trentmkelly/LessWrong-43k huggingface dataset:
Learning Mathematics in Context
by Crux — 2016-01-26T22:27:16.913Z

I have almost no direct knowledge of mathematics. I took various mathematics courses in school, but I put in the minimal amount of effort required to pass and immediately forgot everything afterwards.

When people learn foreign languages, they often learn vocabulary and grammar out of context. They drill vocabulary and grammar in terms of definitions and explanations written in their native language. I, however, have found this to be intolerably boring. I'm conversational in Japanese, but every ounce of my practice came in context: either hanging out with Japanese friends who speak limited English, or watching shows and adding to Anki new words or sentence structures I encounter.

I'm convinced that humans must spike their blood sugar and/or pump their body full of stimulants such as caffeine in order to get past the natural tendency to find it unbearably dull to memorize words and syntax by rote and lifeless connection with the structures in their native language.

I've tried to delve into some mathematics recently, but I get the impression that most of the expositions fall into one of two categories: Either (1) they assume that I'm a student powering my day with coffee and chips and that I won't find it unusual if I'm supposed to just trust that once I spend 300 hours pushing arbitrary symbols around I'll end up with some sort of insight. Or (2) they do enter the world of proper epistemological explanations and deep real-world relevance, but only because they expect that I'm already quite well-versed in various background information.

I don't want an introduction that assumes I'm the average unthinking student, and I don't want an exposition that expects me to understand five different mathematical fields before I can read it. What I want seems likely to be uncommon enough that I might as well simply say: I don't care what field it is; I just want to jump into something which assumes no specifically mathematical background knowledge but nevertheless delves into serious depths that assume a thinking mind and a strong desire for epistemological sophistication.

I bought Calculus by Michael Spivak quite a while ago because the Amazon reviews led me to believe it may fit these considerations. I don't know whether that's actually the case or not though, as I haven't tried reading it yet.

Any suggestions would be appreciated.
Meetup : Sydney Rationality Dojo - February 2016
by luminosity — 2016-01-26T21:15:44.009Z

Discussion article for the meetup : Sydney Rationality Dojo - February 2016

WHEN:
07 February 2016 04:00:00PM (+1100)

WHERE:
10 Shepherd Street, Chippendale

Join us for the first dojo of the new year. Get a head start on accomplishing everything you want to get done this year! After the main session, there will be a group dinner for those who want to come along.

Discussion article for the meetup : Sydney Rationality Dojo - February 2016
Goal completion: algorithm ideas
by Stuart_Armstrong — 2016-01-26T10:01:35.000Z

A putative new idea for AI control; index here.

This post will be extending ideas from inverse reinforcement learning (IRL) to the problem of goal completion. I'll be drawing on the presentation and the algorithm from Apprenticeship Learning via Inverse Reinforcement Learning (with one minor modification).

In that setup, the environment is an MDP (Markov Decision process), and the real reward R is assumed to be linear in the "features" of the state-action space. Features are functions φi from the full state-action space S×A to the unit interval [0,1] (the paper linked above only considers functions from the state space; this is the "minor modification"). These features form a vector φ∈[0,1]^k, for k different features. The actual reward is given by the inner product with a vector w∈ℝ^k, thus the reward at state-action pair (s,a) is

R(s,a)=w.φ(s,a).

To ensure the reward is always between -1 and 1, w is constrained to have ||w||_1 ≤ 1; to reduce redundancy, we'll assume ||w||_1=1.

The advantages of linearity is that we can compute the expected rewards directly from the expected feature vector. If the agent follows a policy π (a map from state to action) and has a discount factor γ, the expected feature vector is

μ(π) = E(Σ_t γ^tφ(s_t,π(s_t)),

where s_t is the state at step t.

The agent's expected reward is then simply

E(R) = w . μ(π).

Thus the problem of computing the correct reward is reduced to the problem of computing the correct w. In practice, to compute the correct policy, we just need to find one whose expected features are close enough to optimal; this need not involve computing w.

