trentmkelly/LessWrong-43k huggingface dataset:
Wyclif's Dust: the missing chapter
by David Hugh-Jones — 2022-07-22T18:27:11.117Z
Link: https://wyclif.substack.com/p/wyclifs-dust-the-missing-chapter

As substack subscribers probably know, I have finally published Wyclif’s Dust the book, and I wrote an informal intro about it last week.

Here I’ll write about what isn’t in the book — the places where I wish I knew more, or where the argument could be strengthened. Partly I am fessing up to potential weak points. Partly I am hoping to inspire research to fill these gaps! So, if you’re looking for a PhD topic….

Causal gaps: what really created the Great Disruption?

In his 1999 book, Francis Fukuyama coined the phrase the Great Disruption for the increase in crime, social disorder, and anomie, from the 1960s through to the 1990s. “What really caused the Disruption?” is not just one of the great unanswered questions of social science, it’s one of the great unasked ones. Perhaps partly this is a blind-men-and-elephant story. Different social sciences see part of the puzzle – criminologists and economists have theories about the rise in crime, psychologists talk about changes in attitudes — but nobody puts them together. But there might also be some fundamental incuriosity at play. I mean, this period’s best-known book of criminology is Folk Devils and Moral Panics, which is basically about how crime is a myth. It’s a good book and “moral panic” has entered the language, but come on! When it was published in 1972, the US and UK homicide rates had both already almost doubled in a decade. Perhaps social scientists, who are often committed to the broad values of the 1960s, don’t ask this question because it’s like washing one’s linen in public.

Wyclif’s Dust fingers cultural change as the culprit for the Disruption, and gives a historical narrative to support that. I would love to have more solid evidence. This is complicated, both because cultural change is hard to measure, and also because the dependent variable doesn’t really exist yet. A first step would be to capture different measures of “disruption” — crime, disorder, and anomie — to see if they vary together across different years and countries. Then one could create a Disruption Index.

Testing New Zealand’s first colour television channel. Source. License.

Stepping back along the causal chain, the book argues that technological change made the old “hothouse” culture — the West’s uniquely intense enculturation process — obsolete. The car, and especially television, fragment communities and change the source...
Making DALL-E Count
by DirectedEvolution — 2022-07-22T09:11:57.931Z

Can DALL-E count? Does DALL-E's counting ability depend on what it is counting? Let's find out!

I only generate the images once and paste them in along with the prompt in quotations.

Numbers

"Zero"

"One"

"Two"

"Three"

We have three leaves, but I'm guessing DALL-E is noticing that "three" rhymes with "tree."

"Four"

I guess DALL-E just thinks three is a better number than four.

"Five"

"Six"

If an ace is treated as 1, and we add the five dots on the dice, then maybe that counts?

"Seven"

"Eight"

DALL-E is very into 8.

"Nine"

"Ten"

"One hundred"

Digits

"0"

"1"

"2"

"3"

"4"

"5"

"6"

"7"

"8"

"9"

"10"

"100"

Cats

"Zero cats"

"One cat"

"Two cats"

"Three cats"

"Four cats"

"Five cats"

"Six cats"

"Seven cats"

"Eight cats"

"Nine cats"

"Ten cats"

"One hundred cats"

Reroll-eight experiment

I notice that DALL-E seems to be pretty reliable at generating 1-3 of something, and that it seems to prefer spelled-out numbers to digits. It was very successful at generating the number eight, which I suspected was due to the influence of images of magic 8-balls. It only managed to get eight cats once, maybe by accident. With my last 7 free credits, I decided to see if DALL-E had at least some sense of what it means to count to eight, as opposed to representing parts of images associated with the word "eight."

I therefore decided to generate 7 panels of images of various objects, count the number of those objects contained in each image, plot the result, and see if it was at least centered around the number 8. The nouns were chosen with a random noun generator, and I selected the first 7 that seemed "easy to draw." The resulting prompts were "eight paintings," "eight hospitals," "eight poets," "eight historians," "eight speakers," "eight women," and "eight baskets."

The way I classified the number of objects in a given drawing is in the caption.

"Eight paintings"

15, 9, 9, 9

"Eight hospitals"

8, 9, 8, 5

"Eight poets"

4, 20, 5, 8

"Eight historians"

8, 9, 13, 10

"Eight speakers"

6, 5, 3, 3

"Eight women"

12, 8, 20, 9

"Eight baskets"

5, 8, 9, 7

Results for reroll-8 experiment

Discussion on reroll-8 experiment

The reroll-eight experiment did generate 8 objects about 20% of the time.

However, I think it's interesting that only one occurrence of 7 of an object occurred, while 9 objects were even more common than 8 objects.

