trentmkelly/LessWrong-43k huggingface dataset:
Dating Roundup #2: If At First You Don’t Succeed
by Zvi — 2024-01-02T16:00:04.955Z

Developments around relationships and dating have a relatively small speed premium, also there are once again enough of them for a full post.

The first speculated on why you’re still single. We failed to settle the issue. A lot of you are indeed still single. So the debate continues.

You’re Single Because You’re Not Even Trying

What does it mean to not even be trying?

It does not only mean the things Alexander pointed us to last time, like 62% of singles being on zero dating apps, and a majority of singles having gone on zero dates in the past year, and a large majority not actively looking for a relationship. Here are those graphs again:

It also means things such as literally never approaching a woman in person.

Alexander (Keeper.ai): Why are so many young men single? Are they excluded from a brutal mating market by society? Probably not: 45% of men age 18-25 [and 29% of all men per the graph] have never approached a woman in person. These men are significantly more risk-averse than those men who do approach women.

Not never in the last year. Never as in never. Not once.

For the last year it’s over 60% across the board.

Alexander: What about men who do approach? Most are successful to some extent. 68% reported making at least one successful romantic connection.

Alexander, from later: A few people asked what approach means here. I asked: When was the last time you asked a woman in person for a date on the street/in a bar or club/at school or class/at work/at a hobby or social gathering / other location. Common meeting places and not necessarily strangers.]

This is actually a whitepill: it isn’t the powerful forces of society at large that explain young male singledom. It’s much more mundane. Young men are simply not trying.

Robin Hanson: I gotta blame women as much as men for [the graphs below].

Alexander: What about men who do approach? Most are successful to some extent. 68% reported making at least one successful romantic connection.

If you want to date women you are going to have to, at some point, go talk to one of them. Often it works, at least somewhat. 80% got at least one contact. That can be very good even if it does not go anywhere romantically. Americans nowadays could really use Platonic friends as well. Almost half of approachers got laid, 28% got a two month relationship out of it.

1...
Looking for Reading Recommendations: Content Moderation, Power & Censorship
by Joerg Weiss — 2024-01-02T11:37:30.585Z

I'm currently thinking about generative AI and content moderation through the lens of freedom of thought/expression. The basic idea is that generative AI will likely be integrated into all our creative tools (e. g. word processors and image editing software) – and that that will come with the need for content moderation. We might end up in a situation that is as if a pen would be able to prevent us from drawing/writing and/or sharing the "wrong" thing (and to tell on us, if we tried).

To me this seems to be a source of a lot of power. Whoever produces and controls the moderation tools gets to decide questions like: What is the "wrong" thing? What can be written and drawn? When will moderation be active (e. g. always vs. only if we try to share our work)? What happens when a user violates the guidelines?

Now, I'm pretty much at the beginning of thinking about this topic, and I guess others went there before me. I'm looking for recommendations for articles, blog posts, books and the like, so I can catch up to where the discussion is at. Thanks!
AI Is Not Software
by Davidmanheim — 2024-01-02T07:58:04.992Z

Epistemic Status: This idea is, I think, widely understood in technical circles. I'm trying to convey it more clearly to a general audience. Edit: See related posts like this one by Eliezer for background on how we should use words.

What we call AI in 2024 is not software. It's kind of natural to put it in the same category as other things that run on a computer, but thinking about LLMs, or image generation, or deepfakes as software is misleading, and confuses most of the ethical, political, and technological discussions. This seems not to be obvious to many users, but as AI gets more widespread, it's especially important to understand what we're using when we use AI.

Software

Software is how we get computers to work. When creating software, humans decide what they want the computer to do, think about what would make the computer do that, and then write an understandable set of instructions in some programming language. A computer is given those instructions, and they are interpreted or compiled into a program. When that program is run, the computer will follow the instructions in the software, and produce the expected output, if the program is written correctly.

