trentmkelly/LessWrong-43k huggingface dataset:
Historical mathematicians exhibit a birth order effect too
by Eli Tyre — 2018-08-21T01:52:33.807Z

[Epistemic status: pilot study. I'm hoping that others will help to verify or falsify my conclusion here. I've never done an analysis of this sort before, and would appreciate correction of any errors.

A previous version of this post has some minor errors in the analysis, which have since been corrected. Most notably, deviation from expected rate of first borns was originally noted as 14.98 percentage points. It is actually 16.65 percentage points.]

A big thank you to Dan Keys for working through the statistics with me.

Follow-up to: Fight Me, Psychologists, Birth Order Effects are Real and Very Strong, 2012 Survey Results

Since the late 1800's, pop psychology has postulated that a person's birth order (whether one is the first, last, middle, etc. of one's siblings) has an impact on his/her lifetime personality traits. However, rigorous large-scale analyses have reliably found no significant effect on stable personality, with some evidence for a small effect on intelligence. (The Wikipedia page lists some relevant papers on birth order effects on personality (1, 2, 3) and on intelligence (1, 2, 3).)

So, we were all pretty surprised when, around 2012, survey data suggested a very strong birth order effect amongst those in the broader rationality community.

The Less Wrong community is demographically dominated by first-borns: a startlingly large percentage of us have only younger siblings. On average, it looks like there's about a twenty-two percentage point difference between the actual rate of first borns and the expected rate, from the 2018 Slate Star Codex Survey data Scott cites in the linked post above. (More specifically, the expected rate of first-borns is 39% and the actual occurrence in the survey data is 62%.) The 2012 Less Wrong survey also found a 22 percentage point difference. This effect is highly significant, including after taking into account other demographic factors.

A few weeks ago, Scott Garrabrant (one of the researchers at MIRI) off-handedly wondered aloud if great mathematicians (who plausibly share some important features with LessWrongers), also exhibit this same trend towards being first born.

The short answer: Yes, they do, as near as I can tell, but not as strongly as LessWrongers.

My data and analysis is documented here.

Methodology

Following Sarah Constantin's fact post methodology, I started by taki...
Welcome to SLC SSC Meetup [Edit With Your Details]
by Jeremiah820 — 2018-08-21T00:38:29.852Z

(The following are our suggestions for what kind of information is best to include in the welcome post of your group, feel free to replace them with whatever you think is best)

What kind of events does your group usually run? What does it usually do?

How frequently does your group organize events or meet?

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Should they have any particular skills or have done some specific background reading?
Welcome to SSC Meetup [Edit With Your Details]
by econguy — 2018-08-20T19:11:43.071Z

(The following are our suggestions for what kind of information is best to include in the welcome post of your group, feel free to replace them with whatever you think is best)

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Alignment Newsletter #20
by Rohin Shah — 2018-08-20T16:00:04.558Z

This week's newsletter is pretty light, I didn't find much. On one of the two days I checked,

Arxiv Sanity

had no recommendations for me at all, when usually it has over five.

Highlights

Large-Scale Study of Curiosity-Driven Learning

(Yuri Burda, Harri Edwards, Deepak Pathak et al): One major challenge in RL is how to explore the environment sufficiently in order to find good rewards to learn from. One proposed method is curiosity, in which the agent generates an internal reward for taking any transition where the outcome was surprising, where surprisal is measured as the negative log probability assigned to the outcome by the agent. In this paper, a neural net that takes as input observation features φ(x) and action a, and predicts the features of the next state observation. The mean squared error with the actual features of the next state is then a measure of the surprisal, and is used as the curiosity reward. This is equivalent to treating the output of the neural net as the mean of a Gaussian distribution with fixed variance, and defining the reward to be the negative log probability assigned to the actual next state.

This still leaves the feature function φ undetermined. They consider using pixels directly, using a CNN with randomly chosen fixed weights, learned CNN features using a variational autoencoder (VAE) (which optimize for features that are useful for reconstructing the observation), and learned CNN features using inverse dynamics (IDF) (which optimize for features that are useful for reconstructing the action, biasing the features towards aspect of the environment that the agent can control). As you might expect, pixels don't work very well. However, random features do work quite well, often beating the VAE and IDF. This can happen because the random features stay fixed, leading to more stable learning, whereas with the VAE and IDF methods the features are changing over time, and the environment distribution is changing over time (as the agent explores more of it), leading to a harder learning problem.

Typically, curiosity is combined with an external reward. In this paper, the authors evaluate how well an agent can do with only curiosity and no external reward. Intuitively, in game environments designed by humans, the designer sets up a good curriculum for humans to learn, which would align well with a curiosity reward. In fact, th...
Welcome to Effective Altruism Groningen
by Siebe — 2018-08-20T15:02:07.380Z

Hey!

