yes: 192 6.254%
No: 1758 57.264%

[I almost wish I hadn't included this option, it'd have been fascinating to learn more about these through write ins.]

Of all the parties who seem like plausible candidates at the moment, Scott Alexander seems most capable to undiaspora the community. In practice he's very busy, so he would need a dedicated team of relatively autonomous people to help him. Scott could court guest posts and start to scale up under the SSC brand, and I think he would fairly easily end up with the lions share of the free floating LWers that way.

Before I call a hearse for LessWrong, there is a glimmer of hope left:

Would you consider rejoining LessWrong?

I never left: 668 40.6%
Yes: 557 33.8%
Yes, but only under certain conditions: 205 12.5%
No: 216 13.1%

A significant fraction of people say they'd be interested in an improved version of the site. And of course there were write ins for conditions to rejoin, what did people say they'd need to rejoin the site?

Rejoin Condition Write Ins [Part One]
Rejoin Condition Write Ins [Part Two]
Rejoin Condition Write Ins [Part Three]
Rejoin Condition Write Ins [Part Four]
Rejoin Condition Write Ins [Part Five]

Feel free to read these yourselves (they're not long), but I'll go ahead and summarize: It's all about the content. Content, content, content. No amount of usability improvements, A/B testing or clever trickery will let you get around content. People are overwhelmingly clear about this; they need a reason to come to the site and right now they don't feel like they have one. That means priority number one for somebody trying to revitalize LessWrong is how you deal with this.

Let's recap.

Future Improvement Wishlist Based On Survey Results

Philosophical

• Pay more attention to mainstream scholarship and ideas.
• Improved intellectual rigor.
• Acknowledge sources borrowed from.
• Be more practical and focus on results.
• Be more humble.

Community

• Less intimidating/aggressive/etc to newcomers,
• Structures that will onboard people into the community.
• Stop being so nitpicky and argumentative.
• Spend more time on getting content indexed in a form where people can actually find it.
• More accepting of outside viewpoints.

While that list seems reasonable, it's quite hard to put into practice. Rigor, as the name implies requires high-effort from participants. Frankly, it's not fun. And getting people to do un-fun things without paying them is difficult. If LessWrong is serious about it's goal of 'advancing the art of human rationality' then it needs to figure out a way to do real investigation into the subject. Not just have people 'discuss', as though the potential for Rationality is within all of us just waiting to be brought out by the right conversation.

I personally haven't been a LW regular in a long time. Assuming the points about pedanticism, snipping, "well actually"-ism and the like are true then they need to stop for the site to move forward. Personally, I'm a huge fan of Scott Alexander's comment policy: All comments must be at least two of true, kind, or necessary.

• True and kind - Probably won't drown out the discussion signal, will help significantly decrease the hostility of the atmosphere.

• True and necessary - Sometimes what you have to say isn't nice, but it needs to be said. This is the common core of free speech arguments for saying mean things and they're not wrong. However, something being true isn't necessarily enough to make it something you should say. In fact, in some situations saying mean things to people entirely unrelated to their arguments is known as the ad hominem fallacy.

• Kind and necessary - The infamous 'hugbox' is essentially a place where people go to hear things which are kind but not necessarily true. I don't think anybody wants a hugbox, but occasionally it can be important to say things that might not be true but are needed for the sake of tact, reconciliation, or to prevent greater harm.

If people took that seriously and really gave it some thought before they used their keyboard, I think the on-site LessWrong community would be a significant part of the way to not driving people off as soon as they arrive.

More importantly, in places like the LessWrong Slack I see this sort of happy go lucky attitude about site improvement. "Oh that sounds nice, we should do that." without the accompanying mountain of work to actually make 'that' happen. I'm not sure people really understand the dynamics of what it means to 'revive' a website in severe decay. When you decide to 'revive' a dying site, what you're really doing once you're past a certain point is refounding the site. So the question you should be asking yourself isn't "Can I fix the site up a bit so it isn't quite so stale?". It's "Could I have founded this site?" and if the answer is no you should seriously question whether to make the time investment.

Whether or not LessWrong lives to see another day basically depends on the level of ground game its last users and administrators can muster up. And if it's not enough, it won't.

Virtus junxit mors non separabit!

What is your opinion on rationality-promoting articles by Gleb Tsipursky / Intentional Insights? Here is what I think:

Cross-posted from my blog

I'd like to coin a term. The Sally-Anne fallacy is the mistake of assuming that somone believes something, simply because that thing is true.¹

The name comes from the Sally-Anne test, used in developmental psychology to detect theory of mind. Someone who lacks theory of mind will fail the Sally-Anne test, thinking that Sally knows where the marble is. The Sally-Anne fallacy is also a failure of theory of mind.

In internet arguments, this will often come up as part of a chain of reasoning, such as: you think X; X implies Y; therefore you think Y. Or: you support X; X leads to Y; therefore you support Y.²

So for example, we have this complaint about the words "African dialect" used in Age of Ultron. The argument goes: a dialect is a variation on a language, therefore Marvel thinks "African" is a language.

You think "African" has dialects; "has dialects" implies "is a language"; therefore you think "African" is a language.

Or maybe Marvel just doesn't know what a "dialect" is.

This is also a mistake I was pointing at in Fascists and Rakes. You think it's okay to eat tic-tacs; tic-tacs are sentient; therefore you think it's okay to eat sentient things. Versus: you think I should be forbidden from eating tic-tacs; tic-tacs are nonsentient; therefore you think I should be forbidden from eating nonsentient things. No, in both cases the defendant is just wrong about whether tic-tacs are sentient.

Many political conflicts include arguments that look like this. You fight our cause; our cause is the cause of [good thing]; therefore you oppose [good thing]. Sometimes people disagree about what's good, but sometimes they just disagree about how to get there, and think that a cause is harmful to its stated goals. Thus, liberals and libertarians symmetrically accuse each other of not caring about the poor.³

If you want to convince someone to change their mind, it's important to know what they're wrong about. The Sally-Anne fallacy causes us to mistarget our counterarguments, and to mistake potential allies for inevitable enemies.

Among those concerned about risks from advanced AI, I've encountered people who would be interested in a career in AI research, but are worried that doing so would speed up AI capability relative to safety. I think it is a mistake for AI safety proponents to avoid going into the field for this reason (better reasons include being well-positioned to do AI safety work, e.g. at MIRI or FHI). This mistake contributed to me choosing statistics rather than computer science for my PhD, which I have some regrets about, though luckily there is enough overlap between the two fields that I can work on machine learning anyway. I think the value of having more AI experts who are worried about AI safety is far higher than the downside of adding a few drops to the ocean of people trying to advance AI. Here are several reasons for this:

1. Concerned researchers can inform and influence their colleagues, especially if they are outspoken about their views.
2. Studying and working on AI brings understanding of the current challenges and breakthroughs in the field, which can usefully inform AI safety work (e.g. wireheading in reinforcement learning agents).
3. Opportunities to work on AI safety are beginning to spring up within academia and industry, e.g. through FLI grants. In the next few years, it will be possible to do an AI-safety-focused PhD or postdoc in computer science, which would hit two birds with one stone.

To elaborate on #1, one of the prevailing arguments against taking long-term AI safety seriously is that not enough experts in the AI field are worried. Several prominent researchers have commented on the potential risks (Stuart Russell, Bart Selman, Murray Shanahan, Shane Legg, and others), and more are concerned but keep quiet for reputational reasons. An accomplished, strategically outspoken and/or well-connected expert can make a big difference in the attitude distribution in the AI field and the level of familiarity with the actual concerns (which are not about malevolence, sentience, or marching robot armies). Having more informed skeptics who have maybe even read Superintelligence, and fewer uninformed skeptics who think AI safety proponents are afraid of Terminators, would produce much needed direct and productive discussion on these issues. As the proportion of informed and concerned researchers in the field approaches critical mass, the reputational consequences for speaking up will decrease.

A year after FLI's Puerto Rico conference, the subject of long-term AI safety is no longer taboo among AI researchers, but remains rather controversial. Addressing AI risk on the long term will require safety work to be a significant part of the field, and close collaboration between those working on safety and capability of advanced AI. Stuart Russell makes the apt analogy that "just as nuclear fusion researchers consider the problem of containment of fusion reactions as one of the primary problems of their field, issues of control and safety will become central to AI as the field matures". If more people who are already concerned about AI safety join the field, we can make this happen faster, and help wisdom win the race with capability.

(Cross-posted from my blog. Thanks to Janos Kramar for his help with editing this post.)

These are my notes and observations after attending the Safety in Artificial Intelligence (SafArtInt) conference, which was co-hosted by the White House Office of Science and Technology Policy and Carnegie Mellon University on June 27 and 28. This isn't an organized summary of the content of the conference; rather, it's a selection of points which are relevant to the control problem. As a result, it suffers from selection bias: it looks like superintelligence and control-problem-relevant issues were discussed frequently, when in reality those issues were discussed less and I didn't write much about the more mundane parts.

SafArtInt has been the third out of a planned series of four conferences. The purpose of the conference series was twofold: the OSTP wanted to get other parts of the government moving on AI issues, and they also wanted to inform public opinion.

The other three conferences are about near term legal, social, and economic issues of AI. SafArtInt was about near term safety and reliability in AI systems. It was effectively the brainchild of Dr. Ed Felten, the deputy U.S. chief technology officer for the White House, who came up with the idea for it last year. CMU is a top computer science university and many of their own researchers attended, as well as some students. There were also researchers from other universities, some people from private sector AI including both Silicon Valley and government contracting, government researchers and policymakers from groups such as DARPA and NASA, a few people from the military/DoD, and a few control problem researchers. As far as I could tell, everyone except a few university researchers were from the U.S., although I did not meet many people. There were about 70-100 people watching the presentations at any given time, and I had conversations with about twelve of the people who were not affiliated with existential risk organizations, as well as of course all of those who were affiliated. The conference was split with a few presentations on the 27th and the majority of presentations on the 28th. Not everyone was there for both days.

Felten believes that neither "robot apocalypses" nor "mass unemployment" are likely. It soon became apparent that the majority of others present at the conference felt the same way with regard to superintelligence. The general intention among researchers and policymakers at the conference could be summarized as follows: we need to make sure that the AI systems we develop in the near future will not be responsible for any accidents, because if accidents do happen then they will spark public fears about AI, which would lead to a dearth of funding for AI research and an inability to realize the corresponding social and economic benefits. Of course, that doesn't change the fact that they strongly care about safety in its own right and have significant pragmatic needs for robust and reliable AI systems.

Most of the talks were about verification and reliability in modern day AI systems. So they were concerned with AI systems that would give poor results or be unreliable in the narrow domains where they are being applied in the near future. They mostly focused on "safety-critical" systems, where failure of an AI program would result in serious negative consequences: automated vehicles were a common topic of interest, as well as the use of AI in healthcare systems. A recurring theme was that we have to be more rigorous in demonstrating safety and do actual hazard analyses on AI systems, and another was that we need the AI safety field to succeed in ways that the cybersecurity field has failed. Another general belief was that long term AI safety, such as concerns about the ability of humans to control AIs, was not a serious issue.

On average, the presentations were moderately technical. They were mostly focused on machine learning systems, although there was significant discussion of cybersecurity techniques.

The first talk was given by Eric Horvitz of Microsoft. He discussed some approaches for pushing into new directions in AI safety. Instead of merely trying to reduce the errors spotted according to one model, we should look out for "unknown unknowns" by stacking models and looking at problems which appear on any of them, a theme which would be presented by other researchers as well in later presentations. He discussed optimization under uncertain parameters, sensitivity analysis to uncertain parameters, and 'wireheading' or short-circuiting of reinforcement learning systems (which he believes can be guarded against by using 'reflective analysis'). Finally, he brought up the concerns about superintelligence, which sparked amused reactions in the audience. He said that scientists should address concerns about superintelligence, which he aptly described as the 'elephant in the room', noting that it was the reason that some people were at the conference. He said that scientists will have to engage with public concerns, while also noting that there were experts who were worried about superintelligence and that there would have to be engagement with the experts' concerns. He did not comment on whether he believed that these concerns were reasonable or not.

An issue which came up in the Q&A afterwards was that we need to deal with mis-structured utility functions in AI, because it is often the case that the specific tradeoffs and utilities which humans claim to value often lead to results which the humans don't like. So we need to have structural uncertainty about our utility models. The difficulty of finding good objective functions for AIs would eventually be discussed in many other presentations as well.

The next talk was given by Andrew Moore of Carnegie Mellon University, who claimed that his talk represented the consensus of computer scientists at the school. He claimed that the stakes of AI safety were very high - namely, that AI has the capability to save many people's lives in the near future, but if there are any accidents involving AI then public fears could lead to freezes in AI research and development. He highlighted the public's irrational tendencies wherein a single accident could cause people to overlook and ignore hundreds of invisible lives saved. He specifically mentioned a 12-24 month timeframe for these issues.