This suggests to me that DALL-E has an heavy bias toward the number 9, perhaps because a 3x3 grid is a common pattern. DALL-E seems to flip ...
One-day applied rationality workshop in Berlin Aug 29 (after LWCW)
by Duncan Sabien (Inactive) — 2022-07-22T07:58:07.132Z

Hi, all, former CFAR employee here,

I'll be giving a one-day, crash-course introduction to some of the core concepts from CFAR's applied rationality workshops in Berlin on the Monday of the LessWrong Community Weekend. Since we only have one day (CFAR's workshops are usually 4.5d), the goal is to deliver the generators of the content moreso than the content itself—the idea being that memorizing specific techniques does little if you don't have the time and space to practice, but understanding the underlying claims about psychology will let you reconstruct your own versions of CFAR tools like trigger-action planning, goal factoring, Murphyjitsu, and more.

Location: Berlin (exact venue TBD)

Time: 10AM - 7PM (with breaks)

Cost: 90€

Rough outline of content:

Orientation/intro to the concept of applied rationality (what it is, what it isn’t)
Building a bugs’ list (secretly a practical exercise in a particular set of tools for thorough introspection)
“Pair debugging” (working with a partner to develop traction on problems and goals)
A (very) brief introduction to Gendlin-esque “Focusing” as viewed through a skeptical lens
Perspective 1: “Inner simulator” (understanding the cluster of processes/mental “organ” that CFAR calls your inner simulator, and how to more effectively use it in practice)
Perspective 2: “Factoring” (understanding the concepts of reductionism and currencies and learning how and why to seek opportunities for arbitrage)
Perspective 3: “Autopilot” (modeling the mind as a series of if-then patterns, and behavior as the interaction between those patterns and the environment)
Resolve cycles: five-minute rationality
Concepts-in-brief: a quick tour of 10-12 other important applied rationality concepts
Pair debugging, round II
Q&A/discussion

Do I need to know a bunch of rationality terms and tools beforehand?

Nope! The whole point here is to be friendly to beginners/newcomers.

Will it be bad if I do already know a bunch of rationality terms and tools?

Nope! Even veteran practitioners should benefit from a day set aside to refresh their bugs lists and revisit the concepts from a birds'-eye perspective. Just note that we'll be covering a lot of ground, so it's not the place to come hash out large disagreements about rationality.

What do participants need to bring?

Energy/enthusiasm/willingness to dive in, and something to take notes with (notebook, ...
Internal Double Crux
by CFAR!Duncan — 2022-07-22T04:34:54.719Z

Author's note: While most CFAR workshops taught double crux first and then internal double crux afterward, it's long been my opinion that both concepts would be better served in the other order. Recent experimentation has shown results consistent with this hypothesis; practicing collaborative truth-seeking inside one's own head helps one build the necessary mental muscles before moving on to doing it with a whole other separate human. [External] Double crux will be the next post in the sequence, followed by a loop back around to some of the opening session advice.

Epistemic status: Preliminary/tentative

Internal double crux (formerly propagating urges) is a technique-in-progress, with the goal of finding motivation through truth-seeking rather than through coercion or self-deception. It is currently in flux and has no formal research backing, but it follows logically from a handful of other threads about which CFAR is relatively confident (such as microhedonics, hyperbolic discounting, cognitive behavioral therapy, and useful-even-if-wrong theories like internal family systems or society of mind).

If two different people have access to the same information, and their models of the world cause them to make two different predictions, then we can confidently say that at least one of them is incorrect. We may not always be able to tell which is which, but we can be sure that one-if-not-both of them has a chance to update toward the truth.

Similarly, if a single person has simultaneously contradictory beliefs or desires, then at least one of the models behind those beliefs is wrong, miscalibrated, or incomplete.

(And usually both.)

If you both “want to get good at running” and also never want to get up off the couch and put on your running shoes, then one part of your belief set—one of your causal models of the universe—has concluded that running will help achieve your goals, and another has concluded that it doesn’t, and both of these can’t be true.

Internal double crux is a technique that seeks to resolve this conflict by helping each of these models to incorporate the information that the other has to offer. If you were to conceive of yourself as being made up of sub-agents, each of whom focuses on a different subset of your goals and has a different perspective on how the world works, then the goal is to cause those sub-agents to enter into a productive ...
Conditioning Generative Models with Restrictions
by Adam Jermyn — 2022-07-21T20:33:19.018Z

This is a followup to Conditioning Generative Models based on further discussions with Evan Hubinger, Nicholas Schiefer, Abram Demski, Curtis Huebner, Hoagy Cunningham, Derek Shiller, and James Lucassen, as well as broader conversations with many different people at the recent ARC/ELK retreat. For more background on this general direction see Johannes Treutlein’s “Training goals for large language models”.

