trentmkelly/LessWrong-43k huggingface dataset:
Funds are available to support LessWrong groups, among others
by Buck — 2021-07-21T01:11:29.981Z

(Cross-posted from the EA Forum)

This post was written by Buck and Claire Zabel but it’s written in Buck’s voice, and “I” here refers to Buck, because it’s about grantmaking that he might do. (Claire contributed in her personal capacity, not as an Open Phil grantmaker).

In addition to

accepting applications for EA groups in some locations

as part of my EAIF grantmaking, I am interested in evaluating applications from people who run groups (in-person or online, full-time or part-time) on a variety of related topics, including:

Reading groups, eg for

The Precipice

or

Scout Mindset

Groups at companies, online-only groups, and other groups not based in particular geographic locations or universities.
Groups discussing blogs or forums that are popular with EAs, such as Slate Star Codex / Astral Codex Ten or LessWrong.
Longtermist-only, AI-centric or biosafety-centric groups, animal welfare groups, or other groups that address only a single EA cause area. (I might refer these applications to the Long-Term Future Fund or the Animal Welfare Fund as appropriate; both of these funds have confirmed to me that they’re interested in making grants of this type.)

I also welcome applications from people who do or want to do work for existing groups, or group organizers who want funding to hire someone else to work with them. Eg:

Maintaining or overhauling group websites, if you think this is worthwhile for your particular group
Working 10hrs/week on a student group
Running group mailing lists

In cases where the project/expense isn’t a good fit for the EA Funds, but I think it’s worth supporting, I am likely able to offer alternative sources of funds.

I might stop doing this if someone appears who’s able to commit more time and thought to funding and supporting these kinds of groups, but for the time being I want to offer folks who want to work on these kinds of things a chance to request support.

I think that people who put serious time into creating high-quality groups deserve compensation for the time they put in, so please don’t let thoughts like “I only work on this for 10 hours a week” or “I’m happy to do this in a volunteer capacity” discourage you from applying. If you’re unsure if something is a reasonable fit, feel free to email me (

bshlegeris@gmail.com

) and ask before applying. Depending on your cost of living, ask for a rate of $20-50 per hour (this includes emplo...
Punishing the good
by KatjaGrace — 2021-07-20T23:30:15.270Z

Should you punish people for wronging others, or for making the wrong call about wronging others?

For example:

A newspaper sends me annoying emails all the time, but suppose that empirically if they didn’t behave like this, they would get markedly fewer subscribers, and may not survive. And suppose their survival is in fact worth a little annoyance for a lot of people, we all agree. Such that if I was in their position, I agree that I would send out the annoying emails. Should I resent them and unsubscribe from their paper for their antisocial behavior, or praise them and be friendly because overall I think they made the right call?

Suppose Bob eats beef, which he thinks makes him feel somewhat better and so be better able to carry out his job as a diplomat negotiating issues in which tens of thousands of lives are at stake. He also thinks it is pretty bad for the cows, but worth it on net. Suppose he’s right about all of this. Five hundred years later, carnivory is illegal and hated, and historians report that Bob, while in other regards a hero, did eat beef. Should the people of 2521 think of Bob as an ambiguous figure, worthy of both pride and contempt? or should they treat him as purely a hero, who made the best choice in his circumstances?

I have one intuition that says, ‘how can you punish someone for doing the very best thing they could have done? What did you want them to do? And are you going to not punish the alternative person, who made a worse choice for the world, but didn’t harm someone in the process? Are you just going to punish everyone different amounts?’

But an argument for the other side—for punishing people for doing the right thing—is that it is needed to get the incentives straight. If Alice does $100 of harm to Bruce to provide $1000 of help to Carrie, then let’s suppose that that’s good (ignoring the potential violation of property rights, which seems like it shouldn’t be ignored ultimately). But if we let such things pass, then Alice might also do this when she guesses that is only worth $60 to Carrie, if she cares about Carrie more than Bruce. Whereas if we always punish Alice just as much as she harmed Bruce, then she will take the action exactly when she would think it worth it if it was her own welfare at stake, rather than Bruce’s. (This is just the general argument for internalizing externalities - having people pay for t...
Entropic boundary conditions towards safe artificial superintelligence
by Santiago Nunez-Corrales — 2021-07-20T22:15:41.664Z
Link: https://www.tandfonline.com/doi/full/10.1080/0952813X.2021.1952653

I wanted to share a recently published paper on mechanisms to decrease the likelihood of ASI being destructive towards living beings in general.

