trentmkelly/LessWrong-43k huggingface dataset:
How to be an amateur polyglot
by arisAlexis — 2024-05-08T15:08:11.404Z

Setting the stage

Being a polyglot is a problem of definition first. Who can be described as a polyglot? At what level do you actually “speak” the given language? Some sources cite that polyglot means speaking more than 4 languages, others 6. My take is it doesn’t matter. I am more interested in the definition of when you speak the language. If you can greet and order a coffee in 20 languages do you actually speak them? I don’t think so. Do you need to present a scientific document or write a newspaper worthy article to be considered? That’s too much. I think the best definition would be that you can go out with a group of native speakers, understand what they are saying and participate in the discussion that would range from everyday stuff to maybe work related stuff and not switching too often to English nor using google translate. It’s ok to pause and maybe ask for a specific word or ask the group if your message got across. This is what I am aiming for when I study a specific language.

Why learn a foreign language when soon we will have AI auto-translate from our glasses and other wearables? This is a valid question for work related purposes but socially it’s not. You can never be interacting with glasses talking in another language while having dinner with friends nor at a date for example. The small things that make you part of the culture are hidden in the language. The respect and the motivation to blend in is irreplaceable.

For reference here are the languages I speak at approximate levels:

Greek - native
English - proficient (C2)
Spanish - high level (C1) active learning
French - medium level (B2) active learning
Italian - coffee+ level (B1) active learning
Dutch - survival level (A2) in hibernation

Get started

Firstly, I think the first foreign language you learn could be taught in a formal way with an experienced teacher. That will teach you the way to structure your thought process and learn how to learn efficiently. It’s common in Europe and non-English speaking countries to learn a second language at school. This guide is not about how to learn formally though. It’s about how to take up new foreign languages without a *permanent teacher (I will expand later). One of the most important things when learning a language is motivation. You either love the culture, the language itself (how it sounds and reads), a loved one or you are moving t...
Dating Roundup #3: Third Time’s the Charm
by Zvi — 2024-05-08T13:30:03.232Z

The first speculated on why you’re still single. We failed to settle the issue. A lot of you were indeed still single. So the debate continues.

The second gave more potential reasons, starting with the suspicion that you are not even trying, and also many ways you are likely trying wrong.

The definition of insanity is trying the same thing over again expecting different results. Another definition of insanity is dating in 2024. Can’t quit now.

You’re Single Because Dating Apps Keep Getting Worse

A guide to taking the perfect dating app photo. This area of your life is important, so if you intend to take dating apps seriously then you should take photo optimization seriously, and of course you can then also use the photos for other things.

I love the ‘possibly’ evil here.

Misha Gurevich: possibly evil idea: Dating app that trawls social media and websites and creates a database of individuals regardless of if they opt in or not, including as many photos and contact information as can be found.

Obviously this would be kind of a privacy violation and a lot of people would hate it.

but I imagine a solid subset of singles who are lonely but HATE the app experience would be grateful to be found this way.

No big deal, all we are doing is taking all the data about private citizens on the web and presenting it to any stranger who wants it in easy form as if you might want to date them. Or stalk them. Or do anything else, really.

And you thought AI training data was getting out of hand before.

All right, so let’s consider the good, or at least not obviously evil, version of this.

There is no need to fill out an intentional profile, or engage in specific actions, other than opting in. We gather all the information off the public web. We use AI to amalgamate all the data, assemble in-depth profiles and models of all the people. If it thinks there is a plausible match, then it sets it up. Since we are in danger of getting high on the creepiness meter, let’s say the woman gets to select who gets contacted first, then if both want to match in succession you put them in contact. Ideally you’d also use AI to facilitate in various other ways, let people say what they actually want in natural language, let the AI ask follow-up questions to find potential matches or do checks first (e.g. ‘I would say yes if you can confirm that he…’) and so on.

There is definitely not ...
Navigating LLM embedding spaces using archetype-based directions
by mwatkins — 2024-05-08T05:54:23.703Z

TL;DR This research presents a novel method for exploring LLM embedding space using the Major Arcana of the tarot as archetypal anchors. The approach generates "archetype-based directions" in GPT-J's embedding space, along which words and concepts "mutate" in meaning, revealing intricate networks of association. These semantic mutation pathways provide insight into the model's learned ontologies and suggest a framework for controlled navigation of embedding space. The work sheds some light on how LLMs represent concepts and how their knowledge structures align (or don't) with human understanding.