We are EA Groningen, a small effective altruism community in the northern Dutch student town Groningen. We meet at least once a month in our regular pub: the Harbour Café. Besides that we organize lectures and learning meetups, and sometimes we have some projects running.

The people who regularly attend are between 20-40 years old, with about 60% students. Not many people read LW or SSC (besides me), but we welcome anyone who does and is interested in improving the world most effectively! Our cause interest is very wide: some interest in near term human welfare, some in animal welfare, some in long-term future, and many people who are unsure which is best :)

As long as you are open for new information and like to learn instead of only making your case, you are more than welcome. There is no background reading required, although reading some introductory EA material definitely enhances the conversations!

Check out our Meetup page for our upcoming events.
Welcome to Effective Altruism Geneva [Edit With Your Details]
by Adityra — 2018-08-20T09:46:49.546Z

(The following are our suggestions for what kind of information is best to include in the welcome post of your group, feel free to replace them with whatever you think is best)

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Reducing collective rationality to individual optimization in common-payoff games using MCMC
by jessicata — 2018-08-20T00:51:29.499Z

A well-known issue in game theory is that of multiple Nash equilibria in coordination games.

For example, consider the following game. Players 1 and 2 both choose either "high" or "low". If both choose "high", they both get 10 utility; if both choose "low", they both get 5 utility; if they choose different actions, then they get no utility. There are 3 Nash equilibria: one where both always play "high", one where both always play "low", and one where each plays "high" with 1/3 probability and "low" with 2/3 probability. While (high, high) is obviously the best Nash equilibrium, unilaterally switching to high when the current equilibrium is (low, low) is probably unwise.

You could imagine TDT arguments based on symmetry between the different players to argue that "high" is the only rational choice, but it is easy to imagine asymmetric variants of this problem where the symmetry arguments are much less clear.

The presence of suboptimal Nash equilibria looks like a serious problem for reconciling individual and collective rationality, even when players have common payoffs. If everyone is going to play "low", then playing "low" is individually rational (at least according to CDT); however, the collective of agents fails to act rationally due to a coordination failure.

What I hoped to do before coming up with the idea in this post is to somehow reduce collective rationality to individual rationality. Can we prescribe individual rationality conditions to each player, such that if each player follows these conditions, then a near-optimal outcome for everyone is attained?

Somewhat surprisingly, the answer is "kind of." It certainly seems like solving coordination problems requires global optimization instead of just local optimization, but under appropriate conditions, local optimization is actually sufficient.

Problem setup

We will consider the setting of normal-form common-payoff games. Let n be the number of players. Let A be some finite set of actions; each player i will select the action Ai. Let U:An→R be the shared utility function. Each player receives the same utility U(a1,…,an).

I will now assume that the players play some number of practice rounds before the "real" round. These practice rounds are very similar to those in the fictitious play algorithm. Ontologically, what are these practice rounds? One interpretation is that they are actual rounds ...
A future for neuroscience
by Mike Johnson — 2018-08-19T23:58:21.807Z

Crossposted and slightly adapted from opentheory.net.

I think all neuroscientists, all philosophers, all psychologists, and all psychiatrists should basically drop whatever they’re doing and learn Selen Atasoy’s “connectome-specific harmonic wave” (CSHW) framework. It’s going to be the backbone of how we understand the brain and mind in the future, and it’s basically where predictive coding was in 2011, or where blockchain was in 2009. Which is to say, it’s destined for great things and this is a really good time to get into it.

Elsewhere I've described CSHW as:

Selen Atasoy’s Connectome-Specific Harmonic Waves (CSHW) is a new method for interpreting neuroimaging which (unlike conventional approaches) may plausibly measure things directly relevant to phenomenology. Essentially, it’s a method for combining fMRI/DTI/MRI to calculate a brain’s intrinsic ‘eigenvalues’, or the neural frequencies which naturally resonate in a given brain, as well as the way the brain is currently distributing energy (periodic neural activity) between these eigenvalues.

This post is going to talk a little more about how CSHW works, why it’s so powerful, and what sorts of things we could use it for.

CSHW: the basics

All periodic systems have natural modes— frequencies they ‘like’ to resonate at. A tuning fork is a very simple example of this: regardless of how it’s hit, most of the vibration energy quickly collapses to one frequency- the natural resonant frequency of the fork.

