trentmkelly/LessWrong-43k huggingface dataset:
What Money Cannot Buy
by johnswentworth — 2020-02-01T20:11:05.090Z

Paul Graham

:

The problem is, if you're not a hacker, you can't tell who the good hackers are. A similar problem explains why American cars are so ugly. I call it the design paradox. You might think that you could make your products beautiful just by hiring a great designer to design them. But if you yourself don't have good taste, how are you going to recognize a good designer? By definition you can't tell from his portfolio. And you can't go by the awards he's won or the jobs he's had, because in design, as in most fields, those tend to be driven by fashion and schmoozing, with actual ability a distant third. There's no way around it: you can't manage a process intended to produce beautiful things without knowing what beautiful is. American cars are ugly because American car companies are run by people with bad taste.

I don’t know how much I believe this claim about cars, but I certainly believe it about software. A startup without a technical cofounder will usually produce bad software, because someone without software engineering skills does not know how to recognize such skills in someone else. The world is full of bad-to-mediocre “software engineers” who do not produce good software. If you don’t already know a fair bit about software engineering, you will not be able to distinguish them from the people who really know what they’re doing.

Same with user interface design. I’ve worked with a CEO who was good at UI; both the process and the results were visibly superior to others I’ve worked with. But if you don’t already know

what good UI design looks like

, you’d have no idea - good design is largely invisible.

Yudkowsky

makes the case

that the same applies to security: you can’t build a secure product with novel requirements without having a security expert as a founder. The world is full of “security experts” who do not, in fact, produce secure systems - I’ve met such people. (I believe they mostly make money by helping companies visibly pretend to have made a real effort at security, which is useful in the event of a lawsuit.) If you don’t already know a fair bit about security, you will not be able to distinguish such people from the people who really know what they’re doing.

But to really drive home the poin...
Effective Altruism QALY workshop materials & outline (and Jan 13 '19 meetup notes)
by samstowers — 2020-02-01T04:42:07.278Z

What the workshop is & a brief overview

Hi all! This is my first post on LessWrong, but I'll be posting here more often with conversation notes & resources from the Effective Altruism Kansas City meetup group.

This workshop was an experiment to give participants an intellectual & intuitive understanding of how EAs typically prioritize between charities - the QALY (and DALY). Participants were told that they're the board for the Hypothetical Foundation and needed to choose a charity to fund between three options to best serve the residents of Hypothetical Town. Discussion is guided from initial impressions, to how to quantify well-being, to how to estimate DALYs, and finally, how to use DALYs to compare charities.

The workshop was fun & engaging, but I've got a few post-mortem revision notes in case anyone wants to use these materials:

Have participants draw their own QALY boxes
Have participants intuit QALY shapes of existing charity models
Walk through an example calculation before having participants do it
Give a worksheet that lays out steps nicely and walks participants through an example calculation

Workshop materials

Rough schedule & content/activity outline

18:15 TALK: Introduce the foundation board meeting premise and the fake charities we'll be evaluating.

All charity operations in Hypothetown have room to scale according to the possible additional funding (to simplify the decision).

18:20 BREAKOUT: Brief discussion of which charities they'd pick based on information they have
18:25 DISCUSS: On what basis would they pick charities? Write ideas on the board.

Lead them to the essential question: "How many are helped, and by how much?"

18:30 TALK: Chief Philosophy Officer [[Josh Rainwater]] presents ways to think about how people are helped and by how much

Will Josh mention the various ways to calculate utility? Should I ask him to?

18:40 BREAKOUT: Have them fill out quality of life forms individually then discuss responses with their partner(s)

Printout link: https://docs.google.com/document/d/18KpMd2qqg5JbVWatcvfxFTTFWdlKHvrEaO0LPM4NasQ/edit?usp=sharing

18:50 DISCUSS: Hand out charity infosheets. Discuss how to incorporate improved life & lengthened life into a single number. Aim for a QALY-like conclusion.

"Ok, board, we've done some due diligence on these charities - I've prepared decision brief

...
More Rhythm Options
by jefftk — 2020-02-01T03:10:02.499Z

Few instruments do a good job as the only rhythm instrument in a dance
band; in my

2014 sample
I only saw guitar and fiddle. I can't play guitar for dancing anymore
because of my wrists, and the piano has to

give up a lot in
exchange for its large range. A goal I've had for a long time is to
figure out how to get the same

full sound from something built
around a mandolin.