Inverse reinforcement learning

The approach of IRL is then to find a way to efficiently compute w, given some "trajectories": examples of good performance, provided by (human) experts. These experts are following the expert policy πE Given these trajectories, the agent can compute an empirical estimate for the μE=μ(πE), by simply averaging the (discounted) feature vectors produced on each trajectory.

The algorithm used is to gradually expand a series of policies π(0), π(1),... until μE is close enough to the convex hull of the μ(π(i)). Then a policy is chosen in the hull of {π(0), π(1),...}, whose expected features are close to those of μE.

Note that since there exists a genuine w such that the experts act as w.μE-maximisers, there must exist a pure deterministic policy that i

...
Meetup : Rationality Meetup Vienna
by AnnaLeptikon — 2016-01-26T09:57:28.958Z

Discussion article for the meetup : Rationality Meetup Vienna

WHEN:
13 February 2016 03:00:00PM (+0100)

WHERE:
Kaisermühlenstraße, Wien

Location:
(often depicted wrong, come to U2 Stadlau)
See picture below!

Event on Facebook: https://www.facebook.com/events/1227373493945585/
(join Rationality Vienna)

Topic:
Not defined yet - therefore probably open microphone (means collecting topics in the beginning and then voting on preferences and order)

Discussion article for the meetup : Rationality Meetup Vienna
The Charity Impact Calculator
by Gleb_Tsipursky — 2016-01-26T05:01:07.180Z

This will be of interest mainly to EA-friendly LWs, and is cross-posted on the EA Forum, The Life You Can Save, and Intentional Insights

The Life You Can Save has an excellent tool to help people easily visualize and quantify the impact of their giving: the Impact Calculator. It enables people to put in any amount of money they want, then click on a charity, and see how much of an impact their money can have. It's a really easy way to promote effective giving to non-EAs, but even EAs who didn't see it before can benefit. I certainly did, when I first played around with it. So I wrote a blog post, copy-pasted below, for The Life You Can Save and for Intentional Insights, to help people learn about the Impact Calculator. If you like the blog, please share this link to The Life You Can Save blog, as opposed to this post. Any feedback on the blog post itself is welcomed!

__________________________________________________________________________

How a Calculator Helped Me Multiply My Giving

It feels great to see hope light up in the eyes of a beggar in the street as you stop to look at them when others pass them by without a glance. Their faces widen in a smile as you reach into your pocket and take out your wallet. "Thank you so much" is such a heartwarming phrase to hear from them as you pull out five bucks and put the money in the hat in front of them. You walk away with your heart beaming as you imagine them getting a nice warm meal at McDonalds due to your generosity.

Yet with the help of a calculator, I learned how to multiply that positive experience manifold! Imagine that when you give five dollars, you don’t give just to one person, but to seven people. When you reach into your pocket, you see seven smiles. When you put the money in the hat, you hear seven people say “Thank you so much.”

The Life You Can Save has an Impact Calculator that helps you calculate the impact of your giving. You can put in any amount of money you want, then click on a charity of your choice, and see how much of an impact your money can have.

When I learned about this calculator, I decided to check out how far $5 can take me. I went through various charities listed there and saw the positive difference that my money can make.

I was especially struck by one charity, GiveDirectly is a nonprofit that enables you to

...
Open thread, Jan. 25 - Jan. 31, 2016
by username2 — 2016-01-25T21:07:02.746Z

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

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

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

4. Open Threads should start on Monday, and end on Sunday.
Goal completion: algorithm ideas
by Stuart_Armstrong — 2016-01-25T17:36:07.364Z

A putative new idea for AI control; index here.

This post will be extending ideas from inverse reinforcement learning (IRL) to the problem of goal completion. I'll be drawing on the presentation and the algorithm from Apprenticeship Learning via Inverse Reinforcement Learning (with one minor modification).