Moore said that verification of AI system safety will be difficult due to the combinatorial explosion of AI behaviors. He talked about meta-machine-learning as a solution to this, something which is being investigated under the direction of Lawrence Schuette at the Office of Naval Research. Moore also said that military AI systems require high verification standards and that development timelines for these systems are long. He talked about two different approaches to AI safety, stochastic testing and theorem proving - the process of doing the latter often leads to the discovery of unsafe edge cases.

He also discussed AI ethics, giving an example 'trolley problem' where AI cars would have to choose whether to hit a deer in order to provide a slightly higher probability of survival for the human driver. He said that we would need hash-defined constants to tell vehicle AIs how many deer a human is worth. He also said that we would need to find compromises in death-pleasantry tradeoffs, for instance where the safety of self-driving cars depends on the speed and routes on which they are driven. He compared the issue to civil engineering where engineers have to operate with an assumption about how much money they would spend to save a human life.

He concluded by saying that we need policymakers, company executives, scientists, and startups to all be involved in AI safety. He said that the research community stands to gain or lose together, and that there is a shared responsibility among researchers and developers to avoid triggering another AI winter through unsafe AI designs.

The next presentation was by Richard Mallah of the Future of Life Institute, who was there to represent "Medium Term AI Safety". He pointed out the explicit/implicit distinction between different modeling techniques in AI systems, as well as the explicit/implicit distinction between different AI actuation techniques. He talked about the difficulty of value specification and the concept of instrumental subgoals as an important issue in the case of complex AIs which are beyond human understanding. He said that even a slight misalignment of AI values with regard to human values along one parameter could lead to a strongly negative outcome, because machine learning parameters don't strictly correspond to the things that humans care about.

Mallah stated that open-world discovery leads to self-discovery, which can lead to reward hacking or a loss of control. He underscored the importance of causal accounting, which is distinguishing causation from correlation in AI systems. He said that we should extend machine learning verification to self-modification. Finally, he talked about introducing non-self-centered ontology to AI systems and bounding their behavior.

The audience was generally quiet and respectful during Richard's talk. I sensed that at least a few of them labelled him as part of the 'superintelligence out-group' and dismissed him accordingly, but I did not learn what most people's thoughts or reactions were. In the next panel featuring three speakers, he wasn't the recipient of any questions regarding his presentation or ideas.

Tom Mitchell from CMU gave the next talk. He talked about both making AI systems safer, and using AI to make other systems safer. He said that risks to humanity from other kinds of issues besides AI were the "big deals of 2016" and that we should make sure that the potential of AIs to solve these problems is realized. He wanted to focus on the detection and remediation of all failures in AI systems. He said that it is a novel issue that learning systems defy standard pre-testing ("as Richard mentioned") and also brought up the purposeful use of AI for dangerous things.

Some interesting points were raised in the panel. Andrew did not have a direct response to the implications of AI ethics being determined by the predominantly white people of the US/UK where most AIs are being developed. He said that ethics in AIs will have to be decided by society, regulators, manufacturers, and human rights organizations in conjunction. He also said that our cost functions for AIs will have to get more and more complicated as AIs get better, and he said that he wants to separate unintended failures from superintelligence type scenarios. On trolley problems in self driving cars and similar issues, he said "it's got to be complicated and messy."

Dario Amodei of Google Deepbrain, who co-authored the paper on concrete problems in AI safety, gave the next talk. He said that the public focus is too much on AGI/ASI and wants more focus on concrete/empirical approaches. He discussed the same problems that pose issues in advanced general AI, including flawed objective functions and reward hacking. He said that he sees long term concerns about AGI/ASI as "extreme versions of accident risk" and that he thinks it's too early to work directly on them, but he believes that if you want to deal with them then the best way to do it is to start with safety in current systems. Mostly he summarized the Google paper in his talk.

In her presentation, Claire Le Goues of CMU said "before we talk about Skynet we should focus on problems that we already have." She mostly talked about analogies between software bugs and AI safety, the similarities and differences between the two and what we can learn from software debugging to help with AI safety.

Robert Rahmer of IARPA discussed CAUSE, a cyberintelligence forecasting program which promises to help predict cyber attacks. It is a program which is still being put together.

In the panel of the above three, autonomous weapons were discussed, but no clear policy stances were presented.

John Launchbury gave a talk on DARPA research and the big picture of AI development. He pointed out that DARPA work leads to commercial applications and that progress in AI comes from sustained government investment. He classified AI capabilities into "describing," "predicting," and "explaining" in order of increasing difficulty, and he pointed out that old fashioned "describing" still plays a large role in AI verification. He said that "explaining" AIs would need transparent decisionmaking and probabilistic programming (the latter would also be discussed by others at the conference).

The next talk came from Jason Gaverick Matheny, the director of IARPA. Matheny talked about four requirements in current and future AI systems: verification, validation, security, and control. He wanted "auditability" in AI systems as a weaker form of explainability. He talked about the importance of "corner cases" for national intelligence purposes, the low probability, high stakes situations where we have limited data - these are situations where we have significant need for analysis but where the traditional machine learning approach doesn't work because of its overwhelming focus on data. Another aspect of national defense is that it has a slower decision tempo, longer timelines, and longer-viewing optics about future events.

He said that assessing local progress in machine learning development would be important for global security and that we therefore need benchmarks to measure progress in AIs. He ended with a concrete invitation for research proposals from anyone (educated or not), for both large scale research and for smaller studies ("seedlings") that could take us "from disbelief to doubt".

The difference in timescales between different groups was something I noticed later on, after hearing someone from the DoD describe their agency as having a longer timeframe than the Homeland Security Agency, and someone from the White House describe their work as being crisis reactionary.

The next presentation was from Andrew Grotto, senior director of cybersecurity policy at the National Security Council. He drew a close parallel from the issue of genetically modified crops in Europe in the 1990's to modern day artificial intelligence. He pointed out that Europe utterly failed to achieve widespread cultivation of GMO crops as a result of public backlash. He said that the widespread economic and health benefits of GMO crops were ignored by the public, who instead focused on a few health incidents which undermined trust in the government and crop producers. He had three key points: that risk frameworks matter, that you should never assume that the benefits of new technology will be widely perceived by the public, and that we're all in this together with regard to funding, research progress and public perception.

In the Q&A between Launchbury, Matheny, and Grotto after Grotto's presentation, it was mentioned that the economic interests of farmers worried about displacement also played a role in populist rejection of GMOs, and that a similar dynamic could play out with regard to automation causing structural unemployment. Grotto was also asked what to do about bad publicity which seeks to sink progress in order to avoid risks. He said that meetings like SafArtInt and open public dialogue were good.

One person asked what Launchbury wanted to do about AI arms races with multiple countries trying to "get there" and whether he thinks we should go "slow and secure" or "fast and risky" in AI development, a question which provoked laughter in the audience. He said we should go "fast and secure" and wasn't concerned. He said that secure designs for the Internet once existed, but the one which took off was the one which was open and flexible.

Another person asked how we could avoid discounting outliers in our models, referencing Matheny's point that we need to include corner cases. Matheny affirmed that data quality is a limiting factor to many of our machine learning capabilities. At IARPA, we generally try to include outliers until they are sure that they are erroneous, said Matheny.

Another presentation came from Tom Dietterich, president of the Association for the Advancement of Artificial Intelligence. He said that we have not focused enough on safety, reliability and robustness in AI and that this must change. Much like Eric Horvitz, he drew a distinction between robustness against errors within the scope of a model and robustness against unmodeled phenomena. On the latter issue, he talked about solutions such as expanding the scope of models, employing multiple parallel models, and doing creative searches for flaws - the latter doesn't enable verification that a system is safe, but it nevertheless helps discover many potential problems. He talked about knowledge-level redundancy as a method of avoiding misspecification - for instance, systems could identify objects by an "ownership facet" as well as by a "goal facet" to produce a combined concept with less likelihood of overlooking key features. He said that this would require wider experiences and more data.

There were many other speakers who brought up a similar set of issues: the user of cybersecurity techniques to verify machine learning systems, the failures of cybersecurity as a field, opportunities for probabilistic programming, and the need for better success in AI verification. Inverse reinforcement learning was extensively discussed as a way of assigning values. Jeanette Wing of Microsoft talked about the need for AIs to reason about the continuous and the discrete in parallel, as well as the need for them to reason about uncertainty (with potential meta levels all the way up). One point which was made by Sarah Loos of Google was that proving the safety of an AI system can be computationally very expensive, especially given the combinatorial explosion of AI behaviors.

In one of the panels, the idea of government actions to ensure AI safety was discussed. No one was willing to say that the government should regulate AI designs. Instead they stated that the government should be involved in softer ways, such as guiding and working with AI developers, and setting standards for certification.

Pictures: https://imgur.com/a/49eb7

In between these presentations I had time to speak to individuals and listen in on various conversations. A high ranking person from the Department of Defense stated that the real benefit of autonomous systems would be in terms of logistical systems rather than weaponized applications. A government AI contractor drew the connection between Mallah's presentation and the recent press revolving around superintelligence, and said he was glad that the government wasn't worried about it.

I talked to some insiders about the status of organizations such as MIRI, and found that the current crop of AI safety groups could use additional donations to become more established and expand their programs. There may be some issues with the organizations being sidelined; after all, the Google Deepbrain paper was essentially similar to a lot of work by MIRI, just expressed in somewhat different language, and was more widely received in mainstream AI circles.

In terms of careers, I found that there is significant opportunity for a wide range of people to contribute to improving government policy on this issue. Working at a group such as the Office of Science and Technology Policy does not necessarily require advanced technical education, as you can just as easily enter straight out of a liberal arts undergraduate program and build a successful career as long as you are technically literate. (At the same time, the level of skepticism about long term AI safety at the conference hinted to me that the signalling value of a PhD in computer science would be significant.) In addition, there are large government budgets in the seven or eight figure range available for qualifying research projects. I've come to believe that it would not be difficult to find or create AI research programs that are relevant to long term AI safety while also being practical and likely to be funded by skeptical policymakers and officials.

I also realized that there is a significant need for people who are interested in long term AI safety to have basic social and business skills. Since there is so much need for persuasion and compromise in government policy, there is a lot of value to be had in being communicative, engaging, approachable, appealing, socially savvy, and well-dressed. This is not to say that everyone involved in long term AI safety is missing those skills, of course.

I was surprised by the refusal of almost everyone at the conference to take long term AI safety seriously, as I had previously held the belief that it was more of a mixed debate given the existence of expert computer scientists who were involved in the issue. I sensed that the recent wave of popular press and public interest in dangerous AI has made researchers and policymakers substantially less likely to take the issue seriously. None of them seemed to be familiar with actual arguments or research on the control problem, so their opinions didn't significantly change my outlook on the technical issues. I strongly suspect that the majority of them had their first or possibly only exposure to the idea of the control problem after seeing badly written op-eds and news editorials featuring comments from the likes of Elon Musk and Stephen Hawking, which would naturally make them strongly predisposed to not take the issue seriously. In the run-up to the conference, websites and press releases didn't say anything about whether this conference would be about long or short term AI safety, and they didn't make any reference to the idea of superintelligence.

I sympathize with the concerns and strategy given by people such as Andrew Moore and Andrew Grotto, which make perfect sense if (and only if) you assume that worries about long term AI safety are completely unfounded. For the community that is interested in long term AI safety, I would recommend that we avoid competitive dynamics by (a) demonstrating that we are equally strong opponents of bad press, inaccurate news, and irrational public opinion which promotes generic uninformed fears over AI, (b) explaining that we are not interested in removing funding for AI research (even if you think that slowing down AI development is a good thing, restricting funding yields only limited benefits in terms of changing overall timelines, whereas those who are not concerned about long term AI safety would see a restriction of funding as a direct threat to their interests and projects, so it makes sense to cooperate here in exchange for other concessions), and (c) showing that we are scientifically literate and focused on the technical concerns. I do not believe that there is necessarily a need for the two "sides" on this to be competing against each other, so it was disappointing to see an implication of opposition at the conference.

Anyway, Ed Felten announced a request for information from the general public, seeking popular and scientific input on the government's policies and attitudes towards AI: https://www.whitehouse.gov/webform/rfi-preparing-future-artificial-intelligence

Overall, I learned quite a bit and benefited from the experience, and I hope the insight I've gained can be used to improve the attitudes and approaches of the long term AI safety community.

This is a new experimental weekly thread.

Guidelines: Top-level comments here should be links to things written by members of the rationalist community, preferably that would be interesting specifically to this community. Self-promotion is totally fine. Including a very brief summary or excerpt is great, but not required. Generally stick to one link per top-level comment. Recent links are preferred.

Rule: Do not link to anyone who does not want to be linked to. In particular, Scott Alexander has asked people not to link to specific posts on his tumblr. As far as I know he's never rescinded that. Do not link to posts on his tumblr.

One or two research groups have published work on carbon dioxide and cognition. The state of the published literature is confusing.

Here is one paper on the topic. The authors investigate a proprietary cognitive benchmark, and experimentally manipulate carbon dioxide levels (without affecting other measures of air quality). They find implausibly large effects from increased carbon dioxide concentrations.

If the reported effects are real and the suggested interpretation is correct, I think it would be a big deal. To put this in perspective, carbon dioxide concentrations in my room vary between 500 and 1500 ppm depending on whether I open the windows. The experiment reports on cognitive effects for moving from 600 and 1000 ppm, and finds significant effects compared to interindividual differences.

