trentmkelly/LessWrong-43k huggingface dataset:
Civil Law and Political Drama
by Benquo — 2022-04-04T20:32:23.417Z
Link: http://benjaminrosshoffman.com/civil-law-and-political-drama/

The purpose of this essay is to clarify to YOU, the mind reading this essay, your relation to law, your characteristic blind spot around adversaries, and what sorts of processes are competing for attention in the humans you’re instantiated through.

For contracts and property not to just be custom and violence we need civil law. We can have civil law without an explicit formalization, just like we could have speech before Kant formulated the Categorical Imperative. But the structure of civil law, like the categorical imperative, is at least implicit in the orientation that permits speech.

Without regular, intelligible, governing principles that are known at least implicitly by the governed, the resolution of a dispute is determined by whatever people happen to decide at the time, not by the text of the original commitment. Therefore, persons subject to the law must be able to demand that it be explicated and applied to specific cases concerning them in a timely manner. This requires open lines of communication for people to clarify who did and said what, and which acts match which words.

In practice, people get defensive and block communication, not just on their own behalf, but in any case that might require them to acknowledge the transgression of a powerful party. So we are effectively in a state of total lawlessness and have neither property nor contracts. Because of the "on demand, in a timely manner" requirement - since otherwise the outcome would be decided by the decision rule for when to give a timely answer - any uncompensated gap constitutes a total rupture. And too many are transgressing for compensation to be plausible.

Therefore, if we are to have law, it must be new, and none can claim the right of law, only custom, to any property, reputation, or relationships they possessed before. Likewise all blame and debt was incurred in a lawless world and cannot be recognized by the legitimate law.

The above account leaves a kind of negative space in which lawlessness is described formally as the complement to law, with the minimal characterization of “people get defensive and block communication […] in any case that might require them to acknowledge the transgression of a powerful party.” This is described as an impediment to or deviation from law. But this negative space would not be important if it were not actively optimized for something; it would simpl...
Ways to do advanced tag filtering
by lizard_brain — 2022-04-04T20:11:36.449Z

Note: All the posts I have found so far describing the tag filtering system are clearly out of date, so I would appreciate a good description of its features in general, preferably one that includes illustrative screenshots, whether or not it answers my specific questions. This would be a useful reference to have however simple the functionality appears.

(How) can I accomplish the following using LessWrong's tag filtering system? If any is not currently possible, consider it a feature request.

Create a whitelist so that I see posts that match any of a list of tags, while all others are hidden.
Use URL parameters to generate filters, so that I can construct arbitrary filters that I can click between at will or share with others.
Is the scaling race finally on?
by p.b. — 2022-04-04T19:53:04.501Z

Exactly one year ago I wrote a lengthy article about the coming phase of massive scaling. Then nothing much happened. GPT-3 was replicated again and again, but nobody went way beyond GPT-3s parameter count (and published what they found). Since last week, this suddenly makes a lot more sense.

Last week Deepmind published a bombshell of a paper "Training Compute-Optimal Large Language Models". In it they detail new scaling laws based on improved best practices - mostly using better learning rate schedules and optimizing with AdamW.

These two differences in training large language models change the learning curve so significantly that the resulting scaling laws come out very different. Suddenly achieving a given loss by scaling data becomes a lot more effective, while scaling the parameters becomes comparably less necessary.

1a3orn has a great post explaining the details of the paper. I don't want to duplicate his effort and instead focus on how these new scaling laws change the perspective on a possible scaling race between AGI-outfits / big tech companies.

The results of the paper mirror the strategic decisions made by OpenAI over the last year or so. Instead of scaling model size further according to their own scaling laws, they have concentrated on using a lot more compute to optimize their language models for commercial use.

What's more, they have also hinted that even the successor models are not going to be significantly larger than GPT-3. I think it is very likely that they realized at the very least that the adoption of AdamW throws a spanner into their scaling laws. Of course by now quite a lot of competitors have blown a part of their compute budget on an oversized and undertrained model.

The old scaling laws imply that there is an intersection point not that far off from GPT-3 where scaling becomes less effective ("the point were you start overfitting even on deduplicated data" was one intuitive explanation).

The new scaling laws instead imply:

Better performance for the same compute. Chinchilla outperforms Gopher by a lot.
Cheaper deployment and fine tuning. Faster response times (at least potentially, probably not true for Chinchilla because it has the same depth as Gopher).
Easier scaling from an engineering perspective. Larger models become more and more difficult to train, not just for pure engineering reasons,

...
Premortem as communication device (e.g. in relationship)
by becausecurious — 2022-04-04T18:53:45.457Z

Epistemic status: My partner and I did 3 premortems for our romantic relationship and we found it surprisingly helpful.

