trentmkelly/LessWrong-43k huggingface dataset:
Making Eggs Without Ovaries
by Niko_McCarty — 2024-09-22T17:44:46.733Z
Link: https://www.asimov.press/p/eggs

This essay was written by @Metacelsus.

In March 2023, a research team at Osaka University

announced an extraordinary achievement

: the production of viable eggs using cells taken from male mice.

Building on years of research into stem cells and reproductive development,

1

the researchers, led by Professor

Katsuhiko Hayashi

, developed a method to induce male stem cells to lose their Y chromosome and duplicate the X chromosome, thus becoming female. From there, researchers took the female cells and grew eggs from them using

an elaborate protocol

they had initially published in 2016.

From the paper and ensuing media fanfare, a question emerged: When will this technology become available for people?

For nearly all of human history, there was only one way to make a baby. When a man and a woman love each other very much . . . well, you know the rest. But for many people, the usual method of baby-making doesn’t work. This is especially problematic for biological women, whose ovaries lose the ability over time to release healthy eggs. By age 40, most biological women are no longer able to reproduce.

2

And in recent years,

rates of infertility have increased

along with the average age of childbearing.

The first method to circumvent some of the difficulties of conceiving a child, in vitro fertilization (IVF), came in 1978. During IVF, eggs are collected from the ovaries and mixed with sperm. As the fertilized eggs become embryos, they are transferred into the uterus. These basic methods have greatly improved in the last four decades, and

more than 10 million people

have been conceived using IVF. About 800,000 children are born from IVF every year. However, this technology still relies upon eggs that are formed naturally, which for many couples is simply not an option.

3

But what if, as the Osaka team’s breakthrough in mice suggests, there was another way to get eggs? In the last decade, several researchers — myself included — have been exploring the tantalizing prospect of growing eggs in cell culture.

Such an approach would take cells from an adult

4

— such as skin or blood — and reprogram them into induced pluripotent stem cells, or iPSCs. Much like embryonic stem cells, iPSCs have the ability to form any cell in the adult body; eggs included. Although generating human iPSCs is now routine, coaxing iPSCs to form eggs in a process known as in vitro oogenesis has

only been

...
Becket First
by jefftk — 2024-09-22T17:10:04.304Z

One of the things I like most about contra dance is how it works with
a very wide range of skill levels, including having one of the best
learning curves of any dance form I know. As a community we are
pretty committed to the idea that people should just be able to show
up and start dancing, no lessons required. Which is why I'm very
excited about a new "Becket first" approach I've been seeing from
several callers.

Contra dance has several common starting formations, which are
traditionally taught as:

Proper: one line of Larks, one line of Robins. It's "proper"
because everyone is on their own ("proper") side.

Improper: start proper, then within each couple that is going
to be going down the hall ("active" or "ones") the Lark and Robin
swap.

Becket: start improper, then circle one place to the left.

These days, proper dances are uncommon, and most are improper or
Becket. While some callers still use the older approach of getting
people to line up proper and then asking the ones to cross over, at
this point it's more common to just teach improper as the starting
formation and have people start that way. But what if we go a step
farther, and start with Becket?

This allows introducing the common duple minor ("hands four")
dances to halls where the average skill level wouldn't support an
improper dance:

Tell people to find a partner and make two long lines facing
each other. Then to slide left or right until they're across from
another couple and can take hands in a circle of four.

Call a dance leaves the dancers back where they started.

To progress, slide left. This is a lot easier than the "pass
through", "California twirl", or "star left" that you tend to see in
easy improper dances.

This lets you choose a dance that doesn't depend on roles, and during
the progression each couple is connected and so is more likely to stay
together.

Luke Donforth started a discussion
on the Contra
Callers List, and here are some role-free examples people shared.
I've put them roughly in increasing order of difficulty.

