trentmkelly/LessWrong-43k huggingface dataset:
Some elements of industrial literacy
by jasoncrawford — 2020-10-08T19:51:05.112Z
Link: https://rootsofprogress.org/some-elements-of-industrial-literacy

Part of industrial literacy might be termed “industrial appreciation”. That is, part of it is learning to appreciate or value certain things that may otherwise be dry, abstract concepts (or even distasteful, to the romantic, anti-industrial mindset). For instance:

Speed and cost. Faster and cheaper is always better. These things aren’t luxuries or “nice to have”; they are essential to life.
As a corollary, other economic and engineering metrics such as productivity (of labor, land, and capital), power, density, etc. These metrics are ultimately tied to human life, health and happiness.
Reliability. Nature is chaotic. Disaster strikes without warning. Even when our needs are met, they aren’t met consistently. A “five 9s” solution is far superior to one that only offers three or four.
Scalability. An option that can’t be scaled up to the whole population is at best a partial solution; it is not a whole solution. Industry must eventually meet the needs of everyone.
Incremental change. A 1% improvement seems small, but these improvements compound. The cumulative difference between a growth rate of 1% and 2% is 3x in a little over a century.

Without industrial literacy, hearing about “a 6% increase in battery energy density” sounds boring and technical. With it, you know that a dozen such improvements mean a doubling; that a doubling in energy density means that our machines and devices can be lighter and cheaper, or that their charge can last longer, or both; that this translates to cost, convenience, and reliability; that those things make a difference in the capabilities and freedoms we enjoy. When you make all those connections, a 6% improvement in energy density can be downright exciting.

What would you add to the above list?
What was your behavioral response to covid-19 ?
by George3d6 — 2020-10-08T19:27:07.460Z

Maybe this question was asked before, but I couldn't really find it. So I hope it might generate some interesting discussion, if it was asked before and the topic is consumed, let me know and I'll delete it.

There's obviously an "opinion" that everyone holds about how themselves and other people should act in the epidemic, but I'm a strong proponent of the stance:

You can't trust thoughts, thoughts are the PR team of your actions, actions are the things that signify one's true beliefs

Which I'd wager most people here agree with to a large extent.

To pin point two very relevant trends regrading travel, flights and hotel revenue:

https://www.oag.com/coronavirus-airline-schedules-data

https://www.statista.com/statistics/206515/us-hotel-revenue-per-available-room-by-month/ (PNG https://cdn.statcdn.com/Statistic/205000/206515-blank-754.png | first data point is for January, min y value 70 max 140)

It seems to me that in Europe and the US I can observe a tiny drop in February (maybe even incidental?) followed by a sharp one starting in March and ending in May-June, when an increase starts being obvious.

As someone that travels a lot this is surprisingly of aligned with my own behavior:

I didn't care much about the epidemic until early March, when the spread was obvious (mid March)
I settled down and was basically on lockdown (contact only with close friends and delivery guys) until early May
I started going to public places (climbing gym, eating on terraces, swimming) early-mid May, by the time mid July was around I started traveling again (but somewhat more cautious than I'd usually be, using a respirator, doing weekly antibody tests and changing places less)
Now that it's October I've pretty much decided to go back to my old schedule with the same measure I took before (antibody tests on a weekly basis, PPP2 no-valve respirator, stick to mid-sized cities and stay in one place for longer to avoid too many flights)

In my mind this is correlated to my usual biases and a "sane" processing of information:

Until early March, my assumption was that, as with most other potential pandemics, nothing will happen. And if it will, chance I'll be among the first to fall would be small.
March to May, once I realized the risk is very pertinent based on real data, rather than just models, I settled down, waiting to see how bad the whole thing is.
Since the end of May I'v

...
Sortition Model of Moral Uncertainty
by B Jacobs — 2020-10-08T17:44:11.208Z

This is a crosspost from the EA forum

The Model

There have been many models proposed to resolve moral uncertainty, but I would like to introduce one more. Instead of acting in accordance to the moral theory we are most confident in (my favorite theory) or making complex electoral systems (MEC, the parliamentary model), we might want to pick a moral theory at random. Just assign to every moral theory you know the probability of how confident you are about this theory, put them in a row from least to most likely (or any sequence really) and pick a random real number between 0 and 100. E.g: Say you have 1% credence in 'Kantian ethics', 30.42% in 'Average utilitarianism' and 68.58% in 'Total utilitarianism' and you generate the random number 31, you will therefore pursue 'Average utilitarianism'. Whenever you update your probabilities you can reroll the dice (another version would be that you reroll at a fixed frequency of intervals, e.g every day). Here are some of the advantages and disadvantages of this model.