Introduction

Despite its questionable association with oracular practices, the Major Arcana (22 non-suited, non-numbered cards) from the traditional tarot present us with a widely documented selection of well-worn, diverse and arguably comprehensive cultural archetypes to experiment with in the context of LLMs. It is in this spirit (rather than as a tarot "believer" or practitioner) that I undertook these experiments – I suspect that the same exercise might be equally illustrative using, e.g., characters from Shakespeare or The Simpsons.

Archetypes associated with 16 of the 22 cards in the Major Arcana of the tarot were used as the basis for a process which produced sixteen directions in GPT-J token embedding space. Token embeddings for each of a diverse set of ten whole-word tokens[1]

[' London', ' transistor', ' broccoli', ' Laos', ' purple', ' radio', ' laundry', ' DNA', ' thief', ' trumpet']

were displaced by incremental amounts in each of these sixteen directions, and the resulting "mutated" embeddings were inserted into a simple prompt template:

A typical definition of '<embedding>' would be '

The "semantic mutation" which occurred across the outputs was then recorded, revealing some intriguing, sometimes dream-like continuums of meaning, similar to those seen when exploring the line segment between two token embeddings. As well as revealing something about LLM ontologies and the latent structures in their knowledge representations, this points to the possibility of archetype-based methods for navigating embedding space to discover locations which encode novel concepts.

Setup

TL;DR For each card, I used ChatGPT to help produce a list of relevant GPT-J tokens. I then calculated the direction in (4096-d) embedding space that the corresponding set of to...
The Social Impact of Trolley Problems
by atomantic — 2024-05-08T03:09:05.063Z

Every few years, someone asks me what I would do to solve a Trolley Problem. Sometimes, they think I’ve never heard of it before—that I’ve never read anything about moral philosophy (e.g. Plato, Foot, Thomson, Graham)—and oh do they have a zinger for me. But for readers who are well familiar with these problems, I have some thoughts that may be new. For those who haven't done the reading, I'll provide links and some minor notes and avoid recapping in great detail.

Ask a dozen people to explain what the trolley problem is and you are likely to get a dozen variations. In this case, my contact presented the scenario as a modified Bystander variation format—though far evolved away from Philippa Foot's original version in “The Problem of Abortion and the Doctrine of the Double Effect*” in 1967, used as a tool to discuss direct versus oblique intention and the moral confusion within abortion doctrine given by the Catholic Church. Foot’s original proposal has then been refined, re-examined, and tested by Judith Jarvis Thompson for the 40+ years following 1976, and practically everyone in philosophy has discussed it since.

The Query

“What is your answer to the famous Trolly Dilemma? A trolly is heading down a track towards five people who are tied to the tracks. There’s a lever and if you pull it it switches tracks where only one person dies. We don’t know who the people are. It could be five Hitlers or five Nobel laureates. What would you do and WHY?”

Now, I have to spend some time going into great detail to break this down because unlike my inquisitor who claims that “It’s a very simple question” and you should flip the switch to “save five people because each of them has equal value and under utilitarianism, you want to maximize good,” I have spent many years researching and thinking about this problem—and while there are many ways to tweak this question or clarify it that end up changing many people’s answers (if it’s your job to operate the lever, or if the one person is also tied to the track, or nobody is actually tied to the tracks, or if you are alone, or if you are pushing a man onto the track instead of pulling a lever, or if there’s another option to jump onto the track and sacrifice yourself, or by changing the question entirely to judges, pilots, large men blocking caves, harvesting organs from dying patients, etc), my own answer to the Bystander variation...
Can Kauffman's NK Boolean networks make humans swarm?
by Yori-92 — 2024-05-08T02:09:58.708Z

With this article, I intend to initiate a discussion with the community on a remarkable (thought) experiment and its implications. The experiment is to conceptualize Stuart Kauffman's NK Boolean networks as a digital social communication network, which introduces a thus far unrealized method for strategic information transmission. From this premise, I deduce that such a technology would enable people to 'swarm', i.e.: engage in self-organized collective behavior without central control. Its realization could result in a powerful tool for bringing about large-scale behavior change. The concept provides a tangible connection between network topology, common knowledge and cooperation, which can improve our understanding of the logic behind prosocial behavior and morality. It also presents us with the question of how the development of such a technology should be pursued and how the underlying ideas can be applied to the alignment of AI with human values. The intention behind sharing these ideas is to test whether they are correct, create common knowledge of unexplored possibilities, and to seek concrete opportunities to move forward. This article is a more freely written form of a paper I recently submitted to the arXiv, which can be found here.

