trentmkelly/LessWrong-43k huggingface dataset:
Intellectual lifehacks repo
by Antoine de Scorraille — 2025-02-25T16:32:09.814Z

I really like dimensional analysis. It's a simple and powerful trick, almost magical, that allows you to distinguish between plausible and chimerical formulas.
I really like the type signature. It's a simple but ontologically important change for classifying different objects. [1]
I really like computational complexity, and its marketing version "Does it scale?" It's a simple but powerful trick for understanding and designing systems and rules. [2]
…

Let's list our ‘intellectual lifehacks’ here, i.e., elements of knowledge that have properties like. :

Very general
Simple to apply, but powerful

I don't have a proper definition other than ‘I feel like it’ to the question ‘Does this count as an intellectual lifehack?’, but I'd like to make it clear that I'm setting the bar very high. I hope you'll be able to connect the dots with n=3... Anyway, share and discuss!

Hereby, our intellectual lifehacks repo:

dimensional analysis
type signature
computational complexity

^

See: How to keep the essence of a theory by looking at the type signature, or to make progress on ontologically unsolved questions

^

See: How to import such intellectual lifehack in another intellectual field
Economics Roundup #5
by Zvi — 2025-02-25T13:40:07.086Z

While we wait for the verdict on Anthropic’s Claude Sonnet 3.7, today seems like a good day to catch up on the queue and look at various economics-related things.

Table of Contents

The Trump Tax Proposals.

Taxing Unrealized Capital Gains.

Extremely High Marginal Tax Rates.

Trade Barriers By Any Name Are Terrible.

Destroying People’s Access to Credit.

Living Paycheck to Paycheck.

Oh California.

Chinese Venture Capital Death Spiral.

There is Someone Elon Musk Forgot to Ask.

Should Have Gone With the Sports Almanac.

Are You Better Off Than You Were Right Before the Election?.

Are You Better Off Than You Were Before the Price Level Rose?.

Most People Have No Idea How Insurance Works.

Do Not Spend Too Much Attention on Your Investments.

Preferences About Insider Training are Weird.

I Will Not Allocate Scarce Resources Via Price.

Minimum Wages, Employment and the Equilibrium.

The National Debt.

In Brief.

The Trump Tax Proposals

The Grumpy Economist goes over Trump’s tax proposals, taking it as given this is not the big tax reform bill America needs and probably will never get (pre-AGI).

No tax on tips. It’s dumb, but it’s a campaign promise. He notes that as long as people still have to declare their tips, and we don’t allow those with high incomes to pretend to take half their income in tips, not taxing tips directly won’t matter much, so we should relax.

I think this is far too big an assumption of competence, but given this has to get through Congress, we’re probably safe from the madness.

No tax on social security. He explains why the benefits shouldn’t be taxed.

I get that, but this is a big benefits increase, in a way that doesn’t seem necessary, that transfers money from young to elderly, and which puts a lie to every other ‘we are running out of money’ complaint.

No tax on overtime pay. This one is sufficiently stupid that he can’t pretend that it would not be a huge disaster, the incentives are so awful.

Renewing the Trump tax cuts. Yeah, yeah. Probably a good idea.

Adjusting the SALT cap. He’s against this because of the incentive it gives to states to raise their income taxes.

I notice that when SALT was capped no states lowered their income taxes? He’s only going to fiddle at margins anyway.

Closing the carried interest “loophole.” He says this one is unclear. He points out actual capital gains taxes are stupid, so we should

...
Making alignment a law of the universe
by Richard Juggins — 2025-02-25T10:44:11.632Z

[Crossposted from my substack Working Through AI. I'm pretty new to writing about AI safety, so if you have any feedback I would appreciate it if you would leave a comment. If you'd rather do so anonymously, I have a feedback form.]

