trentmkelly/LessWrong-43k huggingface dataset:
What Is The Cost of Your Attention?
by Ari Lewis — 2020-05-02T20:50:56.710Z

Before Fake News, before Yellow Journalism, there was Benjamin Day, who ran a newspaper called The Sun. The Sun was started on September 3rd, 1833 with a revolutionary new business model: "Resell the attention of the audience otherwise known as advertising" (Timothy Wu, The Attention Merchants). Before The Sun, newspapers were a form of media, only for the elite. The stories typically focused on three areas: politics, business, and reviews (books and plays). This changed with Benjamin Day who focused on creating stories about real people, but with a twist. Here's how The Sun forever changed the media industry.

The Penny Press, a newspaper that sold for one cent. The typical newspaper in 1833 cost six cents. The everyday man couldn't afford this. Benjamin Day decided to sell his newspaper for one cent; this wasn't a new idea, but it had never succeeded at scale. The Transcript in Boston attempted this but failed. In Britain, The Penny Magazine achieved initial success with gaining circulation of over 200,000 readers, but never earned a profit. However, this seemed to work for The Sun. The paper achieved sustainable success by making a combination of innovative selling techniques and changing demographics.

Changing the way people bought newspapers. Before The Sun, newspapers were delivered to homes on a subscription basis by newspaper publishers or via mail. Benjamin Day, though, popularized the concept of the newspaper hawker. The newspaper hawker would buy The Sun wholesale. They would then sell it on the street or to people who wanted a subscription. Hawkers were typically children and would yell outlandish statements to gain the attention of prospective buyers (the phrase, "extra, extra read all about it" stems from this trend). Great sales require a great product. The Sun learned that early on in its development.

"It shines for all", the slogan of The Sun. The goal was to be the newspaper for the layperson. Benjamin Day was going to make that happen. The middle-class literacy rates were increasing and people wanted news. He wanted to create stories focused on real people. His first hire was George Wisner, the first ever newspaper reporter. His job was to go down to the police court eve...
Rationality vs. Taleb's Antifragilism
by Mellivora — 2020-05-02T14:07:44.344Z

Edited for LessWrong from my Original Post.

Nassim Taleb rails against Rationality frequently in his books, but as someone that finds both Antifragility and Rationality beneficial concepts to aspire to, I feel the need to try to reconcile them.

Bridging the gap between Traditionalism and Modernism

Traditionalism is manifest in a confidence that the way we have always done things is the best. Modernism by contrast is a term used to describe the almost monomaniacal application of “scientific principles” to optimise every aspect of life. Whilst in many ways the opposite of traditionalism, this confidence in the correctness of the approach is something that both modernism and traditionalism very much have in common. The planned, orderly approach to things that modernism advocated, resulted in both huge advances and huge catastrophes in the ‘50s and ‘60s – huge rockets that could reach the moon, monoculture farming turning large areas of the planet into arid dust bowls, glittering skyscrapers providing homes and office space for thousands of people, sterile grid-based cities devoid of culture or community. Clearly modernism was not all bad, but the negative consequences were remarkably far-reaching – if we view traditionalism and modernism as opposite ends of a spectrum, can we find a middle, and will this middle allow us to reap the benefits given by both approaches, avoiding the drawbacks of either?

The main thing we must sacrifice to find a middle-ground is confidence. It is easy to have confidence in an extreme view – the answers are simple, which allows them to be spoken with conviction. “Do it as we’ve always done it” or “Our calculations show this is the best way” leave no room for nuance, therefore little room for thought and little room for doubt. Several practical philosophies have been developed since the fall of modernism, and both Antifragilism and Rationality stand out as particularly instructive. Both of these philosophies appear highly prone to misinterpretation, much to the apparent frustration of their respective advocates, however this is quite possibly a direct result of the sacrifice – people crave confidence, and a theory that does not offer enough of it will be simplified until it does.

E

...
How do you whittle path-dependent (probabilistic) questions?
by Mary Chernyshenko — 2020-05-02T08:53:41.566Z

It's a skill I lack and moreso, a desire I find myself lacking; to structure discussion in such a way that the later probability estimates don't contaminate the earlier ones they really shouldn't.

For example, I am reading Zvi's post on buying/selling/holding on predictions of COVID-19. Besides the object-level stuff, it shows the way the author re-weighs odds according to the change in scope.

Most times I see this IRL, 1) it's not the norm to allocate time to speak about the structure, 2) people aren't used to tracking interconnected events and just lump things together because it's easier, 3) spoken language has its own limitations, like the inability to refer to specific claims very precisely etc. And it's odd to ask people a bunch of questions all mixed up, in order to not lead them Socratically to a conclusion. (I wish not-Socratic talking was not seen as so weird. After all, I know about myself that I'm not always selling something...)

