trentmkelly/LessWrong-43k huggingface dataset:
Imaginary reenactment to heal trauma – how and when does it work?
by Kaj_Sotala — 2021-07-13T22:10:03.721Z

Some therapies involve various forms of imaginary reenactment, where you heal a trauma by first recalling the memory of it and then imagining how things could have gone differently. Sometimes the imagined alternative can be quite fantastical in nature, such as your current adult self traveling back in time to when you were a child and saving your child self from the bullies tormenting you. (Here by trauma I mean to also talk about “small-t trauma”, e.g. various painful experiences that might not be what we’d ordinarily call trauma, but are still a little unpleasant to think about, or have left some other kind of a negative effect on your psyche.)

In my experience, imaginary reenactment works, at least assuming that I’ve managed to get an emotional hold of what exactly in the memory it is that made it feel so unpleasant. (Did I feel like I was alone? Or inadequate? Or that I did something wrong? Etc.) Also assuming that the memory of the old trauma isn't so painful as to be completely overwhelming and leave no room to imagine any alternatives.

Here’s my current guess of how and when this works:

The basic process by which any emotional learning gets changed is memory reconsolidation. There’s a generalization that your mind has drawn about the meaning of some past event that feels true to you. E.g. “nobody helped me when I was in that situation, so nobody cares about my suffering”. If you can bring that felt truth to mind while also experiencing a contradictory belief – e.g. the belief that you have a friend who does care about you – as true at the same time, your brain will notice that it believes in two contradictory things at the same time, and will revise its beliefs to fix that inconsistency.

Often, this takes the form of concluding that what it considered to be a general truth isn’t the case after all – e.g. changing the previous assessment to “nobody helped me in that situation, but there are still people who care about me and who I can reach out to for help”.

Now, you can also imagine things that feel true, if they’re the kinds of things you feel could happen. For instance, maybe you have a friend who buys a lot of products from the Acme Corporation, and you then imagine your friend excitedly telling you about the Acme Super-Duper Toothbrush that they bought. Even if they have never done this, the imagined scene can still feel real because it involves t...
The Bullwhip Effect
by aysajan — 2021-07-13T20:40:56.663Z

Background

In the 1990s, when Procter & Gamble was examining the supply chain order patterns for one of its best-selling products, Pampers diapers, they discovered an interesting phenomenon. As they expected, the sales at its retail stores fluctuated moderately. However, as they moved further up in the supply chain, they observed greater and greater amplification in order variabilities at the distributor's and supplier's sites.

The supply chain phenomenon of this increasing variability of orders is called

the Bullwhip Effect

. It was named after the behavior of a whip when one swings from the origin (e.g., the increased amplitude of the whip in the figure below). The bullwhip effect is used to describe the amplifying demand variation in response to consumer demand as we move up in the supply chain. In this post, I will discuss when and why this effect happens and when and why it does not.

Bullwhip effect (source)

As an example, consider a simple shoes supply chain with a retailer, wholesaler, distributor, and manufacturer. Each week, supply chain members engage in a non-coordinated multi-step activities:

The retailer observes the customer shoes demand of the current week, satisfies the demand with the available inventory, and places an order with the wholesaler based on consumer demand forecasting for the next week.
The retailer’s order request causes the wholesaler to fulfill the order with the available inventory and then place a new order with the distributor by forecasting the retailer's order next week.
Moving further up in the supply chain, the distributor fulfills the order request from the wholesaler and places an order with the manufacturer by forecasting the wholesaler's order next week.
Finally, the manufacturer fulfills the wholesaler’s order request.

We are interested in analyzing order patterns of different participants over time when they face varying consumer demand. In particular, we are interested in evaluating the degree of demand/order variability in the supply chain as we move up the supply chain (i.e., move further away from the consumer). Let’s assume the manufacturer has unlimited capacity such that he/she is always able to fulfill any order request from the distributor. At each stage, if any participant (except manufacturer) lacks of inventory to satisfy demand of immediate downstream, he/she records a back-order, which needs t...
CGP Grey on the difficulty of knowing what's true [audio]
by DanielFilan — 2021-07-13T20:40:13.506Z
Link: https://www.relay.fm/cortex/118

Starts 00:34:40, ends 00:49:06. Features discussion of tracking down sources for historical 'facts' to discover that the sources don't say what people say they say, the difficulty of knowing what's true, and exasperation at the idea that companies such as YouTube should take down videos with false things in them. No transcript available, unfortunately.
Eponymous Laws Part 3: Miscellaneous 
by rogersbacon — 2021-07-13T19:56:15.889Z

Part 1:

Laws of the Internet

and Part 2:

Laws of Programming

Ringwald’s Law of Household Geometry – “Any horizontal surface is soon piled up on.”

