trentmkelly/LessWrong-43k huggingface dataset:
Why correlation, though?
by numpyNaN — 2024-03-06T16:53:09.345Z

This is a very basic question to ask, but I'm not sure I actually understand some fundamental properties people seem to ascribe to correlation.

As far as I understand it, correlation usually refers to Pearson's correlation coefficient, which is, according to Wikipedia, "a correlation coefficient that measures linear correlation between two sets of data." Cool.

But then I see a discussion on whether variables X and Y are related in some way, and reads like:

well, X correlates to Y with r=0.8 so it's a good predictor/proxy/whatever
X and Y have been found to correlate with r = 0.05, so its not a good predictor/proxy/whatever

For the first one, I'm OK. For the second one, not so much:

looking at Anscombe's Quartet:

It would seem that even though correlation (and therefore R²) are the same for all 4 datasets, the upper-rightmost one could be an Y completely dependent on X, probably a polynomial of some kind (in which case, Y could be perfectly explained by X), whereas in the down-rightmost one Y couldn't be explained as a function of X, of any kind.

Now, I understand that correlation only measures how linearly two variables are related, but again, in the same example, it would seem that we would be better served by considering other, non-linear ways they could be related.

Since correlation is such an extended way of measuring the relationships of X and Y, across many levels of competence and certainly among people I know understand this much better than I do, my questions are:

Why is, besides being the most nice/common/useful type of relation, linear relationship privileged in the way I described above? Why is it OK to say that X is not a good predictor of Y because r=0.05, which I'm understanding as "It has a bad linear relation", without adressing other ways they could be related, such as a grade 27 polynimial? Is the fact that they are "badly" linearly related enough to explain that they won't be related any other way?

Again, this a very basic lagoon I've just recently found on myself, so an explanation on any level would be very appreciated
Using axis lines for good or evil
by dynomight — 2024-03-06T14:47:10.989Z
Link: https://dynomight.net/axes/

Say you want to plot some data. You could just plot it by itself:

Or you could put lines on the left and bottom:

Or you could put lines everywhere:

Or you could be weird:

Which is right? Many people treat this as an aesthetic choice. But I’d like to suggest an unambiguous rule.

Principles

First, try to accept that all axis lines are optional. I promise that readers will recognize a plot even without lines around it.

So consider these plots:

Which is better? I claim this depends on what you’re plotting. To answer, mentally picture these arrows:

Now, ask yourself, are the lengths of these arrows meaningful? When you draw that horizontal line, you invite people to compare those lengths.

You use the same principle for deciding if you should draw a y-axis line. As yourself if people should be comparing the lengths:

Years vs. GDP

Suppose your data is how the GDP of some country changed over time, so the x-axis is years and the y-axis is GDP.

You could draw either axis or not. So which of these four plots is acceptable?

Got your answers? Here’s a key:

Why?

GDP is an absolute quantity. If GDP doubles, then that means something. So readers should be thinking about the distance between the curve and the x-axis.

But 1980 is arbitrary. When comparing 2020 to 2000, all that matters is that they’re 20 years apart. No one cares that “2020 is twice as far from 1980 as 2000” because time did not start in 1980.

Years vs. GDP again

Say you have years and GDP again, except all the GDP numbers are much larger—instead of varying between 0 and $3T, they vary between $50T and $53T.

What to do? In principle you could stretch the y-axis all the way down to zero.

But that doesn’t seem like a good idea—you can barely see anything.

Sometimes you need to start the y-axis at $50T. That’s fine. (As long as you’re not using a bar chart.) But then, the right answer changes.

The difference is that $50T isn’t a meaningful baseline. You don’t want people comparing things like (GDP in 1980 - $50T) vs. (GDP in 2000 - $50T) because that ratio doesn’t mean anything.

Years vs temperature

What if the y-axis were temperature? Should you draw a line along the x-axis at zero?

If the temperature is in Kelvin, then probably yes.

If the temperature is in Fahrenheit, then no. No one cares about the difference between the current temperature and the freezing point of some brine that Daniel Fahrenheit may or may not have made.

If the te...
Let's build definitely-not-conscious AI
by lemonhope — 2024-03-06T07:50:01.880Z

Let's build AI with constant run time.

Let's build AI without memory.

Let's build AI that doesn't want to do anything.

Let's build AI like a hammer rather than a DoAnythingNow with hammer skills.

If you insist that you need DoAnythingNow then set up some extremely streamlined data generation & training processes, so it can do anything when you ask it, rather than learning everything in advance just in case.

Most of the stuff I ask GPT etc for doesn't really need natural language. Natural language is not even a very good interface for eg most code stuff. I would much rather click a "fix bug" button.

