trentmkelly/LessWrong-43k huggingface dataset:
Doomsday Argument with Strong Self-Sampling Assumption
by drnickbone — 2012-01-20T23:50:25.636Z

Hello everyone; I'm new to the forum, and have been advised to post this in the "discussion" section. Hope this is OK.

I've found some references to discussions here on Brandon Carter / John Leslie's "Doomsday Argument" and they seemed well-informed. One thing I've noticed about the argument though (but haven't seen discussed before) is that it can be made much sharper by assuming that we are making random *observations*, rather than just that we are a random *observer*.

For those who know the literature, this is a form of Nick Bostrom's Strong Self-Sampling Assumption as opposed to the (basic) Self-Sampling Assumption. Oddly enough, Bostrom discusses SSSA quite a lot in connection with the Doomsday Argument, but I can't see that he's done quite the analysis below.

So here goes:

In the "random observer" model (the Self-Sampling Assumption with the widest reference class of "all observers"), we discover that we are in a human civilization and there have been ~100 billion observers before us in that civilization. We should then predict (crudely) that there will be about ~100 billion observers coming after us in that civilization; also we should predict that a typical civilization of observers won't have much more than ~100-200 billion observers in total (otherwise we'd be in one of the much bigger ones, rather than in a smaller one). So typical civilizations don't expand beyond their planets of origin, and don't even last very long on their planets of origin.

Further, since there are currently ~150 million human births per year that would imply the end of the human race in ~700 years at current population size and birth-rates. Doom soon-ish but not very soon.

But what about the "random observation" model? One difference here is that a large portion of the ~100 billion humans living before us died very young (high infant mortality rate) so made very few observations. For instance, Carl Haub, who calculated the 100 billion number (see http://www.prb.org/Articles/2002/HowManyPeopleHaveEverLivedonEarth.aspx) reckons that for most of human history, life expectancy at birth has been little more than 10 years. By contrast, recent observers have had a life expectancy of 60+ years, so are making many more observations through their lives than average. This means that *observations* are much more concentrate...
Limits on self-optimisation
by RolfAndreassen — 2012-01-20T21:58:05.271Z

Disclaimer: I am a physicist, and in the field of computer science my scholarship is weak. It may be that what I suggest here is well known, or perhaps just wrong.

Abstract: A Turing machine capable of saying whether two arbitrary Turing machines have the same output for all inputs is equivalent to solving the Halting Problem. To optimise a function it is necessary to prove that the optimised version always has the same output as the unoptimised version, which is impossible in general for Turing machines. However, real computers have finite input spaces.

Context: FOOM, Friendliness, optimisation processes.

Consider a computer program which modifies itself in an attempt to optimise for speed. A modification to some algorithm is *proper* if it results, for all inputs, in the same output; it is an optimisation if it results in a shorter running time on average for typical inputs, and a *strict* optimisation if it results in a shorter running time for all inputs.

A Friendly AI, optimising itself, must ensure that it remains Friendly after the modification; it follows that it can only make proper modifications. (When calculating a CEV it may make improper modifications, since the final answer for "How do we deal with X" may change in the course of extrapolating; but for plain optimisations the answer cannot change.)

For simplicity we may consider that the output of a function can be expressed as a single bit; the extension to many bits is obvious. However, in addition to '0' and '1' we must consider that the response to some input can be "does not terminate". The task is to prove that two functions, which we may consider as Turing machines, have the same output for all inputs.

Now, suppose you have a Turing machine that takes as input two arbitrary Turing machines and their respective tapes, and outputs "1" if the two input machines have the same output, and "0" otherwise. Then, by having one of the inputs be a Turing machine which is known not to terminate - one that executes an infinite loop - you can solve the Halting Problem. Therefore, such a machine cannot exist: You cannot build a Turing machine to prove, for arbitrary input machines, that they have the same output.

It seems to follow that you cannot build a fully general proper-optimisation detector.

However, "arbitrary Turing machines" is a strong claim, in fact stronger than we require. No physically real...
A Word to the Resourceful
by Vaniver — 2012-01-20T14:49:11.714Z

Paul Graham has a new article out. Everything he's written is worth reading if you're at all interested in startups, but this article seemed explicitly connected to rationality, by identifying an area where people who are more likely to update / less likely to rationalize will do better than others.

The obvious questions: can this be tested? Noticed early on, rather than in hindsight? Changed by rationality training?
Crocker's Rules: How far to take it?
by lsparrish — 2012-01-20T14:46:30.292Z

Recently I've been considering declaring Crocker's Rules. The wiki page and source document don't suggest any particular time limit or training period, and also don't provide any empirical results of testing it, positive or negative. It sounds good in theory, but how does it affect people in the real world?

If you operate under the Rules for an extended period, does your social status diminish due to behaving like a pushover when insulted?

