trentmkelly/LessWrong-43k huggingface dataset:
Meetup : London social meetup - possibly in a park
by philh — 2014-07-22T17:20:28.288Z

Discussion article for the meetup : London social meetup - possibly in a park

WHEN:
27 July 2014 02:00:00PM (+0100)

WHERE:
Shakespeare's Head, Holborn, WC2B 6BG

The next LW London meetup will be on July 27th. Join us from 2pm to talk about the sorts of things that your other friends will look funny at you for talking about.

If the weather is nice, we'll head to Lincoln's Inn Fields, probably somewhere in the northwest quadrant. If not, we'll be in the usual Shakespeare's Head. If the weather is variable, we might move from one to the other - give me a call or text if you're not sure. My number is 07792009646.

Update: Yay park!

About London LessWrong:

We run this meetup almost every week; these days we tend to get in the region of 5-15 people in attendance. By default, meetups are just unstructured social discussion about whatever strikes our fancy: books we're reading, recent posts on LW/related blogs, logic puzzles, toilet usage statistics....

Sometimes we play The Resistance or other games. We usually finish around 7pm, give or take an hour, but people arrive and leave whenever suits them.

Related discussion happens on both our google group and our facebook group.

Discussion article for the meetup : London social meetup - possibly in a park
Top-Down and Bottom-Up Logical Probabilities
by Manfred — 2014-07-22T08:53:15.473Z

I.

I don't know very much model theory, and thus I don't fully understand Hutter et al.'s logical prior, detailed here, but nonetheless I can tell you that it uses a very top-down approach. About 60% of what I mean is that the prior is presented as a completed object with few moving parts, which fits the authors' mathematical tastes and proposed abstract properties the function should have. And for another thing, it uses model theory - a dead giveaway.

There are plenty of reasons to take a top-down approach. Yes, Hutter et al.'s function isn't computable, but sometimes the properties you want require uncomputability. And it's easier to come up with something vaguely satisfactory if you don't have to have many moving parts. This can range from "the prior is defined as a thing that fulfills the properties I want" on the lawful good side of the spectrum, to "clearly the right answer is just the exponential of the negative complexity of the statement, duh".

Probably the best reason to use a top-down approach to logical uncertainty is so you can do math to it. When you have some elegant description of global properties, it's a lot easier to prove that your logical probability function has nice properties, or to use it in abstract proofs. Hence why model theory is a dead giveaway.

There's one other advantage to designing a logical prior from the top down, which is that you can insert useful stuff like a complexity penalty without worrying too much. After all, you're basically making it up as you go anyhow, you don't have to worry about where it comes from like you would if you were going form the bottom up.

A bottom-up approach, by contrast, starts with an imagined agent with some state of information and asks what the right probabilities to assign are. Rather than pursuing mathematical elegance, you'll see a lot of comparisons to what humans do when reasoning through similar problems, and demands for computability from the outset.

For me, a big opportunity of the bottom-up approach is to use desiderata that look like principles of reasoning. This leads to more moving parts, but also outlaws some global properties that don't have very compelling reasons behind them.

II.

Before we get to the similarities, rather than the differences, we'll have to impose the condition of limited computational resources. A common playing field, as it were. It would probably serve just...
Compiling my writings for Lesswrong and others.
by diegocaleiro — 2014-07-22T08:11:21.095Z

I've just inserted about 50 new links to my list of writings, most of which from Lesswrong, here. For convenience, I'm copying it below.

I write a lot about a variety of topics in English and until 2013 also did in Portuguese, Notice Google Chrome automatically translates texts if you need. This will someday be a compilation of all my writings, divided by Borgean topics. There are also writings I wish I had written:

The ones I really, really want you to read before you read the rest:

000 The Starting Gun

001 A sense that more is possible

002 Russell’s one page autobiography (Português)

003 De onde viemos?

004 10 Razões pelas quais estou indo embora

005 Six Harsh Truths that will make you a better person

006 This blog’s first post: Stop Learning, Start Doing

007 Altruistic Awesomeness, Your Challenge! (O Fantástico Altruista)

Nearly all of these above are intensely motivational in character. If you are truly allergic to motivational writings, read only 003, 004, 005 and 006.

Those that may help you save the world:

100 Troubles with Coherent Extrapolated Volition (CEV)

101 In praise of tribes that pretend to try: counter”-Critique of Effective Altruism. (critique of this)

102 On media and Effective Altruism. (Interacting with media distortion)

104 Amending the Orthogonality Thesis. (Suggests an amendment to this paper)

105 Share your goals (only) with who shares your goals.

Those that are very long and full of ideas:

010 Simulando Dennett (254 pages teaching you to think as Daniel Dennett does about biology, philosophy and AI, in portuguese)

011 On What Selves Are See also Short-term selves versus rationality.

100 Troubles with CEV (a genteral discussion of problems with Coherent Extrapolated Volition, our main strategic plan to save the world up to the time of writing.)

