trentmkelly/LessWrong-43k huggingface dataset:
Weekly LW Meetups
by FrankAdamek — 2016-09-23T15:52:04.563Z

This summary was posted to LW Main on September 23rd. The following week's summary is here.

The following 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:

Baltimore Area / UMBC Weekly Meetup: 25 September 2016 07:00PM
Bay Area Winter Solstice 2016: 17 December 2016 07:00PM
[Moscow] Games in Kocherga club: FallacyMania, Zendo, Tower of Chaos: 28 September 2016 07:40PM
San Francisco Meetup: Mini Talks: 26 September 2016 06:15PM
Sydney Rationality Dojo - October 2016: 02 October 2016 04:00PM
Vienna: 24 September 2016 03:00PM
Washington, D.C.: Outdoor Fun & Games: 25 September 2016 03:30PM

Locations with regularly scheduled meetups: Austin, Berlin, Boston, Brussels, Buffalo, Canberra, Columbus, Denver, Kraków, London, Madison WI, Melbourne, Moscow, New Hampshire, New York, Philadelphia, Research Triangle NC, San Francisco Bay Area, 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 (Los Angeles/Orange Coun...
Meetup : Washington, D.C.: Outdoor Fun & Games
by RobinZ — 2016-09-23T15:11:52.505Z

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

WHEN:
25 September 2016 03:30:00PM (-0400)

WHERE:
Meridian Hill Park

We will be going to the park to hang out, play games, and have fun conversation in the park.

Location: Meridian Hill Park is around 16th & W Streets NW (Google Maps); it's a few blocks away from the Columbia Heights or Woodley Park-Zoo Metro stations. We will be meeting near this entrance, toward the southwest corner.

Upcoming meetups:

Oct. 2: Math Discussion

Oct. 9: Games Discussion

Oct. 16: Fun & Games

Discussion article for the meetup : Washington, D.C.: Outdoor Fun & Games
Meetup : Baltimore Area / UMBC Weekly Meetup
by iarwain1 — 2016-09-23T13:55:38.686Z

Discussion article for the meetup : Baltimore Area / UMBC Weekly Meetup

WHEN:
25 September 2016 08:00:00PM (-0400)

WHERE:
Performing Arts and Humanities Bldg Room 456, 1000 Hilltop Cir, Baltimore, MD 21250

Meeting is on 4th floor of the Performing Arts and Humanities Building. Permit parking designations do not apply on weekends, so park pretty much wherever you want.

Discussion article for the meetup : Baltimore Area / UMBC Weekly Meetup
Meetup : Bay Area Winter Solstice 2016
by ialdabaoth — 2016-09-22T22:12:23.532Z

Discussion article for the meetup : Bay Area Winter Solstice 2016

WHEN:
17 December 2016 07:00:00PM (-0700)

WHERE:
Anna Head Alumnae Hall, 2537 Haste St, Berkeley, CA 94720

It's time to gather together and remember the true Reasons for the Season: axial tilt, orbital mechanics and other vast-yet-comprehensible forces have converged together to bring another year to a close, and as the days grow shorter and colder we remember how profoundly lucky we are to have been forged by blind, impersonal forces into beings that can understand, and wonder, and appreciate ourselves and each other. This year's East Bay Rationalist Winter Solstice will be held in the center of Berkeley, bringing 300 rationalists together in a theatre hall for food, songs, speeches, and conversations. We encourage other Bay denizens who can't make our solstice to put on their own show. Or even if you do come, we encourage people to try out their own ideas. The East Bay Solstice celebration will be on Saturday, December 17th, in the Anna Head Alumnae Hall in Berkeley. Acquire tickets here:
https://www.eventbrite.com/e/2016-bay-area-winter-solstice-tickets-27853776395
We are coordinating with the Bayesian Choir and will be coordinating with various speakers, as in previous years. An MC and schedule will be posted as details solidify.
Kids are welcome. Vegetarian food will be available. Let us know if you have specific accomadation requests or have questions.

