trentmkelly/LessWrong-43k huggingface dataset:
[SEQ RERUN] Heat vs. Motion
by MinibearRex — 2012-03-21T05:08:48.382Z

Today's post, Heat vs. Motion was originally published on 01 April 2008. A summary (taken from the LW wiki):

For a very long time, people had a detailed understanding of kinetics, and they had a detailed understanding of heat. They understood concepts such as momentum and elastic rebounds, as well as concepts such as temperature and pressure. It took an extraordinary amount of work in order to understand things deeply enough to make us realize that heat and motion were really the same thing.

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 Angry Atoms, 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.
Social status hacks from The Improv Wiki
by lsparrish — 2012-03-21T02:56:56.298Z

I can't remember how I found this, just that I was amazed at how rational and near-mode it is on a topic where most of the information one usually encounters is hopelessly far.

LessWrong wiki link on the same topic: http://wiki.lesswrong.com/wiki/Status

The Improv Wiki

Status

Status is pecking order. The person who is lower in status defers to the person who is higher in status.

Status is party established by social position--e.g. boss and employee--but mainly by the way you interact. If you interact in a way that says you are not to be trifled with, the other person must adjust to you, then you are establishing high status. If you interact in a way that says you are willing to go along, you don't want responsibility, that's low status. A boss can play low status or high status. An employee can play low status or high status.

Status is established in every line and gesture, and changes continuously. Status is something that one character plays to another at a particular moment. If you convey that the other person must not cross you on what you're saying now, then you are playing high status to that person in that line. Your very next line might come out low status, as you suggest willingness to defer about something else.

If you analyze your most successful scenes, it's likely they involved several status changes between the players. Therefore, one path to great scenes is to intentionally change status. You can raise or lower your own status, or the status of the other player. The more subtly you can do this, the better the scene.

High-status behaviors

When walking, assuming that other people will get out of your path.

Making eye contact while speaking.

Not checking the other person's eyes for a reaction to what you said.

Having no visible reaction to what the other person said. (Imagine saying something to a typical Clint Eastwood character. You say something expecting a reaction, and you get--nothing.)

Speaking in complete sentences.

Interrupting before you know what you are going to say.

Spreading out your body to full comfort. Taking up a lot of space with your body.

Looking at the other person with your eyes somewhat down (head tilted back a bit to make this work), creating the feeling that you are a parent talking to a child.

Talking matter-of-factly about things that the other person finds displeasing or offensive.

Letting your body be vulnerable, exposing your n

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AI Risk and Opportunity: Humanity's Efforts So Far
by lukeprog — 2012-03-21T02:49:21.315Z

Part of the series AI Risk and Opportunity: A Strategic Analysis.

(You can leave anonymous feedback on posts in this series here. I alone will read the comments, and may use them to improve past and forthcoming posts in this series.)

This post chronicles the story of humanity's growing awareness of AI risk and opportunity, along with some recent AI safety efforts. I will not tackle any strategy questions directly in this post; my purpose today is merely to "bring everyone up to speed."

I know my post skips many important events and people. Please suggest additions in the comments, and include as much detail as possible.

Early history

Late in the Industrial Revolution, Samuel Butler (1863) worried about what might happen when machines become more capable than the humans who designed them:

...we are ourselves creating our own successors; we are daily adding to the beauty and delicacy of their physical organisation; we are daily giving them greater power and supplying by all sorts of ingenious contrivances that self-regulating, self-acting power which will be to them what intellect has been to the human race. In the course of ages we shall find ourselves the inferior race.

...the time will come when the machines will hold the real supremacy over the world and its inhabitants...

This basic idea was picked up by science fiction authors, for example in the 1921 Czech play that introduced the term “robot,” R.U.R. In that play, robots grow in power and intelligence and destroy the entire human race, except for a single survivor.

Another exploration of this idea is found in John W. Campbell’s (1932) short story The Last Evolution, in which aliens attack Earth and the humans and aliens are killed but their machines survive and inherit the solar system. Campbell's (1935) short story The Machine contained perhaps the earlier description of recursive self-improvement:

On the planet Dwranl, of the star you know as Sirius, a great race lived, and they were not too unlike you humans. ...they attained their goal of the machine that could think. And because it could think, they made several and put them to work, largely on scientific problems, and one of the obvious problems was how to make a better machine which could think.

The machines had logic, and they could think constantly, and because of their construction never forgot anything they th

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Meetup : Small weekly Berkeley meetup
by Nisan — 2012-03-21T01:10:21.988Z

Discussion article for the meetup : Small weekly Berkeley meetup

WHEN:
28 March 2012 07:00:00PM (-0700)

WHERE:
2128 Oxford St., Berkeley, CA

We'll meet at the Starbucks on Oxford Street as usual, and then decide where to go from there.