The Good

It represents your probabilities
It is fair, every theory gets an equal chance
It is easy to understand
It is fast
It stops a moral theory from dominating even though it only has slightly more credence than the second largest theory (e.g 49%, 50%)
It stops a moral theory from dominating even though it has a minority credence (e.g 20%, 20%, 20%, 40%)
It stops the problem of theory-individuation
It has no need for intertheoretic comparisons of value
It makes you less fanatical
It is cognitively easy (no need to do complex calculations in your head)

The Bad

Humans need to use something other than their brain (dice/computers) to choose randomly (for an A.I this would not be a problem)
You're not considering a lot of information about the moral theories. This could lead to e.g picking a theory that decides on an option that it doesn't have much stake in while another theory screams from the sideline (this problem could potentially be solved by making the 'stakes' an additional metric for deciding any given option, but that increases the complexity)
It makes you more inconsistent and therefore harder to cooperate with
Someone might get a wrong impression of you because they met you on a day of very low probability

Overall I'm not really convinced this is the path to a better model of moral uncertainty (or value uncertainty, since this model could also...
Help wanted: feedback on research proposals for FHI application
by otto.barten — 2020-10-08T14:42:50.033Z

As some of you will know, the Future of Humanity Institute is looking for researchers. I want to apply to the Foundational Deep Future and AI Governance positions, and as part of the procedure, I am asked to write a research proposal. There are a number of ideas I'm having, but it's hard for me to know which idea is the most relevant. Therefore I'm asking your feedback. The ideas are in the document below, you should be able to comment in the document, or as a reply of course.

Thanks for helping out!
https://docs.google.com/document/d/1vXhclr9Vp28EY4VkOUZZitSootwtCTxCQLrRIBkluCU/edit?usp=sharing
Why isn't JS a popular language for deep learning?
by Will Clark — 2020-10-08T14:36:54.752Z

I've just read this book, which introduces TensorFlow.js: https://www.manning.com/books/deep-learning-with-javascript

Now I'm wondering why JS is so unpopular for deep learning, especially compared to Python. For deep learning there all the usual benefits of using JS, e.g.:

easy to learn
huge community
flexible about paradigms
write code once, run anywhere (especially useful for training/deploying models as well as cool applications like federated learning on client devices).

Plus, compared to Python there's the huge added benefit of proper static types and the rich tooling of TypeScript. I know Python3 has type hints, but it's a really horrible experience compared to any proper typed language. Playing around with TensorFlow.js I've found it catches plenty of mistakes that would require me to read and understand a bunch of code in Python. But with VSCode plugins, I just hover over a variable and it tells me what I'm doing wrong immediately.

So why is no-one really using this? Is it just the momentum of the Python deep learning community? Have I missed some huge downside? Is there a thriving underground JS deep learning community I've not been initiated into?
Covid 10/8: October Surprise
by Zvi — 2020-10-08T13:20:01.665Z

Last week: Covid 10/1: The Long Haul

As part of 2020’s continuing attempt to top itself, President Trump has Covid-19.

It is unclear whether it is more surprising that Trump got Covid-19 now, or that it took this long for him to become infected. It has become very clear that this has been waiting to happen for some time.

The story of Trump getting Covid-19 can be divided into a few different branches.

There’s the story of the timeline. What happened to Trump when, and what did he and the people around him do in response? This is important for the security of our country and for the election, but not necessarily directly impactful for the expected future path of Covid-19.

There’s the story of Trump’s treatment. How sick was he when, with what symptoms, and what treatments did he get? Were those treatments right? How much do they improve his prognosis? Should others get those same treatments? What does it say about his expected future health? We can learn about what Trump can expect, which impacts what the country can expect, and we can learn about medical research and decision making, and what other patients should demand and can expect.

Then there’s the political messaging and impact surrounding all this, and its impact on Covid-19 going forward (as opposed to the impact on the election). This could go in any number of directions. Perhaps Trump getting a serious case of Covid-19 makes his supporters take things more seriously. Perhaps Trump’s messaging that Covid-19 is not serious and we need to learn to live with it makes his supporters irresponsible, and we see large outbreaks. Perhaps nothing matters much, and people do what they would have done anyway.

There’s also who got infected when and how, and what we can infer from this event about how Covid-19 spreads.

Meanwhile, other things didn’t stop happening. Almost none of the people who were infected this week were Donald Trump, and so far none of the people who died were him.

I do my best to avoid politics in these posts, and focus on the pandemic. This week made that extraordinarily difficult. Still, I did my best, and I expect those commenting to do the same.

Before we get to his saga in detail, let’s run the numbers.

