trentmkelly/LessWrong-43k huggingface dataset:
Symbiotic Conflicts
by lsusr — 2019-12-04T00:06:08.777Z

A meme is a self-replicating pattern of information. Some change. Some survive. Some die out.

The most virulent memes often reproduce themselves via their own inverses. Talking about how Flat Earthers are wrong increases awareness of the Flat Earther meme. Increasing awareness of the Flat Earther meme equals proliferating the Flat Earther meme. Anti Flat Earthers[1] spawn Flat Earthers. Flat Earthers spawn Anti Flat Earthers. Every Anti Flat Earther contains a dormant Flat Earther meme.

A scholar is just a library's way of making another library.

— Daniel Dennett

In this way Flat Earthers and Anti Flat Earthers are different stages of a single meme's lifecycle. If the Anti Flat Earthers' objective is to spread the Anti Flat Earth meme then this information reverb benefits the Anti Flat Earthers.

Capitalism and Communism are two halves of the most successful meme in history, the Cold War. Capitalism was defined by its opposition to Communism. Communism is defined by its opposition to Capitalism. It doesn't matter that Communist states spread a twisted version of Capitalism and Capitalist states spread a twisted version of Communism. During the Cold War, the title of your economic ideology mattered more than than your real-world economy.

Fighting a meme makes it stronger. The best way to kill a meme is to ignore it.

This presents a collective action problem. If you tell everyone to ignore the X meme then you've told everyone about X thereby spreading the X meme. Attacking a meme wins you Pyrrhic victories. To kill a meme you have to make the meme irrelevant by transcending it.

The Cold War meme died when the divide between Capitalism and Communism ceased to be meaningful. The Chinese Communist Party manages the world's largest capitalist economy. By 1901 Bolshevik standards, NATO is an alliance of socialist nations.

"Anti Flat Earther" is a different meme from "physics". Anti Flat Earthers refute Flat Earthers' arguments. The physics meme ignores quibble. ↩︎
Long-lasting Effects of Suspensions?
by jefftk — 2019-12-03T20:40:01.264Z

I recently read "The School to Prison Pipeline: Long-Run Impacts of
School Suspensions on Adult Crime" (Bacher-Hicks et. al. 2019,

pdf,

via
Rob Wiblin) which argues that a policy of
suspending kids in middle school leads to more crime as an adult.

Specifically, they found that after controlling for a bunch of things,
students who attended schools with 0.38 more suspensions per student
per year were 20% more likely to be jailed as adults:

A one standard deviation increase in the estimated school effect
increases the average annual number of days suspended per year by
0.38, a 16 percent increase. ... We find that students assigned a
school with a 1 standard deviation higher suspension effect are about
3.2 percentage points more likely to have ever been arrested and 2.5
percentage points more likely to have ever been incarcerated,
which correspond to an increase of 17 percent and 20 percent of
their respective sample means.

This is a very surprising outcome: from a single suspension in three
years they're 20% more likely to go to jail?

The authors look at the Charlotte-Mecklenburg school district, was ordered
by the court to desegregate in the 1970s. In the early 2000s the
court was convinced that busing wasn't needed anymore, and the
district implemented a "School Choice Plan" for beginning of the 2002
year. Students were massively shuffled between the schools and, while
this was generally not randomized, the authors describe it as a
"natural experiment".

The idea is that if a student moves from school A to school B and
you know how often students were suspended at both schools, then you
can look at differences later in life and see how much of that is
explained by the difference in suspension rates. They note:

A key concern is whether variation in "strictness" across schools
arises from policy choices made by administrators versus underlying
variation in school context. Our use of the boundary change partly
addresses this concern, because we show that schools' conditional
suspension rates remain highly correlated through the year of the
boundary change, which provides a very large shock to school
context. We also show that school effects on suspensions are unrelated
to other measures of school quality, such as achievement growth,
teacher turnover and peer characteristics.

And:

We also test directly for the importance

...
(Reinventing wheels) Maybe our world has become more people-shaped.
by aaq — 2019-12-03T20:23:08.493Z

Let's stow our QM caps and pretend Democritus was right: Atoms turn out to be the fundamental, indivisible units of physical reality after all. Let's further pretend that some flavor of hard determininism is in play: There is nothing apart from the motions and interactions of the atoms that governs the way the Universe ambles through time.

Past, perhaps, the first Planck moment of existence, where we might be initializing all of the constants we'll need, our billiard-ball universe is quite amenable to explanations at the atomic level in the language of causality. Why is Atom A here, and not there, at time t? Because it was caused to be there by the actions of the other atoms, and due to certain properties of A itself, in the time leading up to t.

In theory, this means that any level of abstraction we build up from the atomic one should preserve that ability to be described causally. But the amount of computational power we would need to actually pull that off would be staggering, far, far more than we could possibly fit within 3 pounds of grey matter. So even starting from the most deterministic possible model, as agents within the system, we don't really have the ability to directly leverage that causality.

