trentmkelly/LessWrong-43k huggingface dataset:
The Jordan Peterson vs Sam Harris Debate
by lsusr — 2022-04-04T00:55:32.938Z

In 2018, Jordan Peterson and Sam Harris had an 8-hour-long debate about the value of religion. Millions of people watched it.

Peterson and Harris agree on many things. They both oppose nihilism, post-modernism, moral relativism and religious fundamentalism. They are both pro free speech and pro good where "good" is what a reasonable person would think of as "good". They both agree that religion has value. Both of them agree that religious texts should not be taken literally.

They are both familiar with the Western intellectual tradition. They are both familiar with 20th century Western history. Both of them are primarily concerned with People of the Book (especially Christians and Muslims). They argue from a Western point of view. You won't be encountering references to Taiwanese ancestor worship or Zen Buddhism in this debate. This isn't really a big deal for Jordan Peterson when he's arguing in support of Christian narrative but it is a point against Harris when he opposes religion globally. It feels to me like many of Harris' attacks against religion are actually attacks against monotheism.

Peterson and Harris both agree that Western philosophy is in an era of intellectual crisis. They both agree that parts of religion have value. They agree that interpreting religious texts as literally true is bad, but they disagree with what to do about religious texts that contain literal falsehoods.

Peterson believes religious texts should be interpreted as a source of narrative[1] truth.

Harris believes interpreting religious texts as a source of priviledged truth creates a breeding ground for religious fundamentalism.

Narrative Truth

I don't know any religious fundamentalists. The religious people in my social circle include a Protestant Christian, a Pure Land Buddhist, an atheist Jew, a Zen Buddhist and a Taiwanese polytheist. None of these people treat religious texts as a source of literal truth. The polytheist believes in reincarnation but not because it's a dangerous religious dogma. He believes it because he grew up on a primitive rice paddy in the middle of nowhere and never got the complete material reductionist memeplex.

The Jew tells Jewish jokes but I've never heard him quote the Torah. He follows kosher dietary laws because that's what Jews do. He treats it like being vegetarian. His attitude toward religion reminds me of an old Jewish story.

There once wa...
Very Dark Mouth-Shaped Non-fancy Chocolate?
by jefftk — 2022-04-03T22:10:04.922Z

I have somewhat unusual preferences in chocolate:

I want it to be very smooth, and about the right shape to sit
against my hard palate. If it has corners it's difficult to refrain
from chewing (though this isn't the only
factor). This means most of the standard shapes for chocolate
don't work very well: if I break a piece off a larger bar it's going
to have some sharp parts.

I don't want it to be very sweet. I enjoy sweet chocolate, but
I'd like to be eating less sugar. I would also ideally have a piece
and think "that was good, but I don't want more", which is less my
experience with sweeter chocolate.

I don't want something fancy. So far I have managed not to develop a
taste for expensive chocolate, and I'd like to stay that way if
practical.

I haven't found anything that fits these preferences very well. For
example, Hershey's Special Dark nuggets do well at being non-fancy and
smooth, but they're about twice the size I'd like (10g; 5g would be
better) and they're too sweet (~50% sugar). Looking online, two ideas
that might be about right:

Guittard 74%
wafers. At 26% sugar these might be too sweet? Hard to
tell how big they are.

Dove
Deepest Dark 82%. These are 16% sugar, which might be about
right? They're 8g each, which is a little bigger than I'd like but
probably ok. Individually wrapped, which is mildly convenient but not
necessary.

I haven't tried either of these, though, and they're a bit more
expensive than I'd like (both are $0.69/oz, $11/lb). Maybe the best
place to look is chocolate intended for baking? Anyone have
suggestions?

Comment via: facebook
Giving calibrated time estimates can have social costs
by Alex_Altair — 2022-04-03T21:23:46.590Z

In the normal course of my rationalist upbringing, I learned about the classic cognitive biases, including the planning fallacy. This is essentially the fact that it almost always takes longer for a task to get done than people estimate at the beginning. The explanation described in the original sequence post is that people visualize the mainline path of accomplishing the steps of the task, and then just add those times together, whereas in reality, at least one of those steps will have something go wrong, and make the whole thing take much longer.

So I read this, and then just... updated? Typically one should be very skeptical about the feeling that they are not subject to a bias, but as discussed in the original sequence post, this one is empirically reported to be correctable. And I'm sure it took me some time to adjust, but it wasn't too difficult to install a TAP to just consult the outside view instead of the inside view.

And then I went forth into the world, and gave calibrated time estimates, because I want to have true beliefs and I want to say true things. Slowly, over time, I got the sinking sense that this was costing me social points.