I haven't spent much time looking into this (maybe 30 minutes, and another 30 minutes to write this post). I expect that if we spent some time looking into indoor CO2 we could have a much better sense of what was going on, by some combination of better literature review, discussion with experts, looking into the benchmark they used, and just generally thinking about it.

So, here's a proposal:

• If someone looks into this and writes a post that improves our collective understanding of the issue, I will be willing to buy part of an associated certificate of impact, at a price of around $100*N, where N is my own totally made up estimate of how many hours of my own time it would take to produce a similarly useful writeup. I'd buy up to 50% of the certificate at that price.
• Whether or not they want to sell me some of the certificate, on May 1 I'll give a $500 prize to the author of the best publicly-available analysis of the issue. If the best analysis draws heavily on someone else's work, I'll use my discretion: I may split the prize arbitrarily, and may give it to the earlier post even if it is not quite as excellent.

Some clarifications:

• The metric for quality is "how useful it is to Paul." I hope that's a useful proxy for how useful it is in general, but no guarantees. I am generally a pretty skeptical person. I would care a lot about even a modest but well-established effect on performance. 
• These don't need to be new analyses, either for the prize or the purchase.
• I reserve the right to resolve all ambiguities arbitrarily, and in the end to do whatever I feel like. But I promise I am generally a nice guy.
• I posted this 2 weeks ago on the EA forum and haven't had serious takers yet.

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There's a lot of data and research on what makes people successful at online dating, but I don't know anyone who actually tried to wholeheartedly apply this to themselves. I decided to be that person: I implemented lessons from data, economics, game theory and of course rationality in my profile and strategy and OkCupid. Shockingly, it worked! I got a lot of great dates, learned a ton and found the love of my life. I didn't expect dating to be my "rationalist win", but it happened.

Here's the first part of the story, I hope you'll find some useful tips and maybe a dollop of inspiration among all the silly jokes.

P.S.

Does anyone know who curates the "Latest on rationality blogs" toolbar? What are the requirements to be included?

Safely Interruptible Agents

Oxford academics are teaming up with Google DeepMind to make artificial intelligence safer. Laurent Orseau, of Google DeepMind, and Stuart Armstrong, the Alexander Tamas Fellow in Artificial Intelligence and Machine Learning at the Future of Humanity Institute at the University of Oxford, will be presenting their research on reinforcement learning agent interruptibility at UAI 2016. The conference, one of the most prestigious in the field of machine learning, will be held in New York City from June 25-29. The paper which resulted from this collaborative research will be published in the Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI).

Orseau and Armstrong’s research explores a method to ensure that reinforcement learning agents can be repeatedly safely interrupted by human or automatic overseers. This ensures that the agents do not “learn” about these interruptions, and do not take steps to avoid or manipulate the interruptions. When there are control procedures during the training of the agent, we do not want the agent to learn about these procedures, as they will not exist once the agent is on its own. This is useful for agents that have a substantially different training and testing environment (for instance, when training a Martian rover on Earth, shutting it down, replacing it at its initial location and turning it on again when it goes out of bounds—something that may be impossible once alone unsupervised on Mars), for agents not known to be fully trustworthy (such as an automated delivery vehicle, that we do not want to learn to behave differently when watched), or simply for agents that need continual adjustments to their learnt behaviour. In all cases where it makes sense to include an emergency “off” mechanism, it also makes sense to ensure the agent doesn’t learn to plan around that mechanism.

Interruptibility has several advantages as an approach over previous methods of control. As Dr. Armstrong explains, “Interruptibility has applications for many current agents, especially when we need the agent to not learn from specific experiences during training. Many of the naive ideas for accomplishing this—such as deleting certain histories from the training set—change the behaviour of the agent in unfortunate ways.”

In the paper, the researchers provide a formal definition of safe interruptibility, show that some types of agents already have this property, and show that others can be easily modified to gain it. They also demonstrate that even an ideal agent that tends to the optimal behaviour in any computable environment can be made safely interruptible.

These results will have implications in future research directions in AI safety. As the paper says, “Safe interruptibility can be useful to take control of a robot that is misbehaving… take it out of a delicate situation, or even to temporarily use it to achieve a task it did not learn to perform….” As Armstrong explains, “Machine learning is one of the most powerful tools for building AI that has ever existed. But applying it to questions of AI motivations is problematic: just as we humans would not willingly change to an alien system of values, any agent has a natural tendency to avoid changing its current values, even if we want to change or tune them. Interruptibility and the related general idea of corrigibility, allow such changes to happen without the agent trying to resist them or force them. The newness of the field of AI safety means that there is relatively little awareness of these problems in the wider machine learning community.  As with other areas of AI research, DeepMind remains at the cutting edge of this important subfield.”

On the prospect of continuing collaboration in this field with DeepMind, Stuart said, “I personally had a really illuminating time writing this paper—Laurent is a brilliant researcher… I sincerely look forward to productive collaboration with him and other researchers at DeepMind into the future.” The same sentiment is echoed by Laurent, who said, “It was a real pleasure to work with Stuart on this. His creativity and critical thinking as well as his technical skills were essential components to the success of this work. This collaboration is one of the first steps toward AI Safety research, and there’s no doubt FHI and Google DeepMind will work again together to make AI safer.”

For more information, or to schedule an interview, please contact Kyle Scott at fhipa@philosophy.ox.ac.uk

After a 6+ month hiatus driven by grad school and personal projects, I am finally able to continue my sequence on astrobiology.  I was flabbergasted by the positive response my last post got, and despite my status as a biologist with a hobby rather than an astronomer I decided to take a more rigorously mathematical approach to figuring out our biosphere's position in space and time rather than talking in generalizations and impressions.

Post is here:  http://thegreatatuin.blogspot.com/2016/03/space-and-time-revisited.html.  Seeing as this post is an elaboration on the last one, I am posting a link rather than reproducing the text.

To summarize, I found some actual rigorous observational fits to the star formation rate in the universe over time and projected them into the future.  These fits show the Sun as forming after 79% of all stars that will ever exist, and that 90% of all stars that will ever exist already exist.  This makes sense in the light of recent work on 'galaxy quenching' - a process by which galaxies more or less completely shut off star formation through a number of processes - indicating that the majority of gas in the universe probably won't form stars if trends that have held for most of the history of the universe continue to hold.  It relies heavily on analysis I began in comments on this site a few months ago.

I then lift two distinct metallicity normalizations from a paper that was making the rounds here a while back ("On The History and Future of Cosmic Planet Formation"), in an attempt to deal with the fact that that is a measurement of STAR formation, not terrestrial-planet-with-a-biosphere formation.  Depending on which metallicity normalization you use (and how willing you are to take a couple naive assumptions I make in order to slot the math that is too complicated for me to comment on on top of my star formation numbers) the Earth shows up as forming after either 72% or 51% of all terrestrial planets.

These numbers are remarkable in how boring they are.  We find ourselves in an utterly typical position in planet-order, even if I am wrong by quite a bit.  We are not early.  Of interest to many here, explanations of the so called Fermi paradox must go elsewhere, into the genesis of intelligent systems being exceedingly rare or the genesis of intelligent systems not implying interstellar spread.

Now that I seem to have a life again, I will be getting back to my original plan next, talking about our own solar system.

From their site:

OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.

The money quote is at the end, literally—$1B in committed funding from some of the usual suspects.

Cross-posted from my blog here.

One of the greatest successes of mankind over the last few centuries has been the enormous amount of wealth that has been created. Once upon a time virtually everyone lived in grinding poverty; now, thanks to the forces of science, capitalism and total factor productivity, we produce enough to support a much larger population at a much higher standard of living.

EAs being a highly intellectual lot, our preferred form of ritual celebration is charts. The ordained chart for celebrating this triumph of our people is the Declining Share of People Living in Extreme Poverty Chart.

(Source)

However, as a heretic, I think this chart is a mistake. What is so great about reducing the share? We could achieve that by killing all the poor people, but that would not be a good thing! Life is good, and poverty is not death; it is simply better for it to be rich.

As such, I think this is a much better chart. Here we show the world population. Those in extreme poverty are in purple – not red, for their existence is not bad. Those who the wheels of progress have lifted into wealth unbeknownst to our ancestors, on the other hand, are depicted in blue, rising triumphantly.

Long may their rise continue.

Cross-posted from Huffington Post. See also The End of Bullshit at the Hands of Critical Rationalism.

Debating season is in full swing, and as per usual the presidential candidates are playing fast and loose with the truth. Fact-checking sites such as PolitiFact and FactCheck.org have had plenty of easy targets in the debates so far. For instance, in the CNN Republican debate on September 16, Fiorina made several dubious claims about the Planned Parenthood video, as did Cruz about the Iran agreement. Similarly, in the CNN Democratic debate on October 13, Sanders falsely claimed that the U.S. has "more wealth and income inequality than any other country", whereas Chafee fudged the data on his Rhode Island record. No doubt we are going to see more of that in the rest of the presidential campaign. The fact-checkers won't need to worry about finding easy targets.

Research shows that fact-checking actually does make a difference. Incredible as it may seem, the candidates would probably have been even more careless with the truth if it weren't for the fact-checkers. To some extent, fact-checkers are a deterrent to politicians inclined to stretch the truth.

At the same time, the fact that falsehoods and misrepresentations of the truth are still so common shows that this deterrence effect is not particularly strong. This raises the question how we can make it stronger. Is there a way to improve on PolitiFact's and FactCheck.org's model - Fact-Checking 2.0, if you will?

Spencer Greenberg of ClearerThinking and I have developed a tool which we hope could play that role. Greenberg has created an application to embed videos of recorded debates and then add subtitles to them. In these subtitles, I point out falsehoods and misrepresentations of the truth at the moment when the candidates make them. For instance, when Fiorina says about the Planned Parenthood video that there is "a fully formed fetus on the table, its heart beating, its legs kicking, while someone says we have to keep it alive to harvest its brain", I write in the subtitles:

We think that reading that a candidate's statement is false just as it is made could have quite a striking effect. It could trigger more visceral feelings among the viewers than standard fact-checking, which is published in separate articles. To over and over again read in the subtitles that what you're being told simply isn't true should outrage anyone who finds truth-telling an important quality.

Another salient feature of our subtitles is that we go beyond standard fact-checking. There are many other ways of misleading the audience besides playing fast and loose with the truth, such as evasions, ad hominem-attacks and other logical fallacies. Many of these are hard to spot for the viewers. We must therefore go beyond fact-checking and also do argument-checking, as we call it. If fact-checking grew more effective, and misrepresenting the truth less viable a strategy, politicians presumably would more frequently resort to Plan B: evading questions where they don't want the readers to know the truth. To stop that, we need careful argument-checking in addition to fact-checking.

So far, I've annotated the entire CNN Republican Debate, a 12 minute video from the CNN Democratic Debate (more annotations of this debate will come) and nine short clips (1-3 minutes) from the Fox News Republican Debate (August 6). My aim is to be as complete as possible, and I think that I've captured an overwhelming majority of the factual errors, evasions, and fallacies in the clips. The videos can be found on ClearerThinking as well as below.

The CNN Republican debate, subtitled in full.



The first 12 minutes of the CNN Democratic debate.

Nine short clips from the Fox News Debate: Christie and Paul, Bush, Carson, Cruz, Huckabee, Kasich, Rubio, Trump, Walker.

What is perhaps most striking is the sheer number of falsehoods, evasions and fallacies the candidates make. The 2hr 55 min long CNN Republican debate contains 273 fact-checking and argument-checking comments (many of which refer to various fact-checking sites). In total, 27 % of the video is subtitled. Similar numbers hold for the other videos.

Conventional wisdom has it that politicians lie and deceive on a massive scale. My analyses prove conventional wisdom right. The candidates use all sorts of trickery to put themselves in a better light and smear their opponents.

All of this trickery is severely problematic from several perspectives. Firstly, it is likely to undermine the voters' confidence in the political system. This is especially true for voters on the losing side. Why be loyal to a government which has gained power by misleading the electorate? No doubt many voters do think in those terms, more or less explicitly.

It is also likely to damage the image of democracy. The American presidential election is followed all over the world by millions if not billions of people. Many of them live in countries where democracy activists are struggling to amass support against authoritarian regimes. It hardly helps them that the election debates in the U.S. and other democratic countries look like this.

All of these deceptive arguments and claims also make it harder for voters to make informed decisions. Televised debates are supposed to help voters to get a better view of the candidates' policies and track-records, but how could they, if they can't trust what is being said? This is perhaps the most serious consequence of poor debates, since it is likely to lead to poorer decisions on the part of the voters, which in turn will lead to poorer political leadership and poorer policies.

Besides functioning as a more effective lie deterrent to the candidates, improved fact-checking could also nudge the networks to adjust the set-up of the debates. The way the networks lead the debates today hardly encourages serious and rational argumentation. To the contrary, they often positively goad the candidates against each other. Improved fact-checking could make it more salient to the viewers how poor the debates are, and induce them to demand a better debate set-up. The networks need to come up with a format which incentivizes the candidates to argue fairly and truthfully, and which makes it clear who has not. For instance, they could broadcast the debate again the next day, with fact-checking and argument-checking subtitles.