The first general AIs learned all of everything in advance, but I don't see any good reason to keep doing that. I see lots of good reasons to eg learn on demand and then forget.

Let's build a tool that feels like a tool and works like a tool. A magic shapeshifting tool is ok. But I don't want a slave.

I don't need my coding assistant to grok the feelings and motivations of every Shakespeare character in depth. A customer service bot will need some basic people skills but maybe skip reading Machiavelli.

Let's build AI that can only do what we need it to do.
Movie posters
by KatjaGrace — 2024-03-06T06:20:03.034Z

Life involves anticipations. Hopes, dreads, lookings forward.

Looking forward and hoping seem pretty nice, but people are often wary of them, because hoping and then having your hopes fold can be miserable to the point of offsetting the original hope’s sweetness.

Even with very minor hopes: he who has harbored an inchoate desire to eat ice cream all day, coming home to find no ice cream in the freezer, may be more miffed than he who never tasted such hopes.

And this problem is made worse by that old fact that reality is just never like how you imagined it. If you fantasize, you can safely bet that whatever the future is is not your fantasy.

I have never suffered from any of this enough to put me off hoping and dreaming one noticable iota, but the gap between high hopes and reality can still hurt.

I sometimes like to think about these valenced imaginings of the future in a different way from that which comes naturally. I think of them as ‘movie posters’.

When you look fondly on a possible future thing, you have an image of it in your mind, and you like the image.

The image isn’t the real thing. It’s its own thing. It’s like a movie poster for the real thing.

Looking at a movie poster just isn’t like watching the movie. Not just because it’s shorter—it’s just totally different—in style, in content, in being a still image rather than a two hour video. You can like the movie poster or not totally independently of liking the movie.

It’s fine to like the movie poster for living in New York and not like the movie. You don’t even have to stop liking the poster. It’s fine to adore the movie poster for ‘marrying Bob’ and not want to see the movie. If you thrill at the movie poster for ‘starting a startup’, it just doesn’t tell you much about how the movie will be for you. It doesn’t mean you should like it, or that you have to try to do it, or are a failure if you love the movie poster your whole life and never go. (It’s like five thousand hours long, after all.)

This should happen a lot. A lot of movie posters should look great, and you should decide not to see the movies.

A person who looks fondly on the movie poster for ‘having children’ while being perpetually childless could see themselves as a sad creature reaching in vain for something they may not get. Or they could see themselves as right there with an image that is theirs, that they have and love. And that they can nev...
We Inspected Every Head In GPT-2 Small using SAEs So You Don’t Have To
by robertzk — 2024-03-06T05:03:09.639Z

This is an interim report that we are currently building on. We hope this update will be useful to related research occurring in parallel. Produced as part of the

ML Alignment & Theory Scholars Program

- Winter 2023-24 Cohort

Executive Summary

In a previous post we trained

attention SAEs

on

every layer of GPT-2 Small

and we found that a majority of features are interpretable in all layers. We’ve since leveraged our SAEs as a tool to explore individual attention heads through the lens of SAE features.
Using our SAEs, we inspect the roles of every attention head in GPT-2 small, discovering a wide range of previously unidentified behaviors. We manually examined every one of the 144 attention heads and provide brief descriptions in

this spreadsheet

. We note that this is a rough heuristic to get a sense of the most salient effects of a head and likely does not capture their role completely.

We observe that features become more abstract up to layer 9 and then less so after that. We performed this by interpreting and conceptually grouping the top 10 features attributed to all 144 heads.
Working from bottom to top layers, 39 of the 144 heads expressed surprising feature groupings not seen before in a previous head.
We provide

feature dashboards

for each attention head.

To validate that our technique captures legitimate phenomena rather than spurious behaviors, we verified that our interpretations are consistent with previously studied heads in GPT-2 small. These include induction heads, previous token heads, successor heads and duplicate token heads. We note that our annotator mostly did not know a priori which heads had previously been studied.
To demonstrate that our SAEs can enable novel interpretability insights, we leverage our SAEs to develop a deeper understanding of why there are two induction heads in Layer 5. We show that one does standard induction and the other does “long prefix” induction.
We use our technique to investigate the prevalence of attention head polysemanticity. We think that the vast majority of heads (>90%) are performing multiple tasks, but also narrow down a set of 14 candidate heads that are plausibly monosemantic.

Introduction

In

previous work

, we trained and open sourced a set of attention SAEs on all 12 layers of GPT-2 Small. We found that random SAE features in each layer wer...
Does anyone know good essays on how different AI timelines will affect asset prices?
by Tim Liptrot — 2024-03-06T04:21:35.134Z

I often see comments that AI will increase the value of fixed-supply status-enhancing goods, like real estate in downtown Manhattan. I believe I have seen both Roko and Marginal Revolution make that claim.