Introduction

Random NK Boolean networks were first introduced by Stuart Kauffman in 1969 to model gene regulatory systems.[1] The model consists of N automata which are either switched ON (1) or OFF (0). The next state of each automaton is determined by a random boolean function that takes the current state of K other automata as input, resulting in a dynamic network underpinned by a semi-regular and directed graph. It can be applied to model gene regulation, in which the activation of some leads to the activation or suppression of others, but also to physical systems, in which a configuration of spins acting on another will determine whether it flips up or down.

NK Boolean networks evolve deterministically: each following state can be computed based on its preceding state. Since the total number of possible states of the network is finite (although potentially very large), the network must eventually return to a previously visited state, resulting in cyclic behavior. The possible instances of Boolean networks can be subdivided between an ordered and a chaotic regime, which is mainly determined by the number of inputs...
Deep Honesty
by Aletheophile — 2024-05-07T20:31:48.734Z

Most people avoid saying literally false things, especially if those could be audited, like making up facts or credentials. The reasons for this are both moral and pragmatic — being caught out looks really bad, and sustaining lies is quite hard, especially over time. Let’s call the habit of not saying things you know to be false ‘shallow honesty’[1].

Often when people are shallowly honest, they still choose what true things they say in a kind of locally act-consequentialist way, to try to bring about some outcome. Maybe something they want for themselves (e.g. convincing their friends to see a particular movie), or something they truly believe is good (e.g. causing their friend to vote for the candidate they think will be better for the country).

Either way, if you think someone is being merely shallowly honest, you can only shallowly trust them: you might be confident that they aren't literally lying, but you still have to do a bit of reverse engineering to figure out what they actually believe or intend.

This post is about an alternative: deep honesty, and the deep trust that can follow. Deep honesty is the opposite of managing the other party’s reactions for them. Deep honesty means explaining what you actually believe, rather than trying to persuade others of some course of action. Instead, you adopt a sincerely cooperative stance in choosing which information to share, and trust them to come to their own responses.

In this post, we've leaned into the things that seem good to us about deep honesty. Writing while being in touch with that makes it seem easier to convey the core idea. We've tried to outline what we see as disadvantages of deep honesty, but we're still probably a bit partial. We would love to see discussion of the idea, including critical takes (either that our concepts are not useful ones, or that this is less something to be emulated than we imply).

The rest of this post will be:

Some examples of where deep and shallow honesty diverge
Why and when you might want deep honesty
Various disclaimers about what deep honesty is not
A look at some difficult cases for deep honesty
What deep honesty might look like in practice

Examples of shallow (versus deep) honesty

Writing a very optimistic funding application which doesn’t mention your personal concerns about the project

As opposed to being upfront about what you think the weaknesses are

Telling an offic

...
Let's Design A School, Part 2.2
School as Education - The Curriculum (General)
by Sable — 2024-05-07T19:22:21.730Z
Link: https://affablyevil.substack.com/p/lets-design-a-school-part-22

The problem you run into when designing a curriculum is that you have to decide what you want students to learn.

The problem with deciding what you want students to learn is that your decisions will inevitably end up being a reflection of your values. It isn’t a task that can be done objectively.

Do you prioritize STEM topics, hoping to increase the high-tech work force?

Do you go for an education in the classics, because that’s how the aristocrats did it?

Do you focus on art and music, because something something creativity and expression?

Should school life be regimented and strictly scheduled, as public schools currently are, or ad hoc and personalized?

It’s difficult to claim that these questions even have “right” answers, whatever “right” means in this context.

Thus the curriculum that I envision will inevitably be a reflection of what I believe and what I value. Take it as you will.

Matriculation and Grading

Before we get into the subjects students learn, it’s worth asking: how do students matriculate?

The Existing System

In the current American public school system, grading is done with letters representing percentages. An A represents a 90%, a B represents an 80%, and so on. A passing grade is usually a D (60%) although it can instead be a C (70%).

Matriculation - when a student advances in a grade or subject level - happens when the student gets a passing grade in the class.

Grade retention - a misleadingly polite term for being held back - can happen in grades K-6 when a student is failing in most subjects. In grades 7-12, it generally happens on a per-subject basis. The most common reason students are held back, however, is chronic absenteeism - missing too many classes.

That being said, schools are heavily incentivized to get students to pass and graduate. If a student is held back, it effectively adds another student-year’s worth of cost to the school; additionally, it looks bad for teachers and administrators if their students are routinely failing to learn.