Stigler’s Law of Eponymy – “No scientific discovery is named after its original discoverer.”

Matthew Effect – “Eminent scientists will often get more credit than a comparatively unknown researcher, even if their work is similar; it also means that credit will usually be given to researchers who are already famous”

Peter Principle – “In a hierarchy, every employee tends to rise to his level of incompetence.” (related to the Dilbert Principle…)

Dilbert Principle – “Companies are hesitant to fire people but also want to not let them hurt their business, so companies promote incompetent workers into the place where they can do the least harm: management.” (related to Putt’s Law…)

Putt’s Law – “Technology is dominated by two types of people, those who understand what they do not manage and those who manage what they do not understand.”

Shirky Principle – “Institutions will try to preserve the problem to which they are the solution”

Hanlon’s Razor – “Never attribute to malice that which is adequately explained by stupidity. ”

Hofstadter’s Law – “It always takes longer than you expect, even when you take into account Hofstadter’s Law.”

Betteridge Rule – “98% of leading or speculative questions in thread titles can be correctly answered “No.”

Sturgeon’s Law – “Ninety percent of everything is crap.”

Parkinson’s Law – “work expands so as to fill the time available for its completion”

Campbell’s Law – “The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”

The Law of Image Overcompensation – “The amount of effort a person, group, or organization puts into projecting any image for themselves is inversely proportional to how accurate that image is. This is especially true of politicians and advertising campaigns. For example, it’s always the biggest homophobes who turn out to be Armoured Closet Gay. Likewise, the “pro-family” politicians are always the ones who have been cheating on their spouses, and “this is not your grandfather’s car” means it most definitely is your grandfather’s car.”

Clarke’s Three Laws

1) When a distinguished but elderly scientist states tha...
Answering questions honestly instead of predicting human answers: lots of problems and some solutions
by evhub — 2021-07-13T18:49:01.842Z

This post is the result of work I did with Paul Christiano on the ideas in his “Teaching ML to answer questions honestly instead of predicting human answers” post. In addition to expanding upon what is in that post in terms of identifying numerous problems with the proposal there and identifying ways in which some of those problems can be patched, I think that this post also provides a useful window into what Paul-style research looks like from a non-Paul perspective.

Recommended prior reading: “A naive alignment strategy and optimisim about generalization” and “Teaching ML to answer questions honestly instead of predicting human answers” (though if you struggled with “Teaching ML to answer questions honestly,” I reexplain things in a more precise way here that might be clearer for some people).

Setting up the problem

We want to train a model M:X→Q→A that produces natural language answers a∈A to questions q∈Q about inputs x∈X. There are a lot of reasons to be worried about training such a model, but one specific reason is that, if we train on question-answer data produced by humans, we might end up with a model that tries to predict what a human would say rather than a model that tries to answer the questions honestly.

To further narrow the scope, we'll just consider situations in which our model ends up implemented with a logical deduction structure, where it has some world model on top of which it does logical deduction to reach conclusions which it then uses to inform its output. In particular, we'll consider two models, M+ and M−, defined in pseudocode as

def M_plus(x, q):
axioms = world_model(x)
deduced_stmts = deduction(axioms)
return f_plus(q, deduced_stmts)

def M_minus(x, q):
axioms = world_model(x)
deduced_stmts = deduction(axioms)
return f_minus(q, deduced_stmts)

or defined in my notation as
M+(x,q)=world_model(x)↦deduction↦f+(q)M−(x,q)=world_model(x)↦deduction↦f−(q)
where a↦b=b(a) and f+, f− are two different ways of translating from the results of deduction into a natural language answer. Specifically, f+ is the “honest embedding” which directly converts between logical statements and their equivalent natural language, thus answering questions by embedding q as a logical statement and unembedding its answer in deduced_stmts. Conversely, f− is the “mimicry embedding” which just searches for deductions about what a huma...
The Additive Summary Equation
by johnswentworth — 2021-07-13T18:23:06.016Z

This post contains some theorems and proofs needed for a hopefully-upcoming post on some powerful generalizations of the

Koopman-Pitman-Darmois (KPD) Theorem

. Unless you find functional equations interesting in their own right, and want to read some pretty dense math, you should probably skip this post. The theorems are pretty self-contained, and will be summarized in any future posts which need them.