Instead, we are forced by our own limited resources to construct abstractions that are simple enough for us to reason about. These abstractions throw out a lot of detail! And when you throw away even a small amount of detail, you lose the clean isomorphism-to-reality that allows our earlier statement of causality to be preserved. When you're dealing with atoms, in the billiard ball world, you can always predict where they'll be if you have enough power; when you're dealing with aprroximations of atoms, you lose the "always".

So not only is the map not the territory, if you lose the territory, there is no way to perfectly accurately reconstruct it from the map alone. If you ever find yourself in that unenviable place, you'll have to make (dare I say it) aesthetic decisions in the reconstruction process.

And that's a weird thing to think about, not least because that isn't the case in mathematics. If you have a finite-bandwidth mathematical signal, you can perfectly reconstruct it from a finite number of details about it with the correct sampling conditions. That's just the most direct example. Wh...
Russian x-risks newsletter #2, fall 2019
by avturchin — 2019-12-03T16:54:02.784Z

Russia could be seen as an x-risks wonderland, with exploding nuclear facilities and bioweapons storage sites, doomsday weapons, frequent asteroid impacts, Siberian volcanic traps, Arctic methane bomb, military AI programs, and crazy scientists wanting to drill to the Earth’s core. But there are also people who do their best trying to prevent global risks, and one of the most well-known is Petrov, who is remembered on 9/26. Russia can also provide much of the world with lessons in resilience; for example, many of its people, living in villages, can still supply themselves with food, water and energy without access to external sources.

Bio

Explosion in the virology center in Novosibirsk, 16 September 2019. The explosion was caused by a natural gas tank used in the renovation process. The fire was allegedly close to the storage facilities but didn’t directly affect them, so there was no leak of hazardous biological materials.

Why it is important? This seems to be a new type of possible catastrophic event, that has not been previously predicted, one that could produce a “multipandemic”—the simultaneous release of many deadly biological agents. A possible protective measure against new events of this type is the preservation of deadly agents in different places.

What is it? This center is one of two places in the world where live samples of smallpox are preserved. The State Scientific Center for Virology and Biotechnology (SSC WB), also known as "the Vector Center", has one of the most comprehensive collections of viruses in the world, including Ebola, Marburg hemorrhagic fever, severe acute respiratory syndrome (SARS), smallpox, and others. Created in 1974 near Novosibirsk, it was previously a closed institute that led the development of vaccines, as well as strategies for protection against bacteriological and biological weapons. In 1999, Valentin Yevstigneev, the head of the biological defense department of the Russian Ministry of Defense, said that they began to consider the Vector Center as "an industrial base for the production of offensive biological preparations" in the late 1980s. It was assumed that the strains of smallpox, tularemia, plague, anthrax and Ebola developed by the center would be placed in warheads. This work was curtailed in 1992, shortly after USSR ceased to exist.

AI

In October of 2019, ...
Searching Along the Trail of Crumbs
by Zvi — 2019-12-03T13:10:00.419Z

There are two extraordinarily powerful things going on right now in Standard. One of them is Cavaliers, which I played in Twitch Rivals and wrote about here.

The other is the food engine, a core of cards with several possible ways to finish.

The Core Engine

The never-touch-this-actual ever core are these fifteen cards:

4 Witch’s Oven

3 Caldron Familiar

4 Trail of Crumbs

4 Gilded Goose

Duplicate Cats are often low impact, and risk draws that have too much air, so it’s not clear that you want the fourth copy.

If you have the cats but you’re not running green at all, then you’re running Mono-Black Sacrifice or Rakdos Sacrifice. Those are decks, but they’re focusing on a different game plan.

With this engine, you have fifteen one and two mana cards. Trading with any of them without giving you value is usually impossible. Most subsets of the list are continuous advantage engines. Together, these cards give you both good things to do in the early turns and a late game engine that can grind out anyone who doesn’t finish you off. That’s quite the combination.

They also give you a continuous supply of sacrifice triggers, death triggers and food tokens for you to build upon, and also a key artifact worth looking for, which gives you a lot of directions you can go. The rest of this article is about exploring the various options for the remaining 20-21 spell slots.

Instead of fighting over snowballing planeswalker activations, we are now fighting over snowballing Trail of Crumbs activations and who can put more cats into ovens each turn.

Wicked Wolf

The list of additional options starts with Wicked Wolf. People are increasingly moving away from Wicked Wolf. They need to slow down. Wicked Wolf is amazing once you have the twelve sources of food. It was amazing when we only had eight from Gilded Goose and Oko, Thief of Crowns.

Wicked Wolf gives you removal for almost all creatures in the format.