Summary

My partner and I did 3 premortems for our romantic relationship.

The procedure is: define prompts; collect failures; independently estimate risk of each; align on estimates; brainstorm solutions.

In addition to usual benefits of a single person premortem, such session becomes a dedicated safe space to raise and discuss concerns in batch.

What is premortem?

Premortem (aka Murphyjitsu) is "a process for bulletproofing your strategies and plans". (CFAR handbook). The idea is to first think how your plans can fail, then brainstorm ways to prevent these failures. For a deeper introduction please see Murphyjitsu section in the CFAR handbook.

Procedure

My partner and I read the CFAR handbook together. We decided to do a premortem on our relationship. This might have sounded awkward ("Let's brainstorm how our relationship can fail"), but keeping the end goal - improving likelihood of success - helped to avoid this pitfall. Since then we did 3 premortems and converged to a following procedure:

Define prompt(s). The goal is to make sure that we brainstorm in the same direction. Examples:

it is 6 month from now, we broke up, how did this happen?

we came back from a vacation, it was terrible, why?

Independently collect failure scenarios. Separately collect ideas why the failure could occur. I personally do this across multiple days. This way I just notice potential failures during conversations and other activities. My partner prefers to brainstorm right before the discussion. Doing this separately helps to improve our coverage, since we don't bias each other.

Explain scenarios. Add your scenarios to a shared spreadsheet. Make sure that both of you understand all scenarios. If not, give examples or ask questions.

Independently rank scenarios. For each scenario separately estimate expected impact from 1 (negligible) to 10 (crisis). The expected impact already includes both probability and magnitude. It is a product of how likely a scenarios is by how bad it is.

Discuss scenarios with large differences in expected impact; update estimates until converged. Sort scenarios in decreasing order of absolute difference between your estimates. Discuss all scenarios which have difference at least 2. Usually the person whose estimate is larger prese

...
Call For Distillers
by johnswentworth — 2022-04-04T18:25:34.942Z

Many technical alignment researchers are bad-to-mediocre at writing up their ideas and results in a form intelligible to other people. And even for those who are reasonably good at it, writing up a good intuitive explanation still takes a lot of work, and that work lengthens the turn-time on publishing new results. For instance, a couple months ago I wrote

a post

which formalized the idea of abstractions as redundant information, and argued that it’s equivalent to abstractions as information relevant at a distance. That post came out about two months after I had the rough math worked out, because it took a lot of work to explain it decently - and I don’t even think the end result was all that good an explanation! And I still don’t have a post which explains well why that result is interesting.

I think there’s a lot of potential space in the field for people who are good at figuring out what other researchers’ math is saying intuitively, and why it’s interesting, and then communicating that clearly - i.e. the skill of distillation. This post will briefly sketch out what two kinds of distillation roles might look like, what skills are needed, and talk about how one might get started in such a role.

Two Distiller Roles

The two types of distiller role I’ll sketch are:

“Independent” distiller: someone who works independently, understanding work published by other researchers and producing distillations of that work.
“Adjunct” distiller: someone who works directly with one researcher or a small team, producing regular write-ups of what the person/team is thinking about and why.

These two roles add value in slightly different ways.

An independent distiller’s main value-adds are:

Explaining the motivation and intended applications
Coming up with new examples
Boiling down the “key intuitive story” behind an argument
Showing how the intuitive story fits into the context of the intended applications

I expect the ability to come up with novel examples and boil down the core intuitive story behind a bunch of math are the rate-limiting skills here.

Rob Miles

is a good example of an existing independent distiller in the field. He makes YouTube videos intuitively explaining various technical results and arguments. Rob’s work is aimed somewhat more at a popular audience than what I have in mind, but it’s nonetheless been useful for people in the field.

I expect an adj...
Google's new 540 billion parameter language model 
by Matthew Barnett — 2022-04-04T17:49:23.484Z
Link: https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf

Google just announced a very large language model that achieves SOTA across a very large set of tasks, mere days after DeepMind announced Chinchilla, and their discovery that data-scaling might be more valuable than we thought.

Here's the blog post, and here's the paper. I'll repeat the abstract here, with a highlight in bold,

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model (PaLM).