Deaths

Date	WEST	MIDWEST	SOUTH	NORTHEAST
July 30-Aug 5	1831	719	4379	365
Aug 6-Aug 12	1738	663	4554	453
Aug 13-Aug 19	1576	850	4264	422
Aug 20-Aug 26	1503	745	3876	375
Aug 27-Sep 2	1245	759	3631	334
Sep 3-Sep 9

...
Upside decay - why some people never get lucky
by brianlui — 2020-10-08T08:45:02.181Z

Note: This was originally published at https://brianlui.dog/2020/10/06/upside-decay/. This concept is important because many people are self-interested, and this provides a convincing self-interested reason for being virtuous. Being virtuous is good, but it's also practical!

Upside decay means that an organization doesn’t get any lucky breaks.

If we think of average luck as a

normal distribution

of outcomes, this is what it looks like:

The reduction in area under the curve (the orange part) represents the loss of positive outcomes. The total loss is small, but it’s concentrated in the tail of the curve where the extreme successes happen. This is upside decay and it’s catastrophic.

Why upside decay is so harmful

Here’s a per capita GDP chart for the United States.

GDP per capita has increased for two centuries, driven by improvement in

Total Factor Productivity

, also known as technological inventions and breakthroughs.[1] This comes from many people trying out different things, each of them individually unlikely to succeed. The ones that do succeed, though, create an outsized impact. Like in

venture capital

, one big discovery creates enough progress to subsidize all the others that didn’t work out.

This means that progress happens in bursts, when everything goes right and a new invention or discovery happens. For example, we might need six things to go right for a successful discovery. Under normal circumstances each one has a 50% chance to succeed.

1.56% isn’t a very high chance, but we’ll get there with time.

It’s a different story when there’s upside decay. With only a 30% chance of each one working out instead of 50%, this is what happens:

Our 1.56% chance of success is now only 0.07%. Upside decay hits low-probability events hardest because so many things have to go right.

Finding upside decay doesn’t help us

Upside decay is hard to spot. It’s invisible if we’re not specifically looking for it, because the absence of rare positive events is unexceptional. Even when people finally notice something is wrong, they’ll attribute it to malign actors, imaginary enemies or conspiracy theories.

You’d think that being on the lookout for missing positive tail events would be enough to spot upside decay. But it’s too late to be useful. We need to observe an organization over a long period of time or have access to a generous chunk of its history. By the time we ident...
Babble challenge: 50 ways to escape a locked room
by Bird Concept — 2020-10-08T05:13:06.985Z

Once again, it is time to

become stronger

.

This week’s challenge: You find yourself in a locked, empty room. You only have the clothes on your body and a phone in your pocket. You have enough energy to not need food or water for 10 years. Your phone has enough battery power to not need to recharge. It has wi-fi.

Escape.

You have 1 hour to come up with 50 ways.

Looking back

To last week's babble champions, who made it to 50 —

gjm, mr-hire, Tetraspace Grouping, Neel Nanda, ErickBall, Harmless, steven0461, Vanilla_cabs, gogishvilli, justinpombrio, Bautista Cazeaux, tomcatfish, NunoSempere, Unnamed, MikkW, Mati_Roy, James Fadden, James Barry, frontier64, Slider, smiley314.

— I salute you.

Together we produced more than 1300 ways of going to the moon. (Though there’s likely a lot of overlap.)

I found it inspiring. Both in terms of the turnout (which far surpassed my expectations) and in terms of the creativity of individual submissions. Reading your answers made me realise I can do a lot better. There are other levels to reach. I want to get there.

Moving forwards: weekly babble

I am hereby committing to doing a babble challenge on LessWrong every week for the coming six weeks (including today).

(The format might change a bit along the line, but the impetus will remain the same.)

Practice is about building for the long run.

Showing up every week and putting all that deliberateness into your craft.

Showing up for years to tread that subtle incline, until one day you find yourself side by side with a beginner. They look upon your mountain of compound returns and all they see is magic. But you know that “sometimes magic is just someone spending more time on something than anyone else might reasonably expect”.

Eventually I’ll switch away from the babble challenge to other things. But what matters is to retain that will to improve, that burning fire to advance further. And I want LessWrong to be a place that sustains a flourishing culture of practice and self-improvement.

If you want that as well, I invite you to join me. Comment and commit to showing up for this week, and then five more.

(If you just want to try it out once and then reevaluate, that’s also fine. You’re welcome to do that.)

Why creativity?

I feel like so much of our ability to make the world great is bottlenecked by creativity.

We don’t know how...
Political Lottery in Switzerland
by Martin Sustrik — 2020-10-08T05:03:38.824Z

Sortition, that is selection of political officials by lottery, was famously used in ancient Athenian democracy. It was also used, since XIV. century, in couple of city states in northern Italy.

A less well known fact is that it was also used in the Old Swiss Confederacy.