Wicked Wolf gives you an effectively indestructible threat to pressure opponents and planeswalkers. With so much food it can often go very large for a lethal attack.

Wicked Wolf gives you an additional zero-mana way to sacrifice food when you want to activate Trail of Crumbs.

Wicked Wolf with food available can hold the ground on its own or with minimal against a large percentage of aggressive strategies.

Wicked Wolf is the most reliable way to find cards that close the game once you have the en...
MIRI’s 2019 Fundraiser
by Malo — 2019-12-03T01:16:48.652Z

(Crossposted from the MIRI blog)

MIRI’s 2019 fundraiser is now live, December 2–31!

Over the past two years, huge donor support has helped us double the size of our AI alignment research team. Hitting our $1M fundraising goal this month will put us in a great position to continue our growth in 2020 and beyond, recruiting as many brilliant minds as possible to take on what appear to us to be the central technical obstacles to alignment.

Our fundraiser progress, updated in real time (including donations made during the Facebook Giving Tuesday event):

Donate

MIRI is a CS/math research group with a goal of understanding how to reliably “aim” future general-purpose AI systems at known goals. For an introduction to this research area, see Ensuring Smarter-Than-Human Intelligence Has A Positive Outcome and Risks from Learned Optimization in Advanced Machine Learning Systems. For background on how we approach the problem, see 2018 Update: Our New Research Directions and Embedded Agency.

At the end of 2017, we announced plans to substantially grow our research team, with a goal of hiring “around ten new research staff over the next two years.” Two years later, I’m happy to report that we’re up eight research staff, and we have a ninth starting in February of next year, which will bring our total research team size to 20.[1]

We remain excited about our current research directions, and continue to feel that we could make progress on them more quickly by adding additional researchers and engineers to the team. As such, our main organizational priorities remain the same: push forward on our research directions, and grow the research team to accelerate our progress.

While we’re quite uncertain about how large we’ll ultimately want to grow, we plan to continue growing the research team at a similar rate over the next two years, and so expect to add around ten more research staff by the end of 2021.

Our projected budget for 2020 is $6.4M–$7.4M, with a point estimate of $6.8M,[2] up from around $6M this year.[3] In the mainline-growth scenario, we expect our budget to look something like this:

Looking further ahead, since staff salaries account for the vast majority of our expenses, I expect our spending to increase proportionately year-over-year while research team growth continues to be a priority.

Given our...
Open & Welcome Thread - December 2019
by habryka — 2019-12-03T00:00:29.481Z

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.
Call for resources on the link between causation and ontology
by aaq — 2019-12-02T22:03:45.229Z

I'm a little ashamed to admit I only read "Why Correlation Usually ≠ Causation" yesterday. It's very, very good, and you should read it too.

My essential takeaway from it is this: You can find nonzero correlations between almost anything you care to measure. However, it seems unlikely that the number of causal relationships in the universe scales at all proportionally to the number of correlative ones in the Universe.

This question feels like the wrong one to be asking to me, somehow. It feels ontology-flavored, in a way that doesn't make it a great match for how I normally think about statistics, and I would appreciate some book recommendations on the subject in the comments. But first, let me try to explain my thinking on this.

Start with the "base" layer of reality, the movement of atoms, or electrons, or strings, or what-have-you. If we are watching the actions and reactions of that layer from afar, then it seems to me that we have the best possible environment for doing a few experiments to first demonstrate correlation, and then a few more to demonstrate causation afterwards. While we can never be 100% sure, we can asymptotically reach certainty in that world. So far, so good; there's a reason experimental physics can get so precise with its predictions.

When you go one layer of abstraction up -- to molecules, if our base layer was "atoms" -- it seems to me that suddenly the difficulty of ascertaining causation should skyrocket. There are many more confounding variables and possibilities, that make designing an adequate experiment much harder. In addition, it is harder to define "molecule" precisely than it was to define "atom". How far do we move the constituent atoms apart before we turn a molecule into a non-molecule, for example? That seems like a question that you have to sometimes answer differently depending on different scenarios.

The experiments you run for correlation between molecules, on the other hand, might be harder, but I don't get the feeling they experience the same kind of... Combinatorial-superexplosion-y additional difficulty that an experiment designed for causation has to handle.

You should probably try to account for things like thermal noise, and trace impurities and the like, if you have to, but past a certain point it's sort of okay to let go of ...
A list of good heuristics that the case for AI x-risk fails
by David Scott Krueger (formerly: capybaralet) — 2019-12-02T19:26:28.870Z

I think one reason machine learning researchers don't think AI x-risk is a problem is because they haven't given it the time of day. And on some level, they may be right in not doing so!