Abstract:

Artificial superintelligent (ASI) agents that will not cause harm to humans or other organisms are central to mitigating a growing contemporary global safety concern as artificial intelligent agents become more sophisticated. We argue that it is not necessary to resort to implementing an explicit theory of ethics, and that doing so may entail intractable difficulties and unacceptable risks. We attempt to provide some insight into the matter by defining a minimal set of boundary conditions potentially capable of decreasing the probability of conflict with synthetic intellects intended to prevent aggression towards organisms. Our argument departs from causal entropic forces as good general predictors of future action in ASI agents. We reason that maximising future freedom of action implies reducing the amount of repeated computation needed to find good solutions to a large number of problems, for which living systems are good exemplars: a safe ASI should find living organisms intrinsically valuable. We describe empirically-bounded ASI agents whose actions are constrained by the character of physical laws and their own evolutionary history as emerging from H. sapiens, conceptually and memetically, if not genetically. Plausible consequences and practical concerns for experimentation are characterised, and implications for life in the universe are discussed.

Our approach attempts to avoid direct implementation of machine ethics by harnessing the concept of causal entropic forces (i.e. forces that emerge from the microscale as a result of emergent phenomena having a mechanistic origin), and then building a set of boundary conditions for a new class of agents:

Let us define an empirically bounded ASI agent (EBAA) as a type of superintelligent agent whose behaviour is driven by a set of interlocking causal entropic forces and a minimal set of boundary conditions informed by empirical measurements of its accessible portion of the universe. Its entropic forces and its boundary conditions define and constrain its top-level goal satisfaction process.

These agents attempt to satisfy two goals:

(1) Build predictive empirical explanations for events in the accessible universe as sophisticated and generally applicable as possib

...
Working With Monsters
by johnswentworth — 2021-07-20T15:23:20.762Z

This is a fictional piece based on Sort By Controversial. You do not need to read that first, though it may make Scissor Statements feel more real. Content Warning: semipolitical. Views expressed by characters in this piece are not necessarily the views of the author.

I stared out at a parking lot, the pavement cracked and growing grass. A few cars could still be seen, every one with a shattered windshield or no tires or bashed-in roof, one even laying on its side. Of the buildings in sight, two had clearly burned, only blackened reinforced concrete skeletons left behind. To the left, an overpass had collapsed. To the right, the road was cut by a hole four meters across. Everywhere, trees and vines climbed the remains of the small city. The collapsed ceilings and shattered windows and nests of small animals in the once-hospital behind me seemed remarkably minor damage, relatively speaking.

Eighty years of cryonic freeze, and I woke to a post-apocalyptic dystopia.

“It’s all like that,” said a voice behind me. One of my… rescuers? Awakeners. He went by Red. “Whole world’s like that.”

“What happened?” I asked. “Bioweapon?”

“Scissor,” replied a woman, walking through the empty doorway behind Red. Judge, he’d called her earlier.

I raised an eyebrow, and waited for elaboration. Apparently they expected a long conversation - both took a few seconds to get comfortable, Red leaning up against the wall in a patch of shade, Judge righting an overturned bench to sit on. It was Red who took up the conversation thread.

“Let’s start with an ethical question,” he began, then laid out a simple scenario. “So,” he asked once finished, “blue or green?”.

“Blue,” I replied. “Obviously. Is this one of those things where you try to draw an analogy from this nice obvious case to a more complicated one where it isn’t so obvious?”

“No,” Judge cut in, “It’s just that question. But you need some more background.”