Discussion article for the meetup : Small weekly Berkeley meetup
Meetup : Weekly Berkeley meetup
by Nisan — 2012-03-21T01:07:24.742Z

Discussion article for the meetup : Weekly Berkeley meetup

WHEN:
21 March 2012 06:30:00PM (-0700)

WHERE:
2011 Shattuck Avenue Berkeley, CA 94704

This week's Berkeley meetup will be 6:30pm on Wednesday at Biryani House. We'll be meeting jointly with the MPHD seminar folks.

Discussion article for the meetup : Weekly Berkeley meetup
The limited predictor problem
by cousin_it — 2012-03-21T00:15:26.176Z

This post requires some knowledge of logic, computability theory, and K-complexity. Much of the credit goes to Wei Dai. The four sections of the post can be read almost independently.

The limited predictor problem (LPP) is a version of Newcomb's Problem where the predictor has limited computing resources. To predict the agent's action, the predictor simulates the agent for N steps. If the agent doesn't finish in N steps, the predictor assumes that the agent will two-box. LPP is similar to the ASP problem, but with simulation instead of theorem proving.

1. Solving the problem when the agent has a halting oracle

Consider the agent defined in "A model of UDT with a halting oracle", and a predictor that can run the agent's code step-by-step, with oracle calls and all. Turns out that this agent solves LPP correctly if N is high enough. To understand why, note that the agent offloads all interesting work to oracles that return instantly, so the agent's own runtime is provably bounded. If that bound is below N, the agent's oracle will prove that the predictor predicts the agent correctly, so the agent will one-box.

2. Failing to solve the problem when N is algorithmically random

Consider a setting without oracles, with only Turing-computable programs. Maybe the agent should successively search for proofs somehow?

Unfortunately you can't solve most LPPs this way, for a simple but surprising reason. Assume that the predictor's time limit N is a large and algorithmically random number. Then the predictor's source code is >log(N) bits long, because N must be defined in the source code. Then any proof about the world program must also have length >log(N), because the proof needs to at least quote the world program itself. Finding a proof by exhaustive search takes exponential time, so the agent will need >N steps. But the predictor simulates the agent for only N steps. Whoops!

3. Solving the problem when N is large but has a short definition

As usual, let U be the world program that returns a utility value, and A be the agent program that returns an action and has access to the world's source code. Consider the following algorithm for A:

From L=1 to infinity, search for proofs up to length L of the form "if A()=a and runtime(A)<g(L), then U()=u", where g(L) is an upper bound on runtime(A) if A stops the search at length L. Upon finding at least

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A model of UDT without proof limits
by cousin_it — 2012-03-20T19:41:28.937Z

This post requires some knowledge of decision theory math. Part of the credit goes to Vladimir Nesov.

Let the universe be a computer program U that returns a utility value, and the agent is a subprogram A within U that knows the source code of both A and U. (The same setting was used in the reduction of "could" post.) Here's a very simple decision problem:

def U():
if A() == 1:
return 5
else:
return 10

The algorithm for A will be as follows:

Search for proofs of statements of the form "A()=a implies U()=u". Upon finding at least one proof for each possible a, go to step 2.

Let L be the maximum length of proofs found on step 1, and let f(L) be some suitably fast-growing function like 10^L. Search for proofs shorter than f(L) of the form "A()≠a". If such a proof is found, return a.

If we're still here, return the best a found on step 1.

The usual problem with such proof-searching agents is that they might stumble upon "spurious" proofs, e.g. a proof that A()==2 implies U()==0. If A finds such a proof and returns 1 as a result, the statement A()==2 becomes false, and thus provably false under any formal system; and a false statement implies anything, making the original "spurious" proof correct. The reason for constructing A this particular way is to have a shot at proving that A won't stumble on a "spurious" proof before finding the "intended" ones. The proof goes as follows:

Assume that A finds a "spurious" proof on step 1, e.g. that A()=2 implies U()=0. We have a lower bound on L, the length of that proof: it's likely larger than the length of U's source code, because a proof needs to at least state what's being proved. Then in this simple case 10^L steps is clearly enough to also find the "intended" proof that A()=2 implies U()=10, which combined with the previous proof leads to a similarly short proof that A()≠2, so the agent returns 2. But that can't happen if A's proof system is sound, therefore A will find only "intended" proofs rather than "spurious" ones in the first place.

Quote from Nesov that explains what's going on:

With this algorithm, you're not just passively gauging the proof length, instead you take the first moral argument you come across, and then actively defend it against any close competition

By analogy we can see that A coded with f(L)=10^L will correctly solve al...
Better to be testably wrong than to generate nontestable wrongness
by Dmytry — 2012-03-20T19:04:15.290Z

Abstract: Test the world-models [at least somewhat] scientifically by giving others and yourself opportunity to generate straightforwardly and immediately testable factual predictions from the world-model. Read up facts to make sure you are not wrong before posting, not only to persuade.