As a rhythm instrument, the way
I play it, the mandolin has a percussive bite and drive that's
hard to get with the piano. This drive contributes a lot to the
dancing, and is something I really enjoy about a mandolin-piano rhythm
section. Take away the piano, though, and everything is high
frequency.

I've played with a bunch of ideas here for augmenting my mandolin
playing:

DIY organ pedals.

Build a computer vision
system that maps from hand shape and position to chord, and then
choose bass notes from the chord. Trigger the bass
notes with foot pedals.

Make a hat with a tilt
sensor, and use head angle to choose bass notes. Foot pedals as
before.

Use vocals, perhaps
processed, to fill out the sound.

Whistle into a microphone, which controls a bass synthesizer, so I can whistle
bass lines.

Recently I tried a new combination:

Whistle into a microphone to select bass notes, trigger the
bass notes with foot pedals.

Here's a video:

(youtube)

I'm running my standalone pitch
detector which translates the whistling into MIDI, with pitch bend to send fractional
pitch. I tell my MIDI
router what key and mode I'm in, and it listens for I, IV, V, and
either vi (minor) or VII (mixo) by picking the nearest option. I have
this driving both a bass that's triggered by the foot pedals, and an
atmospheric droney pad that just runs. I have the pad set to only
change notes on a pedal tap, however.

It's not as flexible as the bass whistle, because I need to choose in
advance what key and mode to play in and it only does four bass notes,
but it also is much less likely to make weird awkward noises when I
screw up slightly.

Comment via: facebook
Instrumental Occam?
by abramdemski — 2020-01-31T19:27:10.845Z

In The Real Rules have No Exceptions, Said says (apparently speaking of instrumental rules, not epistemic ones):

Prefer simplicity in your rules. Be vigilant that your rules do not grow too complex; make sure you are not relaxing the legitimacy criteria of your exceptions. Periodically audit your rules, inspecting them for complexity; try to formulate simpler versions of complex rules.

This is a very plausible principle. I have had similar thoughts myself. The idea seems to have merit in practice. But is there any theoretical analogue?

Minimum description length (Occam's Razor) is an epistemic principle. In Bayesian terms, probabilities must sum to one, which translates information-theoretically (via optimal encodings) to the idea that there are only so many short codes, so we have to assign them carefully to the most probable things.

However, expected utility is not a limited resource in this way. Updating to think an option is better than previously thought doesn't necessarily make other options worse. Selecting policies is not so different from selecting raw actions. And for Bayesian decision theory, it doesn't really matter whether a policy is specified as a big table of observation/action pairs, vs an elegantly specified rule.

Optimality cares not for elegance!

Yet, even in the relatively formal world of machine learning, the practice seems contrary to this. When you are optimizing a neural network, you don't actually care that much whether it's something like a hypothesis (making predictions) or something like a policy (carrying out actions). You apply the same kind of regularization either way, as far as I understand (regularization being the machine-learner's generalization of Occam). (Correction: this seems not to be the case.)

We might say that this is because (in some sense) the instrumental uncertainty and the epistemic uncertainty are actually being wrapped up together. But (1) this reply seems overly quick to me at present, and I'd want to understand in detail whether this can be justified; (2) I'm curious if there is a purely instrumental version of Occam to be articulated; it seems intuitively really plausible to me, though technically quite mysterious.

So: is it possible to formulate an instrumental version of Occam? Can we justify a simplicity bias in our policies?
REVISED: A drowning child is hard to find
by Benquo — 2020-01-31T18:07:05.348Z
Link: http://benjaminrosshoffman.com/drowning-children-rare/

Substantial revisions to clarify the post's core claim, including but not limited to this summary at the end:

Summary

Effective Altruism claims that there is a large funding gap for cheap well-understood developing-world interventions.

Even the most aggressive plausible construal of this claim implies an annual funding gap that could be covered completely by existing major institutional donors.

If this is true, it implies opportunities for comparatively cheap experiments (relative to the endowments of major donors in the space) with extremely high information value.

Such experiments have not happened either because they are impossible, or because the relevant institutional donors think they have better things to do with their money.

Neither scenario suggests that small donors should try to fill this funding gap. If they trust big donors, they should just give to the big donors. If they don't, why should they believe a story clearly meant to extract money from them?

Original linkpost
Create a Full Alternative Stack
by Zvi — 2020-01-31T17:10:00.543Z

Last time I proposed nine strategies for fighting mazes. All of them were either systemic solutions requiring coordinated and/or political action, or cultural shifts that happen one person at a time.

