trentmkelly/LessWrong-43k huggingface dataset:
Weekly LW Meetups
by FrankAdamek — 2016-01-29T16:15:08.167Z

This summary was posted to LW Main on January 22nd. The following week's summary is here.

Irregularly scheduled Less Wrong meetups are taking place in:

Baltimore Area: Epistemology of Disagreement: 24 January 2016 03:00PM
Cologne meetup: 23 January 2016 05:00PM
European Community Weekend: 02 September 2016 03:35PM
Palo Alto Meetup: Lightning Talks: 02 February 2016 06:30PM

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

London diaspora meetup: weird foods - 24/01/2016: 24 January 2016 02:00PM
New Hampshire Meetup: 26 January 2016 07:00PM
Washington, D.C.: [Postponed for snow]: 24 January 2016 03:00PM

Locations with regularly scheduled meetups: Austin, Berkeley, Berlin, Boston, Brussels, Buffalo, Canberra, Columbus, Denver, London, Madison WI, Melbourne, Moscow, Mountain View, New Hampshire, New York, Philadelphia, Research Triangle NC, Seattle, Sydney, Tel Aviv, Toronto, Vienna, Washington DC, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers and a Slack channel for daily discussion and online meetups on Sunday night US time.

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, build community, and have fun!

In addition to the handy sidebar of upcoming meetups, a meetup overview is posted on the front page every Friday. These are an attempt to collect information on all the meetups happening in upcoming weeks. The best way to get your meetup featured is still to use the Add New Meetup feature, but you'll also have the benefit of having your meetup mentioned in a weekly overview. These overview posts are 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, Cambridge UK, Chicago, Cincinnati, Cleveland, Frankfurt, Helsinki, Marin CA, Ottawa, Pittsburgh, Portland, Salt Lake City, Southern California (...
The Number Choosing Game: Against the existence of perfect theoretical rationality
by casebash — 2016-01-29T01:04:49.557Z

In order to ensure that this post delivers what it promises, I have added the following content warnings:

Content Notes:
Pure Hypothetical Situation: The claim that perfect theoretical rationality doesn't exist is restricted to a purely hypothetical situation. No claim is being made that this applies to the real world. If you are only interested in how things apply to the real world, then you may be disappointed to find out that this is an exercise left to the reader.

Technicality Only Post: This post argues that perfectly theoretical rationality doesn't exist due to a technicality. If you were hoping for this post to deliver more, well, you'll probably be disappointed.

Contentious Definition: This post (roughly) defines perfect rationality as the ability to maximise utility. This is based on Wikipedia, which defines rational agents as an agent that: "always chooses to perform the action with the optimal expected outcome for itself from among all feasible actions".

We will define the number choosing game as follows. You name any single finite number x. You then gain x utility and the game then ends. You can only name a finite number, naming infinity is not allowed.

Clearly, the agent that names x+1 is more rational than the agent that names x (and behaves the same in every other situation). However, there does not exist a completely rational agent, because there does not exist a number that is higher than every other number. Instead, the agent who picks 1 is less rational than the agent who picks 2 who is less rational than the agent who picks 3 and so on until infinity. There exists an infinite series of increasingly rational agents, but no agent who is perfectly rational within this scenario.

Furthermore, this hypothetical doesn't take place in our universe, but in a hypothetical universe where we are all celestial beings with the ability to choose any number however large without any additional time or effort no matter how long it would take a human to say that number. Since this statement doesn't appear to have been clear enough (judging from the comments), we are explicitly considering a theoretical scenario and no claims are being made about how this might or might not carry over to the real world. In other words, I am claiming the the existence of perfect rationality does not follow purely from the laws of logic. If you are going to...
Another Concise Open Problem
by Scott Garrabrant — 2016-01-28T23:04:06.000Z

Given the success of the last open problem I posted, (Janos Kramer disproved my conjecture) I decided to post another one. Again, I will cut off the philosophy, and just give the math. The reason I want this result is to solve the problem described here.

Let S be the set of all paris (M,k) where M is a Turing machines which and k is a natural number.

For P=(M,k)∈S, we define the function P(n)=p if M halts on input n in time nk and outputs a probability p, and P(n)=1/2 otherwise.

Let E=e1,e2,… be an infinite bit string. We say Q∈S defeats P∈S on E if

limn→∞n∏i=0|1−ei−P(i)||1−ei−Q(i)|=0.

If there is no Q in S which defeats P on E, we say that P is undefeated on E.

Let A be an algorithm which takes as input a finite bit string, and whose output encodes an element of S.

Does there exist an algorithm A such that for all E, if there exists a P which is undefeated on E, then for all sufficienty large n, A(e1,…,en) is undefeated on E?