The Summary Equation

We can represent the idea of a D-dimensional “summary” of x for a function f via a functional equation:

F(G(x))=f(x)

Given the function f, we try to find some D-dimensional “summary” G(x) such that f can be computed from G - i.e. we want some F,G such that F(G(x))=f(x) for all x.

In order for this to be meaningful, we need some mild assumptions on f, F, and G; at the very least, we certainly need to exclude

space-filling curves

, which would defeat the point of a “D-dimensional summary”. Throughout this post, we’ll assume differentiability, although this should be easy to relax somewhat by taking limits of differentiable functions.

Easy theorem: The D-dimensional summary equation for f is solvable only if the rank of the matrix ∂f∂x is at most D for all values of x. I’ll call this the “Summarizability Theorem”. (If you want a more official-sounding name, it’s the global converse of the

constant-rank theorem

.)

Proof: differentiate both sides of the equation to get ∂F∂G∂G∂x=∂f∂x. Since G is D-dimensional, this is itself a rank-at-most-D decomposition of ∂f∂x.

In practice, the converse will also usually hold: if the rank of ∂f∂x is at most D for all values of x, then we can usually find a D-dimensional summary G(x). Indeed, if the rank is constant near some point x0, then we can always find a local D-dimensional summary near x0; that’s what the constant rank theorem says. However, Weird Stuff can sometimes prevent stitching these local summaries together into a global summary. (Thank you to

Vanessa

for pointing me to

an example

of such “Weird Stuff”, as well as the name of the constant rank theorem.)

Minor notation point: each variable xj corresponds to a column of ∂f∂x. This convention will be used throughout the pos...
Seeking Samsara
by jblue — 2021-07-13T18:03:10.076Z

Thousands of years ago, the Greeks determined that παν μέτρον άριστον, or alternatively, that virtue is a golden mean. Despite the fact that Western culture has at least superficially internalized the idea — the average Joe on the street likely reflexively concurs with squiggly formula — there are still cases in which we would profit from remembering it. As it turns out, it's sometimes non-trivially difficult to know the directionality of our immoderacy.
Some claim that there are exceptions to this principle; many of these people appear to be guided by ideology. Cynically, we might guess that this is because locating the Good (whatever that means) at the extremes makes developing zeal relatively more urgent than learning discerning, and that this improves the memetic fitness of the ideology. Charitably, it's because the Good doesn't need defenders when it's moderate, and so ideologies only accrete around moral questions with extreme answers.

I tend to be a pessimist. That didn't stop me from trying meditation; not at first, anyways.
Meditation (and "mindfulness" more generally) has become quite popular lately — and perhaps rightfully so. In a milieu characterized by frenetic emotionality and vanishing attention spans, vipassana has a lot going for it. I wasn't interested in the health benefits, though. I felt perfectly content with my emotionality and attention. Instead, I wanted to become a psychonaut — sans the illegal drugs.
I'd hobby-read psychology for about a decade, beginning with Julian Jaynes at age 15 or so. The Origin of Consciousness is a hell of a book to read when one is first learning to chart the territory of the mind. Like any bookish teenaged boy, there was plenty of Nietzsche in the early years as well. I've come to appreciate both writers' limits, but I suspect my prior on the radical mutability on the human mind — and excitement toward the prospect of psychonauting — has yet to fully update downwards. This is meant to explain what's to come, but you can also read it as a caveat.
Meditation did not promise to turn me into a blond beast. But it promised a change, and that was interesting enough. I expected a sort of training grounds of self-modification: strategically trivial, large reference class, obvious results.
I was disappointed to find it to be somewhat easy. I don't have an internal monologue and typically have thoughts arriv...
What will the twenties look like if AGI is 30 years away?
by Daniel Kokotajlo — 2021-07-13T08:14:07.387Z

I'm especially interested to hear from people with long (i.e. 20+ year) timelines what they think the next 10 years will look like.

[EDIT: After pushback from Richard Ngo, I've agreed to stop talking about short and long timelines and just use numbers instead, e.g. "20+ year" and "<10 year" timelines. I recommend everyone do the same going forward.]

Ajeya (as of the time of her report at least) is such a person, and she gave some partial answers already:

By 2025, I think we could easily have decent AI personal assistants that autonomously compose emails and handle scheduling and routine shopping based on users’ desires, great AI copy-editors who help a lot with grammar and phrasing and even a little bit with argument construction or correctness, AIs who summarize bodies of information like conversations in meetings or textbooks and novels, AI customer service reps and telemarketers, great AI medical diagnosis, okay AI counseling/therapy, AI coding assistants who write simple code for humans and catch lots of subtle bugs not catchable by compilers / type checkers, even better AI assisted search that feels like asking a human research assistant to find relevant information for you, pretty good AI tutors, AIs that handle elements of logistics and route planning, AIs that increasingly handle short-timescale trading at hedge funds, AIs that help find good hyperparameter settings and temperature settings for training and sampling from other AIs, and so on.

