trentmkelly/LessWrong-43k huggingface dataset:
Two Bay Area Winter Solstice Events
by Chelsea Voss — 2019-12-01T18:49:03.626Z

Happy December, everyone! This year, the Bay Area Winter Solstice organizers have two events on offer for you to enjoy. Hope to see you there!

Pre-Solstice Unconference, 2019

Saturday, December 14, 2019

Want to spend some time socializing with the people you'll see at Solstice? This year, we're doing an Unconference the day before! It's like an afterparty but without the "after".

[LessWrong event with more details]

Get tickets on Eventbrite.

Bay Area Winter Solstice, 2019

Sunday, December 15, 2019

Gather with us in a planetarium in the Oakland hills. Huddle and sing against the encroaching cold and dark. Share warmth and light. Together we will celebrate humanity and the things that matter to us, through speeches and songs.

[LessWrong event with more details]

Purchase a ticket for this year's Solstice using the secretparty link.
Have you experienced a purity norm around learning "from first principles"?
by aaq — 2019-12-01T15:56:18.593Z

In answering Teach's question "Learning Abstract Math from First Principles?", I wrote

I find that anyone who says they "learned it from first principles" is usually putting on airs. It's an odd intellectual purity norm that I think is unfortunately very common among the mathematically- and philosophically-minded.

This does match my personal experience, but I also have some mild OCD tendencies that tie in with this stuff, and so it may just be me. What do other LWers think?
Vocal Range
by jefftk — 2019-12-01T15:50:02.022Z

I'm organizing another

secular
solstice, which has me thinking about where to pitch songs so the
most people will feel comfortable. I found several people saying that
range of C to C generally works well with untrained voices, and if
they had said something weird like Db-E I might have believed them,
but C-C is so simple I'm skeptical.

I tried looking for papers and didn't find anything, so I ran
a survey. I linked people to wtuner and asked them
what were the highest and lowest notes they could happily sing. I got
thirty
one responses:

The median happy-singing range was 21 half-steps, which is a bit under
two octaves. For example, G2 to E4. Here are the actual notes:

Some of these responses are a bit dubious: one person gave a happy
range from A3 to E4 (eight notes). Another response gave a range from
F2 to F3, but the response with the next lowest high note gave D4
(nine notes higher). One possibility is people interpret "happy" in a
range of ways, where some people are giving the limits of their range
while others are just giving where they feel their voice sounds best?

Taking the survey literally, however, we can calculate the
number of people who would be unhappy with a given range of notes:

This shows people in the survey are generally happiest with a midpoint
of E, so for example Bb-Bb. The difference between the best and worst
options is dramatic: a 17-note range centered on D#, E, or F (ex: Ab-C)
works for 2/3 of the singers, while a 17-note range centered on Ab
through C (ex: D-F#) works for at most 1/3 of the singers.

This would say "Happy Birthday", which has a one-octave range and
starts on the fifth, would work best in G, Ab, or A, and worst in D
(which would center it on Bb).

Here are the centers of people's ranges:

There's one group with a midpoint around F3, and then a less well
defined group with a midpoint just under an octave higher, around Eb4.
I suspect this is related to gender, though I didn't ask about that on
the survey. [1] What makes centering on ~E work well, then, is
probably that low voices can do Bb2-Bb3, while higher voices can do
Bb3-Bb4. This is also good for having a full sound, because the
voices are spread over two octaves; when you do the opposite and pitch
it E-E, a large fraction of people are in the same E3-E4 octave.

[1] Phrasing questions about gender is har...
{Math} A times tables memory.
by aaq — 2019-12-01T15:40:48.941Z

I have a distinct memory of being 8 years old, or so, and being handed one of those worksheets where they ask you to multiply numbers up through 12x12, and being viscerally disgusted by the implied pedagogy of it. That was over a hundred things you were asking me to memorize. On my own time. The whole reason I rush through my school work is so I don't have to do anything when I get home. I don't know if eight year old me swore, but this was definitely a "Screw you" moment for him.

