trentmkelly/LessWrong-43k huggingface dataset:
Optimizing Problem-Solving Strategies Through Prediction Markets
by patrik-cihal — 2024-11-22T19:58:32.930Z

The Current Issue

Suppose you'd like to improve in chess as a total beginner so that within a month training one hour a day your rating in rapids is as high as possible. What do you do? Watch a YouTube video? Read a book? Ask ChatGPT? What about learning a new language in the shortest amount of time, with 20 minutes to spare a day? Or creating a startup full-time for 3 years to maximize net-worth?

The advice is scattered all over the internet, and you don't even have a way of accurately telling which of them have any merit.

In this blog, I propose a platform solution that properly provides incentives and combines beliefs to find the best approaches to problems.

The Solution

A Problem will be defined by:
- Prompt: The set of valid approaches we are looking for (Getting better at chess one hour a day for one month as a total beginner)
- Metric: How do we evaluate individual approaches (Rating on chess.com in rapids at the end)

Anyone can then submit an Approach (Each day play two rapids, and review the game using the chess.com analysis tool, use the remaining time to do puzzles).

The resulting ratings from following this approach form a standard distribution.

If we could then know what the average expected rating is for any given approach, we could rank them and reward the best ones based on the problem demand to incentivize contributions.

Rewarding Contributions

Each problem will have its own demand.

People can boost problems to increase their demand.

Then the total funds for a problem will be slowly over time distributed to the best performing approaches on it.

Evaluating Approaches

How do we determine, however, the expected yield of any approach?

Some of you might have already guessed one possible solution.

That's right. It's prediction markets time.

Each approach will have its own market. People can invest either up or down. Then, once an approach has enough liquidity, the creator will be eligible to receive tokens for it.

Addressing Potential Bottlenecks

Accuracy of Estimates

It's quite hard guessing how much net-worth will an average person have after creating startups for 5 years with a certain approach.

While initially, the estimates might not be very accurate. They will still provide more objective comparison between individual approaches than simple upvotes or downvotes.

Over time if they get tested or put through enough discussion we can expect it to be more and more accur...
Doing Research Part-Time is Great
by casualphysicsenjoyer — 2024-11-22T19:01:15.542Z

This is a crosspost from https://chillphysicsenjoyer.substack.com/p/pursuing-physics-research-part-time.

Intro

Disclaimer - I’m a part-time research associate doing biophysics with a uni research group in the UK. But I have a day job in an unrelated field that pays the bills.

Whilst I’ve read many personal accounts of research from full-time research students in the academic system, I haven’t heard as much from those pursuing research part-time - independently or otherwise.

I’ve always found this weird. Out of the set of people who are really interested in stuff, most people can’t, or don’t want, to go into academia full time. There are loads of valid reasons - financial, skill or geographical constraints. And so, doing unpaid research on the weekends seems like the only way for this kind of person to sate their interests meaningfully. And so I wonder why I haven’t read as much stuff by more people doing this kind of thing.

So as someone doing research part time alongside their day job, I wanted to reflect a bit on my priors about likelihood of success, and about trying to do two things well. The main thing that I wanted to argue is that one's effectiveness doing research part-time is probably a lot higher than the time adjusted effectiveness of a comparable researcher. Specifically, I think there are loads of arguments on why its a lot larger than just (effectiveness of a comparable researcher) * (part time hours / full time hours). And it's more fun!

Background

For the past year, I’ve worked in finance whilst doing biophysics research part-time at a university. I work on spectroscopy.

It took me around four years to get in the place where I could comfortably hold a job in finance and also find a supervisor. After I graduated I worked for big corporations for several years. It got to a point until I could manage my working hours so that I could leave reliably around 5pm, giving a few hours in the day left to work on science. Whilst I was doing this, I published about physics, and continued to study it independently from textbooks. Then I cold emailed supervisors for around two years until a research group at a university was willing to spare me some time to teach me about a field and have me help out.

So why part-time?

First the obvious - I think that part-time scientific research could be a great setup for working people who are still interested in...
Rethinking Laplace's Rule of Succession
by Cleo Nardo — 2024-11-22T18:46:25.156Z

Imagine a sequence of binary outcomes generated independently and identically by some stochastic process. After observing N outcomes, with n successes, Laplace's Rule of Succession suggests that our confidence in another success should be (n+1)/(N+2). This corresponds to a uniform prior over [0,1] for the underlying probability. But should we really be uniform about probabilities?