Another means to improve the debates is further technological innovation. For example, there should be a video annotation equivalent to Genius.com, the web application which allows you to annotate text on any webpage in a convenient way. That would be very useful for fact-checking and argument-checking purposes.

Fact-checking could even become automatic, as Google CEO Eric Schmidt predicted it would be within five years in 2006. Though Schmidt was over-optimistic, Google algorithms are able to fact-check websites with a high degree of accuracy today, whilst Washington Post already has built a rudimentary automatic fact-checker.

But besides new software applications and better debating formats, we also need something else, namely a raised awareness among the public what a great problem politicians' careless attitude to the truth is. They should ask themselves: are people inclined to mislead the voters really suited to shape the future of the world?

Politicians are normally held to high moral standards. Voters tend to take very strict views on other forms of dishonest behavior, such as cheating and tax evasion. Why, then, is it that they don't take a stricter view on intellectual dishonesty? Besides being morally objectionable, intellectual dishonesty is likely to lead to poor decisions. Voters would therefore be wise to let intellectual honesty be an important criterion when they cast their vote. If they started doing that on a grand scale, that would do more to improve the level of political debate than anything else I can think of.

Thanks to Aislinn Pluta, Doug Moore, Janko Prester, Philip Thonemann, Stella Vallgårda and Staffan Holmberg for their contributions to the annotations.

(Part of a sequence on discussion technology and NNTP. As last time, I should probably emphasize that I am a crank on this subject and do not actually expect anything I recommend to be implemented. Add whatever salt you feel is necessary)¹

If there is one thing I hope readers get out of this sequence, it is this: The Web Browser is Not Your Client.

It looks like you have three or four viable clients -- IE, Firefox, Chrome, et al. You don't. You have one. It has a subforum listing with two items at the top of the display; some widgets on the right hand side for user details, RSS feed, meetups; the top-level post display; and below that, replies nested in the usual way.

Changing your browser has the exact same effect on your Less Wrong experience as changing your operating system, i.e. next to none.

For comparison, consider the Less Wrong IRC, where you can tune your experience with a wide range of different software. If you don't like your UX, there are other clients that give a different UX to the same content and community.

That is how the mechanism of discussion used to work, and does not now. Today, your user experience (UX) in a given community is dictated mostly by the admins of that community, and software development is often neither their forte nor something they have time for. I'll often find myself snarkily responding to feature requests with "you know, someone wrote something that does that 20 years ago, but no one uses it."

Semantic Collapse

What defines a client? More specifically, what defines a discussion client, a Less Wrong client?

The toolchain by which you read LW probably looks something like this; anyone who's read the source please correct me if I'm off:

Browser -> HTTP server -> LW UI application -> Reddit API -> Backend database.

The database stores all the information about users, posts, etc. The API presents subsets of that information in a way that's convenient for a web application to consume (probably JSON objects, though I haven't checked). The UI layer generates a web page layout and content using that information, which is then presented -- in the form of (mostly) HTML -- by the HTTP server layer to your browser. Your browser figures out what color pixels go where.

All of this is a gross oversimplification, obviously.

In some sense, the browser is self-evidently a client: It talks to an http server, receives hypertext, renders it, etc. It's a UI for an HTTP server.

But consider the following problem: Find and display all comments by me that are children of this post, and only those comments, using only browser UI elements, i.e. not the LW-specific page widgets. You cannot -- and I'd be pretty surprised if you could make a browser extension that could do it without resorting to the API, skipping the previous elements in the chain above. For that matter, if you can do it with the existing page widgets, I'd love to know how.

That isn't because the browser is poorly designed; it's because the browser lacks the semantic information to figure out what elements of the page constitute a comment, a post, an author. That information was lost in translation somewhere along the way.

Your browser isn't actually interacting with the discussion. Its role is more akin to an operating system than a client. It doesn't define a UX. It provides a shell, a set of system primitives, and a widget collection that can be used to build a UX. Similarly, HTTP is not the successor to NNTP; the successor is the plethora of APIs, for which HTTP is merely a substrate.

The Discussion Client is the point where semantic metadata is translated into display metadata; where you go from 'I have post A from user B with content C' to 'I have a text string H positioned above visual container P containing text string S.' Or, more concretely, when you go from this:

Author: somebody
Subject: I am right, you are mistaken, he is mindkilled.
Date: timestamp
Content: lorem ipsum nonsensical statement involving plankton....

to this:

<h1>I am right, you are mistaken, he is mindkilled.</h1>
<div><span align=left>somebody</span><span align=right>timestamp</span></div>
<div><p>lorem ipsum nonsensical statement involving plankton....</p></div>

That happens at the web application layer. That's the part that generates the subforum headings, the interface widgets, the display format of the comment tree. That's the part that defines your Less Wrong experience, as a reader, commenter, or writer.

That is your client, not your web browser. If it doesn't suit your needs, if it's missing features you'd like to have, well, you probably take for granted that you're stuck with it.

But it doesn't have to be that way.

Mechanism and Policy

One of the difficulties forming an argument about clients is that the proportion of people who have ever had a choice of clients available for any given service keeps shrinking. I have this mental image of the Average Internet User as having no real concept for this.

Then I think about email. Most people have probably used at least two different clients for email, even if it's just Gmail and their phone's built-in mail app. Or perhaps Outlook, if they're using a company system. And they (I think?) mostly take for granted that if they don't like Outlook they can use something else, or if they don't like their phone's mail app they can install a different one. They assume, correctly, that the content and function of their mail account is not tied to the client application they use to work with it.

(They may make the same assumption about web-based services, on the reasoning that if they don't like IE they can switch to Firefox, or if they don't like Firefox they can switch to Chrome. They are incorrect, because The Web Browser is Not Their Client)

Email does a good job of separating mechanism from policy. Its format is defined in RFC 2822 and its transmission protocol is defined in RFC 5321. Neither defines any conventions for user interfaces. There are good reasons for that from a software-design standpoint, but more relevant to our discussion is that interface conventions change more rapidly than the objects they interface with. Forum features change with the times; but the concepts of a Post, an Author, or a Reply are forever.

The benefit of this separation: If someone sends you mail from Outlook, you don't need to use Outlook to read it. You can use something else -- something that may look and behave entirely differently, in a manner more to your liking.

The comparison: If there is a discussion on Less Wrong, you do need to use the Less Wrong UI to read it. The same goes for, say, Facebook.

I object to this.

Standards as Schelling Points

One could argue that the lack of choice is for lack of interest. Less Wrong, and Reddit on which it is based, has an API. One could write a native client. Reddit does have them.

Let's take a tangent and talk about Reddit. Seems like they might have done something right. They have (I think?) the largest contiguous discussion community on the net today. And they have a published API for talking to it. It's even in use.

The problem with this method is that Reddit's API applies only to Reddit. I say problem, singular, but it's really problem, plural, because it hits users and developers in different ways.

On the user end, it means you can't have a unified user interface across different web forums; other forum servers have entirely different APIs, or none at all.² It also makes life difficult when you want to move from one forum to another.

On the developer end, something very ugly happens when a content provider defines its own provision mechanism. Yes, you can write a competing client. But your client exists only at the provider's sufferance, subject to their decision not to make incompatible API changes or just pull the plug on you and your users outright. That isn't paranoia; in at least one case, it actually happened. Using an agreed-upon standard limits this sort of misbehavior, although it can still happen in other ways.

NNTP is a standard for discussion, like SMTP is for email. It is defined in RFC 3977 and its data format is defined in RFC 5536. The point of a standard is to ensure lasting interoperability; because it is a standard, it serves as a deliberately-constructed Schelling point, a place where unrelated developers can converge without further coordination.

Expertise is a Bottleneck

If you're trying to build a high-quality community, you want a closed system. Well kept gardens die by pacifism, and it's impossible to fully moderate an open system. But if you're building a communication infrastructure, you want an open system.

In the early Usenet days, this was exactly what existed; NNTP was standardized and open, but Usenet was a de-facto closed community, accessible mostly to academics. Then AOL hooked its customers into the system. The closed community became open, and the Eternal September began.³ I suspect, but can't prove, that this was a partial cause of the flight of discussion from Usenet to closed web forums.

I don't think that was the appropriate response. I think the appropriate response was private NNTP networks or even single servers, not connected to Usenet at large.

Modern web forums throw the open-infrastructure baby out with the open-community bathwater. The result, in our specific case, is that if we want something not provided by the default Less Wrong interface, it must be implemented by Less Wrongers.

I don't think UI implementation is our comparative advantage. In fact I know it isn't, or the Less Wrong UI wouldn't suck so hard. We're pretty big by web-forum standards, but we still contain only a tiny fraction of the Internet's technical expertise.

The situation is even worse among the diaspora; for example, at SSC, if Scott's readers want something new out of the interface, it must be implemented either by Scott himself or his agents. That doesn't scale.

One of the major benefits of a standardized, open infrastructure is that your developer base is no longer limited to a single community. Any software written by any member of any community backed by the same communication standard is yours for the using. Additionally, the developers are competing for the attention of readers, not admins; you can expect the reader-facing feature set to improve accordingly. If readers want different UI functionality, the community admins don't need to be involved at all.

A Real Web Client

When I wrote the intro to this sequence, the most common thing people insisted on was this: Any system that actually gets used must allow links from the web, and those links must reach a web page.

I completely, if grudgingly, agree. No matter how insightful a post is, if people can't link to it, it will not spread. No matter how interesting a post is, if Google doesn't index it, it doesn't exist.

One way to achieve a common interface to an otherwise-nonstandard forum is to write a gateway program, something that answers NNTP requests and does magic to translate them to whatever the forum understands. This can work and is better than nothing, but I don't like it -- I'll explain why in another post.

Assuming I can suppress my gag reflex for the next few moments, allow me to propose: a web client.

(No, I don't mean write a new browser. The Browser Is Not Your Client.⁴)

Real NNTP clients use the OS's widget set to build their UI and talk to the discussion board using NNTP. There is no fundamental reason the same cannot be done using the browser's widget set. Google did it. Before them, Deja News did it. Both of them suck, but they suck on the UI level. They are still proof that the concept can work.

I imagine an NNTP-backed site where casual visitors never need to know that's what they're dealing with. They see something very similar to a web forum or a blog, but whatever software today talks to a database on the back end, instead talks to NNTP, which is the canonical source of posts and post metadata. For example, it gets the results of a link to http://lesswrong.com/posts/message_id.html by sending ARTICLE message_id to its upstream NNTP server (which may be hosted on the same system), just as a native client would.

To the drive-by reader, nothing has changed. Except, maybe, one thing. When a regular reader, someone who's been around long enough to care about such things, says "Hey, I want feature X," and our hypothetical web client doesn't have it, I can now answer:

Someone wrote something that does that twenty years ago.

Here is how to get it.

The Main / Discussion division has served us well in the past, but traffic to Main has dropped to the point that it's no longer useful. In particular, the low visibility meant that authors would often have to choose between more karma and being seen by more readers. So posting to Main has been disabled, and the successor of Main is on its way. In the meantime, please move everything to discussion.

But I have a great post I've worked really hard on, and I want it to be in Main.

Save it as a draft, let me know, and I'll move it to Main for you.

There's an excellent post that should go on the RSS feed so lots of people read it.

We can still promote posts (and will).  

Okay, so Main is dead. What's next?

What's the point of having multiple subreddits? If you have a single website with several different communities, then having different subreddits allows for different rules, different moderators, and different focuses. But LW has many interests that don't seem to cleanly separate into multiple subreddits. Many distinctions overlap, and tags seem better. So there are two main paths forward:

1) Tagging, 'new to you', and customization based on tags.

1. A tagging system with user input (see Stack Overflow for inspiration) means we can have reliable filtering.
2. We already track when a user last visited a page in order to highlight new comments; we can also use that to remove it from the new posts view if it's already been read. (What about if there's a comment explosion? We can either return it if there are enough new comments, or trust that you'll see the comment explosion through the Recent Comments view.)
3. With everything going to one view, giving users control over that view is critical for keeping it clear of trash. What looks to me like a promising way to do that is subsidies and taxes based on tags; if you want to see parenting posts and don't want to see meetup posts, say, you might give the parenting tag +3 karma and the meetup tag -10 karma, so very popular meetup posts can still appear and even unpopular or new parenting posts will be visible to you.

Summary: My son was stillborn and I don't know why. My wife and I would like to have another child, but would very much not like to try if the probability of this occurring again is above a certain threshold (of which we have already settled on one). All 3 doctors I have consulted were unable to give a definitive cause of death, nor were any willing to give a numerical estimate of the probability (whether for reasons of legal risk, or something else) that our next baby will be stillborn. I am likely too mind-killed to properly evaluate my situation and would very much appreciate an independent (from mine) probability estimate of what caused my son to die, and given that cause, what is the recurrence risk?