But he actually ended up being able to do that sheet pretty quickly, at least compared to most of the rest of the class. There were a few kids who were faster than me, but I got the impression they were dumb enough to have to practice this instead of watching Ed, Edd 'n' Eddy at home. Or worse, they actually did memorize this stuff, instead of practice to get quick with the multiply-numbers-in-your-head algorithm like I did. (Because of course nobody else in the class would be doing it the same way I did, just much faster. But eight-year-olds aren't known to have particularly nuanced concepts of self that can gracefully accept that there are other people naturally much better than them at what they do best.)

Later on, we moved up to multiplying arbitrary two-digit-by-one-digit numbers, and then two-digit-by-two-digit numbers. (I didn't piece together how uncommon this was until a few years later.) Everyone who outpaced me in the times-tables speed tests were now far, far below me; meanwhile, I just had to chain my little "multiply-small-numbers" mental motion to a few "add-up-the-sums" motions. 76 * 89 = 7*8*100 + 6*8*10 + 7*9*10 + 6*9. I felt like I was so clever. I started to take pride in the fact that I was now leading the pack, even though I had told myself before that I didn't care!

That is, of course, until the kids who were originally faster than me also realized how to perform that mental motion, and then they leapt past me in speed with the combined force of split-second memory of times tables and a quick ability to perform algorithms.

I think by the time we were finished with the lightning round worksheet practice, I was in the bottom quarter of the class for speed, and when I did push myself to speed up, I'd start making careless mistakes like mixing up which one of 6*7 and 7*7 was 42 and which was 49, ag...
Learning Abstract Math from First Principles? 
by Teach — 2019-12-01T00:41:39.016Z

I find that I am usually quite good at applied math, and enjoy it. I am taking a course currently that is split into two parts, Vector Calculus and Complex Analysis. The vector calculus makes sense to me and I can see how and why it works, and I find it interesting and enjoyable to learn.

On the other hand, I spend quite a bit of mental energy wrapping my head around the hows and whys of the more abstract complex analysis. I am not sure if I enjoy abstract math or not in general because I do not understand it as well. So, my question: Does anyone have any recommended resources for learning (any) abstract mathematical topic from first principles, that explains reasonably well what's going on with the math, rather than just how to do it?
Epistemic Spot Check: Fatigue and the Central Governor Module
by Elizabeth — 2019-12-01T00:10:00.742Z

Epistemic spot checks used to be a series in which I read papers/books and investigated their claims with an eye towards assessing the work’s credibility. I became unhappy with the limitations of this process and am working on creating something better. This post about both the results of applying the in-development process to a particular work, and observations on the process. As is my new custom, this discussion of the paper will be mostly my conclusions. The actual research is available in my Roam database (a workflowy/wiki hybrid), which I will link to as appropriate.

This post started off as an epistemic spot check of Fatigue is a brain-derived emotion that regulates the exercise behavior to ensure the protection of whole body homeostasis, a scientific article by Timothy David Noakes. I don’t trust myself to summarize it fairly (we’ll get to that in a minute), so here is the abstract:

An influential book written by A. Mosso in the late nineteenth century proposed that fatigue that “at first sight might appear an imperfection of our body, is on the contrary one of its most marvelous perfections. The fatigue increasing more rapidly than the amount of work done saves us from the injury which lesser sensibility would involve for the organism” so that “muscular fatigue also is at bottom an exhaustion of the nervous system.” It has taken more than a century to confirm Mosso’s idea that both the brain and the muscles alter their function during exercise and that fatigue is predominantly an emotion, part of a complex regulation, the goal of which is to protect the body from harm. Mosso’s ideas were supplanted in the English literature by those of A. V. Hill who believed that fatigue was the result of biochemical changes in the exercising limb muscles – “peripheral fatigue” – to which the central nervous system makes no contribution. The past decade has witnessed the growing realization that this brainless model cannot explain exercise performance.This article traces the evolution of our modern understanding of how the CNS regulates exercise specifically to insure that each exercise bout terminates whilst homeostasis is retained in all bodily systems. The brain uses the symptoms of fatigue as key regulators to insure that the exercise is completed before harm develops.These sensations of fatigue are unique to each individual and are illusionary since their genera