We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned stateof-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.
Best informative videos on the Internet
by Ege Erdil — 2022-04-04T17:28:15.918Z

Inspired by Yair Halberstadt's similar question about non-textbooks, I'm asking this question to see if people have any videos that they would want to recommend to others.

The criterion is that the videos must be informative or educational. I don't want a playlist of your favorite songs, but one of your favorite math talks would be fine.

Other than that, the answers can take any form: the videos can be recordings of talks, interviews, panel discussions or educational YouTube videos; they can be linked wholesale in a public playlist or listed individually, you can post a single video and give reasons why people should watch it, you can post an entire channel if you think all of their videos are worth watching, et cetera.

As an example of the kind of content I'm looking for, I'll recommend two YouTube channels that I think are quite underrated: Richard Borcherds and Strategy Stuff.
Not-Useless Advice For Dealing With Things You Don't Want to Do
by Rafael Harth — 2022-04-04T16:37:05.298Z

Sometimes, you want to do something hard, like working on a project, but you can't get yourself to do it in the moment. I think most advice one reads about this problem (at least outside of LW) is useless, hence the title. The aspiration of this post will be to say at least one thing you haven't thought of before.

[Epistemic status: self-experimentation & modelling; no empirical evidence.]

Here is our basic model:

There's [you], and there's [something you want to achieve], but the process of achieving it is unpleasant, so we've drawn a metaphorical "Unpleasantness Barrier".

This image immediately suggests a bunch of ways how to proceed. Let's start with one of the less obvious ones.

1. Avoid the Barrier

1.1. Just don't do it

I am continuously amazed at how often people tell me they will do something only to then not do it. If this is the option you will end up going with, it is almost strictly better to decide this early rather than resolving to do it and failing. Perhaps you couldn't have known how hard it would be at first, but in most cases, who are we kidding? As trivial as this sounds, I think it is one of the most underutilized strategies on this list. Quite often, you can, in fact, just not do the thing.

Related: Should considered harmful; Taboo Should

1.2. Get Around the Barrier

Don't like calling people? Text them instead. Don't like cleaning your house? Hire someone. Don't like your job? Look for another one.

This strategy is arguably even more underutilized, though probably less so among this audience. It's not always applicable, however, which is true for most items in this list. E.g., perhaps what you want to do is study something, in which case getting someone to do it for you won't help at all.

2. Overcome the barrier using BRUTE STRENGTH

You have some tools to do hard things. Let's look at them in turn.

2.1. Motivation

Sometimes you are motivated and can do things that would usually be hard.

This is what most advice focuses on. It's also probably the least useful item on this list. It's not that motivation isn't helpful when you have it, but it's infamously uncontrollable, and telling people a variant of "just be motivated" probably is unhelpful. I mean, just look at that gun; does this really look like something that's going to work when you stop believing in it?

Generally, no amount of intellectual understanding will get you motivated on demand...
Many-Worlds Interpretation and Death with Dignity
by amitlevy49 — 2022-04-04T16:19:36.807Z

The Many-Worlds Interpretation is an interpretation of Quantum Mechanics, that says that the wave function never collapses - every possible universe exists, every quantum coin flip falls both ways. As far as I'm aware, this interpretation is usually (a) regarded to be just a philosophical interpretation, with no practical effect on the physical world, and (b) is usually believed to be false.

But there is a world in which we would all be convinced that this interpretation is correct. Imagine that tomorrow a paper comes out, and it says that actually, the LHC should have killed us all. Colliding protons at the speed and quantity that the LHC does should create an average of 1.2 black holes capable of destroying the planet every month. But the LHC has been running, colliding a billion protons per second for years, and we are still here. Say that paper has been peer-reviewed, the math checked and rechecked, mini black holes created up to the point where they would actually destroy the earth, etcetera. What would this mean? It would mean the Many-Worlds Interpretation is reality, and that we are only alive thanks to there existing an incredibly improbable universe - some universe from the infinite universes - where all the quantum flips came in our favor, and we are there, still alive.

So the Many-Worlds Interpretation should eventually reveal itself as correct in any universe (in this case, multiverse) where the probabilistic tendency in any given universe is for humanity to die out. Or in other words, any universe with a great filter. Is our universe such a universe?

What does Eliezer think? He just made a post called Death with Dignity. He believes, it is incredibly likely, 99.9%+ likely, that humanity will soon die out because of AI. I may not personally agree with this assessment, but from his perspective, if 50 years pass and humanity is still here, this should make him update significantly whatever his current belief is in the different interpretations of quantum mechanics. The same goes for anyone else who believes humanity is faced with a nearly impassable great filter.

