trentmkelly/LessWrong-43k huggingface dataset:
The proper response to mistakes that have harmed others?
by Ruby — 2023-12-31T04:06:31.505Z

I have a tendency to feel very guilty when I have harmed others, especially when the harm was quite large. And I do think I've been legitimately quite hurtful and harmful to a number of people over the course of my life.

Some of my guilt has persisted for years after recognizing the mistake[1]. I think I prefer this to not feeling remorseful at all, but I do also wonder if I'm responding optimally. I suspect that a form of social anxiety might nudge into excessive feelings of guilt.

Guilt done right?

So here are some musings on how to actually respond when you realize you've harmed another person through your own error. I'm writing this to help myself thinking about it, and sharing it partly to maybe benefit answers, and partly to elicit answers from others.

Principal #1: Your guilt and remorse should not make things worse for the person you harmed. If you're now behaving in ways they disprefer, you're only adding more harm to the previous harm. What even? More on this in a moment.

Understand and address the causes of your mistake

If have harmed someone in a way I regret, then I want model why I did that with sufficient accuracy so that I can change something to avoid repeating that mistake. If it was a skill gap, then put in effort to learn the skill. If I had the skill, but failed to notice to apply it, then train myself into better recognition of applying it.

Possibly one ought to apply 5 Why's analysis to their mistake (I haven't done this, but might try it later):

Five whys (or 5 whys) is an iterative interrogative technique used to explore the cause-and-effect relationships underlying a particular problem.[1] The primary goal of the technique is to determine the root cause of a defect or problem by repeating the question "Why?" five times. The answer to the fifth why should reveal the root cause of the problem.[2]

The technique was described by Taiichi Ohno at Toyota Motor Corporation. Others at Toyota and elsewhere have criticized the five whys technique for various reasons (see § Criticism).

An example of a problem is: the vehicle will not start.

Why? – The battery is dead.
Why? – The alternator is not functioning.
Why? – The alternator belt has broken.
Why? – The alternator belt was well beyond its useful service life and not replaced.
Why? – The vehicle was not maintained according to the recommended service schedule. (A root cause)[

Apologi

...
Conversation Visualizer
by nomagicpill — 2023-12-31T01:18:01.424Z
Link: https://210ethan.github.io/thoughts/visualizer.html

Conversation Visualizer

Voice to text to image to reflection.

Contents

Background
Construction
Cost
Examples

Background

niplav and I were discussing what makes good living room decor. It must be thought-provoking and not distracting. Movement is nice, but too much or too quickly violates the distraction rule. The idea of an aquarium or terrarium was brought up.

We (mostly niplav) then came up with the idea of a conversation visualizer (CV): create an image based on the last few minutes of conversation and display it for all to see.

Construction

Overview

The CV consists of three parts that build off of each other:

Voice: Record a conversation
Text: Transcribe the audio recording and summarize
Image: Create an image based on the transcription

Throw this in a simple while loop that continues to go so long as there aren't any errors and power is maintained.

Program

I used OpenAI and its API to keep everything in the same ecosystem: Whisper for transcription, GPT for summarization, and DALL·E for image generation.

Here are the not-quite-foolproof-because-if-you're-reading-this-you-probably-have-some-technological-ability instructions to get the software working:

Download:

Python3
All packages used in visualizer.py
visualizer.py

Create an OpenAI account
Follow OpenAI's Developer quickstart guide
Allow microphone and accessibility access to the program that will run visualizer.py

Future Work

Anyone reading this is encouraged to submit a fork the repo and change it how they see fit. Please contact me if you do so I can see the results!

Some ideas:

Create web app that allows for customizing length/frequency/models used/image quality
Create hardware that plugs into a television and connects to the internet to make the requests

Cost

OpenAI's API fees are fairly cheap, coming in at a whopping $0.06 per picture generated using standard DALL·E 3 images (this price includes transcription, summarization, and image generation).

Variables that will change the cost (along with some suggestions and thoughts on each):

Length of audio recording

30-60 seconds is long enough to capture part of a only single conversation (instead of the second half of convo1 and the first half of convo2)

How often a new image is generated

10 minutes at a minimum. This gives time for talkers to study the image and reflect on it, then get into another conversation before a new image is generated.

Image model used (DALL·E 3, DALL·E 2)

DALL·E

...
Paper Summary: The Koha Code - A Biological Theory of Memory
by jakej — 2023-12-30T22:37:13.865Z

paper link
author’s presentation

This paper introduces the Koha model, which aims to explain how information is stored within the brain and how neurons learn to become pattern detectors. It proposes that dendritic spines serve as computational units that scan for particular temporal codes, or “spike trains”, which are used to dampen or excite signals. A mechanism for learning is proposed, wherein each neuron listens for an “echo” signal that is used to reinforce the physical structure of dendritic spines that contributed to the original signal. Competitive learning then drives neurons to self-organize into emergent algorithms.