Background: V (L and my only biologically related living son) had no complications during birth, nor has he showed any signs of poor health whatsoever. L has a cousin who has had two miscarriages, and I have an aunt who had several stillbirths followed by 3 live births of healthy children. We know of no other family members that have had similar misfortunes.

J (my deceased son) was the product of a 31 week gestation. L (my wife and J's mother) is 28 years old, gravida 2, para 1. L presented to the physicians office for routine prenatal care and noted that she had not felt any fetal movement for the last five to six days. No fetal heart tones were identified. It was determined that there was an intrauterine fetal demise. L was admitted on 11/05/2015 for induction and was delivered of a nonviable, normal appearing, male fetus at approximately 1:30 on 11/06/2015.

Pro-Con Reasoning: According to a leading obstetrics textbook¹, causes of stillbirth are commonly classified into 8 categories: obstetrical complications, placental abnormalities, fetal malformations, infection, umbilical cord abnormalities, hypertensive disorders, medical complications, and undetermined. Below, I'll list the percentage of stillbirths in each category (which may be used as prior probabilities) along with some reasons for or against.

Obstetrical complications (29%)

• Against: No abruption detected. No multifetal gestation. No ruptured preterm membranes at 20-24 weeks.

Placental abnormalities (24%)

• For: Excessive fibrin deposition (as concluded in the surgical pathology report). Early acute chorioamnionitis (as conclused in the surgical pathology report, but Dr. M claimed this was caused by the baby's death, not conversely). L has gene variants associated with deep vein thrombosis (AG on rs2227589 per 23andme raw data).
• Against: No factor V Leiden mutation (GG on rs6025 per 23andme raw data and confirmed via independent lab test). No prothrombin gene mutation (GG on l3002432 per 23andme raw data and confirmed via independent lab test). L was negative for prothrombin G20210A mutation (as determined by lab test). Anti-thrombin III activity results were within normal reference ranges (as determined by lab test). Protein C activity results were withing normal reference ranges (as determined by lab test). Protein S activity results were within normal reference ranges (as determined by lab test). Protein S antigen (free and total) results were within normal references ranges (as determined by lab test).

Infection (13%)

• For: L visited a nurse's home during the last week of August that works in a hospital we now know had frequent cases of CMV infection. CMV antibody IgH, CMV IgG, and Parvovirus B-19 Antibody IgG values were outside of normal reference ranges.
• Against: Dr. M discounted the viral test results as the cause of death, since the levels suggested the infection had occurred years ago, and therefore could not have caused J's death. Dr. F confirmed Dr. M's assessment.

Fetal malformations (14%)

• Against: No major structural abnormalities. No genetic abnormalities detected (CombiSNP Array for Pregnancy Loss results showed a normal male micro array profile).

Umbilical cord abnormalities (10%)

• Against: No prolapse. No stricture. No thrombosis.

Hypertensive disorder (9%)

• Against: No preeclampsia. No chronic hypertension.

Medical complications (8%)

• For: L experienced 2 nights of very painful abdominal pains that could have been contractions on 10/28 and 10/29. L remembers waking up on her back a few nights between 10/20 and 11/05 (it is unclear if this belongs in this category or somewhere else).
• Against: No antiphospholipid antibody syndrome detected (determined via Beta-2 Glycoprotein I Antibodies [IgG, IgA, IgM] test). No maternal diabetes detected (determined via glucose test on 10/20).

Undetermined (24%)

What is the most likely cause of death? How likely is that cause? Given that cause, if we choose to have another child, then how likely is it to survive its birth? Are there any other ways I could reduce uncertainty (additional tests, etc...) that I haven't listed here? Are there any other forums where these questions are more likely to get good answers? Why won't doctors give probabilities? Help with any of these questions would be greatly appreciated. Thank you.

If your advice to me is to consult another expert (in addition to the 2 obstetricians and 1 high-risk obstetrician I already have consulted), please also provide concrete tactics as to how to find such an expert and validate their expertise.

Contact Information: If you would like to contact me, but don't want to create an account here, you can do so at deprimita.patro@gmail.com.

^{[1] Cunningham, F. (2014). Williams obstetrics. New York: McGraw-Hill Medical.}

EDIT 1: Updated to make clear that both V and J are mine and L's biological sons.

EDIT 2: Updated to add information on family history.

EDIT 3: On PipFoweraker's advice, I added contact info.

EDIT 4: I've cross-posted this on Health Stack Exchange.

EDIT 5: I've emailed the list of authors of the most recent meta-analysis concerning causes of stillbirth. Don't expect much.

“In 2015, there were roughly 214 million malaria cases and an estimated 438 000 malaria deaths.”  While we don’t know how many humans malaria has killed, an estimate of half of everyone who has ever died isn’t absurd.  Because few people in rich countries get malaria, pharmaceutical companies put relatively few resources into combating it.   

The best way to eliminate malaria is probably to use gene drives to completely eradicate the species of mosquitoes that bite humans, but until recently rich countries haven’t been motivated to such xenocide.  The Zika virus, which is in mosquitoes in the United States, provides effective altruists with an opportunity to advocate for exterminating all species of mosquitoes that spread disease to humans because the horrifying and disgusting pictures of babies with Zika might make the American public receptive to our arguments.  A leading short-term goal of effective altruists, I propose, should be advocating for mosquito eradication in the short window before rich people get acclimated to pictures of Zika babies.   

Personally, I have (unsuccessfully) pitched articles on mosquito eradication to two magazines and (with a bit more success) emailed someone who knows someone who knows someone in the Trump campaign to attempt to get the candidate to come out in favor of mosquito eradication.  What have you done?   Given the enormous harm mosquitoes inflict on mankind, doing just a little (such as writing a blog post) could have a high expected payoff.

Motte and bailey is a technique by which one protects an interesting but hard-to-defend view by making it similar to a less interesting but more defensible position. Whenever the more interesting position - the bailey - is attacked - one retreats to the more defensible one - the motte -, but when the attackers are gone, one expands again to the bailey. 

In that case, one and the same person switches between two interpretations of the original claim. Here, I rather want to focus on situations where different people make different interpretations of the original claim. The originator of the claim adds a number of caveats and hedges to their claim, which makes it more defensible, but less striking and sometimes also less interesting.* When others refer to the same claim, the caveats and hedges gradually disappear, however, making it more and more motte-like.

A salient example of this is that scientific claims (particularly in messy fields like psychology and economics) often come with a number of caveats and hedges, which tend to get lost when re-told. This is especially so when media writes about these claims, but even other scientists often fail to properly transmit all the hedges and caveats that come with them.

Since this happens over and over again, people probably do expect their hedges to drift to some extent. Indeed, it would not surprise me if some people actually want hedge drift to occur. Such a strategy effectively amounts to a more effective, because less observable, version of the motte-and-bailey-strategy. Rather than switching back and forth between the motte and the bailey - something which is at least moderately observable, and also usually relies on some amount of vagueness, which is undesirable - you let others spread the bailey version of your claim, whilst you sit safe in the motte. This way, you get what you want - the spread of the bailey version - in a much safer way.

Even when people don't use this strategy intentionally, you could argue that they should expect hedge drift, and that omitting to take action against it is, if not ouright intellectually dishonest, then at least approaching that. This argument would rest on the consequentialist notion that if you have strong reasons to believe that some negative event will occur, and you could prevent it from happening by fairly simple means, then you have an obligation to do so. I certainly do think that scientists should do more to prevent their views from being garbled via hedge drift. 

Another way of expressing all this is by saying that when including hedging or caveats, scientists often seem to seek plausible deniability ("I included these hedges; it's not my fault if they were misinterpreted"). They don't actually try to prevent their claims from being misunderstood. 

What concrete steps could one then take to prevent hedge-drift? Here are some suggestions. I am sure there are many more.

1. Many authors use eye-catching, hedge-free titles and/or abstracts, and then only include hedges in the paper itself. This is a recipe for hedge-drift and should be avoided.
2. Make abundantly clear, preferably in the abstract, just how dependent the conclusions are on keys and assumptions. Say this not in a way that enables you to claim plausible deniability in case someone misinterprets you, but in a way that actually reduces the risk of hedge-drift as much as possible. 
3. Explicitly caution against hedge drift, using that term or a similar one, in the abstract of the paper.

* Edited 2/5 2016. By hedges and caveats I mean terms like "somewhat" ("x reduces y somewhat"), "slightly", etc, as well as modelling assumptions without which the conclusions don't follow and qualifications regarding domains in which the thesis don't hold.

I've gotten sufficient evidence from support that voiceofra has been doing retributive downvoting. I've banned them without prior notice because I'm not giving them more chances to downvote.

I'm thinking of something like not letting anyone give more than 5 downvotes/week for content which is more than a month old. The numbers and the time period are tentative-- this isn't my ideal rule. This is probably technically possible. However, my impression is that highly specific rules like that are an invitation to gaming the rules.

I would rather just make spiteful down-voting impossible (or maybe make it expensive) rather than trying to find out who's doing it. Admittedly, putting up barriers to downvoting for past comments doesn't solve the problem of people who down-vote everything, but at least people who downvote current material are easier to notice.

Any thoughts about technical solutions to excessive down-voting of past material?

Note: We now have a website with up to date information here: http://signaldatascience.com/.

(This post is coauthored with Robert Cordwell.)

We’re writing to announce the inaugural run of Signal Data Science’s intensive training program.

The program will train students in the core skills needed to work as a professional data scientist:

• Scraping and cleaning data
• Exploring and analyzing data using statistics
• Presenting findings
• Interviewing

By the end of the course, you’ll will be able to start with raw data and produce analyses like the one in Bayesian Adjustment of Yelp Ratings. More to the point, you’ll understand why Jonah structured the analysis the way he did and be able to do the same yourself.

You’ll also be able to produce cool visualizations like this automatic grouping of Slate Star Codex posts by topic, as shown below.

Why data science?

Making inferences from data is fundamental to understanding the world, and there’s a growing unmet need in industry for people with the relevant skills. With good instruction and peer group, smart, motivated people can quickly develop enough proficiency to get jobs in the tech sector (starting compensation ~$115k in the San Francisco Bay Area).

Why us?

The Program

We offer inquiry-based learning (no boring lecturers or unmotivating problem sets!) and an unusually intellectually curious peer group. Far from what’s typical of college classes, our model has more in common with the Math Olympiad Summer Program, where daily lectures are interspersed with on-the-spot problems and followed by long-form problems designed to build on the lesson.

Robert Cordwell is an IMO gold medalist and educational startup veteran who’s working a Facebook data science job despite his limited, self-taught experience. He’s going to be teaching math problem solving, overall presentation skills, and how to break interviews.

Jonah Sinick is a data scientist with 13 years of experience making advanced math accessible to beginners, a PhD in math from University of Illinois, and an extensive body of published work. He’ll be teaching a comprehensive technical curriculum.

Who is this for?

If you:

• Are interested in data science
• Passionate about learning new things
• Would benefit from a social environment with others working toward the same goal
• Have the programming skills to solve simple algorithms problems
• Plan on applying for data science jobs after the program

our program will be a good fit for you.

Where / When

The first cohort will run in Berkeley for 6 weeks, from Feburary 1st – March 18th. This will be a compressed version of the standard course that we’ll be offering in the future, and is targeted at students who have a high degree of comfort with math.

In the future we’ll be offering longer courses that cover the mathematical / statistical material at a gentler pace.

Cost

For students in our first 6 week cohort, we offer two options:

• Payment of $8,000 at the start of the program.
• A “pay later” model where students pay 8% of their first year’s salary (pretax, spaced over 6 months), contingent on getting a data science job.

This is roughly 50% of the standard price for coding /data science bootcamps.

Next steps

If you’re interested in exploring participating in our first cohort, or keeping posted, please be in touch with us at signaldatascience@gmail.com.

Demis Hassabis gives a great presentation on the state of Deepmind's work as of April 20, 2016. Skip to 23:12 for the statement of the goal of creating a rat-level AI -- "An AI that can do everything a rat can do," in his words. From his tone, it sounds like this is more a short-term, not a long-term goal.

I don't think Hassabis is prone to making unrealistic plans or stating overly bold predictions. I strongly encourage you to scan through Deepmind's publication list to get a sense of how quickly they're making progress. (In fact, I encourage you to bookmark that page, because it seems like they add a new paper about twice a month.) The outfit seems to be systematically knocking down all the "Holy Grail" milestones on the way to GAI, and this is just Deepmind. The papers they've put out in just the last year or so concern successful one-shot learning, continuous control, actor-critic architectures, novel memory architectures, policy learning, and bootstrapped gradient learning, and these are just the most stand-out achievements. There's even a paper co-authored by Stuart Armstrong concerning Friendliness concepts on that list.

If we really do have a genuinely rat-level AI within the next couple of years, I think that would justify radically moving forward expectations of AI development timetables. Speaking very naively, if we can go from "sub-nematode" to "mammal that can solve puzzles" in that timeframe, I would view it as a form of proof that "general" intelligence does not require some mysterious ingredient that we haven't discovered yet.

A lot of my work involves tweaking the utility or probability of an agent to make it believe - or act as if it believed - impossible or almost impossible events. But we have to be careful about this; an agent that believes the impossible may not be so different from one that doesn't.