012 Calibrating against undetectable utilons and goal changing events (how to actually reach your goals even if you change or they are in the far future)

013 Para onde vamos?

Those short:

020 Research is polygamous. The Importance of what you do needn’t be proportional to your awesomeness

021 Why is it rational to invest in retirement, I don’t g

...
Politics is hard mode
by Rob Bensinger — 2014-07-21T22:14:33.503Z

Summary: I don't think 'politics is the mind-killer' works well rhetorically. I suggest 'politics is hard mode' instead.

Some people in and catawampus to the LessWrong community have objected to "politics is the mind-killer" as a framing (/ slogan / taunt). Miri Mogilevsky explained on Facebook:

My usual first objection is that it seems odd to single politics out as a “mind-killer” when there’s plenty of evidence that tribalism happens everywhere. Recently, there has been a whole kerfuffle within the field of psychology about replication of studies. Of course, some key studies have failed to replicate, leading to accusations of “bullying” and “witch-hunts” and what have you. Some of the people involved have since walked their language back, but it was still a rather concerning demonstration of mind-killing in action. People took “sides,” people became upset at people based on their “sides” rather than their actual opinions or behavior, and so on.

Unless this article refers specifically to electoral politics and Democrats and Republicans and things (not clear from the wording), “politics” is such a frightfully broad category of human experience that writing it off entirely as a mind-killer that cannot be discussed or else all rationality flies out the window effectively prohibits a large number of important issues from being discussed, by the very people who can, in theory, be counted upon to discuss them better than most. Is it “politics” for me to talk about my experience as a woman in gatherings that are predominantly composed of men? Many would say it is. But I’m sure that these groups of men stand to gain from hearing about my experiences, since some of them are concerned that so few women attend their events.

In this article, Eliezer notes, “Politics is an important domain to which we should individually apply our rationality — but it’s a terrible domain in which to learn rationality, or discuss rationality, unless all the discussants are already rational.” But that means that we all have to individually, privately apply rationality to politics without consulting anyone who can help us do this well. After all, there is no such thing as a discussant who is “rational”; there is a reason the website is called “Less

...
Knightian Uncertainty and Ambiguity Aversion: Motivation
by So8res — 2014-07-21T20:32:43.289Z

Recently, I found myself in a conversation with someone advocating the use of Knightian uncertainty. I admitted that I've never found the concept compelling. We went back and forth for a little while. His points were crisp and well-supported, my objections were vague. We didn't have enough time to reach consensus, but it became clear that I needed to research his viewpoint and flesh out my objections before being justified in my rejection.

So I did. This is the first in a short series of posts during which I explore what it means for an agent to reason using Knightian uncertainty.

In this first post, I'll present a number of arguments claiming that Bayesian reasoning fails to capture certain desirable behavior. I'll discuss a proposed solution, maximization of minimum expected utility, which is advocated by my friend and others.

In the second post, I'll discuss some more general arguments against Bayesian reasoning as an idealization of human reasoning. What role should "unknown unknowns" play in a bounded Bayesian reasoner? Is "Knightian uncertainty" a useful concept that is not captured by the Bayesian framework?

In the third post, I'll discuss the proposed solution: can rational agents display ambiguity aversion? What does it mean to have a rational agent that does not maximize expected utility, maximizing "minimum expected utility" instead?

In the final post, I'll apply these insights to humans and articulate my objections to ambiguity aversion in general. I'll conclude that while it is possible for agents to be ambiguity-averse, ambiguity aversion in humans is a bias. The maximization of minimum expected utility may be a useful concept for explaining how humans actually act, but probably isn't how you should act.

The following is a stylized conversation that I had at the Stanford workshop on Logic, Rationality, and Intelligent Interaction. I'll anonymize my friend as 'Sir Percy', which seems a fitting pseudonym for someone advocating Knightian uncertainty.

"I think that's repugnant", Sir Percy said. "I can't assign a probability to the simulation hypothesis, because I have Knightian uncertainty about it."

"I've never found Knightian uncertainty compelling" I replied with a shrug. "I don't see how it helps to claim uncertainty about your credence. I know what it means to feel very uncertain (e.g. place a low probability on many different scenarios), ...
[ACTIVITY]: Exploratory Visit to the Bay Area
by Daniel_Burfoot — 2014-07-21T19:49:46.511Z

In my opinion, living anywhere other than the center of your industry is a mistake. A lot of people — those who don’t live in that place — don’t want to hear it. But it’s true. Geographic locality is still — even in the age of the Internet — critically important if you want to maximize your access to the best companies, the best people, and the best opportunities. You can always cite exceptions, but that’s what they are: exceptions.