A neuron. The larger branch pointing down is an axon - the output of the neuron. All of the other forking branches are dendrites, and these serve as the inputs to the cell.

There are many different types of neurons, but they can be generally categorized as inhibitory or excitatory. One of the most common kinds of excitatory neurons is a pyramidal neuron. All mammals, birds, fish, and reptiles have pyramidal neurons.

Pyramidal neurons from all over the brain. They all have a pretty similar structure.

Excitatory and inhibitory neurons can organize to form neural circuits. These circuits can make use of a mechanism called “lateral inhibition” to suppress the activity of other neurons. Networks with these inhibitory configurations (“competitive circuits”) form a winner-takes-all competition between networks where the first circuit to fire can suppress all its competitors.

Pyramidal neurons also have many interesting differences compared to inhibitory neurons. The dendrites (reminder: the inputs) of pyramidal neurons are noteworthy for being absolutely riddled with small mushroom-like protrusions called “spines”. Inhibitory neurons have virtually no such spines, and it is believed these spines allow pyramidal neurons to record information.

Two pyramidal dendrites featuring many mushroom-like “spines” - the memory units of the neuron

Dendritic spines also come in several different phases of development: stubby, thin, and mushroom. Human infants have almost all stubby spines, and adults have almost all mushroom spines. It is thought that mushroom spines, being the largest and most stable, serve as long-term memory and that the more fragile “thin spines” play a role in learning, with their ability to rapidly form and break their connection. Thin ...
shoes with springs
by bhauth — 2023-12-30T21:46:55.319Z
Link: https://www.bhauth.com/blog/industrial%20design/spring%20shoes.html

There's something intuitively intriguing about the concept of shoes with spring elements, something that made many kids excited about getting "moon shoes", but they found the actual item rather disappointing. Using springs somehow with legged movement also makes some logical sense: walking and running involve cyclic energy changes, and the Achilles tendon stores some elastic energy. Is that perspective missing something?

big springs

In a sense, spring elements in shoes are standard: sneakers have elastic foam in the soles, and maximizing the sole bounciness does slightly improve running performance. Jumping stilts are the modern version of spring shoes. They actually work, which also means they're quite dangerous - if what the kids who wanted moon shoes imagined was accurate, their parents wouldn't have bought that for them. The concept is obvious, but they only appeared recently; they weren't being made in 1900, and that's because high-performance materials are necessary for a net increase in performance. Those jumping stilts typically use fiberglass springs and modern aluminum alloys, and keeping weight low is still a problem. As that linked video notes, even with modern materials, the increase in jump height is only moderate.

This guy made a different type of spring boots for increasing running speed, and 16 million views implies that some people find the concept interesting, but it would be better to start from a proper theoretical analysis and then properly optimize materials and structure. This paper argues for a different geometry, where a spring is attached to the foot and hip instead of the foot and shin. It notes:

To reach the theoretical top speed of 20.9 m/s in Fig. 2, the spring should (i) store 930 J energy and (ii) weigh no more than 1.5 kg

and

state-of-the-art fixed stiffness running springs made from carbon fiber offer only about 150 J/kg

It might be possible to use gas springs to get that kind of performance, though matching the desired force curves is an issue. Another obvious issue is transferring vertical forces to the hip or torso without interfering with movement too much or adding too much weight. Of course, 20.9 m/s is very fast and not very realistic in practice, but some sort of setup with a thick waist belt and gas springs + carbon fiber springs could plausibly make people run significantly faster.

heels

A lot of women wear hig...
Techniques to fix incorrect memorization?
by Brendan Long — 2023-12-30T21:32:46.922Z

One of the things I've been using Anki to memorize is friends' birthdays, but I realized after a few weeks that I had entered one of them wrong, in a way that was confusingly similar to another:

Friend A: June 5

Friend B: July 5 (should actually be 6)

Now when I study the cards, for both friend A and friend B I can consistently remember that their birthdays are one of these two dates, but a week later when I review again, I can't consistently remember which one.

It seems like Anki basically did it's job (I did memorize the date), but I memorized "too much". Are there any good techniques to fix this?
How to develop a photographic memory 2/3
by PhilosophicalSoul — 2023-12-30T20:18:14.255Z

As you may have guessed, these first two (2) posts give you the building blocks of mnemonics. The final post will polish some theories I've developed, refine the process in building personal mnemonic techniques, and finally: how to acquire a photographic memory.

As was the case with my first post, I invite suggestions and constructive criticism. Thank you to @Brendan Long for helping me there.