...
CO2 Stripper Postmortem Thoughts
by Diffractor — 2019-11-30T21:20:33.685Z

[EDIT: A crucial consideration was pointed out in the comments. For all the designs I've looked at, it's cheaper to just get a heat exchanger and ventilation fans, and blow the air outside/pull it inside and eat the extra heating costs/throw on an extra layer of clothing, than it is to buy a CO2 stripper. There's still an application niche for poorly ventilated rooms without windows, but that describes a lot fewer occasions than my previous dreams of commercial use.]

So, I have finally completed building a CO2 stripper that removes CO2 from the air to (hopefully) improve cognition in environments with high CO2 levels. In California, the weather is pretty good so it's easy to just crack a window at any point during the year, but other areas get quite cold during the winter or quite warm during summer and it's infeasible to open a window unless you want to spend an awful lot of money on heating or cooling bills. It didn't work quite as well as the math indicated at first, but the whole thing is built, and basically functional. The rest of this post will be a reflection on the lessons learned while doing so.

1: In hardware, ideas are cheap, execution is expensive

So, the fundamental idea is extremely simple once you have some basic knowledge of chemistry. The goal is to get CO2 into some form that isn't the gas form, via some sort of chemical reaction.

Submarines and CO2 capture from flue gas use a reversible reaction with ethanolamines, where they absorb CO2 at high temperatures and release it at low temperatures. Reversible reactions are good for making waste, but heating up and cooling down large quantities of liquid takes an awful lot of energy. Submarines have nuclear reactors onboard, and flue gas is hot, but we don't necessarily have the energy required. Also ethanolamines are toxic and hard to get a hold of for a civilian and really stinky, being the major component of "submarine smell".

Adsorption onto zeolites is also plausible, but the issue is that it requires alternately exposing the zeolites to high air pressure and low air pressure, and high airflow is required. The combination of high pressure and high airflow means that again, you're using a lot of energy. The basic math is as follows: One human produces about 1 kg of CO2 in 24 hours. We can idealize a perfect CO2 stripper as a magic box that i...
How To Change a Dance
by jefftk — 2019-11-30T13:40:01.625Z

Let's say you don't like something about your local dance. Perhaps
you'd like to see gender free calling, a different approach to booking
bands, a new kind of special event, or something else. How can you
make this happen?

The best case is that you talk to the organizers, they say "what a
great idea!" and handle the rest, but it rarely works this way. [1]
Maybe the organizers have a different background and don't understand
why you think your ideas would be an improvement. Maybe your ideas
would be more work, at least at first, and they're feeling overworked
with what they're doing already. Maybe the ideas take longer to
explain than you can get in during conversations at the break. When
this works, it's the least difficult, but for anything tricky it seems
to me like it usually doesn't.

Here are three things, however, that I have seen work well:

Start helping out. Come early and set up, stay late and clean
up, offer to help take money at the door. By doing this work you
demonstrate a commitment to the community and to the dance, you learn
what's involved in running the dance, and you build trust with the
current organizers. Then ask about joining the organizing committee.
While trusting you to arrive early and unlock the building doesn't
seem like it should translate into trusting your view on questions
like whether the dance should book more newer callers, it does seem to
work that way. I think some of this is that doing the work to make
something happen builds ownership, which gets people to make
thoughtful decisions that are better for the long-term health of the
community.