Now for the tenth and final proposal for how to fight mazes. The tenth is a proposed strategy that one dedicated person with sufficient resources could implement on their own.

If you are in a position where you have the resources to implement this, please make an effort to take this proposal seriously. And please contact me if you are potentially interested and wish to discuss it further.

Solution 10: Create a Full Alternative Stack

In some ways this is the most ambitious solution here. It may seem Utopian.

In other ways, it is the least ambitious, and most practical. It could be implemented by a single sufficiently wealthy and committed individual or organization. All other known solutions can be implemented locally, and would help locally, but need general adaptation to succeed in general.

The full alternative stack offers a contract.

Disengage entirely with mazes and traditional distortionary incentives, competitions and signals of all kinds, and discard all zero-sum activity, in favor of doing the thing. Whatever the thing in question may be. Make no compromises to make oneself legible or attractive to outside sources of funding. Tolerate no maze behaviors of any kind. Hire and fire with this deeply in mind.

In exchange, if you keep your end of the bargain, the stack will fully fund you and your operations, at fair prices that do not take advantage of the hold-up opportunity presented by your giving up of other opportunities. Evaluation will be fully on the object-level merits, and the generative processes involved.

This is a form of tenure for the people. If they continue to act with integrity and work to accomplish positive-sum things relevant to the stack’s interests, and spend responsibly, they and their family will have strong financial security.

Think of this as similar to tenure at a university, or to the system of universal employment for partisan hacks. If you are promising the stack gives you the opportunity to prove yourself. Once you have proven yourself, we take care of you, even if you don’t bear as much fruit as we would like, provided you continue to play by the rules of the stack and honor its values. Unlike many tenured professors nowadays, we wo...
[Link] Ignorance, a skilled practice
by romeostevensit — 2020-01-31T16:21:23.062Z

https://carcinisation.com/2020/01/27/ignorance-a-skilled-practice/

Excerpt:

The Global Knowledge Game
To illustrate that global knowledge is a game, consider a story about Alexander Luria, who studied illiterate Russian peasants and their semi-literate children. Consider especially this version of the story, prepared in the 1970s to provide morale and context to reading teachers (John Guthrie, 1977). Essentially, Luria discovered that the illiterate, unschooled peasants were highly resistant to syllogisms and word games. The adult peasants would only answer questions based on their own knowledge, and stubbornly refused to make deductions from given premises. “All bears are white where it is snowy. It is snowy in Nova Zembla. What color are the bears in Nova Zembla?” “I don’t know, I have never been to Nova Zembla.” Children with only a year or two of education, however, were easily able to engage in such abstract reasoning. They quickly answered the syllogisms and drew inferences from hypothetical facts outside of their own observation.
In this story, I argue, Luria’s peasants are indexical geniuses, who refuse to engage in unproven syllogistic games. They are not interested in a global, universal game. Their children, however, are easily introduced to this game by the process of schooling and literacy.
Interestingly, a more recent group of researchers claim that illiterate people do fine at making inferences against experience, if the context is given as a distant planet (Dias et al., 2005). I am not offering this as true, but as a story about how expecting people to operate in the “global knowledge game” might portray them as stupider than they really are, if they simply choose not to play in that game. This is to segue into the next hermeneutic pass, in which we are told that the hype surrounding “cognitive bias” is really a sort of science magic trick, an illusion designed to portray indexical geniuses, like Luria’s peasants and ourselves, as global fools.
The paper is “The Bias Bias in Behavioral Economics,” by Gerd Gigerenzer (2018). If you, like me, have ever been fascinated by cognitive bias research, this is a brutal paper to come to terms with. Gigerenzer examines several purported biases in what I would call analytic reasoning or the global knowledge game, and fin...
[ELDR Tactics] Consider switching to (mostly) decaf.
by aaq — 2020-01-31T15:09:59.734Z

Related post: Everyday belief in diminishing returns is subject to diminishing returns

Growing up as a first-generation American, and generally considered the "smart" one of the family, I started drinking instant coffee when I was 12 to keep awake during homeroom. My usual sleep window now is between 11 pm to 7 am; back then it was more around 1 am to 9; the time I had to wake up to get to school was 5. It was a necessary evil at the time.

One cup in the morning became two, and then four; when I got home, I realized I was suddenly feeling groggy, so I started drinking 1 -> 2 -> 4 once I got back as well. This unsurprisingly made my actual lack of sleep all the worse.