We all need to do meta-level reasoning about what to spend our time and effort on. Even giving an idea or argument the time of day requires it to cross a somewhat high bar, if you value your time. Ultimately, in evaluating whether it's worth considering a putative issue (like the extinction of humanity at the hands (graspers?) of a rogue AI), one must rely on heuristics; by giving the argument the time of day, you've already conceded a significant amount of resources to it! Moreover, you risk privileging the hypothesis or falling victim to Pascal's Mugging.

Unfortunately, the case for x-risk from out-of-control AI systems seems to fail many powerful and accurate heuristics. This can put proponents of this issue in a similar position to flat-earth conspiracy theorists at first glance. My goal here is to enumerate heuristics that arguments for AI takeover scenarios fail.

Ultimately, I think machine learning researchers should not refuse to consider AI x-risk when presented with a well-made case by a person they respect or have a personal relationship with, but I'm ambivalent as to whether they have an obligation to consider the case if they've only seen a few headlines about Elon. I do find it a bit hard to understand how one doesn't end up thinking about the consequences of super-human AI, since it seems obviously impactful and fascinating. But I'm a very curious (read "distractable") person...

A list of heuristics that say not to worry about AI takeover scenarios:

Outsiders not experts: This concern is being voiced exclusively by non-experts like Elon Musk, Steven Hawking, and the talkative crazy guy next to you on the bus.
Ludditism has a poor track record: For every new technology, there's been a pack of alarmist naysayers and doomsday prophets. And then instead of falling apart, the world got better.
EtA: No concrete threat model: When someone raises a hypothetical concern, but can't give you a good explanation for how it could actually happen, it's much less likely to actually happen. Is the paperclip maximizer the best you can do?
It's straight out of science fiction: AI researchers didn't come up with this concern, Hollywood did. Science fiction is constructed bas

...
What I talk about when I talk about AI x-risk: 3 core claims I want machine learning researchers to address.
by David Scott Krueger (formerly: capybaralet) — 2019-12-02T18:20:47.530Z

Recently, as PCSOCMLx, I (co-)hosted a session with the goal of explaining, debating, and discussing what I view as "the case for AI x-risk". Specifically, my goal was/is to make the case for the "out-of-control AI killing everyone" type of AI x-risk, since many or most ML researchers already accept that there are significant risks from misuse of AI that should be addressed. I'm sharing my outline, since it might be useful to others, and in order to get feedback on it. Please tell me what you think it does right/wrong!

EDIT: I noticed I (and others I've spoken to about this) haven't been clear enough about distinguishing CLAIMS and ARGUMENTS. I'm hoping to make this clearer in the future.

Some background/context

I estimate I've spent ~100-400 hours discussing AI x-risk with machine learning researchers during the course of my MSc and PhD. My current impression is that rejection of AI x-risk by ML researchers is mostly due to a combination of:

Misunderstanding of what I view as the key claims (e.g. believing "the case for x-risk hinges on short-timelines and/or fast take-off").
Ignorance of the basis for AI x-risk arguments (e.g. no familiarity with the argument from instrumental convergence).
Different philosophical groundings (e.g. not feeling able/compelled to try and reason using probabilities and expected value; not valuing future lives very much; an unexamined apparent belief that current "real problems" should always take precedence of future "hypothetical concerns" resulting in "whataboutism").

I suspect that ignorance about the level of support for AI x-risk concerns among other researchers also plays a large role, but it's less clear... I think people don't like to be seen to be basing their opinions on other researchers'. Underlying all of this seems to be a mental move of "outright rejection" based on AI x-risk failing many powerful and useful heuristics. AI x-risk is thus commonly viewed as a Pascal's mugging: "plausible" but not plausible enough to compel any consideration or action. A common attitude is that AI take-over has a "0+epsilon" chance of occurring.I'm hoping that being more clear and modest in the claims I/we aim to establish can help move discussions with researchers forward. I've recentl...
Is the rate of scientific progress slowing down? (by Tyler Cowen and Ben Southwood)
by Ben Pace — 2019-12-02T03:45:56.870Z
Link: https://docs.google.com/document/d/1cEBsj18Y4NnVx5Qdu43cKEHMaVBODTTyfHBa8GIRSec

Mini-book-length analysis of the question, by Cowen and Southwood. I'd love to read quotes and summaries of key arguments
The history of smallpox and the origins of vaccines
by philh — 2019-12-01T20:51:29.618Z
Link: https://rootsofprogress.org/smallpox-and-vaccines

Smallpox was one of the worst diseases in human history. It killed an estimated 300 million people or more in the 20th century alone; only tuberculosis and malaria have been more deadly. Its victims were often children, even infants.

It is also, among diseases, humanity’s greatest triumph: the only human disease we have completely eradicated, with zero naturally occurring cases reported worldwide, even in the most remote locations and the poorest countries, for over forty years.

Along the way, it led to fierce religious and political debates, some of the earliest clinical trials and quantitative epidemiology, and the first vaccines.

This is the story of smallpox, and how we killed it.