Types of costs

People may think you're slow

A primary context in which I had to give time estimates was when working as a software engineer. I've worked at multiple companies that regularly used "sprints" to plan work, and we often assigned explicit estimates to the tasks at hand. I regularly gave longer estimates than my coworkers or manager. These estimates were also calibrated; I actually took about that long. Sometimes more, sometimes less. My coworkers usually took longer than their estimates, but their estimates were shorter.[1]

Over time, I believe my managers got a worse impression of me.[2] One simple problem here is that shorter estimates just sound better than longer estimates because the manager wants the thing done sooner. But another slightly subtler problem is that since almost everyone gives optimistic estimates, my manager would reasonably be used to hearing optimistic estimates. So if they hear me give a longer estimate, they don't know that it's more calibrated, so they subconsciously assume it's just as optimistic as my coworkers, and then they would reasonably believe that I am worse.

Ideally, my calibration would eventually be evident through cumulative statistics. But realistically, people aren...
The Case for Frequentism: Why Bayesian Probability is Fundamentally Unsound and What Science Does Instead
by lsusr — 2022-04-03T20:52:50.683Z

Land War in Eurasia

Bayes theorem is a tool for updating probabilities according to evidence.

Consider a real-world example of something important I was recently wrong about. On February 23, I found out Russia had invaded Ukraine. When disruptive events happen, you must respond immediately. I quickly read what the experts were saying on Foreign Affairs and combined it with my existing knowledge of history, technology and geopolitics. I wrote "There is going to be a war. Ukraine is going to lose. The question is how much, how quickly and on what terms."

Then I remembered that war is unpredictable and war experts are often wrong. I added a qualifiers before publishing. "There is probably going to be a war. Ukraine is probably going to lose. The question is how much, how quickly and on what terms."

A week later, reports of under-equipped soldiers began trickling in and I discovered that Russia's economy was much smaller than I had assumed. I updated my analysis. As the war calcified, I finally had time to research current weapons technology and build my own model of the war from a tactics-level foundation.

Bayes' Theorem

Over the course of the war, several observations changed my probabilities of an outcome.

Experts believe Russia will win.

Evidence arrived that Russian troops might be under-equipped.

I discovered the Russian economy is the size of Florida's.

Yep. It's not just propaganda. Russian troops really are under-equipped. Video evidence confirms it.

Russia's assault has slowed to a crawl.

Each time new evidence came in, my analysis of the situation shifted away from a Russia blitzkreig-style fait accopli.

Bayesian probability treats probability as a subjective state that is updated in response to evidence.

Let A denote the a Russian fait accompli.

Let B denote the belief of experts that Russia will perform a fait accompli.

For example, my initial prior probability P(A) of "Russia will perform a fait accompli" started out at 0.5[1].

P(A)=0.5

When I discovered that experts believed Russia would win, I updated my estimate. I estimated a probability of 0.9 that experts would believe in a Russian victory (conditional upon a Russian victory) P(B1|A). I estimated the probability of experts to believe in a Russian victory P(B1) to be 0.5.

P(B1|A)=0.9P(B1)=0.5

After observing experts believed in a Russian victory, I wanted to find out the probability P(A|B1) of a R...
The fingerprints of ideology in science
by Malmesbury — 2022-04-03T20:40:23.345Z

In the Birth of Biopolitics, Foucault tells the story of how, in the Western civilization, the task of figuring out what's good or bad for society has moved from one organ to another. He mentions the 16th century's "reason of state", the physiocrats laissez-faire, 18/19th century bureaucrat-engineers, up to the neoliberal use of markets as some kind of measuring instrument.

Now our civilization has evolved a separate organ dedicated to the pursuit of truth, known as academia. To decide your policy preferences, trust the experts. Unlike technocrats, academics have no direct power, their role is only to provide an accurate model of the world to be used by everyone else. This doesn't sound so bad: now public opinion is a part of the chain of command and acts as a barrier between academics and executives. But, of course, this barrier only works if people don't trust the experts.

Something something biopower locus of veridiction

Full-disclosure: I am myself an academic. I am officially part of the Cathedral, looking at you from the narthex. But this blog is an hermeneutics of suspicion blog, and there is no real power structure above us to check that ideological biases are not deflecting our scientific minds. Therefore, we will have to do the checking ourselves.

1. Drawers

So, how can we watch over academia from below? There are signs. Many papers have obvious methodological flaws, p-hacking and questionable sampling. What's reported in the abstract can be totally different from the actual results (when it's not the outright opposite). Authors make mistakes when calculating p-values, and these mistakes, luckily, change the results in the "right" direction. But all of this can be detected by carefully scrutinizing the papers. However, there is one form of bias that is mostly invisible: selective publication. What if researchers just conduct tons of low-quality studies and only publish the ones that go in the right direction? In that case, the published studies will look perfectly fine.