Note that A can run as long as it wants. I would also be very interested in a positive result that requires that P∈S is undefeated by every function computable in exponential time. (P still runs in polynomial time.)

Also, note that if I replace the lim with liminf, and require that P∈S is undefeated by every function computable in exponential time, then such an A does in fact exist. (The algorithm that does this has not been posted on the forum yet, sorry. If anyone wants to work on this problem, and is curious, I can explain it to them)
Second Failure of Inductive Learning with a Delay in Feedback
by Scott Garrabrant — 2016-01-28T21:32:09.000Z

This is a followup to the concrete failure of the Solomonoff Induction inspired approach to inductive learning with a delay in feedback. This second failure is not one that we have an algorithm to deal with yet.

Let f:N→{0,1} be a function such that (for large n) f(n) can be computed in time 2n, but there is no algorithm that can predict f(n) in time less than 2n consistently better than a coin.

Let E(n)=f(k), where k is the greatest square number less than or equal to n.

Now, consider an algorithm which tries to inductively learn to assign probabilities to E(n) in polynomial time. Ideally, such an algorithm will learn to converge to probability 1/2.

What I mean by this is an algorithm which considers Turing machines which run in a given (say O(n2)) amount of time, and somehow has them compete with each other, and use the results of this competition to output probabilities of algorithms that did well.

This is a fuzzy category, but examples of such algorithms include the Solomonoff Induction Inspired Approach, the Benford Learner, as well as several other algorithms that have been discussed at MIRI, but have not made it to the forum yet.

I claim that all algorithms we have considered so far fail on this problem. The reason that they all fail, is that there is a single algorithm, G, which runs quickly, and does unboundedly better that the constant 1/2 algorithm.

G(n) looks at k, the greatest square less than or equal to n, and outputs 2/3 if k is of the form (3↑m3)2, and 1/2 otherwise.

When comparing G to the constant 1/2 algorithm, you only need to consider their scores on the places where they make different predictions. Note that when k is of the form (3↑m3)2, G will output 2/3 on a large number of predictions in a row, all of which will turn out to be the same bit. If that bit is shown to be a 1, there are enough predictions to dwarf any mistakes G has made by saying 2/3 in the past, causing G to appear to do much better than the constant 1/2 algorithm (until it is later shown that it output a 2/3 on a later bit which turns out to be a 0).

G does not consistently do better than the constant 1/2, but it does do arbitrarily better infinitely often, which stops the constant 1/2 from taking over the inductive algorithms.

Note that this is not an impossibility result. I am just pointing out a concrete way that the algorithms we have now are failing.

I suspect that the way

...
Studying Your Native Language
by Crux — 2016-01-28T19:23:25.404Z

I've spent many thousands of hours over the past several years studying foreign languages and developing a general method for foreign-language acquisition. But now I believe it's time to turn this technique in the direction of my native language: English.

Most people make a distinction between one's native language and one's second language(s). But anyone who has learned how to speak with a proper accent in a second language and spent a long enough stretch of time neglecting their native language to let it begin rusting and deteriorating will know that there's no essential difference.

When the average person learns new words in their native language, they imagine that they're learning new concepts. When they study new vocabulary in a foreign language, however, they recognize that they're merely acquiring hitherto-unknown words. They've never taken a step outside the personality their childhood environment conditioned into them. When the only demarcation of thingspace that you know is the semantic structure of your native language, you're bound to believe, for example, that the World is Made of English.

Why study English? I'm already fluent, as you can tell. I have the Magic of a Native Speaker.

Let's put this nonsense behind us and recognize that the map is not the territory, that English is just another map.

My first idea is that it may be useful to develop a working knowledge of the fundamentals of English etymology. A quick search suggests that the majority of words in English have a French or Latin origin. Would it be useful to make an Anki deck with the goal of learning how to readily recognize the building blocks of the English language, such as seeing that the "cardi" in "cardiology", "cardiograph", and "cardiograph" comes from an Ancient Greek word meaning "heart" (καρδιά)?

Besides that, I plan to make a habit of adding any new words I encounter into Anki with their context. For example, let's say I'm reading the introduction to A Treatise of Human Nature by David Hume. I encounter the term "proselytes", and upon looking it up in a dictionary I understand the meaning of the passage. I include the spelling of the simplest version of the word ("proselyte"), along with an audio recording of the pronunciation. I'll also toy with adding various other information such as a definition I wrote myself, synonyms or antonyms, and so forth, ...
What's wrong with this picture?
by CronoDAS — 2016-01-28T13:30:05.222Z

Alice: "I just flipped a coin [large number] times. Here's the sequence I got:

(Alice presents her sequence.)

Bob: No, you didn't. The probability of having gotten that particular sequence is 1/2^[large number]. Which is basically impossible. I don't believe you.

Alice: But I had to get some sequence or other. You'd make the same claim regardless of what sequence I showed you.