Still, some people not only speak with clarity, but manage to keep track of what events are or are not mutually exclusive and steer the discussion to make it easier for everybody.

...and my question is, how?

I don't think it's always useful or pedagogical or in any way "best", and it only works in some settings. But it's still a thing of beauty.
"Don't even think about hell"
by emmab — 2020-05-02T08:06:35.502Z

I found an interesting article by Eliezer on arbital: https://arbital.com/p/hyperexistential_separation/

One seemingly obvious patch to avoid disutility maximization might be to give the AGI a utility function U=V+W where W says that the absolute worst possible thing that can happen is for a piece of paper to have written on it the SHA256 hash of "Nopenopenope" plus 17

The article appears to be suggesting that in order to avoid an FAI accidentally(?) creating hell while trying to stop hell or think about hell, we could find some solution analogous to making the AI a positive utilitarian, so it would never even invent the idea of hell.

This appears to me to be making an ontological assumption that there's a zero-point between positive and negative value, which seems dubious but I'm confused.

Should AGIs think about hell sometimes? e.g. to stop it?
Stanford Encyclopedia of Philosophy on AI ethics and superintelligence
by Kaj_Sotala — 2020-05-02T07:35:36.997Z
Link: https://plato.stanford.edu/entries/ethics-ai/

The Stanford Encyclopedia of Philosophy - pretty much the standard reference for surveys of philosophical topics - has a brand-new ("First published Thu Apr 30, 2020") article, "Ethics of Artificial Intelligence and Robotics". Section 2.10 is called "Singularity". I think it has a reasonably fair and competent summary of superintelligence discussion:

----

2.10 Singularity

2.10.1 Singularity and Superintelligence

In some quarters, the aim of current AI is thought to be an “artificial general intelligence” (AGI), contrasted to a technical or “narrow” AI. AGI is usually distinguished from traditional notions of AI as a general purpose system, and from Searle’s notion of “strong AI”:

computers given the right programs can be literally said to understand and have other cognitive states. (Searle 1980: 417)

The idea of singularity is that if the trajectory of artificial intelligence reaches up to systems that have a human level of intelligence, then these systems would themselves have the ability to develop AI systems that surpass the human level of intelligence, i.e., they are “superintelligent” (see below). Such superintelligent AI systems would quickly self-improve or develop even more intelligent systems. This sharp turn of events after reaching superintelligent AI is the “singularity” from which the development of AI is out of human control and hard to predict (Kurzweil 2005: 487).

The fear that “the robots we created will take over the world” had captured human imagination even before there were computers (e.g., Butler 1863) and is the central theme in Čapek’s famous play that introduced the word “robot” (Čapek 1920). This fear was first formulated as a possible trajectory of existing AI into an “intelligence explosion” by Irvin Good:

Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion”, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need

...
Meditation: the screen-and-watcher model of the human mind, and how to use it
by Yuye — 2020-05-01T22:36:05.031Z

Background: I started meditating with the app Headspace in 2017, and started using the app Waking Up this past month at the same time I started meditating a lot more. (10h in the past month, vs 30h in the three years before that). I am not an expert, merely an amateur who's seen interesting improvements after relatively little effort.

Part I of this post is a comparison of the two apps, meant to justify why I think someone getting into meditation should start with Headspace. If you are not interested in meditation but enjoy thinking about the human mind, the description in Part I of what Waking Up teaches may still be interesting.

Part II describes my motivation for meditating and what I think other people can get out of it, Part III gives specific recommendations for meditation.

Part I. Headspace vs Waking Up

Note before I go on: Headspace and Waking Up are both paid apps. Headspace is $13/month. Waking Up is $100/year, but many redditors in threads I read about Waking Up before buying it assure me that the team really wants people to meditate and will give it to you for free if you produce a good reason, like “I cannot afford this but I find meditation helpful”.

I think I’m getting about five times more out of Waking Up because I started with Headspace. Some things that I think are very useful before starting Waking Up that Headspace teaches better:

Being sufficiently good at staying at your breath that you can by default stay with your breath for 3 cycles before you get distracted
Being sufficiently good at noticing when you're distracted that distractions are normally <=1m
Either finding body scan (moving your attention down the body, tuning into signals from different subsections) easy/intuitive to begin with, or being familiar enough that you just 'know what to do' when prompted

Headspace teaches you these in a more accessible way. Waking Up asks you to perform new mental motions in almost every session of the introductory sequence, and I think it’s hard to get something out of this if you’re busy struggling on the basics listed above.