I have this theory: there are people with political opinion of some kind, who generate their world-beliefs from that opinion. This is a wrong world-model. It doesn't work for fact finding. It works for tribal affiliations. I think it is fair to say we all been guilty of this on at least several occasions, and that all of us do it for at least some problem domains. Now, suppose you have some logical argument that contradicts other people's world-model, starting from very basic facts. And you are writing an article.

If you source those basic facts, there's what happens: the facts are read and accepted, the reasoning is read, the conclusion is reached, the contradiction with political opinion gets noted, the political opinion does NOT get adjusted, the politically motivated world-model generates a fault inside your argument, you get entirely counter productive and extremely irritating debate about semantics or argumentation techniques. In the end, not a yota changes about the world model of anyone involved in the debate.

If you don't source those basic facts, there's what happens: the facts are read and provisionally accepted, the reasoning is read, the conclusion is reached, the contradiction with political opinion gets noted, the political opinion does not get adjusted, the politically motivated world model generates wrong fact expectations about basic, easily testable facts. The contradiction eventually gets noted, the wrong world-model gets a minor slap on the nose, and actually does decrease in it's weight ever so slightly for generating wrong expectations. The person is, out of necessity, doing some actual science here - generating testable hypotheses from their theory, about the facts they don't know, having them tested (and shown wrong, providing feedback in somewhat scientific manner).

Unfortunately, any alterations to world model are uncomfortable - the world models, as memes, have a form of self preservation - so nobody likes this, and the faulty world-models produce considerable pressure to demand of you to source the basic knowledge upfront, so that the world-model can...
Meetup : First meetup in Budapest
by Joanna Morningstar — 2012-03-20T13:05:38.600Z

Discussion article for the meetup : First meetup in Budapest

WHEN:
25 March 2012 06:00:00PM (+0000)

WHERE:
Szent Istvan ter 4-5, Budapest

Meeting at California Coffee Company Basilica (coffee shop), Szent Istvan ter 4-5.
http://www.californiacoffeeco.net/?page_id=50&lang=en.

Please come and bring friends. If you have questions, contact AlexeyM.

Discussion article for the meetup : First meetup in Budapest
Send me your photos of LessWrongers having fun!
by lukeprog — 2012-03-20T07:37:30.894Z

The Singularity Institute is preparing a booklet for LessWrong meetup organizers called How to Run a Successful Less Wrong Meetup Group.

Right now what we need are your photos of LessWrongers doing things and having fun! If you have photos you don't mind us potentially using in How to Run a Successful Less Wrong Meetup Group, please email them to luke@intelligence.org.

Thanks!
Posts I repent of
by Will_Newsome — 2012-03-20T05:29:31.982Z

"Taking Ideas Seriously": Stylistically contemptible, skimpy on any useful details, contributes to norm of pressuring people into double binds that ultimately do more harm than good. I would prefer it if no one linked to or promoted "Taking Ideas Seriously"; superior alternatives include Anna Salamon's "Compartmentalization in epistemic and instrumental rationality", though I don't necessarily endorse that post either.

"Virtue Ethics for Consequentialists": Stylistically contemptible, written in ignorance of much of the relevant philosophy and psychology literature, contributes to norm of rewarding people who confidently proselytize on subjects of which they do not possess a deep understanding. Thankfully nobody links to this post.

All of my other posts also sucked but not to the extent that they're worth going out of my way to disavow.
[SEQ RERUN] Angry Atoms
by MinibearRex — 2012-03-20T02:36:14.448Z

Today's post, Angry Atoms was originally published on 31 March 2008. A summary (taken from the LW wiki):

It is very hard, without the benefit of hindsight, to understand just how it is that these little bouncing billiard balls called atoms, could ever combine in such a way as to make something angry. If you try to imagine this problem without understanding the idea of neurons, information processing, computing, etc you realize just how challenging reductionism actually is.

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 Hand vs. Fingers, 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.
Emotional regulation Part II: research summary
by Swimmer963 (Miranda Dixon-Luinenburg)  — 2012-03-19T21:51:24.247Z

Abstract: Emotional regulation is a topic currently being studied in the field of psychology. Five different types of emotional regulation strategies have been identified, distinguished by the stage of the emotion-response process in which they occur. To drastically simplify, this strategies are: situation selection, situation modification, deployment of attention, changes in cognition, and modulation of responses.

Introduction

This is a follow-up to my previous post about my problem with emotional regulation. This is also my first outside-of-the-classroom foray into scholarship, lukeprog style. Mainly what I found is that it’s surprisingly time-consuming and frustrating. I suffered a lot of akrasia, compared to my usual, while writing this post–mainly because I kept thinking ‘oh my god, and then I have to cite my sources!’ This may be an area where I need more practice...