TLDR: When something helps us achieve our goals, but is not an end in itself, we can say it is instrumentally valuable. This property is determined by the environment, deriving directly from its structure. For example, knowledge of physical constraints like gravity is widely useful. For an AI, alignment will not generally be instrumentally valuable. This means it will not be self-correcting, making misalignment the default case. We can change this by modifying the environment an AI experiences — by altering the laws of its universe so that desirable capabilities, like being nice to humans, are useful, and undesirable ones, like scheming, are not. For an LLM, this mainly looks like changing the dominant patterns in its training data. In this post, I explore these ideas, and end by proposing some pre-training data curation experiments. I suggest our long-term goal should be to find a kind of basis transformation after which alignment naturally emerges.

When something is a means to an end, rather than an end in itself, we can say it is instrumentally valuable. Money is a great example. We want money either because we can use it to buy other things — things that are intrinsically valuable, like food or housing — or because having lots of it confers social status. The physical facts of having cash in your wallet or numbers in your bank account are not ends in themselves.

You can extend this idea to skills and knowledge. Memorising bus times or being able to type really fast are not intrinsically valuable, but act as enablers unlocking value elsewhere. If you go down the tech tree to more fundamental skills, like fine motor control, reading social cues, or being able to see, then in our subjective experience the distinction collapses somewhat. We have incoherent goals and often value things for their own sake, beyond their ability to bring us core needs like food. But from an evolutionary perspective, where intrinsic value means passing on your genes, these three skills I listed are merely instrumentally valuable. It’s possible to reproduce without any of them, it’s just harder.

Alignment is not self-correcting

A distinction is often made i...
Revisiting Conway's Law
by annebrandes — 2025-02-25T08:33:52.421Z

This is a post about how running companies will change. It seems safe to say that markets are becoming more competitive, since AI tools are raising the floor for incumbents and new market entrants alike. But does this shift raise the floor symmetrically?

And what happens if this shift benefits incumbents? It's easy to imagine a world in which tech giants subsume previously difficult to aggregate assets and edge out new competition due to their ability to resource share under their umbrella. This is a scary world. Social mobility in an ultra-consolidated marketplace is not protected.

It's my belief that we can create tools to empower even individuals to become competitive with incumbents. I'll make that argument here, and I'd be interested to hear what you think.

The Feedback Loop is the Primary Product

The purpose of a company is to learn about its environment.[1] [2] Companies are learning machines, and the best ones focus on extracting more information with less noise as fast as possible. This dynamic has always been true, and promises to become even more true because smaller teams increasingly generate outsized returns. This rising competitive pressure means organizations have an ever-shrinking margin for error in their information uptake rates. If you’re planning on participating in the new world, the feedback loop is your primary product.[3]

Dry powder ready to explode

Companies waste their data, and it’s not for lack of trying. Companies spend billions of dollars extracting and storing information about user interactions. But at the end of this effort they are left with a few colorful graphs designed to persuade, not to inform. And it’s not their fault. Companies are bottlenecked by intelligence. Learning from data has been a hugely expensive effort until recently because of labor costs. “Until recently” because frontier models solve the cost of labor problem.

In other words, companies are learning machines, and now it’s possible for the first time to learn at scale.

Credit attribution systems

Imagine you’re playing chess, you don’t know the rules, and you only get one bit of feedback: whether you won or lost at the end of the game. Over 1,000 iterations of the game, you may improve by classifying every sequence of actions that led to a winning strategy as “good” and every sequence of actions that led to a losing strategy...
Demystifying the Pinocchio Paradox
by Novak Zukowski — 2025-02-25T06:16:57.219Z

I've recently come across the Pinocchio Paradox:
If Pinocchio says "my nose will grow."
Does his nose grow or not grow?

Tracing through this scenario, we can see this is related to the Epimenides Paradox.

The scenario in the Pinnochio Paradox assumes Pinnochio can predict when his nose will grow or not grow with perfect accuracy and achieves 100% certainty (an assumption that is a poor representation of real world agentic systems). This is essentially a scenario where Pinnochio has become self-aware that deception causes his nose to grow and has honed his predictive model on what threshold of deception triggers nose growth.