Festival Reel #2 (Will Mentor)

Becket

A1
(8)
Long lines forward and back
while sliding left

(8)
Long lines forward and back

A2
(8)
Star left

(8)
Star right

B1
(8)
Circle right

(8)
Circle left

B2
(8)
Partner dosido

(8)
Partner swing

Pluc...
On the Role of Proto-Languages
by adamShimi — 2024-09-22T16:50:34.720Z
Link: https://epistemologicalfascinations.substack.com/p/on-the-role-of-proto-languages

I’ve been fascinated recently by

historical linguistics

, and in particular reconstructed

proto-languages

such as

Proto-Indo-European

.

But what even is a “proto-language”?

Well, if we go for the literal operationalization,

Wikipedia defines a proto-language as

:

a postulated ancestral language from which a number of

attested languages

are believed to have descended by evolution, forming a

language family

.

[…]

It is by definition a

linguistic reconstruction

formulated by applying the

comparative method

to a group of languages featuring similar characteristics.

So a proto-language is supposed to be the closest common ancestor of a language family (or subfamily), and it is reconstructed using

the comparative method

(which as the name suggests, works by comparing the different daughter languages to reconstruct (mostly) sound structure (

phonology

) and word structure (

morphology

).

But they’re a weird kind of thing.

First, proto-languages are not attested. This means that we have no example of writing in any proto-language. When old dead languages like

Sumerian

get deciphered (definitely writing a blog post on that one soon!), we have literal texts written in these language from which we can proceed. But that’s not true for any proto-language — instead these are theoretical models of hypothetical parent languages.[1]

Then, proto-languages are even less “languages” per say than most current languages. If you dig into the research on variations and dialects of any current language (let’s say English, even just American English), you end up noticing quickly how many levels of idealization are required to reduce all of that to a single language. You need to idealize variations within one speaker, between speakers, changes happening over time… And with proto-languages, because of the methodology itself, additional layers of idealization are added.

Notably, the comparative method struggles to separate periods of a reconstructed language; so even if the language did exist, the proto-language mixes together features that might never have cohabitated.

One of the book I was reading actually has a nice analogy for this:

The most helpful metaphor to explain this is the ‘constellation’ analogy. Constellations of stars in the night sky, such as The Plough or Orion, make sense to the observer as points on a sphere of a fixed radius around the earth. We see the constellations as two-dimensional

...
I'm creating a deep dive podcast episode about the original Leverage Research - would you like to take part?
by spencerg — 2024-09-22T14:03:22.164Z

Hi all! I'm creating a deep dive podcast episode about (the original) Leverage Research (for my podcast, Clearer Thinking). If you worked at (or currently work at) Leverage, or you have had experiences connected to Leverage, and you would like to either potentially:

(1) do an interview for the podcast

(2) talk to me totally off the record about your experiences (i.e., with your privacy totally protected)

Please reach out privately by using my contact form here. Or you can email our podcast (but multiple team members who work on the podcast will see it) here:

I have known many people involved in Leverage over the years (from its founding to the present day), and so I have heard a great deal of information about it from others. But I've never been a member of it, and there are still many things I don't know about it.

The podcast episode aims to provide a deep exploration (that's as unbiased as we can achieve) into (i) what Leverage was, (ii) what sorts of activities, occurrences, findings, and developments happened there, and (iii) why it ended up falling apart.

Note that Leverage still exists in a modified form, but the focus of the podcast will be on "Leverage 1.0."

Note also that while there are some interviews that I will be able to conduct right away, I will have to wait a while before conducting others (if yours falls into that camp, I will let you know).

Of course, I won't necessarily end up interviewing everyone who reaches out, but I'd be interested to hear from you regardless.
Who Feels More Alone?
by marvinscheffold — 2024-09-22T11:54:06.324Z

Imagine you live in a world with only two types of people. Introverts and extroverts. All introverts are the same. And so are all extroverts. It's a world with only two colors, and no shades in between.

Introverts like to be alone, in our world they want to meet a friend every 14 days. Extroverts love to be around other people. In our experiment they want to hang out with a friend every 2 days.

I´s and e´s also have something in common. They have short term memory loss. Everyday at midnight their brain does a full reset. So they never remember what happened yesterday.

Now that you know the basics of our thought experiment, take a guess: Which personality type feels more alone? Introverts or extroverts?