Headspace has a 30-session introductory course, where each session is 10m. Even if you never do another Headspace pack, I recommend this. I also endorse speedrunning it by doing it 2/day and finishing in 2 weeks.

Each Headspace course (1 course = 10-sessions with a the...
Pueyo: How to Do Testing and Contact Tracing [Summary]
by DerekF — 2020-05-01T22:29:21.331Z

Tomas Pueyo’s

articles

on the Covid-19 pandemic have become widely read and

endorsed

. The most famous of these,

The Hammer and The Dance

, outlined the overall strategy he thinks governments should take: heavy suppression (the Hammer) to quickly reduce transmission, followed by more targeted measures to keep the virus under control while relaxing restrictions (the Dance).

He is now writing a series of follow-up posts, Learning How to Dance, on exactly how we can safely come out of lockdown.

Part 1

asked what we can learn from other countries.

Part 2

looked at some cheap, easy ways of reducing transmission.

Part 3

, on testing and contact tracing, is the longest post yet (~40 minutes’ reading time) so I figured people might appreciate a summary. This one has about 1500 words; if that’s still too much, you can just read the one-paragraph summary from the original text (pasted below), and/or the key points in bold.

Pueyo’s summary

“We can reopen the economy again if we do a few things right, including testing and contact tracing. We need to test all people with symptoms and their contacts, which means at most 3% of our tests should turn out positive. We need to identify as many infected as possible, and 70% to 90% of their contacts, to isolate or quarantine them. If we do all of that really fast (within a day or so), it might be enough to control the epidemic. We should hire lots of people to do that, and also use technology. The technology has some privacy tradeoffs, but they are really reasonable. Most of the bluetooth contact tracing apps built today are amazing pieces of technology that will be useless unless they get some fundamental changes.”

Terminology

Testing finds out who is infected, and they are isolated.
Tracing finds contacts of infected people, and they are quarantined (or isolated if they test positive)

This article examines testing and tracing; Part 4 looks at isolation and quarantine.

Testing

Countries with high transmission rates (e.g. the UK and USA now) should focus on suppression, not widespread testing.

They only need to test sick or high-risk individuals (e.g. healthcare workers), so that they can be isolated and/or treated.
But they need to massively ramp up testing capacity to prepare for coming out of lockdown.

Countries with low transmission rates (e.g. Taiwan, Vietnam and South Korea now, and other coun

...
2020 predictions
by Bucky — 2020-05-01T20:11:04.423Z

EDIT: Ha, just noticed that Zvi has done something similar, I’ll be interested to check another source.

The need for comparison

A couple of posts (1, 2) recently have shown how difficult it is to judge predictions without a baseline to judge against - calibration testing is the only real option.

Having predictions from another source to compare against allows Brier scores or log-likelihoods to be used to see which set of predictions are best. It also allows 50% predictions to be meaningful.

It’s hard to judge predictions in hindsight without accidentally adding what you know now into the discussion (see Scott’s comment on Zvi’s post).

So if you want to assess how good your predictions are, it is best to put them out there in advance against a set of questions that you have some values to compare against.

So I thought I would attempt to put my own probabilities on the SSC predictions from this year (or at least those which I could be reasonably expected to know about). This means that I will be able to see not only if I am well calibrated but also whether I am able to bring in all the evidence I can think of and integrate it into a good prediction. If I get a score close to Scott's then I'll be happy. I don’t know if other people do this too although a quick googling didn’t find anything.

So as not to anchor myself on Scott’s answers (a.k.a. cheating), I deleted Scott’s estimates before going back over and doing my own and then comparing. This is more like the Green Knight test mentioned in Zvi’s post. I have added a comment to this post with the unscored list in case anyone else wants to give it a go before reading on.

I'm tempted to say that no-one is allowed to claim that either of us have made a poor prediction without having tried it off a blank list yourself - it was A LOT harder than I expected it to be! I've done calibration checking myself but putting it out publicly felt really stressful. In truth, feel free to say if you think I have any probability off - simulacrum level 1, agreed?

Comparison of predictions

So here are my predictions. I’ve kept the SSC numbering and indicated where there are any predictions I’ve skipped. Along with any personal predictions, I removed the Reade accusation questions as it isn’t something I’m familiar with (non US ci...
Nicotinamide riboside and SARS-CoV-2
by William Walker — 2020-05-01T16:10:22.046Z

I've been posting Dr. Brenner's recent paper re PARP10 and SARS-CoV-2... are there really no bionerds here? If he's right, he's explained why the virus hits older people harder, and how to counter the effect with a safe supplement. Even the LA Times has noticed:
https://www.latimes.com/california/story/2020-05-01/promises-covid-19-vaccine-therapeutics-cure

Brenner's paper:

https://www.biorxiv.org/content/10.1101/2020.04.17.047480v2
SlateStarCodex 2020 Predictions: Buy, Sell, Hold
by Zvi — 2020-05-01T14:30:00.934Z

Previously: Evaluating Predictions in Hindsight

Epistemic Status: Having fun

Evaluating predictions is hard, especially about the future. Let’s do it.