Publication bias (PB) is usually about how journals are looking for exciting positive results, and non-significant findings are relegated to lower-tier journals. As publication is a long and painful process, it's not always worth the effort. The second kind of PB would be when researchers decide to publish a study or not based on whether they like the results. I will call this "ideo...
How to write a LW sequence to learn a topic?
by PabloAMC — 2022-04-03T20:09:54.992Z

I am thinking of regularly writing a Lesswrong sequence on Causality applied to Machine Learning, as a lesser alternative to doing an AI Safety postdoc on the topic I suggested here.

The purpose of both would be to learn sufficiently about Causality applied to ML such that I can later on contribute with original research. For context, I am finishing a Ph.D. in quantum algorithms so I know how to do research, the issue is learning about a new research area rather.

However, the postdoc, which is still dependent on the OpenPhil decision next week, seems a bit worse career option than a job as a quantum research scientist in a startup in some respects. The main reason for that is that my girlfriend would prefer the relative stability of a well-paying non-academic job in a field I already know, and the ability to work from home. Some have also argued that having one expert in quantum computing in the community might be useful, in the unlikely case it becomes useful, and it would also help me partially skill up as a ML software engineer.

In any case, I would still like to learn about Causality applied to ML and AI Safety because I think there is insufficient research going in this direction in the community; so I was thinking that as a way to learn about it, I might regularly summarize papers or something similar. Lesswrong also supports LATEX which makes it convenient as compared to other platforms.

I was thinking of writing a sequence about this topic to learn. How should I go about it, if I intend to work on this during weekends, say 1 day/week?

Thanks!
Should we push for banning making hiring decisions based on AI?
by ChristianKl — 2022-04-03T19:46:56.022Z

There are fast takeoff scenarios but there are also scenarios where AI slowly take more and more power in society till they have enough society that other stakeholders lack the power to move against AI agents. If we want to prepare for such a world it might be important to reduce the amount of power of AI within organizations and increase the amount of power that humans have within existing organizations.

Amazon gets criticized for firing people based on algorithmic decisions without a human making the decision. Would it be valuable to pick this fight to argue for human decision making when it comes to allocating power (the nature of what hiring decisions are about)?
Working Out in VR Really Works
by Yonatan Cale — 2022-04-03T18:42:37.514Z

Epistemic Status: Based almost entirely on my own experience. Also based on some friends here, here, and in real life.

TL;DR: I now need willpower in order to not-workout (!). I wish someone would have told me this a long time ago when I was looking for some workout that would resonate with me.

Is it actually a workout?

TL;DR: Yes.

I get tired and I sweat, especially in a specific intensive game (Thrill of the Fight, aka TotF)
I saw someone play that game for under 3 minutes and collapse on the sofa in exhaustion for about half a day
Here's a graph from my activity tracker (Oura Ring gen 2, not optimal for tracking workouts!, but still adds some info and also everyone likes graphs, right?) :
To give context to this graph (of me playing TotF several times during the day): When I do a rollerblade trip with a group that's better than me, the hardest part is usually under the "high" line. TotF is often above it.

Can you build muscle with VR?

TL;DR: Unclear.

People who know stuff (I don't qualify) say one needs resistance to build muscles, so maybe using wrist-weights or elastic bands that connect one's limbs to one's torso. I didn't try any of those myself. Other people said "consult with a specialist, some movements that you'd do with resistance can cause harm".

It does seem to be amazing for aerobic exercises, and specifically for interval training.

Which games are good for workouts:

Classic: Beat Saber, almost everyone likes it.

I rate it as a "medium" workout (and if it seems too easy, wait for the harder levels).
I recommend starting from the Tutorial.

I tried Pistol Whip which is somewhat similar but with guns.

Also rated as a "medium" workout.

Thrill of the Fight (TotF) is by far the most intensive workout I found on VR.

This will not be a good fit for everyone, it's scary (at least for me).
I have lots of beginner tips for it (including safety tips and things that I bet the game would explain if it had a tutorial).

FitXR: I played the boxing game a few times and like it so far

They have a subscription model ($10/month + short trial), this is really interesting to me, because they have a strong incentive to get me addicted to working out long-term, which is very aligned with my own desires!

There are reviews of VR games for workouts, like this or this
Adding: See lots of suggestions in the comments

Which VR do I have?

Oculus Quest 2 (Amazon link)

How much does it cost?

$...
On Agent Incentives to Manipulate Human Feedback in Multi-Agent Reward Learning Scenarios
by Francis Rhys Ward — 2022-04-03T18:20:36.769Z

This is an informal (i.e. sans équations) summary of a paper on which I have been working.