I think in the back of my mind, I understood intellectually that this was a Very Bad Idea. Even compared to the average person, I function really poorly when I'm tired relative to when I'm awake. But I also have an extremist bent to my personality; I felt throughout high school and much of college that if I were going to quit coffee, I wanted to do it cold turkey. I would be strong and bear the pain.

The big problem with this is that I Pavloved myself into loving the taste of coffee too much to ever have success with this after high school. (Thank Christ 12 year old me understood this was a possibility, and reflexively refused to drink anything but black coffee -- otherwise I would be several pounds heavier.)

There are downsides to drinking coffee of any variety, decaf or not. It gradually stains your teeth. Last year I paid a few hundred dollars to get my teeth whitened because frankly I found them yellowed enough to be off-putting when I looked at myself in the mirror, especially with a pale sunscreen tint on the surrounding skin creating a contrast.

But let's be honest with ourselves: Slightly stained teeth are much, much less annoying than eternal nights spent in half-sleep.

About 6 weeks ago, undergraduate winter break began here at Northwestern. I didn't have much to do, so I figured this would be a good time to wean myself off of coffee. I understood I had tried to do this before, so I decided to do the opposite thing: Make the minimum possible change that results in the maximum return. Hence the post title; ELDR stands for Everyday Life Diminishing Returns.

I knew from prior experience that I don't suffer strong withdrawal symptoms when I go without it; I...
Existing work on creating terminology & names?
by ozziegooen — 2020-01-31T12:16:32.650Z

I'm in the process of coming up with terminology for various theories, similar to lots of other work on LessWrong and The EA Forum.

Naming things is a bit of a unilateralist action. While community members don't have to accept a specific naming proposal, they are likely to do so if they like the concept. I can't think of many cases where Eliezer or someone named a concept, and the community decided that that name was poor, and renamed it.

However, I can't find much theory on how to figure out great names for things, or even what to consider when doing so. I would have expected there to be comprehensive discussion around Information Architecture, UX Design, or the Library Sciences on this topic, but couldn't identify much outside of card sorting and a few lists of rough heuristics.

This was also an issue for me when I did more software engineering, and I was then also frustrated by the lack of discussion I could find. The best there was work on Software Patterns, which I used primarily for naming conventions.

Some related links I could find:

https://en.wikipedia.org/wiki/Naming_convention

https://www2.staffingindustry.com/eng/Editorial/Archived-Blog-Posts/Adam-Pode-s-Blog/Probably-the-best-file-naming-convention-ever

https://www.invisionapp.com/inside-design/naming-conventions/

https://ux.stackexchange.com/questions/48578/naming-features-of-an-app-or-site
https://www.martyneumeier.com/strong-vs-weak-names
Book Review: Human Compatible
by Scott Alexander — 2020-01-31T05:20:02.138Z

I.

Clarke’s First Law goes: When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.

Stuart Russell is only 58. But what he lacks in age, he makes up in distinction: he’s a computer science professor at Berkeley, neurosurgery professor at UCSF, DARPA advisor, and author of the leading textbook on AI. His new book Human Compatible states that superintelligent AI is possible; Clarke would recommend we listen.

I’m only half-joking: in addition to its contents, Human Compatible is important as an artifact, a crystallized proof that top scientists now think AI safety is worth writing books about. Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies previously filled this role. But Superintelligence was in 2014, and by a philosophy professor. From the artifactual point of view, HC is just better – more recent, and by a more domain-relevant expert.

But if you also open up the books to see what’s inside (not recommended; that’s where the confusing stuff is), the two defy easy comparison.

S:PDS was unabashedly a weird book. It explored various outrageous scenarios (what if the AI destroyed humanity to prevent us from turning it off? what if it put us all in cryostasis so it didn’t count as destroying us? what if it converted the entire Earth into computronium?) with no excuse beyond that, outrageous or not, they might come true. Bostrom was going out on a very shaky limb to broadcast a crazy-sounding warning about what might be the most important problem humanity has ever faced, and the book made this absolutely clear.

HC somehow makes risk from superintelligence not sound weird. I can imagine my mother reading this book, nodding along, feeling better educated at the end of it, agreeing with most of what it says (it’s by a famous professor! I’m sure he knows his stuff!) and never having a moment where she sits bolt upright and goes what? It’s just a bizarrely normal, respectable book. It’s not that it’s dry and technical – HC is much more accessible than S:PDS, with funny anecdotes from Russell’s life, cute vignettes about hypothetical robots, and the occasional dad joke. It’s not hiding any of the weird superintelligence parts. Rereading it carefully, they’re all in there – when I leaf through it for examples, I come across a quote from Mora...