- Marc Andreessen

Like many people in the technology industry, I have been thinking seriously about moving to the Bay Area. However, before I decide to move, I want to do a lot of information gathering. Some basic pieces of information - employment prospects, cost of living statistics, and weather averages - can be found online. But I feel that one's quality of life is determined by a large number of very subtle factors - things like walkability, public transportation, housing quality/dollar of rent, lifestyle options, and so on. These kinds of things seem to require first-hand, in-person examination. For that reason, I'm planning to visit the Bay Area and do an in-depth exploration next month, August 20th-24th.

My guess is that a significant number of LWers are also thinking about moving to the Bay Area, and so I wanted to invite people to accompany me in this exploration. Here are some activities we might do:

Travel around using public transportation. Which places are convenient to get from/to, and which places aren't?

Visit the offices of the major tech companies like Google, Facebook, Apple, and Twitter. Ask some of their employees how they feel about being a software engineer in Silicon Valley.

Eat at local restaurants - not so much the fancy/expensive ones, but the ones a person might go to for a typical, everyday lunch outing.

See some of the sights. Again, the emphasis would be on the things that would affect our everyday lifestyle, should be decide to move, not so much on the tourist attractions. For example, the Golden Gate Bridge is an awesome structure, but I doubt it would improve my everyday life very much. In contrast, living near a good running trail would be a big boost to my lifestyle.

Do some apartment viewing, to get a feel for how much rent a good/medium/student apartment costs in different areas and how good the amenit

...
Open thread, July 21-27, 2014
by polymathwannabe — 2014-07-21T13:15:10.653Z

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one.

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.
How to buy a truth from a liar
by KatjaGrace — 2014-07-21T04:11:34.000Z

Suppose you want to find out the answer to a binary question, such as ‘would open borders destroy America?’, or ‘should I follow this plan?’. You know someone who has access to a lot of evidence on the question. However you don’t trust them, and in particular, you don’t trust them to show you all of the relevant evidence. Let’s suppose that if they purport to show you a piece of evidence, you can verify that it is real. However since they can tell you about any subset of the evidence they know, they can probably make a case for either conclusion. So without looking into the evidence yourself, it appears you can’t really employ them to inform you, because you can’t pay them more when they tell the truth. It seems to be a case of ‘he who pays the piper must know the tune’.

But here is a way to hire them: pay them for every time you change your mind on the question, within a given period. Their optimal strategy for revealing evidence to make money must leave you with the correct conclusion (though maybe not all of the evidence), because otherwise they would be able to get in one more mind-change by giving you the remaining evidence. (And their optimal strategy overall can be much like their optimal strategy for making money, if enough money is on the table).

This may appear to rely on you changing your mind according to evidence. However I think it only really requires that you have some probability of changing your mind when given all of the evidence.

Still, you might just hear their evidence, and refuse to officially change your mind, ever. This way you keep your money, and privately know the truth. How can they trust you to change your mind? If the question is related to a course of action, your current belief (a change of which would reward them) can be tied to a commitment to a certain action, were the period to end without further evidence provided. And if the belief is not related to a course of action, you could often make it related, via a commitment to bet.

This strategy seems to work even for ‘ought questions’, without the employee needing to understand or share your values.
A simple game that has no solution
by James_Miller — 2014-07-20T18:36:54.636Z

The following simple game has one solution that seems correct, but isn’t. Can you figure out why?

The Game

Player One moves first. He must pick A, B, or C. If Player One picks A the game ends and Player Two does nothing. If Player One picks B or C, Player Two will be told that Player One picked B or C, but will not be told which of these two strategies Player One picked, Player Two must then pick X or Y, and then the game ends. The following shows the Players’ payoffs for each possible outcome. Player One’s payoff is listed first.

A 3,0 [And Player Two never got to move.]

B,X 2,0

B,Y 2,2

C,X 0,1

C,Y 6,0

The players are rational, each player cares only about maximizing his own payoff, the players can’t communicate, they play the game only once, this game is all that will ever matter to them, and all of this plus the payoffs and the game structure is common knowledge.

Guess what will happen. Imagine you are really playing the game and decide what you would do as either Player One, or as Player Two if you have been told that you will get to move. To figure out what you would do you must formulate a belief about what the other player has/will do, and this will in part be based on your belief about his belief of what you have/will do.

An Incorrect Argument for A

If Player One picks A he gets 3, whereas if he picks B he gets 2 regardless of what Player Two does. Consequently, Player One should never pick B. If Player One picks C he might get 0 or 6 so we can’t rule out Player One picking C, at least without first figuring out what Player Two will do.