“There was a writer in your time who coined the term ‘scissor statement’,” Red explained, “It’s a statement optimized to be as controversial as possible, to generate maximum conflict. To get a really powerful scissor, you need AI, but the media environment of your time was already selecting for controversy in order to draw clicks.”

“Oh no,” I said, “I read about that… and the question you asked, green or blue, it seems completely obvious, like anyone who’d say green would have to be troll...
An examination of Metaculus' resolved AI predictions and their implications for AI timelines
by CharlesD — 2021-07-20T09:08:43.921Z

Cross-posted from the EA forum

Edit: I was contacted by the author of many unresolved AI questions on Metaculus, who tells me he intends to resolve up to 40 overdue questions on the AI subdomain in the next few weeks. I will update this post if/ when that happens

Introduction

Metaculus

is a forecasting website which aggregates quantitative predictions of future events. One topic of interest on Metaculus is artificial intelligence. Here I look at what we might be able to learn from how the questions on this subject have gone so far, in particular, how the predictions of the Metaculus community have performed. If they have done poorly, it would be of value to making future predictions and interpreting existing ones to know if there are any patterns to this which might reveal common mistakes in making AI related predictions.

Summary

There are three main types of questions I looked at - Date based questions, numeric range questions, and binary questions.

For date questions, slightly fewer than the Metaculus community expected have resolved by now, but the sample size is small.
For numeric questions, it did not seem like the community was biased towards predicting faster or slower timelines than the question resolution implied, but it did look like the community was quite overconfident in their ability to predict the resolution.
For binary questions, it looked like the community expected more developments to occur than actually happened, but they were appropriately not very confident in their predictions.

Overall it looked like there was weak evidence to suggest the community expected more AI progress than actually occurred, but this was not conclusive.

Data I used

I got data from all 259 questions on Metaculus with the category “

Computer Science - AI and Machine Learning

” (169 questions) or on the

AI subdomain

(90 extra questions, there was some overlap). This was quite a manual process; see the appendix for an explanation of the process. The data came in the form of a json object containing both current and historical data for each question.

I took the community prediction 25% of the way through the question lifetime for each question (i.e., 25% of the way from when the question is opened to when it closes). This was to avoid the cases where resolution becomes obvious one way or another and goes to 1% or 99%, and in order to only count each question...
Pi Sound Box
by jefftk — 2021-07-20T04:10:02.545Z

In an effort to reduce clutter, simplify setup, and avoid damaging my
gear, I've packaged up my

whistle synth
and

rhythm stage setup into a
box:

This consolidates:

Raspberry Pi

Power supply

USB hub

Sound card x2

DI box

Internal cables and adapters

The box is aluminum, 10x8x4. I
like working with aluminum: it's very strong, but it's soft enough to
drill without too much trouble. I removed the front and back plates
and clamped them between blocks of wood for drilling.

The DI is a Radial
PRO D2. I removed the shell and reused the original screws to
attach it to my case. I also carefully aligned it so I could reuse
the existing XLR ports; 1/4" jacks are pretty easy but XLR is more
hassle.

I should have included an ethernet port so I can reprogram it without
opening it up. I may come back and add one.

It's very much a prototype: a production version of something like
this would probably only be about 4" on a side. Combining existing
parts does not minimize size or weight.

(In setting this up I moved my rhythm stage setup (github), which I
essentially hadn't touched in a year, from a Mac to the Raspberry Pi.
This meant porting it to Linux, which meant learning how to do MIDI on
Linux. It also meant dropping a few sounds which aren't open source
only
sort of support Linux, primarily the sax and trombone. Since I'm no longer playing the
Axis 49 because of wrist issues, though, this isn't actually
giving up very much.)
Book review: The Explanation of Ideology
by PeterMcCluskey — 2021-07-20T03:42:36.250Z
Link: http://www.bayesianinvestor.com/blog/index.php/2021/07/19/the-explanation-of-ideology/

Book review: The Explanation of Ideology: Family Structure and Social
Systems, by Emmanuel
Todd.