I think a uniform prior is wrong for three reasons:

The uniform prior suggests we should be equally surprised if the underlying probability lies in the interval [0, 0.0001] as in [0.3456, 0.3457]. But this seems wrong. I can think of many process that give probabilities in the first interval — for example, any process that succeeds only in rare edge cases. In contrast, I couldn't list any processes that give probabilities specifically around 0.3456. The uniform prior fails to capture the wide range of log-odds that occur in real-life processes.
Under the uniform prior, the process is almost surely not deterministic — i.e. there is zero prior likelihood of p being exactly 0.0 or 1.0. This seems wrong. Among probabilistic programs that generate binary outcomes, there are very simple deterministic ones (e.g. "always output 0" or "always output 1"). An appropriate prior should have nonzero prior probability on these simple programs.
The uniform prior assigns zero likelihood to simple fractions like p=1/2 or p=5/6. This too seems wrong — simple rational probabilities should have higher weight. To fix this, we should mix in the Thomae distribution, which adds a weight (m·n)^(-α) to each fraction m/(m+n) for every pair 1 ≤ m,n ≤ 100.

I propose this mixture distribution:

w1 * logistic-normal(0, sigma^2) + w2 * 0.5(dirac(0) + dirac(1)) + w3 * thomae_{100}(α) + w4 * uniform(0,1)

where:

The first term captures logistic transformations of normal variables (weight w1), resolving the issue that probabilities should be spread across log-odds
The second term captures deterministic programs (weight w2), allowing for exactly zero and one
The third term captures rational probabilities with simple fractions (weight w3), giving weight to simple ratios
The fourth term captures uniform random number comparisons (weight w4), corresponding to Laplace's original prior

Ideally, our prior should be a mixture of every possible probabilistic program, weighted by 2^(-K) where K is its Kolmogorov complexity. This would prope...
(Salt) Water Gargling as an Antiviral
by Elizabeth — 2024-11-22T18:00:02.765Z

Summary

Over the past year I’ve investigated potential interventions against respiratory illnesses. Previous results include “Enovid nasal spray is promising but understudied”, “Povidone iodine is promising but understudied” and “Humming will solve all your problems no wait it’s useless”. Two of the iodine papers showed salt water doing as well or almost as well as iodine. I assume salt water has lower side effects, so that seemed like a promising thing to check. I still believe that, but that’s about all I believe, because papers studying gargling salt water (without nasal irrigation) are few and far between.

I ended up finding only one new paper I thought valuable that wasn’t already included in my original review of iodine, and it focused on tap water, not salt water. It found a 30% drop in illness when gargling increased in frequency from 1 time per day to 3.6 times, which is fantastic. But having so few relevant papers with such small sample sizes has a little alarm going off in my head screaming publication BIAS publication BIAS. So this is going in the books as another intervention that is promising but understudied, with no larger conclusions drawn.

Papers

Estimating salivary carriage of severe acute respiratory syndrome coronavirus 2 in nonsymptomatic people and efficacy of mouthrinse in reducing viral load: A randomized controlled trial

Note that despite the title, they only gave mouthwashes to participants with symptoms.

This study had 40 participants collect saliva, rinse their mouth with one of four mouthwashes, and then collect more saliva 15 and 45 minutes later . Researchers then compared compared the viral load in the initial collection with the viral load 15 and 45 minutes later. The overall effect was very strong: 3 of the washes had a 90% total reduction in viral load, and the loser of the bunch (chlorhexidine) still had a 70% reduction (error bars fairly large). So taken at face value, salt water was at least as good as the antiseptic washes.

(Normal saline is 0.9% salt by weight, or roughly 0.1 teaspoons salt per 4 tablespoons water)

[ETA 11/19: an earlier version of this post incorrectly stated 1 teaspon per 4 tablespoons. Thank you anonymous]

This graph is a little confusing: both the blue and green bars represent a reduction in viral load relative to the initial collection. Taken at face value, this m...
The Manufactured Crisis: How Society Is Willingly Tying Its Own Noose
by PROPHET — 2024-11-22T17:45:27.941Z

You know me mostly from my analysis on politics, geopolitics, finance and economy. But predictions, or more broadly speaking, forecasting does not end there. We live in a world where economic, political and cultural issues weave together creating a complex web of local and global events. As in The Butterfly Effect, every action, both big and small influences everything else. Each decision we make creates a fork. As individuals, with each action we put a small brick in the collective direction of society.

Taking this into account, I believe that to be a good forecaster, one needs to have a holistic understanding of the world. For example politics is seldom about policies, rather it is about selling a specific narrative on current events, be it cultural or economic. Even here, I have hardly scratched the surface.

Fancying myself a good forecaster I adhere to the above. That is why besides focusing on current events sometimes I think and write about more high-brow issues. So it happens that this “sometimes” comes today, with a topic that has been brewing in my mind for quite a while now. It impacts basically everyone on this planet. And the situation is dire.