Well, but, it would be annoying to wait to become convinced that this interpretation is correct. Is there anyone who has made predictions like these in the past? Have there ever been doomsayers? Interestingly, yes. How many intelligent people, in the peak of the cold war, believed with high c...
Creative nonfiction training exercises
by dynomight — 2022-04-04T16:17:43.791Z
Link: https://dynomight.net/training/

After a mutant spider bite, you take Fluffer to a dog park and realize he is bark-gossiping about you with the other dogs. What is he saying?

Fiction authors like to play around with writing prompts like this. The same thing should exist for creative nonfiction. So I tried to come up with a set of “training exercises” that:

anyone can write about
without doing any research
and provide value
even if lots of others do the same thing.

My heresies

List things that you think might be true, but are out of fashion or against the consensus of your in-group. Do not try to argue that your beliefs are true. Leave truth out of it. Instead, introspect and try to describe why you think you’ve come to different conclusions than others.[1]

My predictions for 30 years in the future

What will the world look like? Will people continue to urbanize? What will happen with the climate? What will technological development look like? How will culture respond to these changes? Will people be more or less religious? Will the world be more peaceful or violent?

Don’t worry too much about probabilities. Instead, your goal is to paint a picture of one possible plausible future that other people shouldn’t totally ignore.[2]

Things you should buy and use

I guess you could talk about how much you enjoy your upper-west side penthouse overlooking Central Park, but it’s unclear who’d benefit from hearing about that. Instead, focus on things with two properties:

They have a high utility/cost ratio.
Many of the people who’d benefit from them don’t know about them.

From these, I learned about small waterproof notebooks and blackout curtains and stannous fluoride toothpaste and the true power of the coatrack. I think I found Frixion erasable pens walking around Tokyu Hands but if I hadn’t, I’d be glad to the dozens of people who’ve recommended them.[3]

Ways you can live differently and make your life better

Don’t worry about creativity! Yes, every permutation of every possible bit of self-help advice has probably been given before. But how did they work for you? Did you try radical honesty and find that it was a disaster and you had to add some exceptions? Did you stop drinking and find that it was better/worse/harder/easier than you expected? Even better, did you try really obvious things, like just making a big effort to be nicer to people or avoiding carbs at lunch?

My review of book X

Go ahead, write the 10...
Project Intro: Selection Theorems for Modularity
by CallumMcDougall — 2022-04-04T12:59:19.321Z

Introduction - what is modularity, and why should we care?

It’s a well-established meme that evolution is a blind idiotic process, that has often resulted in design choices that no sane systems designer would endorse. However, if you are studying simulated evolution, one thing that jumps out at you immediately is that biological systems are highly modular, whereas neural networks produced by genetic algorithms are not. As a result, the outputs of evolution often look more like something that a human might design than do the learned weights of those neural networks.

Humans have distinct organs, like hearts and livers, instead of a single heartliver. They have distinct, modular sections of their brains that seem to do different things. They consist of parts, and the elementary neurons, cells and other building blocks that make up each part interact and interconnect more amongst each other than with the other parts.

Neural networks evolved with genetic algorithms, in contrast, are pretty much uniformly interconnected messes. A big blob that sort of does everything that needs doing all at once.

Again in contrast, networks in the modern deep learning paradigm apparently do exhibit some modular structure.

Top: Yeast Transcriptional Regulatory Modules - clearly modular

Bottom: Circuit diagram evolved with genetic algorithms - non-modular mess

Why should we care about this? Well, one reason is that modularity and interpretability seem like they might be very closely connected. Humans seem to mentally subpartition cognitive tasks into abstractions, which work together to form the whole in what seems like a modular way. Suppose you wanted to figure out how a neural network was learning some particular task, like classifying an image as either a cat or a dog. If you were explaining to a human how to do this, you might speak in terms of discrete high-level concepts, such as face shape, whiskers, or mouth.

How and when does that come about, exactly? It clearly doesn’t always, since our early networks built by genetic algorithms work just fine, despite being an uninterpretable non-modular mess. And when networks are modular, do the modules correspond to human understandable abstractions and subtasks?

Ultimately, if we want to understand and control the goals of an agent, we need to answer questions like “what does it actually mean to explicitly represent a goal”, “what is the ty...