In that setup, the environment is an MDP (Markov Decision process), and the real reward R is assumed to be linear in the "features" of the state-action space. Features are functions φi from the full state-action space S×A to the unit interval [0,1] (the paper linked above only considers functions from the state space; this is the "minor modification"). These features form a vector φ∈[0,1]k, for k different features. The actual reward is given by the inner product with a vector w∈ℝk, thus the reward at state-action pair (s,a) is

R(s,a)=w.φ(s,a).

To ensure the reward is always between -1 and 1, w is constrained to have ||w||1 ≤ 1; to reduce redundancy, we'll assume ||w||1=1.

The advantages of linearity is that we can compute the expected rewards directly from the expected feature vector. If the agent follows a policy π (a map from state to action) and has a discount factor γ, the expected feature vector is

μ(π) = E(Σt γtφ(st,π(st)),

where st is the state at step t.

The agent's expected reward is then simply

E(R) = w . μ(π).

Thus the problem of computing the correct reward is reduced to the problem of computing the correct w. In practice, to compute the correct policy, we just need to find one whose expected features are close enough to optimal; this need not involve computing w.

Inverse reinforcement learning

The approach of IRL is then to find a way to efficiently compute w, given some "trajectories": examples of good performance, provided by (human) experts. These experts are following the expert policy πE Given these trajectories, the agent can compute an empirical estimate for the μE=μ(πE), by simply averaging the (discounted) feature vectors produced on each trajectory.

The algorithm used is to gradually expand a series of policies π(0), π(1),... until μE is close enough to the convex hull of the μ(π(i)). Then a policy is chosen in the hull of {π(0), π(1),...}, whose expected fea...
The map of quantum (big world) immortality
by turchin — 2016-01-25T10:21:04.857Z

The main idea of quantum (the name "big world immortality" may be better) is that if I die, I will continue to exist in another branch of the world, where I will not die in the same situation.

This map is not intended to cover all known topics about QI, so I need to clarify my position.

I think that QI may work, but I put it as Plan D for achieving immortality, after life extension(A), cryonics(B) and digital immortality(C). All plans are here.

I also think that it may be proved experimentally, namely that if I turn 120 years or will be only survivor in plane crash I will assign higher probability to it. (But you should not try to prove it before as you will get this information for free in next 100 years.)

There is also nothing quantum in quantum immortality, because it may work in very large non-quantum world, if it is large enough to have my copies. It was also discussed here: Shock level 5: Big worlds and modal realism.

There is nothing good in it also, because most of my survived branches will be very old and ill. But we could use QI to work for us, if we combine it with cryonics. Just sign up for it or have an idea to sign up, and most likely you will find your self in survived branch where you will be resurrected after cryostasis. (The same is true for digital immortality - record more about your self and future FAI will resurrect you, and QI rises chances of it.)

I do not buy "measure" objection. It said that one should care only about his "measure of existence", that is the number of all branches there he exists, and if this number diminish, he is almost dead. But if we take an example of a book, it still exist until at least one copy of it exist. We also can't measure the measure, because it is not clear how to count branches in infinite universe.

I also don't buy ethical objection that QI may lead unstable person to suicide and so we should claim that QI is false. I think that rational understanding of QI is that it or not work, or will result in severe injuries. The idea of soul existence may result in much stronger temptation to suicide as it at least promise another better world, but I never heard that it was hidden because it may result in suicide. Religions try to stop suicide (which is logical in their premises) by adding additional rule against it. So, QI itself is not promoting suicide and personal instability may be the main c...
Study partner matching thread
by NoSignalNoNoise — 2016-01-25T04:25:03.592Z

Nate Soares recommends pairing up when studying, so I figured it would be useful to facilitate that.

If you are looking for a study partner, please post a top-level comment saying:

What you want to study

Your level of relevant background knowledge

If you have sources in mind (MOOCs, textbooks, etc), what those are

Your time zone
Beware surprising and suspicious convergence
by Thrasymachus — 2016-01-24T19:13:17.813Z

[Cross]

Imagine this:

Oliver: … Thus we see that donating to the opera is the best way of promoting the arts.