The Pinocchio Paradox is resolved by acknowledging that the word "will" in "will grow " indicates an anticipated future nose growth event; intuitively in the imminent future. The "my nose will grow" message must be in reference to some other action that Pinocchio has taken or a message he transmitted prior to the "my nose will grow" message. When Pinocchio transmits a message, he knows with complete certainty if that message is an act of deception or not. Thus, each message transmission event provides the opportunity for nose growth/non-growth to occur.

Deception exists where there is a difference between Pinnochio's internal model and the model that will be compiled out of the transmitted message upon being received by another agent on the social graph. The difference between 2 models is commonly known as relative entropy or KL Divergence.

For this scenario to work, it also requires an assumption that nose growth lags by at least 2 message passing events, giving room for both the 1st message and 2nd message to be transmitted before nose growth happens. Without this assumption, both Pinocchio and the other agent will have the empirical information of whether Pinocchio's nose grew upon the first event, which would invalidate the purpose of sending the 2nd message that says "my nose will grow."

This also means that we should assume that nose growth happens proportional to the number of deceptive messages transmitted within this trailing 2 message window (else, we wouldn't be able to distinguish the number of messages within this window that contain the deception) Here, nose growth is proportional to the total relative entropy deliberately transmitted within the previous 2 message events.

There are 3 sequential events that occur in this scenario.
Brea...
Technical comparison of Deepseek, Novasky, S1, Helix, P0
by Juliezhanggg — 2025-02-25T04:20:40.413Z

Comparing Novasky with S1:

NovaSky by Berkeley club, S1 by Feifei Li (arXiv:2501.19393), are the players who don't have capital or compute, mainly focuses on developing method that finetune a large language model with curated minimal reasoning datasets. Sky-T1 trained an entire model, with datasets from diverse domains, 17K training examples. S1 is focusing more on test-time scaling which is extending the reasoning time when needed through "wait" token repeatedly when it want to terminate the reasoning. But the datasets used to finetune is much smaller, which is only 1000 chosen questions with detailed reasoning traces. One method is more versatile, the other one is focused on math problem by increasing inference time for better performance.

s1:

Inference-Time Enhancement:
Concentrates on leveraging additional computation during inference (test-time scaling) to boost performance without necessarily changing the model’s underlying architecture.

Sky-T1:

End-to-End Training:
Focuses on training a model that already incorporates strong reasoning capabilities across domains. While it might use some techniques to boost inference, the primary innovation lies in a cost-effective and efficient training process that achieves robust performance out-of-the-box.

The players with strongest tech team, but in stealth mode who only do frontier experimentation.

These are the most terrifying players, such as SSI, Keen technology hiding everything behind.

Looking into Deepseek:

Deepseek: A true technical breakthrough has emerged in which reasoning ability is achieved entirely through reinforcement learning (RL) without any reliance on supervised finetuning. This approach is grounded in a novel mathematical framework—Group Relative Policy Optimization (GRPO)—that employs an objective function to ensure the learning process is stable and gradual rather than chaotic, preventing sudden changes in behavior at each step. For every question, the old policy generates a batch of answers, and rewards are assigned based on the relative improvement of the entire set of answers rather than an absolute right-or-wrong measure; the policy is then updated in favor of answers with a positive advantage. The training follows a simple template, typically formatted as a sequence of a “<think>” followed by an “<answer>,” and an “aha moment” emerges as the system develops self-refle...
Upcoming Protest for AI Safety
by Matt Vincent — 2025-02-25T03:04:03.153Z
Link: https://www.pauseai-us.org/protests/

The PauseAI movement is planning protests for Friday, February 28th in several American metropolitan areas.

I understand some people's reservations about protests causing the public to develop negative associations with AI safety. In response, I invite them to join us as a way of directing both the style of the protests and the development of the organization as a whole. Most of the local branches are small (and friendly) enough that you can make a big difference.