It also makes sense to think that many firms that specialize in providing automatable services stand to lose value. Perhaps all the companies that specialize in services stand to lose value, which would be all the tech companies.

I'll start: Robin Hanson's book "Age of Em" should have some chapters relevant to this question.

I'm also interested in the practical side of hedging a portfolio against AI risk.
My Clients, The Liars
by ymeskhout — 2024-03-05T21:06:36.669Z

It’s not just that my clients lie to me a lot, which will only hurt them — it’s that they’re really, really bad at it.

My job as a public defender puts me in a weird place. I am my clients’ zealous advocate, but I’m not their marionette. I don’t just roll into court to parrot whatever my clients tell me — I make sure I’m not re-shoveling bullshit. So for my sake and theirs, I do my homework. I corroborate. I investigate.

A significant portion of my job ironically mirrors that of a police detective. Every case I get requires me to deploy a microscope and retrace the cops’ steps to see if they fucked up somehow (spoiler: they haven’t). Sometimes I go beyond what the cops did to collect my own evidence and track down my own witnesses.

All this puts some of my clients of the guilty persuasion in a bind. Sure, they don’t want me sitting on my ass doing nothing for their case, but they also can’t have me snooping around on my own too much. . . because who knows what I might find? So they take steps to surreptitiously install guardrails around my scrutiny, hoping I won’t notice.

You might wonder why any chicanery from my clients is warranted. After all, am I not professionally obligated to strictly maintain client confidentiality? It’s true, a client can show me where they buried their dozen murder victims and I wouldn’t be allowed to tell a soul, even if an innocent person is sitting in prison for their crimes. Part of my clients’ clammed-up demeanors rests on a deluded notion that I won’t fight as hard for their cases unless I am infatuated by their innocence. Perhaps they don’t realize that representing the guilty is the overwhelmingly banal reality of my job.[1] More importantly, it’s myopic to forget that judges, prosecutors, and jurors want to see proof, not just emphatic assurances on the matter.

But clients still lie to me — exclusively to their own detriment

Marcel was not allowed to possess a firearm. And yet mysteriously, when the police arrested him — the details are way too complicated to explain, even by my standards — in his sister’s vehicle, they found a pistol under the passenger seat.

“The gun is not mine. I don’t even like guns. I’m actually scared of guns.” He told me this through the jail plexiglass as I flipped through his remarkable résumé of gun-related crimes. Marcel spent our entir...
Making Connections with ChatGPT: The Macksey Game 
by Bill Benzon — 2024-03-05T18:15:52.342Z

Cross-posted from New Savanna.

In my most recent piece for 3 Quarks Daily, Western Metaphysics is Imploding. Will We Raise a Phoenix from The Ashes? [Catalytic AI], I introduce what I’ve called The Macksey Game, named after my undergraduate teacher and mentor, Dick Macksey. Macksey was something of a polymath. It seemed like he’s read everydamnthing. No one has, of course, note even the Chatster – not everything that’s been written down is on the internet. But it seemed like it to me, coming from a small steel city in Western Pennsylvania, Johnstown.

Macksey had read a lot, and could call it up at will and often at the most unexpected times. He could ramble on from topic to topic in a way that fascinated me. Here’s how I put it in the 3QD piece:

I often thought that, if you picked any two topics, Macksey could create a bridge between the two in no more than five conceptual jumps. Quantum entanglement to Paul Bocuse, Ernest Shackleton to The Tale of Genji, Pareto efficiency to C. Elegans – leaps like that. I never tested it out, but it just seemed like the way Macksey’s mind worked.

And so The Macksey Game was born. I gave ChatGPT those three pairs of endpoints and asked it to construct a conceptual chain between the ends. I put the result into an appendix to the article. But of course I didn’t leave it at that. I had to tinker around.

That’s what this post is about, the original game and a bit of tinkering around. First, I present the first time through, just as I listed it in the 3QD piece. Then I offer some variations.

The Macksey Game: Queen to Newton

This is the first time I played the game. This is the version I appended to my 3 Quarks Daily article. As always, my prompt is in boldface while ChatGPT’s response is plainface.

_______

If someone asked me to make conceptual connections between, say, Queen Elizabeth and the Voyager spacecraft, I might do it like this:

1. Elizabeth was Queen of England, the home of Isaac Newton.
2. Newton was a physicist who formulated 3 laws of motion and the law of gravitation.
3. The laws of motion are important in understanding how rockets work.
4. The law of gravitation tells us how to track the motions of the planets and bodies moving through the solar system.
5. NASA used both the laws of motion and the law of gravitation in the Voyager mission.