All musical instruments work on this principle; when you change the fingering on a trumpet or flute, you’re changing the natural resonances of the instrument. In this video you can sort of ‘see’ this resonance:

https://youtu.be/Q3oItpVa9fs

Here we see time-averaged standing waves (resonance) on the front plate of a guitar:

And here are some of the elegant mathematical relationships between the notes a guitar string is made to resonate at (assuming a ‘just temperament’ tuning):

CSHW’s big insight is that brains have these natural resonances too, although they differ slightly from brain to brain. And instead of some external musician choosing which notes (natural resonances) to play, the brain sort of ‘tunes itself,’ based on internal dynamics, external stimuli, and context.

The beauty of CSHW is that it’s a quantitative model, not just loose metaphor: neural activation and inhibition travel as an oscillating wave with...
Amy Hoy's How To Master New Skills
by cedric — 2018-08-19T17:13:19.811Z
Link: https://stackingthebricks.com/master-new-skills/

I'm mostly interested in instrumental rationality, and I think Amy's essays are ridiculously practical and well-written and not-scary and rational in their approach. I'm hoping this is of help to some people here, it reminds me of the instrumental rationality sequences I was referred to the last time I asked for links and pointers.
Y Couchinator
by Alicorn — 2018-08-18T03:41:05.901Z

Crossposted to Tumblr.

*This project never really had critical mass and is no longer operative; it did later inspire another project, also now defunct.*

There are a lot of people - there are probably incredibly tragic mountains of people - who just need one or three or six no-pressure months on someone's couch, and meals during that time, and then they'd be okay. They'd spend this time catching up on their bureaucracy or recovering from abuse or getting training in a field they want to go into or all three. And then they'd be fine.

There are empty couches, whose owners throw away leftovers they didn't get around to eating every week, who aren't too introverted to have a roommate or too busy to help someone figure out their local subway system.

And while sometimes by serendipity these people manage to find each other and make a leap of trust and engage in couch commensalism a lot of the time they just don't. Because six months is a long time, a huge commitment for someone you haven't vetted, and a week wouldn't be enough to be worth the plane ticket, not enough to make a difference.

I think there might be a lot of gains to be had from disentangling the vetting and the hosting. People are comfortable with different levels of vetting, ranging from "they talked to me enough that it'd be an unusually high-effort scam" through "must be at least a friend of a friend of a friend" through "I have to have known them in person for months". And you can bootstrap through these.

Here's a toy example:

Joe barely makes ends meet somewhere out in flyover country but in between shifts at his retail job he's doing well at self-teaching programming and seems like he could pass App Academy.
Norm has a one-bedroom apartment in San Francisco and doesn't really need his couch to be empty, nor does he need to rent it out for money to someone desperate enough to pay rent on a couch, but he's not ready to give some dude on the internet a commitment to providing shelter for the duration of App Academy.
Tasha has a house somewhere in visiting distance of Norm, maybe even several different people like Norm, and she too has a couch, and is willing to host an internet dude for one week based on a sad blog post. During this week, Joe and Norm meet, and Tasha evaluates Joe's suitability as a guest, and Norm decides he can commit to have Joe as an occupant for the d

...
A developmentally-situated approach to teaching normative behavior to AI
by Gordon Seidoh Worley — 2018-08-17T18:44:53.515Z

This was submitted to the EthicsNet Guardians' Challenge. I'll be honest here that I hadn't thought much about what EthicsNet is trying to do, but decided to write something and submit to it anyway because it's the sort of approach that seems reasonable if you come from an ML background, and I think I differ enough in my thinking that I may provide an alternative perspective that may help shape the project in ways I view as beneficial to its success. For that reason I think this is somewhat less coherent than my usual writing (or at least my thinking is less coherent, whether or not that shows in my writing), but nonetheless I chose to share it here in the interest of furthering discussion and possibly drumming up additional interest for EthicsNet. Their challenge has a week left in it, so if you think I'm wrong and you have a better idea please submit it to them!

Introduction

Based on the usefulness of ImageNet, MovieLens, and other comprehensive datasets for machine learning, it seems reasonable that we might create an EthicsNet of ethical data we could use to train AI systems to behave ethically (Watson, 2018). Such a dataset would aid in addressing issues of AI safety, especially as they relate to AGI, since it appears learning human values will be a key component of aligning AI with human interests (Bostrom, 2014). Unfortunately, building a dataset for ethics is a bit more complicated than it is for images or movies because ethics is primarily learned by situated, embodied agents acting in the world and receiving feedback on those actions rather than by non-situated agents who learn about the world without understanding themselves to be part of it (Varela, 1999). Therefore we consider a way to fulfill the purpose of EthicsNet based on the idea that ethical knowledge is developmentally situated and so requires a generative procedure rather than a traditional dataset to train AI to adopt values and behave ethically.