The most frustrating part of predictions is defining them carefully. A lot of Scott’s 2020 predictions seem like they have a high enough probability of a disputed outcome that they’d require clarification before betting on them. A bunch of others say they’re explicitly Scott’s decision. Thus, I’ll try to clarify how I interpret such proposals as part of my evaluation.

I’ll be looking at the predictions as if they were markets, and asking whether I would buy (bet on the thing happening at those odds plus some fee), sell (bet against the thing happening at those odds plus some fee) or hold (not inclined to wager), and about where I’d put my fair. Note that this doesn’t mean I’d bet against Scott because Scott believes the prices are fair. So we’d have to give him good enough odds that he’d be willing to bet.

First up, we have the Coronavirus predictions. You’d pay to know what you really think! Hence, betting markets.

1. Bay Area lockdown (eg restaurants closed) will be extended beyond June 15: 60%

Sell to 40%, if I’m interpreting this correctly. I’m reading this as “no major relaxation of lockdown conditions” with things extended as they are or harsher. Certainly allowing restaurants to open at any level of capacity would mean it fails.

Right now, California is running in place at very low levels. Almost no herd immunity is being built, and most hospital capacity is not being used at all. The economy is being sacrificed in the hopes that conditions improve, but how long can that continue? How long should it continue? How long would people continue to abide it under such conditions, with no end in sight?

This is soon enough that there’s a decent chance that these realizations have not yet come at that time. And there’s some chance that there’s a treatment or vaccine that looks sufficiently promising that ‘tough it out until the end’ becomes reasonable. But I’m guessing, as I noted last time, that a partial reopening does little or no damage if done wisely, and I expect California to end up doing something of that type.

2. …until Election Day: 10%

Hold. If anything, that seems high, assuming it means continuous lockdown until then rather than being locked down on election day. This has to both be necessary and sufficient to...
Open & Welcome Thread—May 2020
by roland — 2020-05-01T12:01:33.876Z

If it’s worth saying, but not worth its own post, here’s a place to put it. (You can also make a shortform post)

And, if you are new to LessWrong, here’s the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are welcome.

If you want to explore the community more, I recommend reading the Library, checking recent Curated posts, seeing if there are any meetups in your area, and checking out the Getting Started section of the LessWrong FAQ.

The Open Thread sequence is here.
Interview with Aubrey de Grey, chief science officer of the SENS Research Foundation
by emanuele ascani — 2020-05-01T09:04:44.979Z

Introduction

In the last post, I explained why I think SENS Research Foundation is probably a great choice for its cost-effectiveness, and I announced that I would interview its chief science officer, Aubrey de Grey, for a further evaluation of this charity. This is the interview, preceded by a small glossary.

Glossary

SENS: Strategies for Engineered Negligible Senescence. SENS divides aging into seven categories of damage, each with its own corresponding line of research. This categorization is similar to the one described in The Hallmarks of Aging, which represents the current scientific consensus.

SRF: SENS Research Foundation.

Damage repair approach: Repairing the damages of aging, as opposed to geriatrics, which only alleviates disease before death, and old-style biogerontology, which is more focused on studying and tweaking metabolism.

LEV: Longevity Escape Velocity, which is the minimum rate of medical progress such that individual life expectancy is raised by at least one year per year if medical interventions are used. This does not refer to life expectancy at birth; it refers to life expectancy calculated from a person's statistical risk of dying at any given time. This is equivalent to saying that a person's expected future lifetime remains at least constant despite the passing years.

ALT: Alternative mechanisms for telomere lengthening that about 10% of all cancers use to achieve uncontrolled replication.

Macular degeneration: Age-related disease that results in the loss of the central visual field.

NIH: National Institutes of Health, the primary medical research agency of the U.S.

NIA: National Institute on Aging. A division of the NIH focused on aging.

Senescent cells: Cells that have stopped replicating and play a pathological role in aging.

Senolytics: Drugs that eliminate senescent cells.

Interview

How do you choose the staff to hire for intramural research projects?

No differently than any other lab would - we look for track record, domain expertise, references, the usual. We don’t particularly prioritise people who specifically want to dedicate their lives to defeating aging, not least because people who join our team without that mindset rather often acquire it over time. The one thing we do try to avoid is hiring people whose longer-term goal is something else, such as a standard faculty position: we like people to stick around, and su...