Discussion article for the meetup : Bay Area Winter Solstice 2016
Meetup : SF Meetup: Mini Talks
by maia — 2016-09-22T15:23:22.590Z

Discussion article for the meetup : SF Meetup: Mini Talks

WHEN:
26 September 2016 06:15:00PM (-0700)

WHERE:
1597 Howard St., SF

We’ll be meeting to give and listen to very short talks!

We’ll do 7-minute lightning talks with 3 additional minutes allowed for questions. We’ll also limit the number of programming-related talks to no more than half of all talks, in order to promote variety.

A talk doesn’t have to be formal, planned, or even something that you’d expect someone to Give A Talk About; it can be as simple as telling the group about something you find interesting or cool. In the past, we’ve had people talk about topics like: how complicated the process of organizing fresh food for airplane flights is, their experience volunteering for a local political campaign, a video game they were designing and writing, and many others.

We don't expect any sort of preparation or practice for these kinds of talks. They're very casual and the expectations are low. If your talk isn't great, it's okay because we'll just move on to another one in a few minutes. If it helps, think of it this way: you're just being given the conversational floor for a few minutes, in a slightly more organized way than usual.

For help getting into the building, please call (or text, with a likely-somewhat-slower response rate): three zero one, three five six, five four two four.

Format:

We meet and start hanging out at 6:15, but don’t officially start doing the meetup topic until 6:45-7 to accommodate stragglers. Usually there is a food order that goes out before we start the meetup topic.

About these meetups:

The mission of the SF LessWrong meetup is to provide a fun, low-key social space with some structured interaction, where new and non-new community members can mingle and have interesting conversations. Everyone is welcome.

We explicitly encourage people to split off from the main conversation or diverge from the topic if that would be more fun for them (moving side conversations into a separate part of the space if appropriate). Meetup topics are here as a tool to facilitate fun interaction, and we certainly don’t want them to inhibit it.

Discussion article for the meetup : SF Meetup: Mini Talks
Heroin model: AI "manipulates" "unmanipulatable" reward
by Stuart_Armstrong — 2016-09-22T10:27:01.091Z

A putative new idea for AI control; index here.

A conversation with Jessica has revealed that people weren't understanding my points about AI manipulating the learning process. So here's a formal model of a CIRL-style AI, with a prior over human preferences that treats them as an unchangeable historical fact, yet will manipulate human preferences in practice.

Heroin or no heroin

The world

In this model, the AI has the option of either forcing heroin on a human, or not doing so; these are its only actions. Call these actions F or ~F. The human's subsequent actions are chosen from among five: {strongly seek out heroin, seek out heroin, be indifferent, avoid heroin, strongly avoid heroin}. We can refer to these as a++, a+, a0, a-, and a--. These actions achieve negligible utility, but reveal the human preferences.

The facts of the world are: if the AI does force heroin, the human will desperately seek out more heroin; if it doesn't the human will act moderately to avoid it. Thus F→a++ and ~F→a-.

Human preferences

The AI starts with a distribution over various utility or reward functions that the human could have. The function U(+) means the human prefers heroin; U(++) that they prefer it a lot; and conversely U(-) and U(--) that they prefer to avoid taking heroin (U(0) is the null utility where the human is indifferent).

It also considers more exotic utilities. Let U(++,-) be the utility where the human strongly prefers heroin, conditional on it being forced on them, but mildly prefers to avoid it, conditional on it not being forced on them. There are twenty-five of these exotic utilities, including things like U(--,++), U(0,++), U(-,0), and so on. But only twenty of them are new: U(++,++)=U(++), U(+,+)=U(+), and so on.

Applying these utilities to AI actions give results like U(++)(F)=2, U(++)(~F)=-2, U(++,-)(F)=2, U(++,-)(~F)=1, and so on.

Joint prior

The AI has a joint prior P over the utilities U and the human actions (conditional on the AI's actions). Looking at terms like P(a--| U(0), F), we can see that P defines a map μ from the space of possible utilities (and AI actions), to a probability distribution over human actions. Given μ and the marginal distribution PU over utilities, we can reconstruct P entirely.

For this model, we'll choose the simplest μ possible:

The human is rational.