(Technical note: PauseAI and PauseAI US are legally distinct entities. I've also linked to the global organization's website because it provides more detailed resources.)
what an efficient market feels from inside
by DMMF — 2025-02-25T02:38:40.129Z
Link: https://danfrank.ca/how-an-efficient-market-feels-from-the-inside/

“I often think of the time I met Scott Sumner and he said he pretty much assumes the market is efficient and just buys the most expensive brand of everything in the grocery store.” - a Tweet

It’s a funny quip, but it captures the vibe a lot of people have about efficient markets: everything’s priced perfectly, no deals to sniff out, just grab what’s in front of you and call it a day. The invisible hand’s got it all figured out—right?

Well, not quite. This isn’t to say efficient markets are a myth, but rather that their efficiency is a statistical property, describing the average participant, and thus leaving ample room for individuals to strategically deviate and find superior outcomes.

I recently moved to New York City, and if there’s one thing people here obsess over, it’s apartments. Everyone eagerly shares how competitive, ruthless, and “efficient” the rental market is. What’s unique about NYC is that nearly every unit gets listed on the same website, which shows you the rental history for every apartment—not just the ones you’re looking at, but nearly every unit in the city (and, awkwardly, how much all your friends are paying). You’d think with all that transparency, every place would be priced at its true value. But when you start looking, one thing jumps out: so many apartments are terrible, offering downright bad “value”—and still, they get rented, often at the same prices as the place you’d actually want to live in.

This bugged me. If the market’s so efficient, why are there so many seemingly bad apartment deals out there? Or does the mere existence of bad deals not necessarily imply there are good deals? I don’t think so. What I’ve come to realize is that being inside an efficient market doesn’t feel as airtight as it sounds. There’s still plenty of room to find better value, even in a ruthlessly competitive market like NYC rentals.

The Interior View of Market Efficiency

Here are some of the opportunities to “exploit” an efficient market that I thought about when looking for apartments in NYC.

Preference divergence

The biggest and most obvious is this: everyone’s got different preferences.

Markets aggregate preferences into a single price, but your preferences aren’t the aggregate. It’s important to spell out very clearly: everyone has different preferences, so we all have a different sense of what value actually is.

Some people work from home and cra...
Metacompilation
by Donald Hobson — 2025-02-24T22:58:00.085Z

A post that is going to be part of my sequence on rethinking programming languages.

The baisc idea

A compiler is a piece of machine code C, that takes as input a text string describing a program p and returns the compiled machine code C(p)

Let Opt be a function that takes in a machine code program and returns another potentially faster or smaller program.

A metacompliler has the formula C(p)=Opt("C(p).run()")

To understand how this works, first let's look at a less self referential case. Let Cr be a regular compiler. C1(p)=Opt("Cr(p).run()")

p is just a string. Maybe it's "print(1+2)"

"Cr(p).run()" is a machine code program. This program, if run, would first compile p into machine code, and then would run that machine code. Therefore it is a machine code program that does the same thing as p. It has a fairly significant size for even a small program, as it contains a complete copy of the compiler.

What does Opt("Cr(p).run()") do? It optimizes that machine code. The first thing it can do is cut out big chunks of the compiler. At least in simple cases. If the code is running arbitrary eval statements, all the compiler might be needed. In the case of this simple program, the parts of the compiler that handle floats, loops etc are just not used. If the optimizer is good, it could simplify the code all the way down to "[MACHINE CODE OUTPUT 3]". Some programming languages (see zig) already run code at compile time. The difference between compile and run time is just in what variables you currently know the value of, and the relative cost of compute.

For code with a finite runtime that just runs by itself, not interacting with the outside world, it can all, in principle be simplified down to a single print statement. In practice computer programs interact with the "outside world" in all sorts of ways. In some contexts, writes to disk or sending data to a GPU might be considered interactions with an external world. But for simplicity, assume the only form of interaction is input() and print()

Self reference and infinite regress

So that's what a metacompiler does. But does it actually do anything. The most naive metacompiler implimentation has Opt(x)=x. When we call C(p) we get the program"C(p).run()". And when we proceed to run ...
The manifest manifesto
by dkl9 — 2025-02-24T22:13:53.342Z
Link: https://dkl9.net/essays/manifest_manifesto.html

Followup to: If you weren't such an idiot...