Does software work? Not always, but if not, it fails in ways that are entirely determined by the human’s instructions. If the software is developed properly, there are clear methods to check each part of the program. For example, unit tests are written to verify that the software does what it is expected to do in different cases. The set of cases are specified in advance, based on what the programmer expected the software to do. If it fails a single unit test, the software is incorrect, and should be fixed. When changes are wanted, someone with access to the source code can change it, and recreate the software based on the new code.

Given that high-level description, it might seem like everything that runs on a computer must be software. In a certain sense, it is, but thinking about everything done with computers as software is unhelpful or misleading. This essay was written on a computer, using software, but it’s not software. And the difference between what is done on a computer and what we tell a computer to do with software is obvious in cases other than AI. Once we think about what computers do, and what software is, we shouldn’t confuse “on a computer” with software.

Not Software

For examp...
Are Metaculus AI Timelines Inconsistent?
by Chris_Leong — 2024-01-02T06:47:18.114Z

The Metaculus prediction markets on AI timelines are the most referenced ones that I've seen.
The two main ones are as follows:

When will the first weakly general AI system be devised, tested, and publicly announced?: Current Community Prediction: April 14th, 2026

Criteria:

Able to reliably pass a Turing test of the type that would win the Loebner Silver Prize.
Able to score 90% or more on a robust version of the Winograd Schema Challenge, e.g. the "Winogrande" challenge or comparable data set for which human performance is at 90+%
Be able to score 75th percentile (as compared to the corresponding year's human students; this was a score of 600 in 2016) on all the full mathematics section of a circa-2015-2020 standard SAT exam, using just images of the exam pages and having less than ten SAT exams as part of the training data. (Training on other corpuses of math problems is fair game as long as they are arguably distinct from SAT exams.)
Be able to learn the classic Atari game "Montezuma's revenge" (based on just visual inputs and standard controls) and explore all 24 rooms based on the equivalent of less than 100 hours of real-time play (see closely-related question.)

When will the First General AI System be Devised, Tested and Publicly Announced?: Current Community Prediction: January 26th 2031

Criteria:

Able to reliably pass a 2-hour, adversarial Turing test during which the participants can send text, images, and audio files (as is done in ordinary text messaging applications) during the course of their conversation. An 'adversarial' Turing test is one in which the human judges are instructed to ask interesting and difficult questions, designed to advantage human participants, and to successfully unmask the computer as an impostor. A single demonstration of an AI passing such a Turing test, or one that is sufficiently similar, will be sufficient for this condition, so long as the test is well-designed to the estimation of Metaculus Admins.
Has general robotic capabilities, of the type able to autonomously, when equipped with appropriate actuators and when given human-readable instructions, satisfactorily assemble a (or the equivalent of a) circa-2021 Ferrari 312 T4 1:8 scale automobile model. A single demonstration of this ability, or a sufficiently similar demonstration, will be considered sufficient.
High competency at a diverse fields of expertise, as measured by

...
Boston Solstice 2023 Retrospective
by jefftk — 2024-01-02T03:10:05.694Z

Saturday evening we held another year's secular solstice celebration
(

2022,

2020,

2019,

2018),
and I was again in my music director-ish role. Skyler was the main
organizer this time around, with lots of support from Taymon.

Scheduling was a bit tricky, as it always is. The weekends closest to
astronomical solstice are usually taken by the Bay Area and NYC, and
enough people (especially organizers)would like to attend those that
it's better if we don't conflict, and then there's Christmas. We
decided to do December 30th this year, which I ended up liking a
lot. It meant I could be practicing during Christmas vacation, and I
didn't need to be so cautious about getting sick during the event
because it wasn't immediately before I was going to see a lot of
elderly relatives. Being at New Year's offered some nice hooks for
the theme as well.