Consider for instance an agent that assigns a prior probability of zero to JFK ever having been assassinated. No matter what evidence you present to it, it will go on disbelieving the "non-zero gunmen theory".

Initially, the agent will behave very unusually. If it was in charge of JFK's security in Dallas before the shooting, it would have sent all secret service agents home, because no assassination could happen. Immediately after the assassination, it would have disbelieved everything. The films would have been faked or misinterpreted; the witnesses, deluded; the dead body of the president, that of twin or an actor. It would have had huge problems with the aftermath, trying to reject all the evidence of death, seeing a vast conspiracy to hide the truth of JFK's non-death, including the many other conspiracy theories that must be false flags, because they all agree with the wrong statement that the president was actually assassinated.

But as time went on, the agent's behaviour would start to become more and more normal. It would realise the conspiracy was incredibly thorough in its faking of the evidence. All avenues it pursued to expose them would come to naught. It would stop expecting people to come forward and confess the joke, it would stop expecting to find radical new evidence overturning the accepted narrative. After a while, it would start to expect the next new piece of evidence to be in favour of the assassination idea - because if a conspiracy has been faking things this well so far, then they should continue to do so in the future. Though it cannot change its view of the assassination, its expectation for observations converge towards the norm.

If it does a really thorough investigation, it might stop believing in a conspiracy at all. At some point, the probability of a miracle will start to become more likely than a perfect but undetectable conspiracy. It is very unlikely that Lee Harvey Oswald shot at JFK, missed, and the president's head exploded simultaneously for unrelated natural causes. But after a while, such a miraculous explanation will start to become more likely than anything else the agent can consider. This explanation opens the possibility of miracles; but again, if the agent is very thorough, it will fail to find evidence of other miracles, and will probably settle on "an unrepeatable miracle caused JFK's death in a way that is physically undetectable".

But then note that such an agent will have a probability distribution over future events that is almost indistinguishable from a normal agent that just believes the standard story of JFK being assassinated. The zero-prior has been negated, not in theory but in practice.

How to do proper probability manipulation

This section is still somewhat a work in progress.

So the agent believes one false fact about the world, but its expectation is otherwise normal. This can be both desirable and undesirable. The negative is if we try and control the agent forever by giving it a false fact.

To see the positive, ask why would we want an agent to believe impossible things in the first place? Well, one example was an Oracle design where the Oracle didn't believe its output message would ever be read. Here we wanted the Oracle to believe the message wouldn't be read, but not believe anything else too weird about the world.

In terms of causality, if X designates the message being read at time t, and B and A are event before and after t, respectively, we want P(B|X)≈P(B) (probabilities about current facts in the world shouldn't change much) while P(A|X)≠P(A) is fine and often expected (the future should be different if the message is read or not).

In the JFK example, the agent eventually concluded "a miracle happened". I'll call this miracle a scrambling point. It's kind of a breakdown in causality: two futures are merged into one, given two different pasts. The two pasts are "JFK was assassinated" and "JFK wasn't assassinated", and their common scrambled future is "everything appears as if JFK was assassinated". The non-assassination belief has shifted the past but not the future.

For the Oracle, we want to do the reverse: we want the non-reading belief to shift the future but not the past. However, unlike the JFK assassination, we can try and build the scrambling point. That's why I always talk about messages going down noisy wires, or specific quantum events, or chaotic processes. If the past goes through a truly stochastic event (it doesn't matter whether there is true randomness or just that the agent can't figure out the consequences), we can get what we want.

The Oracle idea will go wrong if the Oracle conclude that non-reading must imply something is different about the past (maybe it can see through chaos in ways we thought it couldn't), just as the JFK assassination denier will continue to be crazy if can't find a route to reach "everything appears as if JFK was assassinated".

But there is a break in the symmetry: the JFK assassination denier will eventually reach that point as long as the world is complex and stochastic enough. While the Oracle requires that the future probabilities be the same in all (realistic) past universes.

Now, once the Oracle's message has been read, the Oracle will find itself in the same situation as the other agent: believing an impossible thing. For Oracles, we can simply reset them. Other agents might have to behave more like the JFK assassination disbeliever. Though if we're careful, we can quantify things more precisely, as I attempted to do here.

Related to: The Correct Contrarian Cluster, The General Factor of Correctness

(Content note: Explicitly about spreading rationalist memes, increasing the size of the rationalist movement, and proselytizing. I also regularly use the word 'we' to refer to the rationalist community/subculture. You might prefer not to read this if you don't like that sort of thing and/or you don't think I'm qualified to write about that sort of thing and/or you're not interested in providing constructive criticism.)

I've tried to introduce a number of people to this culture and the ideas within it, but it takes some finesse to get a random individual from the world population to keep thinking about these things and apply them. My personal efforts have been very hit-or-miss. Others have told me that they've been more successful. But I think there are many people that share my experience. This is unfortunate: we want people to be more rational and we want more rational people.

At any rate, this is not about the art of raising the sanity waterline, but the more general task of spreading rationalist memes. Some people naturally arrive at these ideas, but they usually have to find them through other people first. This is really about all of the people in the world who are like you probably were before you found this culture; the people who would care about it, and invest in it, as it is right now, if only they knew it existed.

I'm going to be vague for the sake of anonymity, but here it goes:

I was reading a book review on Amazon, and I really liked it. The writer felt like a kindred spirit. I immediately saw that they were capable of coming to non-obvious conclusions, so I kept reading. Then I checked their review history in the hope that I would find other good books and reviews. And it was very strange.

They did a bunch of stuff that very few humans do. They realized that nuclear power has risks but that the benefits heavily outweigh the risks given the appropriate alternative, and they realized that humans overestimate the risks of nuclear power for silly reasons. They noticed when people were getting confused about labels and pointed out the general mistake, as well as pointing out what everyone should really be talking about. They acknowledged individual and average IQ differences and realized the correct policy implications. They really understood evolution, they took evolutionary psychology seriously, and they didn't care if it was labeled as sociobiology. They used the word 'numerate.'

And the reviews ranged over more than a decade of time. These were persistent interests.

I don't know what other people do when they discover that a stranger like this exists, but the first thing that I try to do is talk to them. It's not like I'm going to run into them on the sidewalk.

Amazon had no messaging feature that I could find, so I looked for a website, and I found one. I found even more evidence, and that's certainly what it was. They were interested in altruism, including how it goes wrong; computer science; statistics; psychology; ethics; coordination failures; failures of academic and scientific institutions; educational reform; cryptocurrency, etc. At this point I considered it more likely than not that they already knew everything that I wanted to tell them, and that they already self-identified as a rationalist, or that they had a contrarian reason for not identifying as such.

So I found their email address. I told them that they were a great reviewer, that I was surprised that they had come to so many correct contrarian conclusions, and that, if they didn't already know, there was a whole culture of people like them.

They replied in ten minutes. They were busy, but they liked what I had to say, and as a matter of fact, a friend had already convinced them to buy Rationality: From AI to Zombies. They said they hadn't read much relative to the size of the book because it's so large, but they loved it so far and they wanted to keep reading.

(You might postulate that I found a review by a user like this on a different book because I was recommended this book and both of us were interested in Rationality: From AI to Zombies. However, the first review I read by this user was for a book on unusual gardening methods, that I found in a search for books about gardening methods. For the sake of anonymity, however, my unusual gardening methods must remain a secret. It is reasonable to postulate that there would be some sort of sampling bias like the one that I have described, but given what I know, it is likely that this is not that. You certainly could still postulate a correlation by means of books about unusual gardening methods, however.)

Maybe that extra push made the difference. Maybe if there hadn't been a friend, I would've made the difference.

Who knew that's how my morning would turn out?

As I've said in some of my other posts, but not in so many words, maybe we should start doing this accidentally effective thing deliberately!

I know there's probably controversy about whether or not rationalists should proselytize, but I've been in favor of it for awhile. And if you're like me, then I don't think this is a very special effort to make. I'm sure sometimes you see a little thread, and you think, "Wow, they're a lot like me; they're a lot like us, in fact; I wonder if there are other things too. I wonder if they would care about this."

Don't just move on! That's Bayesian evidence!

I dare you to follow that path to its destination. I dare you to reach out. It doesn't cost much.

And obviously there are ways to make yourself look creepy or weird or crazy. But I said to reach out, not to reach out badly. If you could figure out how to do it right, it could have a large impact. And these people are likely to be pretty reasonable. You should keep a look out in the future.

Speaking of the future, it's worth noting that I ended up reading the first review because of an automated Amazon book recommendation and subsequent curiosity. You know we're in the data. We are out there and there are ways to find us. In a sense, we aren't exactly low-hanging fruit. But in another sense, we are.

I've never read a word of the Methods of Rationality, but I have to shoehorn this in: we need to write the program that sends a Hogwarts acceptance letter to witches and wizards on their eleventh birthday.

This is a response to ingres' recent post sharing Less Wrong survey results. If you haven't read & upvoted it, I strongly encourage you to--they've done a fabulous job of collecting and presenting data about the state of the community.

So, there's a bit of a contradiction in the survey results.  On the one hand, people say the community needs to do more scholarship, be more rigorous, be more practical, be more humble.  On the other hand, not much is getting posted, and it seems like raising the bar will only exacerbate that problem.

I did a query against the survey database to find the complaints of top Less Wrong contributors and figure out how best to serve their needs.  (Note: it's a bit hard to read the comments because some of them should start with "the community needs more" or "the community needs less", but adding that info would have meant constructing a much more complicated query.)  One user wrote:

[it's not so much that there are] overly high standards,  just not a very civil or welcoming climate . why write content for free and get trashed when I can go write a grant application or a manuscript instead?

ingres emphasizes that in order to revitalize the community, we would need more content.  Content is important, but incentives for producing content might be even more important.  Social status may be the incentive humans respond most strongly to.  Right now, from a social status perspective, the expected value of creating a new Less Wrong post doesn't feel very high.  Partially because many LW posts are getting downvotes and critical comments, so my System 1 says my posts might as well.  And partially because the Less Wrong brand is weak enough that I don't expect associating myself with it will boost my social status.

When Less Wrong was founded, the primary failure mode guarded against was Eternal September.  If Eternal September represents a sort of digital populism, Less Wrong was attempting a sort of digital elitism.  My perception is that elitism isn't working because the benefits of joining the elite are too small and the costs are too large.  Teddy Roosevelt talked about the man in the arena--I think Less Wrong experienced the reverse of the evaporative cooling EY feared, where people gradually left the arena as the proportional number of critics in the stands grew ever larger.

Given where Less Wrong is at, however, I suspect the goal of revitalizing Less Wrong represents a lost purpose.

ingres' survey received a total of 3083 responses.  Not only is that about twice the number we got in the last survey in 2014, it's about twice the number we got in 2013, 2012, and 2011 (though much bigger than the first survey in 2009).  It's hard to know for sure, since previous surveys were only advertised on the LessWrong.com domain, but it doesn't seem like the diaspora thing has slowed the growth of the community a ton and it may have dramatically accelerated it.

Why has the community continued growing?  Here's one possibility.  Maybe Less Wrong has been replaced by superior alternatives.

• CFAR - ingres writes: "If LessWrong is serious about it's goal of 'advancing the art of human rationality' then it needs to figure out a way to do real investigation into the subject."  That's exactly what CFAR does.  CFAR is a superior alternative for people who want something like Less Wrong, but more practical.  (They have an alumni mailing list that's higher quality and more active than Less Wrong.)  Yes, CFAR costs money, because doing research costs money!
• Effective Altruism - A superior alternative for people who want something that's more focused on results.
• Facebook, Tumblr, Twitter - People are going to be wasting time on these sites anyway.  They might as well talk about rationality while they do it.  Like all those phpBB boards in the 00s, Less Wrong has been outcompeted by the hot new thing, and I think it's probably better to roll with it than fight it.  I also wouldn't be surprised if interacting with others through social media has been a cause of community growth.
• SlateStarCodex - SSC already checks most of the boxes under ingres' "Future Improvement Wishlist Based On Survey Results".  In my opinion, the average SSC post has better scholarship, rigor, and humility than the average LW post, and the community seems less intimidating, less argumentative, more accessible, and more accepting of outside viewpoints.
• The meatspace community - Meeting in person has lots of advantages.  Real-time discussion using Slack/IRC also has advantages.

Less Wrong had a great run, and the superior alternatives wouldn't exist in their current form without it.  (LW was easily the most common way people heard about EA in 2014, for instance, although sampling effects may have distorted that estimate.)  But that doesn't mean it's the best option going forward.

Therefore, here are some things I don't think we should do:

• Try to be a second-rate version of any of the superior alternatives I mentioned above.  If someone's going to put something together, it should fulfill a real community need or be the best alternative available for whatever purpose it serves.
• Try to get old contributors to return to Less Wrong for the sake of getting them to return.  If they've judged that other activities are a better use of time, we should probably trust their judgement.  It might be sensible to make an exception for old posters that never transferred to the in-person community, but they'd be harder to track down.
• Try to solve the same sort of problems Arbital or Metaculus is optimizing for.  No reason to step on the toes of other projects in the community.