Today I will write about one of the most pressing issues we as a society have. The housing cost crisis was one of the main US election campaign topics as part of the affordability debate. Along with high interest rates (relatively, compared to the last 10Y), housing is on the minds of many young people, struggling to afford a once mediocre accommodation.

I may have just hinted at my thesis in the article’s title, but there is plenty to unpack here.

Prisoners by Choice

My thesis is very simple - we purposefully inflate cost of housing by socially enforcing suboptimal behaviors. This is a crisis of our own device and we have every power to reverse it. But first, why are actually real estate prices so high.

Short Intro to Microeconomics

Real Estate Supply

Real estate is a specific type of good. There are two main reasons for that:

Real Estate is immovable - we cannot take a house from Tennessee and move it to New York.
Real Estate supply is perfectly inelastic in the short term - it takes a lot of time and effort to build housing. This means that if demand increases, it will only affect the price of real estate. Increased demand will incentivize building of new housing, but it will take years to complete. In the short t

...
Sideloading: creating a model of a person via LLM with very large prompt
by avturchin — 2024-11-22T16:41:28.293Z

TLDR. We can create a relatively good model of a person by prompting a long-context LLM with a list of facts about this person. We can get much better results by iteratively improving the prompt based on the person's feedback. Sideloading is the only technology for immortality that actually works as of 2024.

Fig. 1. Here are my sideload’s comments on the draft of this article

Summary

Sideloading is the creation of a digital model of a person during their life via iterative improvements of this model based on the person's feedback. The progress of LLMs with large prompts allows the creation of very large, book-size prompts which describe a personality. We will call mind-models created via sideloading "sideloads"; they often look like chatbots, but they are more than that as they have other output channels, like internal thought streams and descriptions of actions.

By arranging the predictive power of the facts about a person, we can write down the most important facts first. This allows us to get a high level of fidelity in a person's model by writing down a finite number of facts and running it as an LLM prompt with instructions to create a person's chatbot. This instruction also includes an extensive general sideload prompt (we will call it a prompt-loader), which explains how the person's sideload should work, but it is universal and works as an operating system for different persons.

I created my personal sideload to test all this and it started working surprisingly quickly.

We found that there are three main levels of facts about a person: core facts, long-term memory, and historical facts. Core facts should go to the main prompt of the LLM, long-term memory belongs to RAG, and historical facts – which a person often does not remember – should be only used to extract new relevant data. The most important thing in preparing data for sideloading is to get a full list of the core facts, and not to mix it with long-term memory and historical facts.
The LLM-sideload project is inherently future-oriented. New state-of-the-art LLMs with large prompt capabilities emerge quarterly. This allows for the preparation of prompts that exceed current LLM capabilities, anticipating smooth execution in the following year.

Below we present the key findings of this work in the form of an executive summary:

Quality of sideloads is evaluated using three metrics:

Facts: Accuracy of

...
Neuroscience of human social instincts: a sketch
by Steven Byrnes — 2024-11-22T16:16:52.552Z

(If you’re in a hurry, you can just read the “Background and summary” section, and skip the other 85%.)

0. Background and summary

0.1 Background: What’s the problem and why should we care?

My primary neuroscience research goal for the past couple years has been to solve a certain problem, a problem which has had me stumped since the very beginning of when I became interested in neuroscience at all (

as a lens into Artificial General Intelligence safety

) back in 2019. In this post I offer a hypothesis for what the solution might generally look like, at least in the big picture. I don’t have all the details pinned down, but I feel like this post is major progress. (Unless it’s all wrong! Like

the last one

was.[1] Very happy for feedback!)

What is this grand problem? As described in

Intro to Brain-Like-AGI Safety

, I believe the following:

We can divide the brain into a

“Learning Subsystem”

(cortex, striatum, amygdala, cerebellum, and a few other areas) that houses a bunch of

randomly-initialized

within-lifetime learning algorithms, and a

“Steering Subsystem”

(hypothalamus, brainstem, and a few other areas) that houses a bunch of specific, genetically-specified

“business logic”

. A major role of the Steering Subsystem is as the home for the brain’s “innate drives”, a.k.a. “primary rewards”, roughly equivalent to the reward function in reinforcement learning—things like eating-when-hungry being good (other things equal), pain being bad, and so on.
Some of those “innate drives” are related to human social instincts—a suite of reactions and drives that are upstream of things like compassion, friendship, love, spite, sense of fairness and justice, etc.
The grand problem is: how do those human social instincts work? Ideally, an answer to this problem would look like legible pseudocode that’s simultaneously compatible with behavioral observations (including everyday experience), with evolutionary considerations, and with a neuroscience-based story of how that pseudocode is actually implemented by neurons in the brain.[2]
Explaining how human social instincts work is tricky mainly because of the “symbol grounding problem”. In brief, everything we know—all the interlinked concepts that constitute our understanding of the world and ourselves—is created

“from scratch”

in the cortex by a learning algorithm, and thus winds up in

...
Rebutting Every Objection To Giving To The Shrimp Welfare Project

by Bentham's Bulldog — 2024-11-22T16:12:48.868Z

Crosspost of this on my blog.