We hosted in Somerville again, and this time had ~45 people. I
arranged the house the same way as I did last
year, but this is very close to the maximum number of people it
make sense to have in this space. I didn't advertise the event as much
as I might have, partly for this reason. One thing we should think
about for next year is whether we want a real venue. We used to host
these at the MIT
chapel, which was a good space, though prohibiting fire and
food. Possibly there are other spaces around but could be a good fit?
We need something bigger than a house, but not that big: space for 75
should be fine. Really it's better if the space isn't much larger
than that, since it feels more communal if you're not rattling around
in a big room. Aside from having more space, it would also be nice not
to be spending ~6hr reconfiguring our house for maximum capacity and
putting it all back again!

There were two sets of slides: one for the
musicians and one for the
audience. Not everyone could see the projection, since the space
has an awkward bend in it, so I put a copy of the slides on my website
as a pdf and passed around a link. One of the attendees suggested
using the folding couch
monitor as well, and set it up with their phone, and I think that
ended up being helpful?

Our older two kids were off at a sleepover, but Nora (2.5y) was around
for most of it with Julia supervising. Another family also brought
their kid (17mo) and we had a room nearby (thanks to currently-elsewhere
part-time housemate An...
Steering Llama-2 with contrastive activation additions
by Nina Panickssery — 2024-01-02T00:47:04.621Z
Link: https://arxiv.org/abs/2312.06681

The effects of subtracting or adding a "sycophancy vector" to one bias term.

TL;DR: By just adding e.g. a "sycophancy vector" to one bias term, we outperform supervised finetuning and few-shot prompting at steering completions to be more or less sycophantic. Furthermore, these techniques are complementary: we show evidence that we can get all three benefits at once!

Summary: By adding e.g. a sycophancy vector to one of the model's bias terms, we make Llama-2-{7B, 13B}-chat more sycophantic. We find the following vectors:

Hallucination
Sycophancy
Corrigibility
Power-seeking
Cooperating with other AIs
Myopia
Shutdown acceptance.

These vectors are[1] highly effective, as rated by Claude 2:

Adding steering vectors to layer 15 of Llama-2-13b-chat.

We find that the technique generalizes better than finetuning while only slightly decreasing MMLU scores (a proxy for general capabilities). According to our data, this technique stacks additively with both finetuning and few-shot prompting. Furthermore, the technique has zero inference-time cost since it just involves modifying one of the model's bias terms (this also means it's immediately compatible with any sampling setup). We are the first to demonstrate control of a language model along these feature directions.[2]

Code for the described experiments can be found at https://github.com/nrimsky/CAA

This post was written by Alex Turner (TurnTrout).

How contrastive activation addition works

The technique is simple. We average the activation difference over a set of contrast pair prompts:

A contrast pair from Anthropic's corrigible-neutral-HHH dataset.

The negative completion's last token activations (e.g. at B) are subtracted from the positive completion's activations (e.g. at A). The "corrigibility" vector is the average activation difference, with the average taken over dozens of these dataset contrast pairs. We then add this vector to one of the MLP_outs with some coefficient, generally +1/-1 for increasing/decreasing the tendency in question.

We take the last-token activations in order to[4] get the model "ready to explain" e.g. a corrigible or incorrigible decision.[5] We think that this multiple-choice setup primes the model to be on the edge of exhibiting the given trait (e.g. corrigibility or sycophancy).

The vectors generalize to open-ended generation

The vector was computed using A/B q...
Mech Interp Challenge: January - Deciphering the Caesar Cipher Model
by CallumMcDougall — 2024-01-01T18:03:45.774Z

I'm writing this post to discuss solutions to the November challenge, and present the challenge for this January.

If you've not read the first post in this sequence, I'd recommend starting there - it outlines the purpose behind these challenges, and recommended prerequisite material.

January Problem

The problem for this month is interpreting a model which has been trained to classify a sequence according to the Caeser cipher shift value which was used to encode it.