But that doesn't mean there's nothing to be done.  Here are some possible weaknesses I see with our current setup:

• If you've got a great idea for a blog post, and you don't already have an online presence, it's a bit hard to reach lots of people, if that's what you want to do.
• If we had a good system for incentivizing people to write great stuff (as opposed to merely tolerating great stuff the way LW culture historically has), we'd get more great stuff written.
• It can be hard to find good content in the diaspora.  Possible solution: Weekly "diaspora roundup" posts to Less Wrong.  I'm too busy to do this, but anyone else is more than welcome to (assuming both people reading LW and people in the diaspora want it).

ingres mentions the possibility of Scott Alexander somehow opening up SlateStarCodex to other contributors.  This seems like a clearly superior alternative to revitalizing Less Wrong, if Scott is down for it:

• As I mentioned, SSC already seems to have solved most of the culture & philosophy problems that people complained about with Less Wrong.
• SSC has no shortage of content--Scott has increased the rate at which he creates open threads to deal with an excess of comments.
• SSC has a stronger brand than Less Wrong.  It's been linked to by Ezra Klein, Ross Douthat, Bryan Caplan, etc.

But the most important reasons may be behavioral reasons.  SSC has more traffic--people are in the habit of visiting there, not here.  And the posting habits people have acquired there seem more conducive to community.  Changing habits is hard.

As ingres writes, revitalizing Less Wrong is probably about as difficult as creating a new site from scratch, and I think creating a new site from scratch for Scott is a superior alternative for the reasons I gave.

So if there's anyone who's interested in improving Less Wrong, here's my humble recommendation: Go tell Scott Alexander you'll build an online forum to his specification, with SSC community feedback, to provide a better solution for his overflowing open threads.  Once you've solved that problem, keep making improvements and subfora so your forum becomes the best available alternative for more and more use cases.

And here's my humble suggestion for what an SSC forum could look like:

As I mentioned above, Eternal September is analogous to a sort of digital populism.  The major social media sites often have a "mob rule" culture to them, and people are increasingly seeing the disadvantages of this model.  Less Wrong tried to achieve digital elitism and it didn't work well in the long run, but that doesn't mean it's impossible.  Edge.org has found a model for digital elitism that works.  There may be other workable models out there.  A workable model could even turn in to a successful company.  Fight the hot new thing by becoming the hot new thing.

My proposal is based on the idea of eigendemocracy.  (Recommended that you read the link before continuing--eigendemocracy is cool.)  In eigendemocracy, your trust score is a composite rating of what trusted people think of you.  (It sounds like infinite recursion, but it can be resolved using linear algebra.)

Eigendemocracy is a complicated idea, but a simple way to get most of the way there would be to have a forum where having lots of karma gives you the ability to upvote multiple times.  How would this work?  Let's say Scott starts with 5 karma and everyone else starts with 0 karma.  Each point of karma gives you the ability to upvote once a day.  Let's say it takes 5 upvotes for a post to get featured on the sidebar of Scott's blog.  If Scott wants to feature a post on the sidebar of his blog, he upvotes it 5 times, netting the person who wrote it 1 karma.  As Scott features more and more posts, he gains a moderation team full of people who wrote posts that were good enough to feature.  As they feature posts in turn, they generate more co-moderators.

Why do I like this solution?

• It acts as a cultural preservation mechanism.  On reddit and Twitter, sheer numbers rule when determining what gets visibility.  The reddit-like voting mechanisms of Less Wrong meant that the site deliberately kept a somewhat low profile in order to avoid getting overrun.  Even if SSC experienced a large influx of new users, those users would only gain power to affect the visibility of content if they proved themselves by making quality contributions first.
• It takes the moderation burden off of Scott and distributes it across trusted community members.  As the community grows, the mod team grows with it.
• The incentives seem well-aligned.  Writing stuff Scott likes or meta-likes gets you recognition, mod powers, and the ability to control the discussion--forms of social status.  Contrast with social media sites where hyperbole is a shortcut to attention, followers, upvotes.  Also, unlike Less Wrong, there'd be no punishment for writing a low quality post--it simply doesn't get featured and is one more click away from the SSC homepage.

TL;DR - Despite appearances, the Less Wrong community is actually doing great.  Any successor to Less Wrong should try to offer compelling advantages over options that are already available.

2016 LessWrong Diaspora Survey Analysis

Overview

• Results and Dataset
• Meta
• Demographics (You are here)
• LessWrong Usage and Experience
• LessWrong Criticism and Successorship
• Diaspora Community Analysis
• What it all means for LW 2.0
• Mental Health Section
• Basilisk Section/Analysis
• Blogs and Media analysis
• Politics
• Calibration Question And Probability Question Analysis
• Charity And Effective Altruism Analysis

Survey Meta

Introduction

Hello everybody, this is part one in a series of posts analyzing the 2016 LessWrong Diaspora Survey. The survey ran from March 24th to May 1st and had 3083 respondents.

Almost two thousand eight hundred and fifty hours were spent surveying this year and you've all waited nearly two months from the first survey response to the results writeup. While the results have been available for over a week, they haven't seen widespread dissemination in large part because they lacked a succinct summary of their contents.

When we started the survey in march I posted this graph showing the dropoff in question responses over time:

So it seems only reasonable to post the same graph with this years survey data:

(I should note that this analysis counts certain things as questions that the other chart does not, so it says there are many more questions than the previous survey when in reality where are about as many as last year.)

2016 Diaspora Survey Stats

Survey hours spent in total: 2849.818888888889

Average number of minutes spent on survey: 102.14404619673437

Median number of minutes spent on survey: 39.775

Mode minutes spent on survey: 20.266666666666666

The takeaway here seems to be that some people take a long time with the survey, raising the average. However, most people's survey time is somewhere below the forty five minute mark. LessWrong does a very long survey, and I wanted to make sure that investment was rewarded with a deep detailed analysis. Weighing in at over four thousand lines of python code, I hope the analysis I've put together is worth the wait.

Credits

I'd like to thank people who contributed to the analysis effort:

Bartosz Wroblewski

Kuudes on #lesswrong

Obormot on #lesswrong

Two anonymous contributors

And anybody else who I may have forgotten. Thanks again to Scott Alexander, who wrote the majority of the survey and ran it in 2014, and who has also been generous enough to license his part of the survey under a creative commons license along with mine.

Demographics

Age

The 2014 survey gave these numbers for age:

Age: 27.67 + 8.679 (22, 26, 31) [1490]

In 2016 the numbers were:

Mean: 28.108772669759592
Median: 26.0
Mode: 23.0

Most LWers are in their early to mid twenties, with some older LWers bringing up the average. The average is close enough to the former figure that we can probably say the LW demographic is in their 20's or 30's as a general rule.

Sex and Gender

In 2014 our gender ratio looked like this:

Female: 179, 11.9%
Male: 1311, 87.2%

In 2016 the proportion of women in the community went up by over four percent:

Male: 2021 83.5%
Female: 393 16.2%

One hypothesis on why this happened is that the 2016 survey focused on the diaspora rather than just LW. Diaspora communities plausibly have marginally higher rates of female membership. If I had more time I would write an analysis investigating the demographics of each diaspora community, but to answer this particular question I think a couple of SQL queries are illustrative:

(Note: ActiveMemberships one and two are 'LessWrong' and 'LessWrong Meetups' respectively.)
sqlite> select count(birthsex) from data where (ActiveMemberships_1 = "Yes" OR ActiveMemberships_2 = "Yes") AND birthsex="Male";
425
sqlite> select count(birthsex) from data where (ActiveMemberships_1 = "Yes" OR ActiveMemberships_2 = "Yes") AND birthsex="Female";
66
>>> 66 / (425 + 66)
0.13441955193482688

Well, maybe. Of course, before we wring our hands too much on this question it pays to remember that assigned sex at birth isn't the whole story. The gender question in 2014 had these results:

F (cisgender): 150, 10.0%
F (transgender MtF): 24, 1.6%
M (cisgender): 1245, 82.8%
M (transgender FtM): 5, 0.3%
Other: 64, 4.3%

In 2016:

F (cisgender): 321 13.3%
F (transgender MtF): 65 2.7%
M (cisgender): 1829 76%
M (transgender FtM): 23 1%
Other: 156 6.48%

Some things to note here. 16.2% of respondents were assigned female at birth but only 13.3% still identify as women. 1% are transmen, but where did the other 1.9% go? Presumably into the 'Other' field. Let's find out.

sqlite> select count(birthsex) from data where birthsex = "Female" AND gender = "Other";
57
sqlite> select count(*) from data;
3083
>>> 57 / 3083
0.018488485241647746

Seems to be the case. In general the proportion of men is down 6.1% from 2014. We also gained 1.1% transwomen and .7% transmen in 2016. Moving away from binary genders, this surveys nonbinary gender count gained in proportion by nearly 2.2%. This means that over one in twenty LWers identified as a nonbinary gender, making it a larger demographic than binary transgender LWers! As exciting as that may sound to some ears the numbers tell one story and the write ins tell quite another.

It pays to keep in mind that nonbinary genders are a common troll option for people who want to write in criticism of the question. A quick look at the write ins accompanying the other option indicates that this is what many people used it for, but by no means all. At 156 responses, that's small enough to be worth doing a quick manual tally.

So depending on your comfort zone as to what constitutes a countable gender, there are 90 to 96 valid 'other' answers in the survey dataset. (Labeled dataset)

>>> 90 / 3083
0.029192345118391177

With some cleanup the number trails behind the binary transgender one by the greater part of a percentage point, but only by. I bet that if you went through and did the same sort of tally on the 2014 survey results you'd find that the proportion of valid nonbinary gender write ins has gone up between then and now.

Some interesting 'esoteric' answers: Attack Helocopter, Blackstar, Elizer, spiderman, Agenderfluid

For the rest of this section I'm going to just focus on differences between the 2016 and 2014 surveys.

2014 Demographics Versus 2016 Demographics

Country

United States: -1.000% 1298 53.700%
United Kingdom: -0.100% 183 7.600%
Canada: +0.100% 144 6.000%
Australia: +0.300% 141 5.800%
Germany: -0.600% 85 3.500%
Russia: +0.700% 57 2.400%
Finland: -0.300% 25 1.000%
New Zealand: -0.200% 26 1.100%
India: -0.100% 24 1.000%
Brazil: -0.300% 16 0.700%
France: +0.400% 34 1.400%
Israel: +0.200% 29 1.200%
Other: 354 14.646%

[Summing these all up to one shows that nearly 1% of change is unaccounted for. My hypothesis is that this 1% went into the other countries not in the list, this can't be easily confirmed because the 2014 analysis does not list the other country percentage.]

Race

Asian (East Asian): -0.600% 80 3.300%
Asian (Indian subcontinent): +0.300% 60 2.500%
Middle Eastern: 0.000% 14 0.600%
Black: -0.300% 12 0.500%
White (non-Hispanic): -0.300% 2059 85.800%
Hispanic: +0.300% 57 2.400%
Other: +1.200% 108 4.500%

Sexual Orientation

Heterosexual: -5.000% 1640 70.400%
Homosexual: +1.300% 103 4.400%
Bisexual: +4.000% 428 18.400%
Other: +3.880% 144 6.180%

[LessWrong got 5.3% more gay, 9.1% if you're more loose with the definition. Before we start any wild speculation, the 2014 question included asexuality as an option and it got 3.9% of the responses, we spun this off into a separate question on the 2016 survey which should explain a significant portion of the change.]

Are you asexual?

Yes: 171 0.074
No: 2129 0.926

[Scott said in 2014 that he'd probably 'vastly undercounted' our asexual readers, a near doubling in our count would seem to support this.]

Relationship Style

Prefer monogomous: -0.900% 1190 50.900%
Prefer polyamorous: +3.100% 426 18.200%
Uncertain/no preference: -2.100% 673 28.800%
Other: +0.426% 45 1.926%

[Polyamorous gained three points, presumably the drop in uncertain people went into that bin.]

Number of Partners

0: -2.300% 1094 46.800%
1: -0.400% 1039 44.400%
2: +1.200% 107 4.600%
3: +0.900% 46 2.000%
4: +0.100% 15 0.600%
5: +0.200% 8 0.300%
Lots and lots: +1.000% 29 1.200%

Relationship Goals

...and seeking more relationship partners: +0.200% 577 24.800%
...and possibly open to more relationship partners: -0.300% 716 30.800%
...and currently not looking for more relationship partners: +1.300% 1034 44.400%

Are you married?

Yes: 443 0.19
No: 1885 0.81

[This question appeared in a different form on the previous survey. Marriage went up by .8% from last year.]

Who do you currently live with most of the time?

Alone: -2.200% 487 20.800%
With parents and/or guardians: +0.100% 476 20.300%
With partner and/or children: +2.100% 687 29.400%
With roommates: -2.000% 619 26.500%

[This would seem to line up with the result that single LWers went down by 2.3%]

How many children do you have?