Does it usually become unbearable after a particular period of time? Or is there a temporary discomfort that you get over quickly?

Is there a list of signatories who have declared Crocker's Rules on an indefinite or time-limited basis?

Where can I find examples of dialogue that has benefited (or suffered) from this?

It seems like an "obviously cool" idea but the risk to one's reputation is worth taking into consideration. If it is clear that the risk is low, and if the value to be gained is clearly very high, we should probably be doing more to encourage it as an explicit norm.

On the other hand, if it is just one of those ideas that sounds better in theory than it is in practice (because the theory does not correctly model reality), or is just yet another signaling game with a net negative value, that is worth knowing as well.

I haven't seen anyone argue against Crocker's Rules or claim it ruined their life, so my estimation is that the risk is low (although there is a small sample size to start with). Also, I have seen at least one statement from lukeprog implying that it has been instrumental in triggering updates during live conversations he has observed, indicating that the value is high (though its causal role is not firmly established in that example).

Does anyone have further data points to add?
Weekly LW Meetups: Portland, Berkleley, San Diego, Pittsburgh, Houston, Seattle, Madison, Fort Collins, Sydney, Melbourne
by FrankAdamek — 2012-01-20T14:42:35.758Z

There are upcoming irregularly scheduled Less Wrong meetups in:

Portland Meetup?: 14 January 2012 12:00PM
San Diego experimental meetup: 15 January 2012 01:00PM
Pittsburgh Meetup: Big Gaming Fun 2!: 15 January 2012 01:00PM
Houston Meetup - 11/15: 15 January 2012 02:00PM
Seattle, Diseased Thinking and evidence on parenting: 15 January 2012 04:00PM
Monday Madison Meetup: 16 January 2012 06:30PM
Fort Collins Meetup: 18 January 2012 07:00PM
First Sydney 2012 meetup.: 18 January 2012 06:00PM
First Salt Lake City Meetup: 22 January 2012 03:00PM: 22 January 2012 03:00PM
Columbus or Cincinnati Meetup: 22 January 2012 05:00PM
Fort Collins Meetup: 25 January 2012 07:00PM
First Brussels meetup: 11 February 2012 11:00AM

The following meetups take place in cities with regularly scheduled meetups, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:

Monthly San Francisco Bay Area meetup: 14 January 2012 07:49PM
Melbourne social meetup: 20 January 2012 06:30PM

Cities with regularly scheduled meetups: Austin, Berkeley, Cambridge, MA, London, Madison, WI, Marin, CA (uses the Bay Area List), Melbourne, Mountain View, New York, Ottawa, Oxford, San Francisco, Seattle, Toronto, Washington, DC, and West Los Angeles.

If you'd like to talk with other LW-ers face to face, and there is no meetup in your area, consider starting your own meetup; it's easy (more resources here). Check one out, stretch your rationality skills, and have fun!

In addition to the handy sidebar of upcoming meetups, a meetup overview will continue to be posted on the front page every Friday. These will be an attempt to collect information on all the meetups happening in the next weeks. The best way to get your meetup featured is still to use the Add New Meetup feature, but you'll now also have the benefit of having your meetup mentioned in a weekly overview. These overview posts will be moved to the discussion section when the new post goes up.

Please note that for your meetup to appear in the weekly meetups feature, you need to post your meetup before the Friday before your meetup!

If you check Less Wrong irregularly, consider subscribing to one or more city-specific mailing list in order to be notified when an irregular meetup is happening: Atlanta, Chicago, Helsinki, London, Pittsburgh, Southe...
PredictionBook: Feature Request and Bug Report
by Jayson_Virissimo — 2012-01-20T09:20:59.786Z

Introduction

It occurred to me (and ahartell) that it would be convenient if there were a place where we could report bugs and request features for the prediction tracking website PredictionBook. I propose to make this that place. If you post a comment in this thread pointing out a bug or requesting the addition of a new feature, then I will update this article to include that bug or feature.

Disclaimer: This thread isn't a promise that any particular bug will be fixed or feature added.

Feature Request

1. The ability to tag prediction statements to allow sorting by topic.

(a). Prevent prediction statements that are tagged personal from showing up on the main feed or happenstance.

(b). Allow users to form groups of "friends" that can follow prediction statements tagged as personal.

2. A voting system or flag button to discourage the use of PredictionBook for advertising purposes (and other types of spam).

3. A threaded comment system for discussing particular prediction statements (this will be much more important as the size of the community grows).

4. A Brier score visible on the user's page.

(a). Brier scores by topic.

(b). Brier score over time (say, quarterly).