I want you to do the same thing with, say, Quantum entanglement and Paul Bocuse. Construc...
Good taxonomies of all risks (small or large) from AI?
by Aryeh Englander — 2024-03-05T18:15:29.933Z

Are there any good taxonomies or categorizations of risks from AI-enabled systems (broadly defined) that aren't focused solely on risks to society as a whole / global catastrophic risks? Ideally the taxonomy should cover things like accident risks from individual factory robots, algorithmic bias against individuals or groups, privacy and cybersecurity issues, misuse by hackers or terrorists, incidental job loss due to AI, etc. It should ideally also cover the big society-wide or global catastrophic risks, just that it shouldn't only be about those.
Making 2023 ACX Prediction Results Public
by Legionnaire — 2024-03-05T17:56:18.437Z

They say you shouldn't roll your own encryption, which is why I'm posting this here, so it can be unrolled if it's too unsafe.

Problem: Astral Codex Ten finished scoring the 2023 prediction results, but the primary identifier most used for people's score was their email address. Since people wouldn't want those published, what's an easy way to get people their score?

You could email everyone, but then you have to interact with an email server, and then nobody can do cool analysis of the scores and whatever other data is the document.

My proposal:

There are ~10,000 email addresses. Hash the passwords using a hash that only maps to ~10 million values.
Replace the emails in the document with the hashes. Writing a python script to this could be done in a few minutes.
Give everyone access to that file.

If you know the email address of a participant, it's trivial to check their score. And if you forgot which email address you used, just try each one! Odds are you will not have had a collision.

But at the same time, with 8 billion email addresses worldwide, any given hash in the document should collide with ~1000 other email addresses (because the 10,000 real addresses will have used 0.1% of the space of the hash output), meaning you can't just brute force and figure out each persons address. Out of the 8 billion real addresses you try, ~8 million will be real and appear hashed in the document, but only 10,000 of those (~0.1%) will be the originals. So finding an address-hash that appears in the document is highly unlikely to be the actual address of the participant.

If there are a few victims of the birthday paradox, they could probably just email request for their line number in the document. It may be better to use a larger hash space to avoid an internal (in the data set) collisions, but then you lower the number of external collisions. My back of the envelope expects at least several collisions with a 10 mil output space. 100 mil makes it 0 or 1.

Which hash? Not sure. Maybe SHA256 then just delete N characters off the end until the space is ~10,000,000?

Please discuss how safe/unsafe this is. Thanks for your time.
Social status part 2/2: everything else
by Steven Byrnes — 2024-03-05T16:29:19.072Z

2.1 Post summary / Table of contents

This is the second of two blog posts where I try to make sense of the whole universe of social-status-related behaviors and phenomena. The previous one was: “Social status part 1/2: negotiations over object-level preferences”.

In that previous post, I was focusing on the simplified special case of two people interacting, where they have different “object-level preferences”, but where we ignore their higher-level preferences regarding the interaction itself (e.g. the desire not to be rude). In that post, I offered a series of vignettes and toy models, covering the ideas of “mostly leading” versus “mostly following”, “ask versus guess culture”, “pushiness”, and some other topics.

Now that we have that foundation, we’re ready to talk about the universe of social behaviors built on top of that basic dynamic, including dominance-versus-prestige, getting offended, passive-aggressiveness, status, self-deprecation, and more.

This post is organized as follows:

Section 2.2 surveys the wide variety of reasons why people might want to “mostly lead” or “mostly follow” in an interaction. I offer six categories. First, leading is nice for the obvious reason that you get to fulfill your object-level desires (see previous post). Second, it’s possible that there is an “innate drive” to lead. Third, you may want to “lead” or “follow” if you think your object-level desires are better- or worse-informed than the other person’s, respectively. Fourth, you may want to “follow” due to general admiration of the other person, or “lead” due to general scorn of the other person. Fifth, you may want to “follow” to avoid blame, or “lead” to get credit. Sixth, you may want to accommodate (or undermine) the other person’s preference to lead or follow.
Section 2.3 introduces “dominance versus prestige” of

Dual Strategies Theory

, and how they relate to the idea of “mostly leading” versus “mostly following” from the previous post. Specifically, “Alice is being dominant” means: Alice is “mostly leading”, and Beth wishes that Alice wouldn’t lead so much, but Beth doesn’t do anything about it because she’s afraid of offending Alice. By contrast “Alice has prestige” means: Beth directly wants to “follow”, and wants Alice to lead, because Beth likes and admires Alice.
Section 2.4, following

this 2018 blog post

, defines “status” as a tacit shared understa

...