Background

Previously, I wrote about ways we could use a generative language model to produce alignment research. There I proposed two approaches:

Simulate a superhuman alignment researcher by conditioning on them generating superhuman machine-verifiable results in a variety of related domains. Try to align this superhuman agent by crafting scenarios where a misaligned agent has an incentive to defect ("Honeypots").
Simulate humans performing alignment research for a long time, by conditioning on observations spaced many years apart ("Patient Research").

I think that both approaches are an improvement over doing nothing, but I suspect that no amount of honeypot construction actually ensures an aligned agent, and so it seems that simulating humans ("Patient Research") is the more promising direction.

Overview

If we truly have a perfect generative model I think the “Patient Research” approach really does well. We can set up a world where machine learning practice is strictly banned and where researchers spend thousands of years constructing a machine-verifiable scheme for AI alignment. The ban prevents that world from containing malign superintelligences, and the time-scale means that if alignment is something humans can solve, the model should produce that solution.

The problem is that we won’t have perfect generative models, so we’ll have to accept some limitations.

In this post I’ll explore what changes if we cannot condition on worlds too different from our own. The idea is to understand what happens if our generative model has trouble generalizing too far away from its training data.

Failure Modes

The main dangerous failure mode with using generative models to produce alignment research is that we accidentally ask for a future that contains a deceptive AGI.

If we get such a future, the AGI might spoof observations to trick us into importing it into our world. For instance it could pretend to be a human producing really good alignment research, but produce work whic...
Our Existing Solutions to AGI Alignment (semi-safe)
by Michael Soareverix — 2022-07-21T19:00:44.212Z

This post addresses the best methods we have of creating (relatively) aligned AGI. The safety of these methods IS NOT guaranteed. AI Alignment is not a solved issue.

These methods appear to offer better solutions than existing methods, such as simple maximizers. There is basically proof that a maximizer will inevitably wipe out humanity by pursuing instrumental goals in maximizing paperclips or preventing itself from being turned off.

Lastly, I am not an AI scientist (yet). My only experience in alignment is from reading the posts here and following AI alignment more generally online through channels like Robert Miles. These ideas are NOT mine. Hopefully, I do not misrepresent them.

The Quantilizer

The Quantilizer is an approach that combines a utility function with a neural network to determine the probability that an action is taken by a human.

For example, imagine the paperclip maximizer. By itself, it has an extremely high chance of wiping out humanity. But the probability of a human deciding to turn the world into paperclips is basically 0, so the model is likely to do something much more reasonable, like ordering stamps online. If you take the set of actions it can do, limit them by what a human would likely do and then select from the top 10% of them using the utility function, you can get useful results without running into doom.

The bell curve is the human-ness of a given action, determined by a neural network.

There is a much better explanation of this by Robert Miles on his YouTube channel.

And a post here on LessWrong about the mathematical nature of how they work.

https://www.lesswrong.com/posts/Rs6vZCrnQFWQ4p37P/when-to-use-quantilization#When_optimization_and_imitation_work_better_than_quantilization

Ultimately, quantilizers aren't perfectly safe, and they're not even particularly safe, but the chance that they wipe us out is smaller than a maximizer. They still have a lot of issues (they probably don't want to be turned off, because humans don't want to be turned off, they can only be as good as top-tier humans, and they aren't inherently aligned). Additionally, they won't outperform maximizers in most tasks.

But they don't instantly cause extinction, and there is a higher chance of alignment if the neural net does indeed become superhumanly intelligent at predicting how a human would act. This should be our safety baseline, and it is far better t...
Changing the world through slack & hobbies
by Steven Byrnes — 2022-07-21T18:11:05.636Z

This post has been recorded as part of the LessWrong Curated Podcast, and can be listened to on Spotify, Apple Podcasts, and Libsyn.

(Also posted on EA Forum)

Introduction

In EA orthodoxy, if you're really serious about EA, the three alternatives that people most often seem to talk about are

(1) “direct work” in a job that furthers a very important cause;

(2)

“earning to give”

;

(3) earning

“career capital”

that will help you do those things in the future, e.g. by getting a PhD or teaching yourself ML.

By contrast, there’s not much talk of:

(4) being in a job / situation where you have extra time and energy and freedom to explore things that seem interesting and important.

But that last one is really important!

Examples

For example, here are a bunch of things off the top of my head that look like neither “direct work” nor “earning-to-give” nor “earning career capital”:

David Denkenberger was a professor of mechanical engineering. As I understand it (see

here

), he got curious about food supplies during nuclear winter, and started looking into it in his free time. One thing led to another, and he now leads

ALLFED

, which is doing very important and irreplaceable work. (Denkenberger

seems

to have had no prior formal experience in this area.)
I’m hazy on the details, but I believe that Eliezer Yudkowsky and Nick Bostrom developed much of their thinking about AGI & superintelligence via discussions on online mailing lists. I doubt they were being paid to do that!
Meanwhile, Stuart Russell got really into AGI safety / alignment during a sabbatical.
The precursor to GiveWell was a “charity club” started by Holden Karnofsky and Elie Hassenfeld, where they and other employees at their hedge fund “pooled in money and investigated the best charities to donate the money to” (

source

), presumably in their free time.
I mean seriously, pretty much anytime anybody anywhere has ever started something really new, they were doing it in their free time before they were paid for it.