Thus, given U(++), the human ...
Heroin model: AI "manipulates" "unmanipulatable" reward
by Stuart_Armstrong — 2016-09-21T18:10:47.000Z

A putative new idea for AI control; index here.

A conversation with Jessica has revealed that people weren't understanding my points about AI manipulating the learning process. So here's a formal model of a CIRL-style AI, with a prior over human preferences that treats them as an unchangeable historical fact, yet will manipulate human preferences in practice.

Heroin or no heroin

The world

In this model, the AI has the option of either forcing heroin on a human, or not doing so; these are its only actions. Call these actions F or ¬F. The human's subsequent actions are chosen from among five: {strongly seek out heroin, seek out heroin, be indifferent, avoid heroin, strongly avoid heroin}. We can refer to these as a++,a+,a0,a−, and a−−. These actions achieve negligible utility, but reveal the human preferences.

The facts of the world are: if the AI does force heroin, the human will desperately seek out more heroin; if it doesn't the human will act moderately to avoid it. Thus F→a++ and ¬F→a−.

Human preferences

The AI starts with a distribution over various utility or reward functions that the human could have. The function U(+) means the human prefers heroin; U(++) that they prefer it a lot; and conversely U(−) and U(−−) that they prefer to avoid taking heroin (U(0) is the null utility where the human is indifferent).

It also considers more exotic utilities. Let U(++,−) be the utility where the human strongly prefers heroin, conditional on it being forced on them, but mildly prefers to avoid it, conditional on it not being forced on them. There are twenty-five of these exotic utilities, including things like U(−−,++), U(0,++), U(−,0), and so on. But only twenty of them are new: U(++,++)=U(++), U(+,+)=U(+), and so on.

Applying these utilities to AI actions give results like U(++)(F)=2, U(++)(¬F)=−2, U(++,−)(F)=2, U(++,−)(¬F)=1, and so on.

Joint prior

The AI has a joint prior P over the utilities U and the human actions (conditional on the AI's actions). Looking at terms like P(a−−|U(0),F), we can see that P defines a map μ from the space of possible utilities (and AI actions), to a probability distribution over human actions. Given μ and the marginal distribution PU over utilities, we can reconstruct P entirely.

For this model, we'll choose the simplest μ possible:

The human is rational.

Thus, given U(++), the human will always choose a++; given U(++,−), the human

...
Problems with learning values from observation
by David Scott Krueger (formerly: capybaralet) — 2016-09-21T00:40:49.102Z

I dunno if this has been discussed elsewhere (pointers welcome).
Observational data doesn't allow one to distinguish correlation and causation.
This is a problem for an agent attempting to learn values without being allowed to make interventions.
For example, suppose that happiness is just a linear function of how much Utopamine is in a person's brain.
If a person smiles only when their Utopamine concentration is above 3 ppm, then an value-learner which observes both someone's Utopamine levels and facial expression and tries to predict their reported happiness on the basis of these features will notice that smiling is correlated with higher levels of reported happiness and thus erroneously believe that it is partially responsible for the happiness.
------------------
an IMPLICATION:
I have a picture of value learning where the AI learns via observation (since we don't want to give an unaligned AI access to actuators!).
But this makes it seem important to consider how to make an un unaligned AI safe-enough to perform value-learning relevant interventions.
Logical Inductors that trust their limits
by Scott Garrabrant — 2016-09-20T23:17:55.000Z

Here is another open question related to Logical Inductors. I have not thought about it very long, so it might be easy.

Does there exist a logical inductor {Pn} over PA such that for all ϕ:

PA proves that P∞(ϕ) exists and is in [0,1], and

En(P∞(ϕ))≂nPn(ϕ)?

Note that P∞(ϕ) need not be computable so this does not happen by default.

For example, consider the logical inductor described in the paper with the extra condition that if ever the deductive state is discovered to be inconsistent, all probabilities are set to 0 forever. This clause will never be executed, since PA is consistent, but since this clause exists, PA can prove that P∞(ϕ) exists. (PA proves that if PA is consistent, the limit exists using the proof in the paper, and if PA is inconsistent, the limit exists and equals 0.)