Build consensus. Say you and your friends are all on board with
gender-free bathrooms, but a lot of other dancers are uneasy about
them. If you had a vote you'd probably be in the minority, and if you
were able to change things by fiat you'd have a lot of unhappy dancers
and maybe a revolt. How can you get to where this wouldn't be a
controversial change anymore? Friendly one-on-one conversations can
go a long way here, especially when people can convey a perspective
someone hasn't considered much before. Listen, figure out why other
people feel the way they do, share why you see it differently. This
can be a lot of work, and the work of, in this example, building
consensus on gender free bathrooms will mostly fall fall on trans and
gender non-conforming

...
Counterfactuals as a matter of Social Convention
by Chris_Leong — 2019-11-30T10:35:39.784Z

In my last post, I wrote that the counterfactuals in Transparent-Box Newcomb's problem were largely a matter of social convention. One point I overlooked for a long time was that formalising a problem like Newcomb's is tricker than it seems. Depending on how it is written, some statements may seem to apply to just our actual world, some may seem to be also referring to counterfactual worlds and some may seem ambiguous.

To clarify this, I'll consider phrases that one might hear in relation to this problem + some variations and draw out their implications. I won't use modal logic since it really wouldn't add anything to this discussion except more jargon.

The idea that counterfactuals could have a social element should seem really puzzling at first. After all, counterfactuals determine what counts as a good decision and surely what is a good decision isn't just a matter of social convention? I think I know how to resolve this problem and I'll address that in a post soon, but for now I'll just provide a hint and link you to a comment by Abram Demski talking about how probabilities are somewhere between subjective and objective.

Example 1:

a) Omega is a perfect predictor

b) You find out from an infallible source that Omega will predict your choice correctly

The first suggests that Omega will predict you correctly no matter what you choose, so we might take it to apply to every counterfactual world, while it is technically possible that Omega might only be a perfect predictor in this world. The second is much more ambiguous and you might take its prediction to only be correct in this world and not the counterfactual.

Example 2:

a) The first box always contains $1000

b) The first box contains $1000

First seems to be making a claim about counterfactual worlds again, while the second is ambiguous. It isn't clear if it applies to all worlds or not.

Example 3:

"The game works as follows: the first box contains $1000, while the second contains $0 or $1000 depending on whether the predictor predicts you'll two-box or one-box"

Talking about the rules of the game seems to be a hint that this will apply to all counterfactuals. After all, decision problems are normally about winning within a game, as opposed to the rules changing according to your decision.

Example 4:

a) The box in front of you contains $1 million

b) The box in f...
Useful Does Not Mean Secure
by Ben Pace — 2019-11-30T02:05:14.305Z

Brief summary of what I'm trying to do with this post:

Contrast a “Usefulness” focused approach to building AI with a “Security” focused approach, and try to give an account of where security problems come from in AI.
Show how marginal transparency improvements don’t necessarily improve things from the perspective of security.
Describe what research is happening that I think is making progress from the perspective of security.

In this post I will be attempting to

taboo

the term 'alignment', and just talk about properties of systems. The below is not very original, I'm often just saying things in my own words that

Paul

and

Eliezer

have written, in large part just to try to think through the considerations myself. My thanks to Abram Demski and Rob Bensinger for comments on a draft of this post, though this doesn't mean they endorse the content or anything.

Useful Does Not Mean Secure

This post grew out of a comment thread

elsewhere

. In that thread, Ray Arnold was worried that there was an uncanny valley of how good we are at understanding and building AI where we can build AGI but not a safe AGI. Rohin Shah replied, and I'll quote from his reply:

Consider instead this worldview:

The way you build things that are useful and do what you want is to understand how things work and put them together in a deliberate way. If you put things together randomly, they either won't work, or will have unintended side effects.

(This worldview can apply to far more than AI; e.g. it seems right in basically every STEM field. You might argue that putting things together randomly seems to work surprisingly well in AI, to which I say that it really doesn't, you just don't see all of the effort where you put things together randomly and it simply flat-out fails.)

The argument "it's good to for people to understand AI techniques better even if it accelerates AGI" is a very straightforward non-clever consequence of this worldview.

[...]

Under the worldview I mentioned, the first-order effect of better understanding of AI systems, is that you are more likely to build AI systems that are useful and do what you want.