We believe in staying alive — at least when life is or will be worth it — to do things that are useful and/or fun.

Protect yourself from extreme cold by techniques such as wearing clothes. Avoid looking directly at the sun, except when using eye-protecting equipment. Prefer to avoid addictive drugs, especially those with acute overdose risk. Prefer to keep yourself and others alive. Use a vehicle to travel a long distance, if you must do it quickly and/or thru a dangerous environment.

Wash your hands sometimes, at least after you defecate and before you perform surgery. Clean your body, by shower or equivalent, at least once per week.

Sleep sometimes, some amount between 20 hours per week and 14 hours per day, in a place at most as dangerous as where you tend to go when awake. Drink water-based liquid, some amount between 5 litres per week and 10 litres per day, with liquid at most as contaminated as that you use to clean your hands or body. Eat food, some amount between 10 kilograms per month and 10 kilograms per day, which food should, in total, contain at least fibre, protein, α-linolenic acid, and ascorbic acid. At least some of the food should be plant-based. Exercise sometimes. If your environment is safe enough, go outside at least once per month.

Think, at least a little, before you act in a new or complex way. Observe new things, at least a little, before you handle them. If an action hurts, consider stopping that action. If an action is fun, consider doing more of that action. Where you see a way to do a task at equal quality with fewer resources (time is a resource), or at better quality with the same resources, do it that way.

Speak to people, in at least those cases where you are spoken to and you have a wise and/or useful response. Prefer to tell the truth. Know how to read and write your native language. Know the basics of arithmetic on natural numbers.

If there is to be a government, it should leave some autonomy to the people. If the government will punish people, it should do so in positive correlation with their crimes. A government should legalise some vehicles for private use and/or provide public transportation.

The following should generally be illegal:

taking others' personal property
killing people

The following should generally be legal:

thinking
reading
walking
wearing clothes

There are at most as many ...
Credit Suisse collapse obfuscated Parreaux, Thiébaud & Partners scandal

by pocock — 2025-02-24T21:28:39.617Z
Link: https://juristgate.com/en/2025/02/24/credit-suisse-collapse-obfuscated-parreaux-thiebaud-partners-scandal/

Is it just coincidence that scandals are swept under the carpet when a bigger scandal dominates the news cycle? The linked article has direct links to the official sources for this story.

15 March 2023, the value of shares in Credit Suisse plunged. Four days later UBS announced the purchase of their competitor Credit Suisse.

It was an enormous scandal for Switzerland.

The risk of a scandal had been known for some months. For a few weeks after 15 March 2023, the Swiss media published multiple reports about Credit Suisse each day.

Two weeks later, on 4 April 2023, the trade registry in Canton Geneva received the decision of FINMA for the immediate liquidation of Parreaux, Thièbaud & Partners / Justicia SA.

We looked at the web site of RTS (Swiss public broadcaster) using the Wayback Machine. Below, we publish some screen shots from the week from 3 to 7 April 2023. We didn't find any reports about the closure of Parreaux, Thièbaud & Partners.

20,000 clients had suddenly lost their Swiss legal services insurance. Some of the insured clients were in the middle of trials and other procedures funded by the insurance.

The Swiss media helped the jurists with free publicity in 2018.

But the media said nothing when the jurists were shut down.
According to the judgment of 4 April 2023, FINMA had knowledge of the situation at Parreaux, Thiébaud & Partners since 2021. For two years they looked for a solution. They didn't find a solution but they had the chance to do a cover-up when the Credit Suisse crisis arrived.

The linked post includes screenshots showing how the legal insurance scandal appears to be smothered by news reports about the much bigger banking scandal.