5. The ability to compare calibration between users.

6. The ability to delete comments (perhaps, for a certain time period after the comment is made).

7. A button on the login page to reset the user's password.

8. An option on the user page to sort by judged/unjudged/upcoming.

9. Display a probability for the prediction statement weighed by the users' calibration scores.

Bug Reports

1. The email notification system appears not to be working.
[SEQ RERUN] Feel the Meaning
by MinibearRex — 2012-01-20T06:09:50.195Z

Today's post, Feel the Meaning was originally published on 13 February 2008. A summary (taken from the LW wiki):

You think a word has a meaning, as a property of the word itself; rather than there being a label that your brain associates to a particular concept. When someone shouts, "Yikes! A tiger!", evolution would not favor an organism that thinks, "Hm... I have just heard the syllables 'Tie' and 'Grr' which my fellow tribemembers associate with their internal analogues of my own tiger concept and which aiiieeee CRUNCH CRUNCH GULP." So the brain takes a shortcut, and it seems that the meaning of tigerness is a property of the label itself. People argue about the correct meaning of a label like "sound".

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Disputing Definitions, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
Breaking the chain of akrasia
by lukeprog — 2012-01-20T04:12:06.380Z

I'd like to share my specific motivation for writing Can the Chain Still Hold You?

I agree with Yvain that akrasia is probably a major reason that rationality alone doesn't create superheroes. You might be much better than average at making good decisions based on an accurate model of reality, but that doesn't mean you can follow through with them.

Many people report that their thinking is clearer and better as a result of Less Wrong. But despite our many, many attempts to hack away at the problem of akrasia (more: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), I haven't heard of many LWers conquering akrasia.

But I still have hope that this is possible. In 2006, we finally got a decent psychological theory of procrastination, much better than the old decisional-avoidant-arousal theory. On the timescale of progress in psychology, 2006 is basically yesterday. The first book on how to apply this new theory to daily life was published in late 2010. There is no community of people systematically practicing these techniques and reporting their results.

So it seems to me there is a lot of low-hanging fruit to be scooped up in the field of procrastination research. If we try and test enough things, and especially if our tests our theory-guided, we may be able to learn new things and flip a few causal factors such that the chain of akrasia no longer holds us — at least, not as tightly as before.
[META] ajax.googleapis.com
by Pavitra — 2012-01-20T02:24:24.930Z

Apparently, by not unblocking scripts for "ajax.googleapis.com", I am unable to vote on LW. I generally dislike enabling scripting for domains that are used in many places -- unblocking Google APIs would unblock it everywhere, not just here -- so the result is that I am no longer voting. I suspect that I am not alone in this.

(Apparently I can't post without enabling it either. Looks like I'll have make an exception and do the script-on-script-off dance after all. Whee.)
Quixey Challenge - Fix a bug in 1 minute, win $100. Refer a winner, win $50.
by Liron — 2012-01-19T19:39:58.264Z

Hiring is so hard that we spent a man-month creating a sub-startup to do it. The product is the Quixey Challenge which is running today until 7pm PST (GMT-8).

Benefits of playing:

You can learn something from our craftsmanship of the algorithms (we work hard on them)

The 1-minute challenge is a rush

You can make money

If you do well you can interview at Quixey

Even if you have zero engineering skills, you can get $50 for referring someone who wins.
Zeckhauser's roulette
by cousin_it — 2012-01-19T19:22:50.716Z

Imagine you're playing Russian roulette. Case 1: a six-shooter contains four bullets, and you're asked how much you'll pay to remove one of them. Case 2: a six-shooter contains two bullets, and you're asked how much you'll pay to remove both of them. Steven Landsburg describes an argument by Richard Zeckhauser and Richard Jeffrey saying you should pay the same amount in both cases, provided that you don't have heirs and all your remaining money magically disappears when you die. What do you think?
Q&A with experts on risks from AI #4
by XiXiDu — 2012-01-19T16:29:53.996Z

[Click here to see a list of all interviews]

Professor Michael G. Dyer is an author of over 100 publications, including In-Depth Understanding, MIT Press, 1983. He serves on the editorial board of the journals: Applied Intelligence, Connection Science, Knowledge-Based Systems, International Journal of Expert Systems, and Cognitive Systems Research. His research interests are centered around semantic processing of natural language, through symbolic, connectionist, and evolutionary techniques. [Homepage]

Dr. John Tromp is interested in Board Games and Artificial Intelligence, Algorithms, Complexity, Algorithmic Information Theory, Distributed Computing, Computational biology. His recent research has focused on the Combinatorics of Go, specifically counting the number of legal positions. [Homepage]

Dr. Kevin Korb both developed and taught the following subjects at Monash University: Machine Learning, Bayesian Reasoning, Causal Reasoning, The Computer Industry: historical, social and professional issues, Research Methods, Bayesian Models, Causal Discovery, Epistemology of Computer Simulation, The Art of Causal. [Curriculum vitae] [Bayesian Artificial Intelligence]