Techniques covered: Sirianni, Sirianni-Soul, Dark Room, Memory Board, Shaper System, Association System. Alongside this, a brief summary of tools; Spreeder, Notion, Anki, Trello, Obsidian, Bionic Reading, arXiv Vanity, and ChatGPT. (Credits to @Algon for suggesting this.)

CHAPTER II: UNORTHODOX TECHNIQUES

I've called these three (3) techniques 'unorthodox' because there is not substantial research behind them, nor is there widespread use. While my first post dealt with techniques that had studies directly dedicated to them, these are backed by indirect conclusions drawn by a cluster of vaguely related studies. These three (3) seem to be effective for the same reasons that 'orthodox techniques' work (I've dedicated a section in this post listing those reasons titled 'foundations of memory'.) Importantly, these follow the general rule of thumb: it shouldn't be more difficult to remember the memory tools, than it is to remember in itself.

(1) SIRIANNI

The Sirianni technique was created by youtuber 'State of Mind'. It falls under the 'library' category.[1] Those familiar with the Zettelkasten technique, will recognise that this is heavily inspired by it.

While this technique is effective, it has several biases. As a result, I'll explain some of those. After, I'll explain why (and probably against all your rationality ringing bells) some biases are beneficial when it comes to memory. Last, I'll propose what I think is an even better working model: 'SIRIANNI-SOUL'.

I'm outlining this process (a)-(c) so that you understand how I invented the technique for photographic memory. It's always been; know the problems with memory, understand the anatomy of other memory techniques, and give your own theories a nice title like 'SIRIANNI-SOUL'. The Solakios technique is just the magnum opus of refining this analytical process.

(a) PROBLEMS WITH MEMORY

Our memory isn't perfect, as you've probably discovered. Either through overwritten child memories with displaced people a...
If Clarity Seems Like Death to Them
by Zack_M_Davis — 2023-12-30T17:40:42.622Z
Link: http://unremediatedgender.space/2023/Dec/if-clarity-seems-like-death-to-them/

"—but if one hundred thousand [normies] can turn up, to show their support for the [rationalist] community, why can't you?"

I said wearily, "Because every time I hear the word community, I know I'm being manipulated. If there is such a thing as the [rationalist] community, I'm certainly not a part of it. As it happens, I don't want to spend my life watching [rationalist and effective altruist] television channels, using [rationalist and effective altruist] news systems ... or going to [rationalist and effective altruist] street parades. It's all so ... proprietary. You'd think there was a multinational corporation who had the franchise rights on [truth and goodness]. And if you don't market the product their way, you're some kind of second-class, inferior, bootleg, unauthorized [nerd]."

—"Cocoon" by Greg Egan (paraphrased)[1]

Recapping my Whole Dumb Story so far: in a previous post, "Sexual Dimorphism in Yudkowsky's Sequences, in Relation to My Gender Problems", I told you about how I've always (since puberty) had this obsessive erotic fantasy about being magically transformed into a woman and how I used to think it was immoral to believe in psychological sex differences, until I read these great Sequences of blog posts by Eliezer Yudkowsky which incidentally pointed out how absurdly impossible my obsessive fantasy was ...

—none of which gooey private psychological minutiæ would be in the public interest to blog about, except that, as I explained in a subsequent post, "Blanchard's Dangerous Idea and the Plight of the Lucid Crossdreamer", around 2016, everyone in the community that formed around the Sequences suddenly decided that guys like me might actually be women in some unspecified metaphysical sense, and the cognitive dissonance from having to rebut all this nonsense coming from everyone I used to trust drove me temporarily insane from stress and sleep deprivation ...

—which would have been the end of the story, except that, as I explained in a subsequent–subsequent post, "A Hill of Validity in Defense of Meaning", in late 2018, Eliezer Yudkowsky prevaricated about his own philosophy of language in a way that suggested that people were philosophically confused if they disputed that men could be women in some unspecified metaphysical sense.

Anyone else being wrong on the internet like that wouldn't have seemed like a big deal, but Scott Alexander had semi-j...
When Can Optimization Be Done Safely?
by StrivingForLegibility — 2023-12-30T01:24:30.234Z

When facing a problem in computer science, one can often write a program that solves that problem directly. Usually, a programmer understands why their programs do what they do. Not always, but usually. One can even use tools from formal verification to prove that programs have certain properties, such as correctly implementing a specification.

For some problems, it's really hard to explicitly write a program to solve them, but it's easy to write down a metric for how good a program is at solving that problem. The field of machine learning studies programs that themselves search over a restricted class of programs, looking for programs that are good at solving the given problem. A classic example is using gradient descent to search over neural networks.

In general, solving an optimization problem involves an optimization process, an optimization metric, and a domain of candidate solutions. In some cases, we can safely specify an optimization metric, and delegate our choice of solution to an optimization process. But in other cases, we know that this process leads to bad results. This is the classic pattern of Goodhart's Law that arises in principal-agent problems.