In canton Glarus, offices were originally filled by voting by show of hand at a public gathering (Landesgemeinde). However, buying of votes was widespread and the people have, over time, tried different measures to remedy the problem.

In 1623, taxes were introduced. Those who were elected to an office had to pay certain amount. The measure haven't had the desired effect. In fact, it has strengthened the aristocratization of politics.

In 1640, selecting the officials by lottery was introduced in the protestant part of the canton. It was done in two stages. First, eight candidates were selected by show of hands. Then, an underage boy gave each candidate a ball rimmed in black. Seven of them were silver, one was gold. Whoever got the golden ball got the office.

In 1764, a resolution passed by Landesgemeinde warns the candidates that they should "no longer exchange the balls, but rather everyone should keep the ball that he gets". That suggests that the candidates were no longer buying the votes of the entire community, but rather the votes of their counter-candidates.

In 1791, the Landesgemeinde adopted a new procedure for some offices. It removed the pre-selection phase and drew the lots directly from all citizens. However, it sometimes happened that people were selected who didn't want or weren't able to exercise the office (e.g. because they were illiterate). In many cases they auctioned the office to the highest bidder.

One can thus think of sortition in canton Glarus not as of a democratization measure, but rather as a way to make buying of votes more cost effective. After all, election bribery led to an increasingly costly arms race between powerful individuals and it was threatening to ruin entire families.

Taken from a great article by Lukas Leuzinger. The article (in German) contains much more interesting detail and can be found here.
Sunday October 11th, 12:00PM (PT) — talks by Alex Zhu, Martin Sustrik and Steve Byrnes
by habryka — 2020-10-07T23:22:14.050Z

This Sunday at 12pm (PT), we're running another session of "lightning talks" by curated LessWrong authors (see here for previous weeks' transcripts). This time fully in Gather Town!

For the first hour, we will have a series of lightning talks each lasting about 5 minutes followed by discussion. The talks will be short and focus on presenting one core idea well, rather than rushing through a lot of content.
From 1PM to 2PM, we'll just be casually hanging out in Gather Town. If you are not interested in the talks, feel free to just show up for this part (or the other way around).
We want to give top LessWrong writers an interesting space to discuss their ideas, and have more fruitful collaboration between users. Think of it like a cross between an academic colloquium and some friends chatting by a whiteboard.

If you're a curated author and interested in giving a 5-min talk at a future event, which will then be transcribed and edited, sign up here.

Speakers

Alex Zhu: The Internet Oasis of Sanity
Martin Sustrik: The Swiss Political System
Steve Byrnes: "Little glimpses of empathy" as the foundation for social emotions

Details

When? Sunday October 11th, 12:00PM (PT)

Where?

https://gather.town/app/Dgohwk4BUg5HjGZE/lesswrong-clubhouse
Thoughts on ADHD
by romeostevensit — 2020-10-07T20:46:24.827Z

A few different things that have occurred while investigating. It seems weird to me there are lots of posts about akrasia and productivity but not adhd.

1. High Rejection Sensitivity makes my attention in general more flinchy. Rather than just explicit rejection, you can think of this as a high sensitivity to negative feelings in general with rejection just being of extra highlight. This becomes a bit of a closed loop if I'm avoid noticing how flinchy my attention is because that would also imply unpleasant things.

2. Imbalanced novelty seeking dovetails really nicely with 1 because it gives me more buckets to run to. Find something I don't like in one tab? There is another tab a muscle memory shortcut away. It also encourages multitasking in the sense of listening to music or eating at the same time as I do other things. Novelty feels somewhat additive, so that 3 minor novelties can combine to make me feel 'stimulated enough.' In the same way that insulin insensitivity is a problem this feels a bit like a sort of dopamine insensitivity (probably not literally literally since dopamine doesn't work the way the folk version of it does). When I'm not stimulated enough I'm more likely to notice unwanted stimuli. Novelty, or surprise, is 'spiky' (activating, pulling of attention) enough to keep attention away from the unpleasant.

3. Higher than average branch factor + completionism = bad combo. By branch factor I mean the average number of thoughts activated by each thought. When this is less than 1 I tend towards idle relaxation, between 1 and 2 and I have a coherent chain + the ability to analyze a few possible alternatives. 3+ and I'm a bit all over the place. This isn't so bad if/when my pruning heuristic scales with the branching, but if it doesn't I'm in for a bad time. Thoughts tend to just expand without healthy cycles of contraction until they become unwieldy for working memory, at which point novelty seeking becomes a nice relief, at which point I lose most of my cache and am back to square one the next time I go to load the problem. But the next time I go to load the problem I run into exactly the same issue but now even worse because reloading the cache doesn't have any novelty to make it fun. So now the task feels really big and boring. And I don't have a strong sense of why I failed last time, so attention remains diffuse etc. This also results in avoi...