Dr. Leo Pape is a postdoc in Jürgen Schmidhuber's group at IDSIA (Dalle Molle Institute for Artificial Intelligence). He is interested in artificial curiosity, chaos, metalearning, music, nonlinearity, order, philosophy of science, predictability, recurrent neural networks, reinforcement learning, robotics, science of metaphysics, sequence learning, transcendental idealism, unifying principles. [Homepage] [Publications]

Professor Peter Gacs is interested in Fault-tolerant cellular automata, algorithmic information theory, computational complexity theory, quantum information theory. [Homepage]

Professor Donald Loveland does focus his research on automated theorem proving, logic programming, knowledge evaluation, expert systems, test-and-treatment problem. [Curriculum vitae]

Eray Ozkural is a computer scientist whose research interests are mainly in parallel computing, data mining, artificial intelligence, information theory, and computer architecture. He has an Msc. and is trying to complete a long overdue PhD in his field. He also has a keen interest in philosophical foundations of artificial intelligence. With regards to AI, his current goal is to complete an AI system based on the Alpha architecture of...
A call for solutions and a tease of mine
by HonoreDB — 2012-01-19T14:40:39.158Z

So here's the problem: Given a well-defined group charter, how should groups make decisions? You have an issue, you've talked it over, and now it's time for the group to take action. Different members have different opinions, because they're not perfect reasoners and because their interests don't reliably align with those of the group. What do you do? Historical solutions include direct democracy, representative democracy, various hierarchies, dictatorships, oligarchies, consensus...But what's the shoes-with-toes solution? How do they do it in Weirdtopia? What is the universally correct method that could be implemented by organizations, corporations, and governments alike?

This Tuesday, I posted an idea. I came up with it about ten, maybe fifteen years ago, decided it was awesome and revolutionary, and spent a few years doing some extremely ineffectual advocacy for it. I've pushed for it in various contexts on and off since then, but I'd basically shelved it until I had a better platform to talk about it. And recently I realized, belatedly, that Less Wrong is probably the perfect audience. You're going to be open to it, and you'll be able to competently critique it. And maybe some of you will use it.

I posted this tease first because the idea is a solution to a problem you may not have thought for five minutes about. I want to give you the opportunity to come up with other solutions, or discuss how to rigorously evaluate ideas. Also, I'm really curious to see whether anybody independently reinvents mine. Over the years, I've had the experience several times of reading someone else's work and thinking they're about to start pitching my idea. But they never do. It all gives me complicated feelings.

Please discuss in the comments. Remember, we're aggregating the opinions, the judgment, of the group members, not their personal preferences. So answers like “iterated runoff voting,” “bargaining theory X,” or “coherent extrapolated volition” are not in themselves what I'm looking for. If you happen to know what my idea is, or manage to find it by google-stalking me, please don't give it away yet.
The MIT Mystery Hunt and the Illusion of Transparency
by novalis — 2012-01-19T07:34:41.661Z

The MIT Mystery Hunt is a collection of puzzles, solved in teams over a long weekend every year. The prize for winning is that your team gets to write next year's hunt. Mystery Hunt puzzles are generally designed to take a few hours for a few people. A hunt typically has around 100 such puzzles, organized into a dozen or so metapuzzles; the metapuzzles can typically be solved with only a subset of the answers from the puzzles for that round, so not every puzzle needs to be solved to win.

My team, Codex, won in 2011 and thus wrote the 2012 hunt, which has just concluded. I wanted to share some thoughts about the hunt, and also share one of the puzzles that didn't make it in, but that I think Less Wrong will appreciate.

Edward Z. Yang compared the process of solving puzzles to science. It's not always that way -- in particular, Duck Konundrum is the prototype of a class of puzzle which merely requires following a very complicated set of instructions, while Square Mess is a simple matter of programming (well, and univat n ovt rabhtu qvpgvbanel). But it's a pretty good way of looking at things.

This year, I was a puzzle editor as well as an author. One of the things I learned about puzzles is that authors always think their puzzles are solvable, whether or not they are. This is the Illusion of Transparency in action -- it's obvious to the author how the puzzle ought to be solved. One job of editors is to ensure that every aha is properly clued, and that there is internal confirmation that solvers are on the right track. Internal confirmation means that when there are two steps to solving a puzzle, the intermediate result contains something intelligible even with omissions or errors. For example, if an intermediate result is a set of trigrams, those trigrams should be plausibly English-like. In nature, internal confirmation comes naturally, since all of nature follows a single set of rules. But in a puzzle, the rules are entirely arbitrary, so internal confirmation must be added.

In past hunts, a number of puzzles went completely unsolved, because there wasn't a rigorous testsolving process. Some puzzles were released with serious undetected errors, and some puzzles were simply too hard. In 2012, every puzzle was solved forwards (that is, without inferring the answer from the constraints in the metapuzzle) at least once.

The only way to tell if a puzzle really works i...