I recently wrote an article making the case for

giving to shrimp welfare

. Lots of people were convinced to give (enough to help around

half a billion shrimp

avoid a painful death), but because the idea sounds silly, many people on

Twitter

,

hacker news

, and various other places made fun of it, usually with very lame objections, mostly just pointing and sputtering. Here, I’ll explain why all the objections are wrong.

(Note: if any of the people making fun of shrimp welfare want to have a debate about it, shoot me a dm.)

Before I get to this, let me clarify: this is not satire. While shrimp welfare sounds weird, there’s nothing implausible about the notion that spending a dollar making sure that tens of thousands of conscious beings don’t experience a slow and agonizing death is good. If shrimp looked cute and cuddly, like puppies, no one would find this weird.

The basic argument I gave in the post was relatively simple:

If a dollar given to an organization has a sizeable probability of averting an enormous amount of suffering and averts an enormous amount of

expected suffering

, it’s very good to donate to the organization.
A dollar given to the shrimp welfare project has a sizeable probability of averting an enormous amount of suffering and averts an enormous amount of expected suffering.
So it’s very good to give to the shrimp welfare project.

The second premise is very plausible. A dollar given to the shrimp welfare project makes painless about

1,500 shrimp deaths per year

—probably totaling around 15,000 per dollar. It looks like the

marginal dollar

is even better, probably preventing around 20,000 shrimp from painfully dying. The most detailed report on the intensity of shrimp pain concluded, on average, that they suffer about

19% as intensely as we do

, and as I’ve

argued recently

at considerable length, that’s probably an underestimate. This means that the average dollar given to the shrimp welfare project averts about as much agony as making painless ~2850 deaths per dollar, and the marginal dollar probably averts as much agony as making painless ~3,800 deaths per dollar.

If a dollar made it so that almost 4,000 people were spared an excruciating death by slowly suffocating, that would avert an extreme amount of suffering. But that’s the average estimate of how much agony a dollar given to the SWP averts. Even if you think shrim...
A very strange probability paradox
by Terence Coelho — 2024-11-22T14:01:36.587Z

Which of the following do you think is bigger?

A: The expected number of rolls of a fair die until you roll two 6s in a row, given that all rolls were even.

B: The expected number of rolls of a fair die until you roll the second 6 (not necessarily in a row), given that all rolls were even.

If you are unfamiliar with conditional expectation, think of it this way: Imagine you were to perform a million sequences of die rolls, stopping each sequence when you roll two 6s in a row. Then you throw out all the sequences that contain an odd roll. The average number of rolls in the remaining sequences should be close to A. Next, perform a million sequences of die rolls, stopping each sequence when you roll the second 6. Throw out all the sequences among these that contain an odd roll. The average number of rolls in the remaining sequences should be close to B.

I asked something like this on r/math about a year ago, and even with the hint that the answer was paradoxical, the early consensus was that A must be larger. The justification was more or less the following: any time you roll until reaching two 6s in a row, you will have also hit your second 6 at or before then. So regardless what the conditions are, A must be larger than B.

But the correct answer is actually B. What on earth is going on?

A quick verification

Before we proceed, let's write some code to estimate A and B. The only goal here is to be as unambiguous as possible, so the code will be almost comically unoptimized in both run-time and length.

import random
def estimate_A(n):
#Rolls 'n' sequences of die rolls, stopping each when two 6s in a row.
#Tracks number of sequences with no odds
#Tracks sum of number of rolls in all sequences with no odds
#Computes average by dividing the two.
num_sequences_without_odds=0
sum_rolls_without_odds=0
for i in range(n):
counter=0
last_roll_six=False
has_odd=False

#Roll until two sixes in a row
while True:
x=random.randint(1,6)
counter+=1
if x%2==1:
has_odd=True

if x==6:
if last_roll_six:
break
last_roll_six=True
else:
last_roll_six=False

if not has_odd:
sum_rolls_without_odds+=counter
num_sequences_without_odds+=1

A_estimate=(sum_rolls_without_odds)/(num_sequences_without_odds)
return A_estimate

def estimate_B(n):
#Rolls 'n' sequences

...