However, this logical inductor will not satisfy the second property. Consider the sentence ⊤. Pn(⊤) will converge to 1, while En(P∞(⊤)) will converge to the probability according to P∞ that PA is consistent. (PA proves that if PA is consistent, the limit is 1 using the proof in the paper, and that if PA is inconsistent, the limit is 0.)

If a Logical Inductor with the above property is found, there are many follow up questions you could ask.

Can you make an analogue of the self trust property that works for P∞? Does the above property imply that self trust property?

Is there some simple extra condition that could be added to the definition of a Logical Inductor, that implies everything we could want about beliefs about P∞?

A good place to start with this question might be to analyze a logical inductor that in the spirit of the Demski prior adds sentences to the deductive state only if they are propositionally consistent with all previous sentences. This way, PA will prove that the algorithm defines a logical inductor over some consistent propositional theory (even if it does not know if that theory is PA).
Meetup : Games in Kocherga club: FallacyMania, Zendo, Tower of Chaos
by Alexander230 — 2016-09-20T19:33:18.835Z

Discussion article for the meetup : Games in Kocherga club: FallacyMania, Zendo, Tower of Chaos

WHEN:
28 September 2016 07:40:00PM (+0300)

WHERE:
Moscow, B.Dorogomilovskaya, 5-2

Welcome to Moscow LW community makeshift games! In that games, some rationality skills are involved, so you can practise while you playing!

FallacyMania: it is a game where you guess logical fallacies in arguments, or practise using logical fallacies yourself (depending on team in which you will be).

Details about the game: http://goo.gl/BtRVhB

Zendo: table game with guessing the rules about items placement on the table.

Game rules: https://goo.gl/RW2Fx7

Tower of Chaos: funny game with guessing the rules of human placement on a Twister mat.

Game rules: https://goo.gl/u9qgc3

Come to antikafe "Kocherga", ul.B.Dorogomilovskaya, 5-2. The map is here: http://kocherga-club.ru/#contacts . Nearest metro station is Kievskaya. If you are lost, call Sasha at +7-905-527-30-82.

Games begin at 19:40, the length is 3.5 hours.

Discussion article for the meetup : Games in Kocherga club: FallacyMania, Zendo, Tower of Chaos
Superintelligence FAQ
by Scott Alexander — 2016-09-20T19:00:00.000Z

Editor's note: this post is several years out of date and doesn't include information on modern systems like GPT-4, but is still a solid layman's introduction to why superintelligence might be important, dangerous and confusing.

1: What is superintelligence?

A superintelligence is a mind that is much more intelligent than any human. Most of the time, it’s used to discuss hypothetical future AIs.

1.1: Sounds a lot like science fiction. Do people think about this in the real world?

Yes. Two years ago, Google bought artificial intelligence startup DeepMind for $400 million; DeepMind added the condition that Google promise to set up an AI Ethics Board. DeepMind cofounder Shane Legg has said in interviews that he believes superintelligent AI will be “something approaching absolute power” and “the number one risk for this century”.

Many other science and technology leaders agree. Astrophysicist Stephen Hawking says that superintelligence “could spell the end of the human race.” Tech billionaire Bill Gates describes himself as “in the camp that is concerned about superintelligence…I don’t understand why some people are not concerned”. SpaceX/Tesla CEO Elon Musk calls superintelligence “our greatest existential threat” and donated $10 million from his personal fortune to study the danger. Stuart Russell, Professor of Computer Science at Berkeley and world-famous AI expert, warns of “species-ending problems” and wants his field to pivot to make superintelligence-related risks a central concern.

Professor Nick Bostrom is the director of Oxford’s Future of Humanity Institute, tasked with anticipating and preventing threats to human civilization. He has been studying the risks of artificial intelligence for twenty years. The explanations below are loosely adapted from his 2014 book Superintelligence, and divided into three parts addressing three major questions. First, why is superintelligence a topic of concern? Second, what is a “hard takeoff” and how does it impact our concern about superintelligence? Third, what measures can we take to make superintelligence safe and beneficial for humanity?