Eleanor: Okay, but I’m principally interested in improving human welfare.

Oliver: Oh! Well I think it is also the case that donating to the opera is best for improving human welfare too.

Generally, what is best for one thing is usually not the best for something else, and thus Oliver’s claim that donations to opera are best for the arts and human welfare is surprising. We may suspect bias: that Oliver’s claim that the Opera is best for the human welfare is primarily motivated by his enthusiasm for opera and desire to find reasons in favour, rather than a cooler, more objective search for what is really best for human welfare.

The rest of this essay tries to better establish what is going on (and going wrong) in cases like this. It is in three parts: the first looks at the ‘statistics’ of convergence - in what circumstances is it surprising to find one object judged best by the lights of two different considerations? The second looks more carefully at the claim of bias: how it might be substantiated, and how it should be taken into consideration. The third returns to the example given above, and discusses the prevalence of this sort of error ‘within’ EA, and what can be done to avoid it.

Varieties of convergence

Imagine two considerations, X and Y, and a field of objects to be considered. For each object, we can score it by how well it performs by the lights of the considerations of X and Y. We can then plot each object on a scatterplot, with each axis assigned to a particular consideration. How could this look?

At one extreme, the two considerations are unrelated, and thus the scatterplot shows no association. Knowing how well an object fares by the lights of one consideration tells you nothing about how it fares by the lights of another, and the chance that the object that scores highest on consideration X also scores highest on consideration Y is very low. Call this no convergence.

At the other extreme, considerations are perfectly correlated, and the ‘scatter’ plot has no scatter, but rather a straight line. Knowing how well an object fares by consideration X tells you exactly how well it fares by consideration Y, and the object that scores highest on consideration X is certain to be scored highest on consideration Y. Call this strong convergence.

In most ca...
"Why Try Hard" Essay targeted at non rationalists
by  — 2016-01-24T16:40:52.442Z

Hello everyone,

This is a follow-up to my last post about optimizing, which I intended to spread to nonrationalist friends of mine. Initial feedback let me know that a lot of the language was a put-off, as well as the dry style and lack of opposing counters to arguments against optimization.

I've tried to take some of those ideas and put it into a new essay, one that tries to get across the idea that planning is important to accomplish goals.

I'd appreciate any/all critiques-- the comments last time were very helpful in learning what I could improve on:

Why Try Hard?

Life is pretty hard. Seriously, it scores an 11/10 on the Mohs Scale. Scratch that (eyy), it’s more like a 12/10. Which is probably why many people go through life without too many dreams and ambitions. I mean, it’s hard to just deal with daily problems of living, not to mention those pesky social interactions no one seems to get the hang of (“So you grasp their hand and apply pressure while vigorously shaking it a few inches up and down?”).

But you’re going to be different.

You’re going to try and make a difference, and change the world. Except that everyone around you seems to be talking about the “naivete of youth” and seems pretty jaded (6.5 Mohs) about life. Obviously their cynicism isn’t sharp enough to scratch all of life (6.5<12), so you listen their warnings and run off to face the final boss anyways.

After all, if you really try hard, things should work out, right? You’ve got the drive, the dream, the baseball cap, and the yellow electrical mouse. Why wouldn’t things work out for you, the main character?

Deep down though, you probably also already realize the futility of trying to create systemic change. I mean, those jaded mentors of yours once had your idealism too. And they also wanted to make the world a better place. But they ventured out into the world, and came back, battered and wizened-- more attuned to the reality we live in.

“Might it be smarter,” that little voice in your head asks, “to bow to the reality of the situation, and lower your sights?”

And the reality of the situation is terrible. We have hundreds of thousands of lives being lost each day. Terrible diseases that cripple our livelihood and te...
Desperation
by So8res — 2016-01-24T02:00:00.000Z
Link: https://mindingourway.com/desperation/

The next three posts will discuss what I dub the three dubious virtues: desperation, recklessness, and defiance. I call them dubious, because each can easily turn into a vice if used incorrectly or excessively. As you read these posts, keep in mind the law of equal and opposite advice. Though these virtues are dubious, I have found each of them to be a crucial component of a strong and healthy intrinsic motivation system.