Sum: 598 or greater
0: +5.400% 2042 87.000%
1: +0.500% 115 4.900%
2: +0.100% 124 5.300%
3: +0.900% 48 2.000%
4: -0.100% 7 0.300%
5: +0.100% 6 0.300%
6: 0.000% 2 0.100%
Lots and lots: 0.000% 3 0.100%

[Interestingly enough, childless LWers went up by 5.4%. This would seem incongruous with the previous results. Not sure how to investigate though.]

Are you planning on having more children?

Yes: -5.400% 720 30.700%
Uncertain: +3.900% 755 32.200%
No: +2.800% 869 37.100%

[This is an interesting result, either nearly 4% of LWers are suddenly less enthusiastic about having kids, or new entrants to the survey are less likely and less sure if they want to. Possibly both.]

Work Status

Student: -5.402% 968 31.398%
Academics: +0.949% 205 6.649%
Self-employed: +4.223% 309 10.023%
Independently wealthy: +0.762% 42 1.362%
Non-profit work: +1.030% 152 4.930%
For-profit work: -1.756% 954 30.944%
Government work: +0.479% 135 4.379%
Homemaker: +1.024% 47 1.524%
Unemployed: +0.495% 228 7.395%

[Most interesting result here is that 5.4% of LWers are no longer students or new survey entrants aren't.]

Profession

Art: +0.800% 51 2.300%
Biology: +0.300% 49 2.200%
Business: -0.800% 72 3.200%
Computers (AI): +0.700% 79 3.500%
Computers (other academic, computer science): -0.100% 156 7.000%
Computers (practical): -1.200% 681 30.500%
Engineering: +0.600% 150 6.700%
Finance / Economics: +0.500% 116 5.200%
Law: -0.300% 50 2.200%
Mathematics: -1.500% 147 6.600%
Medicine: +0.100% 49 2.200%
Neuroscience: +0.100% 28 1.300%
Philosophy: 0.000% 54 2.400%
Physics: -0.200% 91 4.100%
Psychology: 0.000% 48 2.100%
Other: +2.199% 277 12.399%
Other "hard science": -0.500% 26 1.200%
Other "social science": -0.200% 48 2.100%

[The largest profession growth for LWers in 2016 was art, that or this is a consequence of new survey entrants.]

What is your highest education credential earned?

None: -0.700% 96 4.200%
High School: +3.600% 617 26.700%
2 year degree: +0.200% 105 4.500%
Bachelor's: -1.600% 815 35.300%
Master's: -0.500% 415 18.000%
JD/MD/other professional degree: 0.000% 66 2.900%
PhD: -0.700% 145 6.300%
Other: +0.288% 39 1.688%

[Hm, the academic credentials of LWers seems to have gone down some since the last survey. As usual this may also be the result of new survey entrants.]

Footnotes

1. The 2850 hour estimate of survey hours is very naive. It measures the time between starting and turning in the survey, a person didn't necessarily sit there during all that time. For example this could easily be including people who spent multiple days doing other things before finally finishing their survey.

2. The apache helicopter image is licensed under the Open Government License, which requires attribution. That particular edit was done by Wubbles on the LW Slack.

3. The first published draft of this post made a basic stats error calculating the proportion of women in active memberships one and two, dividing the number of women by the number of men rather than the number of women by the number of men and women.

For some time now, "Promoted" has been reserved for articles written by MIRI staff, mostly about MIRI activities.  Which, I suppose, would be reasonable, if this were MIRI's blog.  But it isn't.  MIRI has its own blog.  It seems to me inconvenient both to readers of LessWrong, and to readers of MIRI's blog, to split MIRI's material up between the two.

People visiting lesswrong land on "Promoted", see a bunch of MIRI blogs, mostly written by people who don't read LessWrong themselves much anymore, and get a mistaken impression of what people talk about on LessWrong.  Also, LessWrong looks like a dying site, since often months pass between new posts.

I suggest the default landing page be "New", not "Promoted".

My Business Insider article titled 10 tips from a Silicon Valley bootcamp that aims to make smart, successful people more productive.

There were some concerns here (http://lesswrong.com/lw/2po/selfimprovement_or_shiny_distraction_why_less/) regarding value of LessWrong community from the perspective of instrumental rationality. 

In the discussion on the relevant topic I've seen the story about how community can help  http://lesswrong.com/lw/2p5/humans_are_not_automatically_strategic/2l73 from this perspective.

And I think It's a great thing that local community can help people in various ways to achieve their goals. Also it's not the first time I hear about how this kind of community is helpful as a way of achieving personal goals.

Local LessWrong meetups and communities are great, but they have kind of different focus. And a lot of people live in places where there are no local community or it's not active/regular.

So I propose to form small groups (4-8 people). Initially, groups would meet (using whatever means that are convenient for a particular group), discuss the goals of each participant in a long and in a short term (life/year/month/etc). They would collectively analyze proposed strategies for achieving these goals. Discuss how short term goals align with long term goals. And determine whether the particular tactics for achieving stated goal is optimal. And is there any way to improve on it?

Afterwards, the group would meet weekly to:

Set their short term goals, retrospect on the goals set for previous period. Discuss how successfully they were achieved, what problems people encountered and what alterations to overall strategy follows. And they will also analyze how newly set short-term goals coincide with long-term goals. 

In this way, each member of the group would receive helpful feedback on his goals and on his approach to attaining them. And also he will fill accountable, in a way, for goals, he have stated before the group and this could be an additional boost to productivity.

I also expect that group would be helpful from the perspective of overcoming different kind of fallacies and gaining more accurate beliefs about the world. Because it's easier for people to spot errors in the beliefs/judgment of others. I hope that group's would be able to develop friendly environment and so it would be easier for people to get to know about their errors and change their mind. Truth springs from argument amongst friends.

Group will reflect on it's effectiveness and procedures every month(?) and will incrementally improve itself. Obviously if somebody have some great idea about group proceedings it makes sense to discuss it after usual meeting and implement it right away. But I think regular in-depth retrospective on internal workings is also important.

If there are several groups available - groups will be able to share insights, things group have learned during it's operation. (I'm not sure how much of this kind of insights would be generated, but maybe it would make sense to once in a while publish post that would sum up groups collective insights.)

There are some things that I'm not sure about: 

• I think it would be worth to discuss possibility of shuffling group members (or at least exchanging members in some manner) once in a while to provide fresh insight on goals/problems that people are facing and make the flow of ideas between groups more agile.
• How the groups should be initially formed? Just random assignment or it's reasonable to devise some criteria? (Goals alignment/Diversity/Geography/etc?)

I think initial reglament of the group should be developed by the group, though I guess it's reasonable to discuss some general recommendations.

So what do you think? 

If you interested - fill up this google form:

https://docs.google.com/forms/d/1IsUQTp_6pGyNglBiPOGDuwdGTBOolAKfAfRrQloYN_o/viewform?usp=send_form

Naming things! Naming things is hard. It's been claimed that it's one of the hardest parts of computer science. Now, this might sound surprising, but one of my favoritely named concepts is Kahneman's System 1 and System 2.

I want you to pause for a few seconds and consider what comes to mind when you read just the bolded phrase above.

Got it?

If you're familiar with the concepts of S1 and S2, then you probably have a pretty rich sense of what I'm talking about. Or perhaps you have a partial notion: "I think it was about..." or something. If you've never been exposed to the concept, then you probably have no idea.

Now, Kahneman could have reasonably named these systems lots of other things, like "emotional cognition" and "rational cognition"... or "fast, automatic thinking" and "slow, deliberate thinking". But now imagine that it had been "emotional and rational cognition" that Kahneman had written about, and the effect on the earlier paragraph.

It would be about the same for those who had studied it in depth, but now those who had heard about it briefly (or maybe at one point knew about the concepts) would be reminded of that one particular contrast between S1 and S2 (emotion/reason) and be primed to think that was the main one, forgetting about all of the other parameters that that distinction seeks to describe. Those who had never heard of Kahneman's research might assume that they basically knew what the terms were about, because they already have a sense of what emotion and reason are.

This is related to a concept known as overshadowing, when a verbal description of a scene can cause eyewitnesses to misremember the details of the scene. Words can disrupt lots of other things too, including our ability to think clearly about concepts.

An example of this in action is Ask and Guess Culture model (and later Tell, and Reveal). People who are trying to use the models become hugely distracted by the particular names of the entities in the model, which only have a rough bearing on the nuanced elements of these cultures. Even after thinking about this a ton myself, I still found myself accidentally assuming that questions an Ask Culture thing.

So "System 1" and "System 2" have several advantages:

• they don't immediately and easily seem like you already understand them if you haven't been exposed to that particular source
• they don't overshadow people who do know them into assuming that the names contain the most important features

Another example that I think is decent (though not as clean as S1/S2) is Scott Alexander's use of Red Tribe and Blue Tribe to refer to culture clusters that roughly correspond to right and left political leanings in the USA. (For readers in most other countries: the US has their colors backwards... blue is left wing and red is right wing.) The colors make it reasonably easy to associate and remember, but unless you've read the post (or talked with someone who has) you won't necessarily know the jargon.

Jargon vs in-jokes

All of the examples I've listed above are essentially jargon—terminology that isn't available to the general public. I'm generally in favour of jargon! If you want to precisely and concisely convey a concept that doesn't already have its own word, then you have two options.

"Coining new jargon words (neologisms) is an alternative to formulating unusually precise meanings of commonly-heard words when one needs to convey a specific meaning." — fubarobfusco on a LW thread

Doing the latter is often safe when you're in a technical context. "Energy" is a colloquial term, but it also has a precise technical meaning. Since in technical contexts, people will tend assume that all such terms have technical meanings (or even learn said meanings early on) there is little risk of confusion here. Usually.

I'm going to make a case that it's worth treating nuanced concepts like in-jokes: don't make the meaning feel like it's in the term. Now, I'm not sold that this is a good idea all the time, but it seems to have some merit to it. I'm interested in where it works and where it doesn't; don't take this article to suggest I think it's unilaterally good. Let's jam on where it's good.

Communication is built on shared understanding. Much of this comes from the commons: almost all of words you're reading in this blog posts are not words that you and I had to guarantee we both understood with each other, before I could write the post. Sometimes, blog posts (or books, lectures, etc) will contain definitions, or will try to triangulate a concept with examples. The author hopes that the reader will indeed have a similar handle on the word they're using after reading the definition. (The reader may not, of course. Also they they might think they do. Or be confused.)

When you have the chance to interact with someone in real-time, 1-on-1, you can often gauge their understanding because they'll try to paraphrase the thing, and you can usually tell if the thing that they say is the kind of thing someone who understood would say. This is great, because then you can feel confident that you can use that concept as a building block in explaining further concepts.

One common failure mode of communication is when people assume that they're using the same building blocks as each other, when in fact, they're using importantly different concepts. The is the issue that rationalist taboo is designed to combat: forbid use of a confounding word and force the conversationalists to build the concept up from component parts again.

Another way to reduce the occurrence of this sort of thing is to use jargon and in-jokes, because then the person is going to draw a blank if they don't already have the shared understanding. Since you had to be there, and if you weren't, something key is obviously missing.

I once had a long conversation with someone, and we ended up using a lot of the objects we had with us as props when explaining certain concepts. This had the curious effect that if we wanted to reference our shared understanding of the earlier concept, we could refer to the object and it became really clear that it was our shared understanding we were referencing, not some more general thing. So I could say "the banana thing" to refer to him having explored the notion that evilness is a property of the map, not the territory, by remarking that a banana can't be evil but that we can think it evil.

The important thing here is that it felt like it was easier to point clearly at that topic by saying "the banana thing", because we both knew what that was and didn't need to accidentally overshadow it, by saying "the objects aren't evil thing" which might eventually get turned into a catchphrase that seems to contain meaning but never actually contained the critical insight.

This prompted me to think that it might be valuable to buy a bunch of toys from a thrift store, and to keep them at hand when hanging out with a particular person or small group. When you have a concept to explore, you'd grab an unused toy that seemed to suit it decently well, and then you'd gesture with it while explaining the concept. Then later you could refer to "the pink sparkly ball thing" or simply "this thing" while gesturing at the ball. Possibly, the other person wouldn't remember, or not immediately. But if they did, you could be much more confident that you were on the same page. It's a kind of shared mnemonic handle.

In some ways, this is already a natural part of human communication: I recall years ago talking to a friend and saying "oh, it's like the thing we talked about on my porch last summer" and she immediately knew what I meant. I'm basically proposing to take it further, by using props or by inventing new words.

Unfortunately, terms often end up losing their nuance, for various reasons. Sometimes this happens because the small concept they were trying to point at happens to be surrounded by a vacuum, so it expands. Other times because of shibboleths and people wanting to use in-group words. Or the words are used playfully and poetically, for humor purposes, which then makes it less clear that they once had a precise meaning.

This suggests there might be a kind of terminological inflation thing going on. And to the extent that signalling by using jargon is anti-inductive, that'll dilute things too.

I think if you're trying to think complex thoughts, it's worth developing specialized language, not just with groups of people, but even in 1-on-1 contexts. Of course, pay attention so you don't use terms with people who totally don't know them.

And this, this developing of shared language beyond what's strictly necessary but still worthwhile... this, perhaps, we might call the pink and purple ball thing.

(this article crossposted from malcolmocean.com)