What does this question even mean? Let's say I'm an introvert. I wake up one morning and want to meet a friend. But when I call my friends, they don’t want to hang out with me.

That day I will go to bed feeling alone. But as I said above, the next morning will be a fresh start.

Here's two more ways to ask the same question. Who, on average, feels alone on more days, i’s or e’s? Whose interpersonal needs are being met less, i´s or e´s?

Did you take a guess? Maybe you picked e’s 🦅. Because they need more human interaction in general. Or you picked i’s 🏴‍☠️. Because they shoot themselves in the foot by being such loners?

Let’s take a closer look at our protagonists:

Introvert	Nickname: i as in ay ay, captain 🏴‍☠️
Wants to meet a friend once every

14

days
Probability wants to meet a friend today

7.14%

Extrovert	Nickname: e as in eagle 🦅
Wants to meet a friend once every

2

days
Probability wants to meet a friend today

50.00%

Are You Going To Meet a Friend Today?

To answer this question I calculated the probability of “meeting a friend today” for each friend group mix.

What is a “friend group mix”?

If you have one introverted and one extroverted friend, your friend group mix is 1/1, one i 🏴‍☠️ & one e 🦅.
If you have 5 extroverted friends, your friend group mix is 0/5, zero i 🏴‍☠️ & five e 🦅.

Each table cell (probability of meeting a friend today) is calculated like this: probability_self * sum_probability_friends.

If an introvert has one introvert friend its: 7.14% * (1-(1-7.14%)^1) = 0.51%.
If an introvert has two extrovert friends it’s: 7.14% * (1-(1-50%)^2)

...
Another argument against utility-centric alignment paradigms
by Fiora Sunshine — 2024-09-22T07:28:27.856Z

expectation calibrator: freeform draft, posting partly to practice lowering my own excessive standards.

So, a few months back, I finally got around to reading Nick Bostrom's Superintelligence, a major player in the popularization of AI safety concerns. Lots of the argument was stuff I'd internalized a long time ago, reading the sequences and even living with AI alignment researchers for a few months. But I wanted to see the circa-2015 risk-case laid out as thorougly as possible, partly as an excusion to learn more about my own subculture, and partly to see if I'd missed anything crucial to why all of us consider future advanced AI systems so dangerous.

But for the most part, the main effect of reading that book was actually to finally shatter a certain story I'd been telling myself about the AI doom scenario, the cracks in which had been starting to show for several months prior. Reading Superintelligence for myself made me notice just how much of AGI X-risk concern grew out of an extremely specific model of how advanced AI was actually going to work, one which had received very little supporting evidence (and even some countervailing evidence) since the rise of large language models.

Of course, I'm talking about how, when it comes to explaining why AI systems are risky, Bostrom's book leans hard on expected utility maximization (like Yudkowsky's sequences before it). Bostrom does step into discussions about other risks from time to time, for example pointing out wireheading as a potential failure mode of reinforcement learning systems (which are importantly not AIXI-style utility maximizers and pose an at least slightly different set of risks). However, most of the book's discussion of AI risk frames the AI as having a certain set of goals from the moment it's turned on, and ruthlessly pursuing those to the best of its ability.

It'd be easy to get sidetracked here with a debate about whether or not current AI systems are on-track to post that exact kind of x-risk. Indeed, modern deep learning systems don't have explicit utility functions; instead they're trained with loss functions, which are plausibly quite different in their dynamics.

We could talk about MIRI's Risks from Learned Optimization paper and its arguments that deep learning systems could plausibly develop algorithms explicit optimization anyway. We could try to analyze that argument in more depth, o...
My hopes for YouCongress.com
by Nathan Helm-Burger — 2024-09-22T03:20:20.939Z

Background

For background on YouCongress.com see this post by Hector Perez Arenas.

I love this general concept, and have a lot of ideas for how this implementation could be expanded. I'm hoping that writing out some of my ideas might inspire someone to jump in an contribute code. I would myself if I didn't feel full to the brim on trying to work on AI alignment / control / safety ideas.