Abstract

In settings without well-defined goals, methods for reward learning allow reinforcement learning agents to infer the goal from human feedback. Past work has discussed the problem that such agents may manipulate humans, or the reward learning process, in order to gain higher reward. In this paper, we introduce the neglected problem that, in multi-agent settings, agents may have incentives to manipulate each other’s reward functions in order to change each other’s behavioral policies. We focus on the setting with humans acting alongside assistive artificial agents who must learn the reward function by interacting with these humans. We propose a solution to manipulation of human feedback in the multi-agent reward learning setting: the Shared Value Prior (SVP). The SVP equips agents with an assumption that the reward functions of all humans are similar. Given this assumption, the actions of opposing humans provide information to an agent about her own reward, and so she wishes to observe these actions rather than to manipulate them. We present an example in which the SVP prevents manipulation and show that, in the case of “arbitrary” manipulation, nothing can be learned about the target’s reward function by observing her behavior.

1 Intro

Background: Single agent Manipulation of the Reward

A well known problem for alignment (of reinforcement learning agents) is that agents might “tamper with” their reward functions in order to satisfy the reward without completing the intended task.
Reward learning is a solution to alignment in which the reward function is taken as something to be learned from human-feedback.
In the reward learning setting, agents might manipulate the human in order to “tamper with” their reward function (i.e., to change which reward is learned).

(Neglected) Problem: Manipulation in the Multi-Agent Reward Learning Case

In multi-agent settings, agents may have incentives to manipulate each other’s reward functions in order to change each other’s behavioral policies.
We focus on the setting with humans acting alongside assistive artificial agents who must learn the reward function by interacting with these humans.
Here, agents may be incentivized to manipulate the human actors in order to change which rewards are learned by o

...
Science is Mining, not Foraging
by Seb Farquhar — 2022-04-03T18:19:38.911Z

Tl;dr: My model for science is that it is like mining outwards from a point. This offers predictions that extend beyond Scott Alexander’s foraging metaphor and sometimes disagree. The mining metaphor emphasises the fact that research exposes new research problems; that research is much slower than learning; and research can facilitate future learning. The model offers actionable advice to researchers (e.g., “avoid the piles of skeletons” and “use new tunnels to unexplored rock-faces”). In part II, I also object to the idea that it is concerning that a smaller proportion of people are now “geniuses”, arguing that this would be true even if the importance and difficulty of intellectual feats achieved was constant because of the way in which genius is socially constructed.

This post is a response to some recent interesting posts that are well worth reading:

Why we stopped making Einsteins by Erik Hoel

Contra Hoel on Aristocratic Tutoring by Scott Alexander

Follow up: Why we stopped making Einsteins by Erik Hoel

The Low-Hanging Fruit Argument: Models and Predictions by Scott Alexander

I. Science as Mining

Scott proposed a neat model for how research happens, which is similar to mine but has some important differences.

Imagine some foragers who have just set up a new camp. The first day, they forage in the immediate vicinity of the camp, leaving the ground bare. The next day, they go a little further, and so on. There’s no point in traveling miles and miles away when there are still tasty roots and grubs nearby. But as time goes on, the radius of denuded ground will get wider and wider. Eventually, the foragers will have to embark on long expeditions with skilled guides just to make it to the nearest productive land.

He adds the idea that some people are taller (smarter) than others and can reach higher branches. There’s also a time limit (death), which means speed matters.

Scott explains that this model makes some predictions:

Early scientists should make more (and larger) discoveries than later scientists.

Early scientists should be relatively more likely to be amateurs; later scientists, professionals.

Early scientists should make discoveries younger (on average) than later scientists.

These trends should move more slowly for the most brilliant scientists.

These trends should fail to apply in fields of science that were impossible for previous generations

...
A simple guide to life
by jasoncrawford — 2022-04-03T18:09:18.264Z
Link: https://jasoncrawford.org/guide-to-life

I first made a version of this chart seven years ago today. It’s worth a re-up.

The meaning of this chart is:

Everything you do should be justified either by being inherently enjoyable, or by being important for some other purpose. Absolutely minimize activities that satisfy neither of these criteria: things that are neither fun nor important. (This seems obvious, but think of how often it’s violated: online flame wars, doomscrolling and general overconsumption of news, long sob stories about trivial inconveniences, endless stewing over long-ago wrongs, etc.)
Spend the vast majority of your time on things that are both enjoyable and important, such as (hopefully) career and family. Some time on chores, taxes, etc. is unavoidable. Some time on games and diversions is fine. But both should be small relative to the big, meaningful, deeply rewarding things.

(And just to anticipate one reaction: if you enjoy arguments on the Internet, then they can go under “fun and games”.)

It’s not a complete guide to life, but it’s important and something I apply often.