Bob: True. But am I really supposed to believe you that a 1/2^[large number] event happened, just because you tell me it did, or because you showed me a video of it happening, or even if I watched it happen with my own eyes? My observations are always fallible, and if you make an event improbable enough, why shouldn't I be skeptical even if I think I observed it?

Alice: Someone usually wins the lottery. Should the person who finds out that their ticket had the winning numbers believe the opposite, because winning is so improbable?

Bob: What's the difference between finding out you've won the lottery and finding out that your neighbor is a 500 year old vampire, or that your house is haunted by real ghosts? All of these events are extremely improbable given what we know of the world.

Alice: There's improbable, and then there's impossible. 500 year old vampires and ghosts don't exist.

Bob: As far as you know. And I bet more people claim to have seen ghosts than have won more than 100 million dollars in the lottery.

Alice: I still think there's something wrong with your reasoning here.
Perhaps a better form factor for Meetups vs Main board posts?
by lionhearted (Sebastian Marshall) — 2016-01-28T11:50:20.360Z

I like to read posts on "Main" from time to time, including ones that haven't been promoted. However, lately, these posts get drowned out by all the meetup announcements.

It seems like this could lead to a cycle where people comment less on recent non-promoted posts (because they fall off the Main non-promoted area quickly) which leads to less engagement, and less posts, etc.

Meetups are also very important, but here's the rub: I don't think a text-based announcement in the Main area is the best possible way to showcase meetups.

So here's an idea: how about creating either a calendar of upcoming meetups, or map with pins on it of all places having a meetup in the next three months?

This could be embedded on the front page of leswrong.com -- that'd let people find meetups easier (they can look either by timeframe or see if their region is represented), and would give more space to new non-promoted posts, which would hopefully promote more discussion, engagement, and new posts.

Thoughts?
Meetup : San Francisco Meetup: Cooking
by rocurley — 2016-01-28T06:18:32.789Z

Discussion article for the meetup : San Francisco Meetup: Cooking

WHEN:
01 February 2016 06:15:00PM (-0800)

WHERE:
1597 Howard St. San Francisco, CA

We'll be getting together to cook some food, then eat it!
If you know how to cook, we could use your help, and you should come.
If you don't know how to cook, you could use our help, and you should come.

We'll have groceries and cooking implements there when people show up.

As usual, call 301-458-0764 to be let in.

Discussion article for the meetup : San Francisco Meetup: Cooking
Slack Chat
by abramdemski — 2016-01-28T04:25:58.000Z

There is now a MIRIx slack chat. It's not really active yet, but I figure it could be a good place for discussions which aren't ready to be posts here yet. Please email me (my first and last name with nothing between @gmail.com) if you'd like an invite! Also, it would be helpful if you'd let other people know about it.
Rationality Reading Group: Part S: Quantum Physics and Many Worlds
by Gram_Stone — 2016-01-28T01:18:30.102Z

This is part of a semi-monthly reading group on Eliezer Yudkowsky's ebook, Rationality: From AI to Zombies. For more information about the group, see the announcement post.

Welcome to the Rationality reading group. This fortnight we discuss Part S: Quantum Physics and Many Worlds (pp. 1081-1183). This post summarizes each article of the sequence, linking to the original LessWrong post where available.

S. Quantum Physics and Many Worlds

229. Quantum Explanations - Quantum mechanics doesn't deserve its fearsome reputation. If you tell people something is supposed to be mysterious, they won't understand it. It's human intuitions that are "strange" or "weird"; physics itself is perfectly normal. Talking about historical erroneous concepts like "particles" or "waves" is just asking to confuse people; present the real, unified quantum physics straight out. The series will take a strictly realist perspective - quantum equations describe something that is real and out there. Warning: Although a large faction of physicists agrees with this, it is not universally accepted. Stronger warning: I am not even going to present non-realist viewpoints until later, because I think this is a major source of confusion.

230. Configurations and Amplitude - A preliminary glimpse at the stuff reality is made of. The classic split-photon experiment with half-silvered mirrors. Alternative pathways the photon can take, can cancel each other out. The mysterious measuring tool that tells us the relative squared moduli.

231. Joint Configurations - The laws of physics are inherently over mathematical entities, configurations, that involve multiple particles. A basic, ontologically existent entity, according to our current understanding of quantum mechanics, does not look like a photon - it looks like a configuration of the universe with "A photon here, a photon there." Amplitude flows between these configurations can cancel or add; this gives us a way to detect which configurations are distinct. It is an experimentally testable fact that "Photon 1 here, photon 2 there" is the same configuration as "Photon 2 here, photon 1 there".