A brief summary of the current concept is that it is a political polling platform (which could in theory be used to guide the decision making of political representatives). The platform allows users to create polls. Polls are answered by users and by 'digital twins' of famous people. Currently, the 'digital twins' seem to do an ok-ish job at making short statements and placing votes on the polls, based on the publicly expressed opinions of the relevant source person. It's also possible to initiate discussions with these 'digital twins' to explore your agreements and differences in more depth. Also, you can do topic-specific vote deference to other users or digital twins, which helps paint a clearer picture of topic-specific preferences for a wide segment of users.

Ideas for taking this further

Added realism for the digital twins

1. Adding to the training data

I imagine something like a set of 'trusted editors' who are allowed to upload additional existing writings of the target famous person being twinned. Also, any dialogues that can be found, and expressed as
"nontarget person: blah blah
target person: response to blah blah"

where the model would use the off-target writing as context, and be trained to predict just the target person's response.

2. Human Feedback

The 'trusted editors' could also look at responses of the current digital twin fine-tune to various prompts. Picking the more accurately representative of two possible responses would provide feedback that could be used to further fine-tune the model (probably using a LoRA on top of a base model).

Ability for Users to create their own digital twin

I think that potentially you could have an option for users to pay (or subscribe to premium, or enter their LLM API key, etc) in order to be allowed to make a digital twin of their own (not necessarily of themselves). The user would be allowed to upload documents and dialogues, and then queue a fine-tune. They'd be able to see how their digital representative responded...
How Often Does Taking Away Options Help?
by niplav — 2024-09-21T21:52:40.822Z

In small normal-form games, taking away an option from a player
improves the payoff of that player usually <⅓ of the time, but improves
the payoffs for the other player ½ of the time. The numbers depend on
the size of the game; plotted here.

There's
been
some
discussion
about
the origins
of
paternalism.

I believe that there's another possible justification for paternalism:
Intervening in situations between different actors to bring about
Pareto-improved games.

Let's take the game of
chicken between Abdullah
and Benjamin. If a paternalist Petra favors Abdullah, and Petra has
access to Abdullah's car before the game, Petra can remove the steering
wheel to make Abdullah's commitment for them — taking an option
away. This improves Abdullah's situation by forcing Benjamin to swerve
first, and guaranteeing Abdullah's victory (after all, it's a strictly
dominant strategy
for Benjamin to swerve).

In a less artificial context, one could see minimum wage
laws as an example of
this. Disregarding potential effects from increased unemployment, having
higher minimum wage removes the temptation of workers to accept lower
wages. Braess' paradox
is another case where taking options away from people helps.

Frequency

We can figure this out by running a Monte-Carlo
simulation.

First, start by generating random normal form
games
with payoffs in [0,1]. Then, compute the Nash
equilibria for both players
via vertex
enumeration
of the best response
polytope (using
nashpy)—the
Lemke-Howson
algorithm was
giving me duplicate results. Compute the payoffs for both Abdullah and Benjamin.

Then, remove one option from Abdullah (which translates to deleting a
row from the payoff matrix).

Calculate the Nash equilibria and payoffs again.

We assume that all Nash equilibria are equally likely, so for each player
we take the mean payoff across Nash equilibria.

For a player, taking away one of Abdullah's options is considered an
improvement iff the mean payoff in the original game is stricly lower
than the mean payoff in the game with one option removed. Thus, one can
improve the game for Abdullah by taking away one of his options, and
one can improve the game for Benjamin by taking away one of Abdullah's
options, or both.

Plots

For games originally of size n×m, how often is it the case that taking an option away from Abdullah improves the payoffs for Abdullah?

For games originally of size n×m, how often...
A Rational Company - Seeking Advisors
by AlignmentOptimizer — 2024-09-21T19:51:22.476Z

The purpose of this post is to hopefully get some feedback, advice or even advisors, for how to optimize a business using rationalist principles.

Background

We are a team of three people running a small tech/AI company and currently dedicate the profits to:

Donate to AI alignment research.
Reinvest in the business to expand the business.