Player Two should assume that Player One will never pick B. Consequently, if Player Two gets to move he should assume that C was played and therefore Player Two should respond with X. If Player One believes that Player Two will, if given the chance to move, pick X, then Player One is best off picking A. In conclusion, Player One will pick A and Player Two will never get to move.

Why the Game Has No Solution

I believe that the above logic is wrong, and indeed the game has no solution. My reasoning is given in rot13. (Copy what is below and paste at this link to convert to English.)

http://rot13.com/

Vs gur nobir na...
Experiments 1: Learning trivia
by casebash — 2014-07-20T10:31:38.377Z

There has been some talk of a lack of content being posted to Less Wrong, so I decided to start a series on various experiments that I've tried and what I've learned from them as I believe that experimentation is key to being a rationalist. My first few posts will be adapted from content I've written for /r/socialskills, but as Less Wrong has a broader scope I plan to post some original content too. I hope that this post will encourage other people to share detailed descriptions of the experiments that they have tried as I believe that this is much more valuable than a list of lessons posted outside of the context in which they were learned. If anyone has already posted any similar posts, then I would really appreciate any links.

Trivia Experiment

I used to have a lot of trouble in conversation thinking of things to say. I wanted to be a more interesting person and I noticed that my brother uses his knowledge of a broad range of topics to engage people in conversations, so I wanted to do the same.

I was drawn quite quickly towards facts because of how quickly they can be read. If a piece of trivia takes 10 seconds to read, then you can read 360 in an hour. If only 5% are good, then that's still 18 usable facts per hour. Articles are longer, but have significantly higher chances of teaching you something. It seemed like you should be able to prevent ever running out of things to talk about with a reasonable investment of time. It didn't quite work out this way, but this was the idea.d

Another motivation was that I have always valued intelligence and learning more information made me feel good about myself.

Sources

Today I learned: #1 recommended source

The straight dope: Many articles in the archive are quite interesting, but I unsubscribed because I found the more recent ones boring

Damn interesting

Now I know

Cracked: Not the most reliable source and can be a huge time sink, but occasionally there are articles there that will give you 6 or 7 interesting facts in one go

Dr Karl: Science blog

Skeptics Stackexchange

Mythbusters results

The future is now

I read through the top 1000 links on Today I learned, the entire archive of the straight dope, maybe half of damn interesting and now I know, half of Karl and all the mythbusters results up to about a year or two ago. We are pretty much talking about months of solid reading.

Results

You probably guessed it, but

...
LINK: Top HIV researcher killed in plane crash
by polymathwannabe — 2014-07-19T17:03:21.443Z

As most of you may already know, the plane that recently crashed on disputed Ukrainian soil carried some of the world's top HIV researchers.

One part of me holds vehemently that all human beings are of equal value.

Another part of me wishes there could be extra-creative punishments for depriving the world of its best minds.

Opinions?

http://www.huffingtonpost.com/2014/07/17/joep-lange-dead-aids-expert-plane-crash_n_5597834.html
[QUESTION]: Academic social science and machine learning
by VipulNaik — 2014-07-19T15:13:28.704Z

I asked this question on Facebook here, and got some interesting answers, but I thought it would be interesting to ask LessWrong and get a larger range of opinions. I've modified the list of options somewhat.

What explains why some classification, prediction, and regression methods are common in academic social science, while others are common in machine learning and data science?

For instance, I've encountered probit models in some academic social science, but not in machine learning.

Similarly, I've encountered support vector machines, artificial neural networks, and random forests in machine learning, but not in academic social science.

The main algorithms that I believe are common to academic social science and machine learning are the most standard regression algorithms: linear regression and logistic regression.

Possibilities that come to mind:

(0) My observation is wrong and/or the whole question is misguided.

(1) The focus in machine learning is on algorithms that can perform well on large data sets. Thus, for instance, probit models may be academically useful but don't scale up as well as logistic regression.

(2) Academic social scientists take time to catch up with new machine learning approaches. Of the methods mentioned above, random forests and support vector machines was introduced as recently as 1995. Neural networks are older but their practical implementation is about as recent. Moreover, the practical implementations of these algorithm in the standard statistical softwares and packages that academics rely on is even more recent. (This relates to point (4)).

(3) Academic social scientists are focused on publishing papers, where the goal is generally to determine whether a hypothesis is true. Therefore, they rely on approaches that have clear rules for hypothesis testing and for establishing statistical significance (see also this post of mine). Many of the new machine learning approaches don't have clearly defined statistical approaches for significance testing. Also, the strength of machine learning approaches is more exploratory than testing already formulated hypotheses (this relates to point (5)).

(4) Some of the new methods are complicated to code, and academic social scientists don't know enough mathematics, computer science, or statistics to cope with the methods (this may change if they're taught more about these methods in graduate school,

...