What features distinguish countries that embraced communism from
countries that resisted?

Why did Islam spread rapidly for a century and a half, then see
relatively few changes in its boundaries for more than a millennium?

Todd's answer is that the structure of the family is a good deal more
stable than ideologies and religions, and different family structures
create different constraints on what ideologies and religions will be
accepted. Published in 1983, it still seems little-known.

Maybe this neglect is most pronounced in the English-speaking parts of
the world, where one family structure is overwhelmingly popular, and
alternatives are often dismissed as primitive relics. France seems more
conducive to Todd's insights, since France has four different family
structures, each dominating in various regions.

Here are the main dimensions that he uses to categorize family
structures:

Exogamous: marriages between
cousins are heavily
discouraged, versus endogamous: marriages between cousins are
common.

Nuclear versus community: Are children expected to move away from
the parental home upon marriage?

Equal versus unequal. Beware that this is a nonstandard meaning,
focused on relations between brothers, especially on whether
inheritances are split equally. Todd says this is inversely
correlated with sexual equality. He seems willing to accept sexual
inequality as not worth trying to eliminate ("male dominance, a
principle ... which is in practice much more universal than the
incest taboo").

Liberty versus authority. This is mostly about parental authority
over children.

Here are his categories, listed in roughly descending order of how many
Europeans practice them (this is Todd's order; the book is a bit
Eurocentric).

Exogamous Community

This system is equal, authoritarian, and universalist. It mostly
coincides with countries that adopted communism at some point, plus
Finland and northern India.

It is relatively unstable, tending to produce features such as
communism, which wages war on the family, and urbanization, which pushes
toward a more nuclear family. But then why is it the most populous
family system (41% of the world population when the book was written)?
Todd does not ask. Some of it might be due to generating population
growth, but that can't be a full explanati...
In search of benevolence (or: what should you get Clippy for Christmas?)
by Joe Carlsmith — 2021-07-20T01:12:12.162Z

(Cross-posted from Hands and Cities)

Suppose that you aspire to promote the welfare of others in a roughly impartial way, at least in some parts of your life. This post examines a dilemma that such an aspiration creates, especially given subjectivism about meta-ethics. If you don’t use idealized preference-satisfaction as your theory of welfare, your “helping someone” often ends up “imposing your will” on them (albeit, in a way they generally prefer over nothing) — and for subjectivists, there’s no higher normative authority that says whose will is right. But if you do use idealized preference satisfaction as your theory of welfare, then you end up with a host of unappealing implications — notably, for example, you can end up randomizing the universe, turning it into an ocean of office supplies, and causing suffering to one agent that a sufficient number of others prefer (even if they never find out).

I don’t like either horn of this dilemma. But I think that the first horn (e.g., accepting some aspect of “imposing your will,” though some of the connotations here may not ultimately apply) is less bad, especially on further scrutiny and with further conditions.

I. “Wants” vs. “Good for”

Consider two ontologies — superficially similar, but importantly distinct. The first, which I’ll call the “preference ontology,” begins with a set of agents, who are each assigned preferences about possible worlds, indicating how much an idealized version of the agent would prefer that world to others. The second, which I’ll call the “welfare ontology,” begins with a set of patients, who are each assigned “welfare levels” in possible worlds, indicating how “good for” the patient each world is.

On a whiteboard, and in the mind’s eye, these can look the same. You draw slots, representing person-like things; you write numbers in the slots, representing some kind of person-relative “score” for a given world. Indeed, the ontologies look so similar that it’s tempting to equate them, and to run “Bob prefers X to Y” and “X is better than Y for Bob” together.

Conceptually, though, these are distinct — or at least, philosophers treat them as such. In particular: philosophers generally equate “welfare” with concepts like “rational self-interest” and “prudence” (see, e.g., here). Bob’s preferences always track Bob’s welfare levels, that is, only if Bob is entirely selfish. But Bob need not be e...
Future Of Work
by specbug — 2021-07-19T23:16:20.109Z
Link: https://sixeleven.in/work

The current employment model is outdated. For the majority of workers, vocation and avocation are incongruent vectors. Here, we describe a set of tools that can be used to form a better-integrated work model, to dispense high-quality work to everyone.