What is emotion anyway?

Apparently there are quite a lot of competing definitions for ‘emotion’. Maybe this shouldn’t be surprising–the concept of emotion seem simple because most of the processing happens below conscious awareness, but emotions are as complex as the brains that create them.

The definition that most research in emotional regulation uses is the ‘response tendency’ definition: emotions are adaptive behavioral and/or physiological responses, and they happen when the organism is put in evolutionarily significant situations. The internal experience of emotion may lead to a particular behaviour, but may not: emotion is a feedback mechanism that leads to various behaviours, rather than the direct cause of behaviour. Recent research has covered the specific purposes that emotions accomplish. They can facilitate decision-making, prepare the individual for a fast response to a given situation, inform on the match between organism and environment, and serve a social function; in general, they allow for learning. Emotional responses are not set in stone, and can be modulated on the way to taking their final shape. (Gross, 1988).

What does this mean for me, personally? One, emotions exist for a reason. They are adaptive, and attempting to turn mine off entirely or prevent them from affecting my decisions would likely not be adaptive. Two, emotio

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Saturating utilities as a model
by Dmytry — 2012-03-19T21:17:31.857Z

Okay, it is a very raw idea, but consider the utility processing that works as following:

1: The utility i'm speaking of is not 'happiness', nor is it 'strength of the compulsion', the utility is only used for the purpose of comparing between futures to pick the one with larger utility. Applying same monotonously increasing function to both sides of comparison does not change outcome of comparison, and works as if the function was not there.

The utility is an array of n numbers. The arrays are compared after pseudo-summing them using sigmoid function like:

a[1]+k[1]*sigmoid(a[2]+k[2]*sigmoid(a[3] + ...))

This has a bunch of nasty properties (i.e. it is not clear how to deal with probabilities here), but may capture the human view on the torture and dust specks, and similar problems like pascal's wager, where arguments of low quality may just go into a[n] where n is large, rather than be assigned any defined low probability.

Note that usually, two future worlds being compared are identical up to some n , and so the comparison can be made starting from the n, disregarding the equal smaller terms.

Furthermore, the comparison allows for 'short evaluation', as after few steps no further values need to be considered.

The obvious model that comes to mind if you observe this comparator as a black box, is the linear sum where weights are k[1] >> k[2] , k[2] >> k[3] , and so on, which is a fairly good approximation but breaks down when you start using really huge numbers like 3^^^^3 . The sigmoid eats uparrows for breakfast and asks for more.

It seems to me that this does accurately capture the behaviour which is not generally very impressed by Knuth's up arrow notation, and the sigmoids are biologically plausible. Other monotonously growing functions can be employed.

One could probably come up with nicer model which results in identical outcomes, whereby n does not need to be integer.
Difference between CDT and ADT/UDT as constant programs
by gRR — 2012-03-19T19:41:30.982Z

After some thinking, I came upon an idea how to define the difference between CDT and UDT within the constant programs framework. I would post it as a comment, but it is rather long...

The idea is to separate the cognitive part of an agent into three separate modules:

1. Simulator: given the code for a parameterless function X(), the Simulator tries to evaluate it, spending L computation steps. The result is either proving that X()=x for some value x, or leaving X() unknown.

2. Correlator: given the code for two functions X(...) and Y(...), the Correlator checks for proofs (of length up to P) of structural similarity between the source codes of the functions, trying to prove correlations X(...)=Y(...).

[Note: the Simulator and the Correlator can use the results of each other, so that:
If simulator proves that A()=x, then correlator can prove that A()+B() = x+B()
If correlator proves that A()=B(), then simulator can skip simulation when proving that (A()==B() ? 1 : 2) = 1]

3. Executive: allocates tasks and resources to Simulator and Correlator in some systematic manner, trying to get them to prove the "moral arguments"
Self()=x => U()=u
or
( Self()=x => U()=ux AND Self()=y => U()=uy ) => ux > uy,
and returns the best found action.

Now, CDT can be defined as an agent with the Simulator, but without the Correlator. Then, no matter what L it is given, the Simulator won't be able to prove that Self()=Self(), because of the infinite regress. So the agent will be opaque to itself, and will two-box on Newcomb's problem and defect against itself in Prisoner's Dilemma.

The UDT/ADT, on the other hand, have functioning Correlators.

If it is possible to explicitly (rather than conceptually) separate an agent into the three parts, then it appears to be possible to demonstrate the good behavior of an agent in the ASP problem. The world can be written as a NewComb's-like function:

def U():
box2 = 1000
box1 = (P()==1 ? 1000000 : 0)
return (A()==1 ? box1 : box1 + box2)

where P is a predictor that has much less computational resources than the agent A. We can assume that the predictor has basically the same source code as the agent, except for the bound on L and ...