My intuition is that it should be possible to use rationalist principles of decision making, in order to create a scalable and replicable system for decision making, in order to successfully grow the business. We believe our chances of success will increase if we collaborate with external advisors well-versed in rationality.

Benefit of Becoming Our Advisor

A chance to engage in discussing rationalist ideas in the context of business.
Opportunity to help spread rational discourse in the corporate world.
Collaborate with a team dedicated to impactful causes like AI alignment.
Monetary compensation.

What We are Looking For

An ideal advisor would:

Have business expertise
Genuinely enjoy exploring new ideas
Have a rationalist mindset

Some Rationality Inspired Ideas for Running a Company We are Considering

Prediction Markets
Internal prediction markets, where employees get to make predictions, and those predictions guide the company’s decision making for major decisions, and could possibly lead to improved results. As with any prediction market, well calibrated predictions would over time get more weight in their votes.

Performance Based Project Funding (with A/B testing)
Through modularization of different projects, the performance of each project can be better measured and rewarded. With time we collect more and more data on what makes a good/bad project.

Rationalist Culture
By fostering a culture of rational thinking, overall decision making can be improved. Culture can be fostered in many ways, a few of which are:

Hiring people with a rationalist inclination (Possibly through LessWrong?)
Leading by example
Give positive feedback when rationalist principles are applied
Allow some work hours

If you're interested in helping us integrate rationalist principles into our business, please reach out via direct message. We'd love to discuss how we can collaborate.

Or, feel free to share relevant posts/ideas in the comments.
Seeking mentorship
by Kevin Afachao — 2024-09-21T16:54:58.353Z

Hey!

I’m Kevin, an aspiring software engineer from Ghana. I’ve recently done the 80k 8 weeks career course and I’m excited to start earning-to-give. I’m looking for a mentor to help me land my first job and help me plan my career to maximise my expected donations!

I’m in GMT +2. If you’d like to meet and figure out if we’re a good fit, please drop me an email on "thekevin [dot] afachao [at] gmail [dotcom]" or message me on LinkedIn.
Applications of Chaos: Saying No (with Hastings Greer)
by Elizabeth — 2024-09-21T16:30:07.415Z

Previously Alex Altair and I published a post on the applications of chaos theory, which found a few successes but mostly overhyped dead ends. Luckily the comments came through, providing me with an entirely different type of application: knowing you can’t, and explaining to your boss that you can’t.

Knowing you can’t

Calling a system chaotic rules out many solutions and tools, which can save you time and money in dead ends not traveled. I knew this, but also knew that you could never be 100% certain a physical system was chaotic, as opposed to misunderstood.

However, you can know the equations behind proposed solutions, and trust that reality is unlikely to be simpler[1] than the idealized math. This means that if the equations necessary for your proposed solution could be used to solve the 3-body problem, you don’t have a solution.

[[1] I’m hedging a little because sometimes reality’s complications make the math harder but the ultimate solution easier. E.g. friction makes movement harder to predict but gives you terminal velocity.]

I had a great conversation with trebuchet and math enthusiast Hastings Greer about how this dynamic plays out with trebuchets.

Transcript

Note that this was recorded in Skype with standard headphones, so the recording leaves something to be desired. I think it’s worth it for the trebuchet software visuals starting at 07:00

My favorite parts:

If a trebuchet requires you to solve the double pendulum problem (a classic example of a chaotic system) in order to aim, it is not a competition-winning trebuchet. ETA 9/22: Hastings corrects this to “If a simulating a trebuchet requires solving the double pendulum problem over many error-doublings, it is not a competition-winning trebuchet”
Trebuchet design was solved 15-20 years ago; it’s all implementation details now. This did not require modern levels of tech, just modern nerds with free time.
The winning design was used by the Syrians during Arab Spring, which everyone involved feels ambivalent about.
The national pumpkin throwing competition has been snuffed out by insurance issues, but local competitions remain.
Learning about trebuchet modeling software.

Explaining you can’t

One reason to doubt chaos theory’s usefulness is that we don’t need fancy theories to tell us something is impossible. Impossibility tends to make itself obvious.

But some people refus...