2: AIs aren’t as smart as rats, let alone humans. Isn’t it sort of early to be worrying about this kind of thing?

Maybe. It’s true that although AI has had some recent successes – like DeepMind’s newest creation AlphaGo defeating the human Go champion in April – it still has nothing lik...
Seven Apocalypses
by Commander Zander — 2016-09-20T02:59:20.173Z

0: Recoverable Catastrophe

An apocalypse is an event that permanently damages the world. This scale is for scenarios that are much worse than any normal disaster. Even if 100 million people die in a war, the rest of the world can eventually rebuild and keep going.

1: Economic Apocalypse

The human carrying capacity of the planet depends on the world's systems of industry, shipping, agriculture, and organizations. If the planet's economic and infrastructural systems were destroyed, then we would have to rely on more local farming, and we could not support as high a population or standard of living. In addition, rebuilding the world economy could be very difficult if the Earth's mineral and fossil fuel resources are already depleted.

2: Communications Apocalypse

If large regions of the Earth become depopulated, or if sufficiently many humans die in the catastrophe, it's possible that regions and continents could be isolated from one another. In this scenario, globalization is reversed by obstacles to long-distance communication and travel. Telecommunications, the internet, and air travel are no longer common. Humans are reduced to multiple, isolated communities.

3: Knowledge Apocalypse

If the loss of human population and institutions is so extreme that a large portion of human cultural or technological knowledge is lost, it could reverse one of the most reliable trends in modern history. Some innovations and scientific models can take millennia to develop from scratch.

4: Human Apocalypse

Even if the human population were to be violently reduced by 90%, it's easy to imagine the survivors slowly resettling the planet, given the resources and opportunity. But a sufficiently extreme transformation of the Earth could drive the human species completely extinct. To many people, this is the worst possible outcome, and any further developments are irrelevant next to the end of human history.

5: Biosphere Apocalypse

In some scenarios (such as the physical destruction of the Earth), one can imagine the extinction not just of humans, but of all known life. Only astrophysical and geological phenomena would be left in this region of the universe. In this timeline we are unlikely to be succeeded by any familiar life forms.

6: Galactic Apocalypse

A rare few scenarios have the potential to wipe out not just Earth, but also all nearby space. This usually come

...
Adding and removing complexity from models
by Elo — 2016-09-19T23:31:41.296Z

Original post: http://bearlamp.com.au/adding-and-removing-complexity-from-models/

I had a really interesting conversation with a guy about modelling information. what I did when talking to him is in one case insist that his model be made more simple because adding more variation in the model was unhelpful, and then in another case in the same conversation, insist his model be made more complicated to account for the available information that didn't fit his model.

On reflection I realised that I had applied two opposing forces to models of information and could only vaguely explain why. With that in mind I decided to work out what was going on. The following is obvious, but that's why I am writing it out, so that no one else has to do the obvious thing.

Case where a model should be simplified

This all comes down to what you are measuring or describing. If you are trying to describe something rather general, like "what impact do number of beach-goers have on the pollution at the beach?", it's probably not important what gender, age, race, time spent at the beach or socioeconomic status the beach goers are. (With the exception of maybe socioeconomic status of the surrounding geopolitical territory), what is important is maybe two pieces of information:

A measure of the number of beach goers

A measure of the pollution

That's it. This would be a case for reducing the survey of beach goers down to a counter of beach goers and a daily photo of the remaining state of the beach at the end of the day (which could be compared to other similar photos). Or even just - 3 photos, one at 9am (start), one at 1pm (peak) and one at 5pm (end). This model needs no more moving parts. The day you want to start using historic information to decide how many beach cleaners you want to employ, you can do that from the limited but effective data you have gathered.

Case where a model should have more moving parts added to it.

Let's continue the same example. You have 3 photos of each day, but sometimes the 1pm photo is deserted. Nearly no one is at the beach, and you wonder why. It's also messing with your predictions because there is still a bit of rubbish at 5pm even though very few people were at the beach. The model no longer explains the&nb...