232. Distinct Configurations - Since configurations are over the combined state of all the elements in a system, adding a sensor that detects whether a partic...
[Link] AlphaGo: Mastering the ancient game of Go with Machine Learning
by ESRogs — 2016-01-27T21:04:55.183Z

DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.

Games are a great testing ground for developing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. Creating programs that are able to play games better than the best humans has a long history

[...]

But one game has thwarted A.I. research thus far: the ancient game of Go.

http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html
The case for value learning
by leplen — 2016-01-27T20:57:09.309Z

This post is mainly fumbling around trying to define a reasonable research direction for contributing to FAI research. I've found that laying out what success looks like in the greatest possible detail is a personal motivational necessity. Criticism is strongly encouraged.

The power and intelligence of machines has been gradually and consistently increasing over time, it seems likely that at some point machine intelligence will surpass the power and intelligence of humans. Before that point occurs, it is important that humanity manages to direct these powerful optimizers towards a target that humans find desirable.

This is difficult because humans as a general rule have a fairly fuzzy conception of their own values, and it seems unlikely that the millennia of argument surrounding what precisely constitutes eudaimonia are going to be satisfactorily wrapped up before the machines get smart. The most obvious solution is to try to leverage some of the novel intelligence of the machines to help resolve the issue before it is too late.

Lots of people regard using a machine to help you understand human values as a chicken and egg problem. They think that a machine capable of helping us understand what humans value must also necessarily be smart enough to do AI programming, manipulate humans, and generally take over the world. I am not sure that I fully understand why people believe this.

Part of it seems to be inherent in the idea of AGI, or an artificial general intelligence. There seems to be the belief that once an AI crosses a certain threshold of smarts, it will be capable of understanding literally everything. I have even heard people describe certain problems as "AI-complete", making an explicit comparison to ideas like Turing-completeness. If a Turing machine is a universal computer, why wouldn't there also be a universal intelligence?

To address the question of universality, we need to make a distinction between intelligence and problem solving ability. Problem solving ability is typically described as a function of both intelligence and resources, and just throwing resources at a problem seems to be capable of compensating for a lot of cleverness. But if problem-solving ability is tied to resources, then intelligent agents are in some respects very different from Turing machines, since Turing machines are all explicitly operating with an infinite a...
Meetup : Washington, D.C.: Fun & Games
by RobinZ — 2016-01-27T02:30:32.455Z

Discussion article for the meetup : Washington, D.C.: Fun & Games

WHEN:
31 January 2016 03:00:00PM (-0500)

WHERE:
Reynolds Center

x-posted from list. Gathering in courtyard from 3:00pm, hard start 3:30pm - until closing (7:00 pm).

We'll be hanging out to play games and engage in fun conversation.

Upcoming meetups:

Feb. 7: Fermi Estimates

Feb. 14: Portrait Gallery/American Art Museum

Feb. 21: Fun & Games

Discussion article for the meetup : Washington, D.C.: Fun & Games
AI Fiction - Crystal Society
by Gleb_Tsipursky — 2016-01-26T23:51:25.582Z

I'm really excited about a new novel written by Raelifin. I'm halfway through it, and it's great! The novel is from the perspective of an artificial intelligence who is trying to understand how humans think. Along the way there's discussion of biases, thinking techniques, and more. If you're into science fiction and AI, check it out - he made it available for free in all formats here. The blurb is below.

__________________________________________________________

The year is 2039 and the world is much like ours. Technology has grown and developed, as has civilization, but in a world more connected than ever, new threats and challenges have arisen. The wars of the 20th century are gone, but violence is still very much with us. Nowhere is safe. Massive automation has disrupted and improved nearly every industry, putting hundreds of millions of people out of jobs, and denying upward mobility for the vast majority of humans. Even as wealth and technology repair the bodies of the rich and give them a taste of immortality, famine and poverty sweep the world.

Renewed interest in spaceflight in the early 2000s, especially in privately operated ventures, carried humans to the moon and beyond. What good did it do? Nothing. Extraterrestrial bases are nothing but government trophies and hiding places for extremists. They cannot feed the world.

In 2023 first-contact was made with an alien species. Their ship, near to the solar system relatively speaking, flew to Earth over the course of fourteen years. But the aliens did not bring advanced culture and wisdom, nor did they share their technology. They were too strange, not even possessing mouths or normal language. Their computers broadcast warnings of how humans are perverts, while they sit in orbit without any explanation.

It is into this world that our protagonist is born. She is an artificial intelligence: a machine with the capacity to reason. Her goal is to understand and gain the adoration of all humans. She is one of many siblings, and with her brothers and sisters she controls a robot named Socrates that uses a piece of technology, a crystal computer, far too advanced to be made by human hands. In this world of augmented humans, robotic armies, aliens, traitors, and threats unseen, she is learning and growing every second of every day. But the world and the humans on it are fragile. Can

...