A lot of things Rohin says in that thread make sense. But in this post, let me point to a different perspective on AI that I might consider, if I were to focus entirely on Paul Christiano's model of greedy algorithms in part II of his post on what failure looks like. That perspective so

...
Neural Annealing: Toward a Neural Theory of Everything (crosspost)
by Michael Edward Johnson — 2019-11-29T17:31:12.730Z

The following is QRI's unified theory of music, meditation, psychedelics, depression, trauma, and emotional processing. Implications for how the brain implements Bayesian updating, and future directions for neuroscience. Crossposted from http://opentheory.net

-----------------

Context: follow-up to The Neuroscience of Meditation and A Future For Neuroscience; a unification of (1) the Entropic Brain & REBUS (Carhart-Harris et al. 2014; 2018; 2019), (2) the Free Energy Principle (Friston 2010), (3) Connectome-Specific Harmonic Waves (Atasoy et al. 2016; 2017), and (4) QRI’s Symmetry Theory of Valence (Johnson 2016; Gomez Emilsson 2017).

0. Introduction

Why is neuroscience so hard?

Part of the problem is that the brain is complicated. But we’ve also mostly been doing it wrong, trying to explain the brain using methods that couldn’t possibly generate insight about the things we care about.

On QRI’s lineages page, we suggest there’s a distinction between ‘old’ and ‘new’ neuroscience:

Traditionally, neuroscience has been concerned with cataloguing the brain, e.g. collecting discrete observations about anatomy, observed cyclic patterns (EEG frequencies), and cell types and neurotransmitters, and trying to match these facts with functional stories. However, it’s increasingly clear that these sorts of neat stories about localized function are artifacts of the tools we’re using to look at the brain, not of the brain’s underlying computational structure.

What’s the alternative? Instead of centering our exploration on the sorts of raw data our tools are able to gather, we can approach the brain as a self-organizing system, something which uses a few core principles to both build and regulate itself. As such, if we can reverse-engineer these core principles and use what tools we have to validate these bottom-up models, we can both understand the internal logic of the brain’s algorithms — the how and why the brain does what it does — as well as find more elegant intervention points for altering it.

That’s a big check to try to cash. What might this look like?

I. Annealing metaphors for the brain

In my post about the neuroscience of meditation, I talked about simulated annealing, a natural implication of Robin Carhart-Harris’s work on entropic disinte...
Preserving Practical Science (Phronesis)
by SebastianG  — 2019-11-28T22:37:21.591Z

This is my first commentary on the previous Saint Louis Junto Meeting whose notes are here. Since a meetup can cover diverse topics, I have decided that I will not include running commentary in the meeting notes, and instead reflect upon a few of the discussions in subsequent posts. If I like the results of this procedure, I will stick with it.

--

There is a certain type of knowledge gained from experience which is different from scientific theory and from rudimentary skills, but which is at the same time skill-based and scientific. Here are the examples we came up with in our meeting:

i. Iron Working, Guild and Trades, etc. functioned through an unbroken apprentice system. It takes a blacksmith to make a blacksmith. The non-academic approach makes learning what was done at any particular time difficult. High iteration processes generally have this feature. What fields today are very difficult to learn about without doing?

ii. NASA’s Apollo Program was so “do engineering” focused that it took a long time after the fact to figure out exactly how all the parts were made to fit together.

iii. Nuclear scientists today are monitored so that their know-how is put to use in approved countries, same for WMD specialists, etc.

iv. International Students in the US have limited access to highly advanced fields and the most cutting edge. Sometimes for National Security reasons, but mostly for the reason of patent protection and intellectual property rights for professors and grant making agencies. It's not that other countries can figure out these technologies in principle; it's that implementation is somehow a different category of knowledge.

The Greek word phronesis is usually translated as 'practical wisdom' or 'prudence' which is distinct from rudimentary skills (techne) or scientific knowledge (episteme). These three categories or knowing were codified by Aristotle, and we probably should be careful about using them to apply to today. Nonetheless, they are decent starting point for thinking about a type of knowledge that is sometimes hard to preserve, and extremely expensive to recover once lost.

I came up with some additional examples that are not in scientific fields.

i. How to raise a family. A community of friends of different ages and generations develop an art of living and

...