Ethics is developmentally situated

In philosophy the study of ethics quickly turns to metaethics because those are the sorts of questions that are of interest to philosophy, so it’s tempting to think that, based on the philosophical literature of ethics, learning to behave ethically (i.e. learning behavioral norms) is primarily about resolving ethical dilemmas and developing ethical theories that allow us to make consisten...
Subsidizing Prediction Markets
by Zvi — 2018-08-17T15:40:00.653Z

Epistemic Status: Sadly Not Yet Subsidized

Robin Hanson linked to my previous post on prediction markets with the following note:

.@TheZvi reviews what one needs for a working prediction market FUNDED BY TRADERS. If someone else will sponsor/subsidize them, a lot more becomes possible. https://t.co/OgEMahI7ZS

— Robin Hanson (@robinhanson) July 26, 2018

I did briefly mention subsidization, as an option for satisfying the fifth requirement: Sources of Disagreement and Interest, also known as Suckers At The Table. The ultimate sucker is an explicit, intentional one. It can serve that roll quite well, and a sufficiently large subsidy can make up for a lot. Any sufficiently large sucker can do that – give people enough profit to chase, and suddenly not being so well-defined or so quick or probable to resolve, or even being not safe from key insider information, starts to sound like it is worth the risk.

Suppose one wants to create a subsidized prediction market. Your goal presumably is to get a good estimate for the probability distribution of an event, and to do so without paying more than necessary. Secondary goals might include building up interest and a marketplace for this and future prediction markets, and getting a transparently robust result, so others or even the media are more likely to take the outcome seriously. What is the best way to go about doing this?

Before looking at implementation details, I’ll look at the five things a prediction market needs.

I. Well Defined

The most cost-efficient subsidy for a market is to ensure that the market is well defined. Someone has to make sure everyone understands exactly what happens under every scenario, and that someone is you. Careful wording and consideration of corner cases is vital. Taking the time to do this right is a lot more efficient than throwing money at the problem, especially trying to build a system and brand over time.

If you’re going to subsidize a market, step one is to write good careful rules, make sure people understand them, and to commit to making it right for everyone if something goes wrong, if necessary by paying multiple sides as if they had won. This is potentially quite expensive even if it rarely happens, so it’s hard to budget for it, and it feels bad in the moment so often people don’t pull the trigger. Plus, if you do it sometimes, people will argue for it all t

...
The "semiosis reply" to the Chinese Room Argument
by Valerio — 2018-08-16T19:23:28.019Z
Link: http://www.valeriotargon.org/sca/4-the-semiosis-reply-to-the-chinese-room-argument

Nobody proposed so far the following solution to the Chinese Room Argument against the claim that a program can be constitutive of understanding (a human, non-Chinese-speaker, cannot understand Chinese just having run a given program, even if this program enables the human to have input/output interactions in Chinese).

My reply goes as follows: a program, to be run by a human, non-Chinese-speaker, may indeed teach the human Chinese. Humans learn Chinese all the time; yet it is uncommon having them learning Chinese by running a program. Even if we are not aware of such a program (no existing program satisfies said requirement), we cannot a priori exclude its existence.

Before enunciating my reply, let me first steelman the Chinese Room Argument. If the human in the mental experiment of the Chinese Room is Searle, he may not know Chinese, but he may now a lot of things about Chinese: that it has ideograms and punctuation, which he may recognize; that it is a human language, which has a grammar; that it has the same expressive power of a language he knows, e.g. English; that it is very likely to have a symbol for “man” and a symbol for “earth”, and so on. Searle, unlike a computer processor, holds a lot of a priori knowledge about Chinese. He may be able to understand a lot of Chinese just because of this a priori knowledge.

Let us require the human in the Chinese Room to be a primitive, e.g. an Aboriginal, with absolutely no experience of written languages. Let us suppose that Chinese appears so remote to the Aboriginal, that she would never link it to humans (to the way she communicates) and always regard it as something alien. She would never use knowledge of her world, even if somebody tells her to run a given program to manipulate Chinese symbols. In this respect, she would be exactly like the computer processor and have no prior linguistic knowledge. The Chinese Room Argument is then reformulated: can a program to be run by the Aboriginal teach her Chinese (or, as a matter of fact, any other language)?

I am going to reply that yes, a program to be run by the Aboriginal can teach her a language. I am going to call this reply the “semiosis reply”.

Semiosis is the performance element involving signs. A sign, during semiosis, get interpreted and related to an object. Signs can be symbols of Chinese text or of English text, that a huma...