Grade inflation is the most common antidote to grade retention - squeezing as many students as possible past the passing line, even if it requires lowering standards or fudging the numbers a bit.

The whole subject deserves a more thorough review, but that’s not our aim here.

The New System

We’re already doing away with grade levels - no 1st grade, 2nd grade, and so on, so we don’t have to worry about matricu...
Designing for a single purpose
by Itay Dreyfus — 2024-05-07T14:11:22.242Z
Link: https://productidentity.co/p/designing-for-a-single-purpose

If you’ve ever been to Amsterdam, you’ve probably visited, or at least heard about the famous cookie store that sells only one cookie. I mean, not a piece, but a single flavor.

I’m talking about

Van Stapele Koekmakerij

of course—where you can get one of the world's most delicious chocolate chip cookies. If not arriving at opening hour, it’s likely to find a long queue extending from the store’s doorstep through the street it resides. When I visited the city a few years ago, I watched the sensation myself: a nervous crowd awaited as the rumor of ‘out of stock’ cookies spreaded across the line.

Owner Vera Van Stapele with fresh-baked cookies, via

store website

The store, despite becoming a landmark for tourists, stands for an idea that seems to be forgotten in our culture: crafting for a single purpose.

In the tech scene where I’m coming from, and which you might too, this approach is often perceived as singular, and not in its positiveness. We’ve been taught to go big or go home—raise millions in funding, build a big company, hire more and more employees, and hope for the desired exit.

Anything less is considered a mind of a failure.

From a personal perspective I’ve seen this attitude in almost every branding session I ran with startup founders. Again and again, they struggled to distill their primary focus. Moreover, when discussing competitors, it often seemed their startup competed in every possible field.

In a way, that fear of committing reflects the human nature of FOMO—deliberately giving up on something(s) and experiencing the potential loss of other benefits.

This mindset has also seeped into our collective body of work, especially in software. A product, which often starts as a weird small creature, gradually evolves into a multi-arm octopus, which sadly became the norm for

VCware

1

.

And so we’ve been left with bloated, bigger, and… worse software.

The idea of maintaining a small scope in product has already appeared in my writing in various forms; in

niche product design

I explored the effect of growth on design; and

in defense of Twitter

, I wrote about the bloated era of incumbent culture. But in between there seems to be a different attitude that not many choose to embrace, which like in Van Stapele’s case, seeks a real purpose.

Going back to basics as a way to find purpose

In a tweet posted a few months ago,

Jeff Sheldon

described his renewed approach to pho...
Reviewing the Structure of Current AI Regulations
by Deric Cheng — 2024-05-07T12:34:17.820Z

This report is one in a series of ~10 posts comprising a 2024 State of the AI Regulatory Landscape Review, conducted by the

Governance Recommendations Research Program

at

Convergence Analysis

. Each post will cover a specific domain of AI governance (such as

incident reporting

,

safety evals

, model registries, and more). We’ll provide an overview of existing regulations, focusing on the US, EU, and China as the leading governmental bodies currently developing AI legislation. Additionally, we’ll discuss the relevant context behind each domain and conduct a short analysis.

This series is intended to be a primer for policymakers, researchers, and individuals seeking to develop a high-level overview of the current AI governance space. We’ll publish individual posts on our website and release a comprehensive report at the end of this series.

In this post, we’ll discuss a multifaceted, high-level topic: How are current AI regulatory policies structured, and what are the advantages and disadvantages of their choices? By focusing on the existing regulatory choices of the EU, US, and China, we’ll compare and contrast key decisions in terms of classifying AI models and the organization of existing AI governance structures.

What are possible approaches to classify AI systems for governance?

Before passing any regulations, governments must answer for themselves several challenging, interrelated questions to lay the groundwork for their regulatory strategy:

How will we classify AI systems - by their capabilities, amount of compute, domain of application, risk level, underlying architecture, or otherwise?
Who will these regulations apply to – organizations, individuals, or companies?
Who will possess legal responsibility for harm generated by AI systems - the AI lab developing the core model, the enterprise business deploying it, or the customer using it?
What is the correct tradeoff between encouraging development & innovation and mitigating risks from AI systems?

Complicating the matter, even precisely defining what is an AI system is challenging: as a field, AI today encompasses many different forms of algorithms and structures. You’ll find overlapping and occasionally conflicting definitions on what constitutes

“

models

”, “

algorithms

”, “

AI

”, “

ML

”,

and more. In particular, the latest wave of foundational large-language mod...