The first of the three dubious virtues is desperation. There are bad ways to be desperate: visible desperation towards people can put you in a bad social position, strain your relationships, or otherwise harm you. Desperation towards a goal, on the other hand, is vital for a guilt-free intrinsic drive.

By "desperation towards a goal" I mean the possession of a goal so important to you that you can commit yourself to it fully, without hesitation, without some part of you wondering whether it's really worth all your effort. I mean a goal that you pursue with both reckless abandon and cautious deliberation in fair portions. I mean a goal so important that it does not occur to you to spare time wondering whether you can achieve it, but only whether this path to achieving it is better or worse than that path.

In my experience, the really powerful intrinsic motivations require that you're able to struggle as if something of incredible value is on the line. That's much easier if, on a gut level, you believe that's true.

Desperate people have a power that others lack: they have the ability to go all out, to put all their effort towards a task without reservation. Most people I have met don't have the ability to go all out for anything, not even in their imagination.

Ask yourself: is there anything you would go all out for? Is there anything some antagonist could put in danger, such that you would pull out all your stops? Is there any threat so dire that you would hold nothing back, in your struggle to make things right?

I have met many people who cannot honestly answer "yes" to this question, not even under imaginary circumstances. If I ask them to imagine their family being kidnapped, they say they would call the police and wait anxiously. If I ask them to imagine the world threatened by an asteroid, they say they would do their best to enjoy their remaining time. These are fine and prudent answers. Yet, even if I ask them to imagine strange scenarios w...
[Link] Video Presentation: Rationality 101 for Secular People
by Gleb_Tsipursky — 2016-01-24T00:45:50.587Z

Secular people are a natural target group for pitching rationality, since they don't suffer from one of the most debilitating forms of irrationality and also because they have warm fuzzies toward the concept of reason. From reason, it's easy to transition toward what it would be reasonable to do, namely be reasonable about how our minds work and how we should improve them. I did a Rationality 101 for Secular People presentation that was pretty successful, with a number of people following up and showing an interest in gaining further rationality knowledge. Here's a video of the presentation I made, and it has the PP slides I made uploaded into SlideShare. Anyone who wishes to do so is free to use these materials for their own needs, whether sharing the video with secular friends or doing a version of this workshop for local secular groups.
Meetup : LessWrong Aus Online Hangout
by Calien — 2016-01-23T12:55:34.292Z

Discussion article for the meetup : LessWrong Aus Online Hangout

WHEN:
06 February 2016 07:30:00PM (+1100)

WHERE:
Canberra

(Facebook event: https://www.facebook.com/events/1064379460250772/)

Greetings! I haven't done one of these for far too long, but it's back. Tell us what you've learned recently, ask for rationalist advice, talk about cool stuff, and so on. I'll post a Google Hangouts link on Facebook close to the time.

We tend to go until late, so if you can't make it for the official time, you're welcome to pop in anyway.

People outside Australia are welcome too! We've heard from New Zealanders, South Americans, Europeans, North Americans...

Time zone is 7.30pm Canberra/Melbourne/Sydney, 6.30pm Brisbane.

Discussion article for the meetup : LessWrong Aus Online Hangout
Meetup : San Francisco Meetup: Group Debugging
by rocurley — 2016-01-23T06:17:56.350Z

Discussion article for the meetup : San Francisco Meetup: Group Debugging

WHEN:
25 January 2016 06:15:00PM (-0800)

WHERE:
1597 Howard St. San Francisco, CA

We'll be meeting to try and solve each others problems! As usual, call 301-458-0764 to be let in.

This previously (incorrectly) said 1390 Market st.

Discussion article for the meetup : San Francisco Meetup: Group Debugging