An Optimisation Problem

The number of jobs is ever-mutating. There is no finite number of enterprises being segmented since ancient times. Evolving cultures yield evolving problems, increasing opportunities for innovation. Synchronously, an increasing knowledge base curates more individuals with unique competencies and interests.

So the issue of optimal employment is neither of quantity nor quality. As a crude formulation, we have n tasks to be solved and k agents to solve them. The problem is

optimally

matching the k agents to solve those n tasks — the problem is constrained optimisation.

Constrained optimisation is the process of optimising an objective function with respect to some variables, in the presence of constraints on those variables.

Here, we won't be formulating any linear equations or defining any statistical metrics. Instead, we'll follow a more descriptive approach and peek at a set of components we could leverage. We can gain insights into the solution by gauging the pitfalls of current model.

First Principles

The idea of (voluntarily) working for someone, even at the expense of one's freedom and contentment, is not intrinsic to us. We started out as a tight-knit band of hunter-gatherers, for subsistence, some 200,000 years ago.

The genesis of the Agriculture Age, some 10,000 years ago, conceptualised a tree-like work culture. The following Industrial Age, merely 300 years ago, only concretised the model.

Are Hierarchies Inevitable?

Hierarchies skirt some underlying principles of the work model (to be discussed). To analyse its effects on work efficiency, it is crucial to understand the implications of the question: Why is a company the size that it is?

Ronald Coase argues that the size of a firm depends on finding an optimal balance between the internal transaction costs vs the external transaction costs to the company. A firm can outsource some one-off task, but any resource that requires frequent ministration is better off in-house.

Pre-Information Age, there was friction to outsource some task — constrained by low bandwidth mediums in communication & commute....
Some thoughts on David Roodman’s GWP model and its relation to AI timelines
by Tom Davidson — 2021-07-19T22:59:06.861Z

[Cross posted from the EA forum.]

I’ve been working on a report (see blog) assessing possible trajectories for GWP out to 2100. A lot of my early work focussed on analysing a paper of my colleague David Roodman. Roodman fits a growth model to long-run GWP; the model predicts a 50% probability that annual GWP growth is >= 30% by 2043.

I was thinking about whether to trust this model’s GWP forecasts, compared with the standard extrapolations that predict GWP growth of ~3% per year or less.[1] I was also thinking about how the model might relate to AI timelines.

This post briefly describes some of my key takeaways, as they don’t figure prominently in the report. I explain them briefly and directly, rather than focussing on nuance or caveats.[2] I expect it to be useful mostly for people who already have a rough sense for how Roodman’s model works. Many points here have already been made elsewhere.

Although for brevity I sometimes refer to “Roodman’s extrapolations”, what I really mean is the extrapolations of his univariate model once it’s been fitted to long-run GWP data. Of course, David does not literally believe these extrapolations. More generally, this post is not about David’s beliefs at all but rather about possible uses and interpretations of his model.

[Views are my own, not my employers]

Economic theory doesn’t straightforwardly support Roodman’s extrapolation over standard extrapolations

Early on in the project, I had the following rough picture in my mind (oversimplifying for readability):

Standard extrapolations use what are called ‘exogenous growth models’. These fit the post-1900 data well. However, the exponential growth is put in by hand and isn’t justified by economic theory. (Exogenous growth models assume technology grows exponentially but don’t attempt to justify this assumption; the exponential growth of technology then drives exponential growth of GDP/capita.)

On the other hand, endogenous growth models can explain growth without putting in the answer by hand. They explain technological progress as resulting from economic activity (e.g. targeted R&D), and they find that exponential growth is implausible - a knife-edge case. Ignoring this knife-edge case, growth is either sub- or super- exponential. Roodman fits an endogenous growth model to the data and finds super-exponential growth (because growth has increased over the long-run o

...