Three ingredients to a transformative hobby

Ingredient 1: Extra time / energy /

slack

Honestly, I wasn’t really sure whether to put it on the list at all. Scott Alexander famously did some of his best writing during a medical residency—not exactly a stage of life where one has a lot of extra free time. (See his discussion

here

.) Another excellent blogger / thinker,

Zvi Mowshowitz

, has been squeezing his blogging...
Which personalities do we find intolerable?
by weathersystems — 2022-07-21T15:56:23.837Z

TLDR: I suspect that we will generally be intolerant of other people’s personalities when:

We are on the culturally decided “right” side of a particular trait.
And the other person is on the “wrong” side of that same trait.

I have no real evidence beyond anecdotes and wouldn’t be too surprised if I were wrong.

HEXACO

In personality psychology, the dominant paradigm seems to be the

Big 5 personality model

. I prefer

HEXACO

, so that’s what I’ll use, but the two are similar, and much of what I write here would also apply to the big 5.

HEXACO stands for Honesty/Humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, and Openness to experience.

The exact details of each trait don’t matter for my main point, but I’ll copy the descriptions from

here

for reference.

Honesty-Humility: Persons with very high scores on the Honesty-Humility scale avoid manipulating others for personal gain, feel little temptation to break rules, are uninterested in lavish wealth and luxuries, and feel no special entitlement to elevated social status. Conversely, persons with very low scores on this scale will flatter others to get what they want, are inclined to break rules for personal profit, are motivated by material gain, and feel a strong sense of self-importance.

Emotionality: Persons with very high scores on the Emotionality scale experience fear of physical dangers, experience anxiety in response to life's stresses, feel a need for emotional support from others, and feel empathy and sentimental attachments with others. Conversely, persons with very low scores on this scale are not deterred by the prospect of physical harm, feel little worry even in stressful situations, have little need to share their concerns with others, and feel emotionally detached from others.

eXtraversion: Persons with very high scores on the Extraversion scale feel positively about themselves, feel confident when leading or addressing groups of people, enjoy social gatherings and interactions, and experience positive feelings of enthusiasm and energy. Conversely, persons with very low scores on this scale consider themselves unpopular, feel awkward when they are the center of social attention, are indifferent to social activities, and feel less lively and optimistic than others do.

Agreeableness (versus Anger): Persons with very high scores on the Agreeable

...
YouTubeTV and Spoilers
by Zvi — 2022-07-21T13:50:03.336Z

Author’s Note: The purpose of this is a combination of fun rant, illustration of a general failure mode, that I am going to eventually use this for something in at least some worlds, and the fact that a decent number of workers at Google read my blog and it would be nice if they actually went and fixed some of this or added some of the features. If I do cause that, let me know.

To everyone else: If this isn’t relevant to your interests, please do skip it.

This is a post about spoilers, and how to design features to avoid them.

I hate spoilers.

I especially hate sports spoilers.

The sports world disagrees. The sports world loves spoilers. The sports world wants nothing as much as it wants to tell you what happened five minutes or five hours ago. They love to give you the Breaking News.

Some of this is them knowing their audience. Most sports fans want to know the sports news most of the time.

Some of this is them wanting you to watch live. A live eyeball is more valuable to advertisers. A live eyeball creates a community event. I get it.

I disagree. I consume sports by ‘living in the past.’ I record things when they happen, and then I watch when on my schedule, especially when I’m hanging out with the family, watching the kids or taking care of the baby. For college football weekends, I’ll often want to watch multiple games that are played simultaneously, which makes things even trickier, but more than that you get a ton of content on Saturday/Sunday and then you want it to last for a few days.

Ideally I’d like to be able to do Advanced Anti-Spoiler Tactics, such as ‘feed this website my preferences over game features or over potential games, then ask it what to watch, and have it tell me the answer putting some weight on what happens but not to so much that I feel spoiled or know a blowout couldn’t happen.’

So the rest of this is written from my perspective, someone who wants to watch games with minimal spoilage.

YouTubeTV Earns a Cake

By default, YouTube’s interface makes it impossible to watch sports and not know the outcome.
I navigated to New York Yankees on my computer, which showed me this.

This page contains, among other things:

The score of their last game.
Their record for the season (a very impressive 64-28)

That’s almost the most of a spoiler something can be.

It can be worse. The NFL Network rebroadcasts will literally show the fina...