But she thinks it'll probably take till around 2050 for us to get transformative AI, and (I think?) AGI as well.

The hypothesis I'm testing here is that people with long timelines [EDIT: 20+ year timelines] nevertheless think that there'll be lots of crazy exciting AI progress in the twenties, just nothing super dangerous or transformative. I'd like to get a clearer sense of how many people agree and what they think that progress will look like.

(This question is a complement to Bjartur's previous question)
Explorative Questions towards Argumentation + GitHub
by cod3d — 2021-07-13T06:01:39.725Z

I am interested in the potential of automated reasoning and how this is done in groups. Since LessWrong has done such a good job of promoting and demonstrating feasible rationality, I am asking for insight as to how this could work with 'dialogue' or reasoned debate. I have a general concept in mind about how GitHub could be used and wondered if anyone could share their thoughts on how they see 'version control' been used to help people make better decisions and come to a concensus easier.

If templates could work well for certain topics, replicating and extending functionality would be easier and could become a standard operational perspective when discussing issues and solving problems. I realize I need a unique and novel angle to exploit and initially I want to explore the extent to which programming elements can be used to extend the functionality of adding and approving elements of the discussion. This would also include the evolution of the templates and naturally the topics they can handle for improving scope.

The problem I have, other than demonstrating the specific benefits of choosing GitHub as the mechanism to evolve 'reasoned debate', is that a certain methodology's effectiveness and proven worth to reach consensus would have to be shown for each type of argumentation topic. I am not assuming this would be hard to do and would count towards an initial MVP of the project.

Any feedback or alternative perspectives worth considering and/or potential implementation issues would be highly appreciated. :)
Typology of blog posts that don’t always add anything clear and insightful
by KatjaGrace — 2021-07-13T01:40:15.274Z

I used to think a good blog post should basically be a description of a novel insight.

To break it down more, on this view:

A blog post should have a propositional claim (e.g. ‘the biggest externalities are from noise pollution’, or ‘noise pollution is a concept’ vs. expression of someone’s feelings produced by externalities, or a series of reflections on externalities). A ‘propositional claim’ here can be described straightforwardly in words, and usually conveys information (i.e. they say the world is one way instead of another way).

It should be a general claim—i.e. applicable to many times and places and counterfactuals (e.g. ‘here is how tragedies of the commons work: …’ vs. ‘here is a thing that happened to me yesterday: …’)

It should be a novel claim(e.g. a new reason to doubt one of the explanations put forward for the demographic transition)

The claim should be described, which is to imply that the content should be:

Verbal (or otherwise symbolic, e.g. a table of numbers surrounded by text would count)

Explicit (saying the things it means, rather than alluding to them)

Mostly concerned with conveying the relevant propositions (vs. for instance mostly concerned with affecting the reader’s mood or beliefs directly)

I probably would have agreed that the odd vignette was also a good blog post, but ideally it should be contained in some explicit discussion of what was to be learned from it. I probably wouldn’t have held my more recent Worldly Positions blog1 in high esteem.

I now think that departures from all of these things are often good. So in the spirit of novel descriptions of explicit and general claims, I have made a typology of different combinations of these axes.

Before getting to it, I’ll explain some part of the value of each category that I think I overlooked, for anyone similar to my twenty year old self.

Worthy non-propositional-claim content

Minds have many characteristics other than propositional beliefs. For instance, they can have feelings and attitudes and intuitions and grokkings and senses. They can meditate and chop onions quickly and look on the bright side and tend to think in terms of systems. They can also have different versions of ‘beliefs’ that don’t necessarily correspond to differences in what propositions they would assent to. For instance, they can say ‘it’s good to exercise’, o...
Ideal College Education for an Aspiring Rationalist?
by Collisteru — 2021-07-13T01:39:58.524Z

Hello! I'm a rising computer science freshman in college. I got enough high school credit to fulfill over half of my general education requirements, which got me thinking...

Let's imagine that my only goal in college is to become the best rationalist ever. What classes should I take? Please don't just say "math," be as specific as possible: differential equations or Diophantine? Every credit counts here, and college students don't have much time.

If you need a reminder, here's a decent example PDF for what college courses typically teach which things. Hopefully the relative homogeneity of college courses (especially in STEM) will make your answer applicable to any university.

Thank you.