Analogy to Paying for Results

Using an optimization program is also structurally analogous to the economic technique of paying for results. When your country has a cobra problem, and you start paying people to bring you dead cobras, sometimes they'll bring you wild cobras they killed. And sometimes they'll start breeding cobras to kill and bring to you. An optimization program is in some sense incentivized to bring you solutions that score highly on the metric you gave it. Which can be a problem if that metric isn't aligned with what you actually want.

Robin Hanson describes some factors that lead paying-for-results to actually deliver results the principal is happy with:

We have some good basic theory on paying for results. For example, paying your agents for results works better when you can measure the things that you want sooner and more accurately, when you are more risk-averse, and when your agents are less risk-averse. It is less less useful when you can watch your agents well, and you know what they should be doing to get good outcomes.

In other words, when you know what your delegate should do and can effectively supervise them, you should just ask them to do that. This is the model used in many ...
Optimization Markets
by StrivingForLegibility — 2023-12-30T01:24:01.777Z

How can we coordinate on solving important computational problems? And how can we do this safely?

Computing Markets

We can think of computing power as a resource, and one way to get people to contribute their resources to a project you think is important is to pay them! The global computing power market is already a $45 billion industry, and this is expected to roughly double in the next decade. One popular model for cloud computing is to rent the services of someone else's computer, paying by the hour.

Another approach is to pay for results, using a bounty. A legible delegated software system, such as a smart contract, can act as an escrow service, which releases funds under two conditions:

Someone has submitted a solution, which the delegated system can verify. In which case the contract pays out to the solver.
The bounty has expired, and only the principal that originally deposited the funds can reclaim them.

Optimization Markets

Optimization problems have a measure of "how good a solution is", in addition to criteria for what makes a solution valid. And we can use this additional structure to pay solvers more for better solutions. The creator of an optimization market likely has some sense of what they'd be willing to pay for solutions of varying quality. And they can use that information to structure a payout function.

One example architecture would be to allow anyone in a large pool of participants to submit solutions, with top solutions being published to the entire pool as they're submitted. The market creator seeds the market with an initial solution, a payout function, and funds to pay solvers. If a participant has a better solution than the current best, they can submit it and claim the corresponding marginal payout for their marginal improvement. If the optimization metric goes from 0 to 100, and the slope of the payout function is a constant $1/unit of improvement, then the optimization market pays out $5 for improving the top solution from a score of 45 to 50.

Another architecture would be to keep solutions private, and award prizes to the participants that submit the best solutions. Kaggle runs competitions where teams compete to submit the best machine learning models, with prizes in the tens of thousands of dollars for big competitions. To prevent overfitting, there is a public training data set, and a private validation data set. Teams have a ...
The Plan - 2023 Version
by johnswentworth — 2023-12-29T23:34:19.651Z

Background: The Plan, The Plan: 2022 Update. If you haven’t read those, don’t worry, we’re going to go through things from the top this year, and with moderately more detail than before.

1. What’s Your Plan For AI Alignment?

Median happy trajectory:

Sort out our fundamental confusions about agency and abstraction enough to do interpretability that works and generalizes robustly.
Look through our AI’s internal concepts for a good alignment target, then

Retarget the Search

[1].
…
Profit!

We’ll talk about some other (very different) trajectories shortly.

A side-note on

how I think about plans

: I’m not really optimizing to make the plan happen. Rather, I think about many different “plans” as possible trajectories, and my optimization efforts are aimed at robust bottlenecks - subproblems which are bottlenecks on lots of different trajectories. An example from the linked post:

For instance, if I wanted to build a solid-state amplifier in 1940, I’d make sure I could build prototypes quickly (including with weird materials), and look for ways to visualize the fields, charge densities, and conductivity patterns produced. Whenever I saw “weird” results, I’d first figure out exactly which variables I needed to control to reproduce them, and of course measure everything I could (using those tools for visualizing fields, densities, etc). I’d also look for patterns among results, and look for models which unified lots of them.

Those are strategies which would be robustly useful for building solid-state amplifiers in many worlds, and likely directly address bottlenecks to progress in many worlds.

Main upshot of approaching planning this way: subproblems which are robust bottlenecks across many different trajectories we thought of are more likely to be bottlenecks on the trajectories we didn’t think of - including the trajectory followed by the real world. In other words, this sort of planning is likely to result in actions which still make sense in hindsight, especially in areas with lots of uncertainty, even after the world has thrown lots of surprises at us.

2. So what exactly are the “robust bottlenecks” you’re targeting?

For the past few years, understanding

natural abstraction

has been the main focus. Roughly speaking, the questions are: what structures in an environment will a wide variety of adaptive systems trained/evolved in that environment ...