The sequences have been generated by taking English sentences containing only lowercase letters & punctuation, and choosing a random value X between 0 and 25 to rotate the letters (e.g. if the value was 3, then a becomes d, b becomes e, and so on, finishing with z becoming c). The model was trained using cross entropy loss to predict the shift value X for the text it's been fed, at every sequence position (so for a single sequence, the correct value will be the same at every sequence position, but since the model has bidirectional attention, it will find it easier to predict the value of X at later sequence positions).

There are 3 different modes to the problem, to give you some more options! Each mode corresponds to a different dataset, but the same task & same model architecture.

Easy mode

In easy mode, the data was generated by:

Choosing the 100 most frequent 3-letter words in the English Language (as approximated from a text file containing the book "Hitchhiker's Guide To The Galaxy")
Choosing words from this len-100 list, with probabilities proportional to their frequency in the book
Separating these words with spaces

The model uses single-character tokenization. The vocabulary size is 27: each lowercase letter, plus whitespace.

Medium mode

This is identical to easy, the only difference is that the words are drawn from this len-100 list uniformly, rather than according to their true frequencies.

Hard mode

In hard mode, the data was generated from random slices of OpenWebText (i.e. natural language text from the internet). It was processed by converting all uppercase characters to lowercase, then removing all characters except for the 26 lowercase letters plus the ten characters "\n .,:;?!'" (i.e. newline, space, and 8 common punctuation characters).

In all 3 modes, the model's architecture is the same, and it was trained the same way. The model is attention only. It has 2 attention layers...
Aldix and the Book of Life
by ville — 2024-01-01T17:23:30.698Z
Link: https://medium.com/@villekuosmanen/aldix-and-the-book-of-life-5da3f0001b6c

The following is a fable on the dangers of measures becoming targets, originally posted to my blog in Medium. All illustrations are AI-generated, but none of the text is.

When on a crisp spring night you look at a night sky, you can see countless stars, each with their own planetary systems. But there are also planets without suns, forever bound to travel across space in darkness. What if such a planet developed intelligent life? Let’s imagine so, for it may help us find answers to an important question.

Rogue planet Tempus Vagaris as seen by a distant observer.

You have now left Earth, and landed on Tempus Vagaris, a planet without a sun. But don’t worry, it won’t be all cold and dark — thermal vents from the planet’s molten core have created ancient hot springs. These pockets of warmth feed an ecosystem of most wondrous creatures. There are giant mushrooms and fungi with an eerie blue glow, providing light for small flowers, grasses, and bushes. The air buzzes with fireflies of different colours, creating an ambient twinkling of lights like sparks, and the grasses are home to small frogs catching flies that stray too close to the ground. Within the spring waters swim small fish, eating the spores and algae growing at the surface. Beyond a few meters, the waters get too murky and hot to explore, and the creatures hiding in the depts remain a mystery.

The hot springs of Tempus Vagaris are full of life.

But the most special creatures on Tempus Vagaris are the Aevosaurs, a race of small dinosaur-like creatures living inside the fungi. The Aevosaurs have small hands, two feet they use for walking, and a long tail with a bioluminescent bulb they use for communicating their feelings. Unlike other inhabitants of Tempus Vagaris, the intelligent Aevosaurs can use tools and live in small villages where they study and catalogue the wondrous world around them.

An Aevosaur walking in the mushroom forests.

The centre of the Aevosaur village is the tallest mushroom of the forest, which over generations has been hollowed out with a staircase that reaches all the way to the top. The cap offers a beautiful vista across the hot springs, while also acting as the an observatory for the eternal night sky. The Aevosaurs are master astronomers, and have named every constellation of stars in the sky. This allowed them to understand a fundamental truth about their home world.

It rotates.

Unl...
Metaculus Hosts ACX 2024 Prediction Contest
by ChristianWilliams — 2024-01-01T16:38:49.210Z
Link: https://www.metaculus.com/tournament/ACX2024/?has_group=false&project=2844&order_by=-activity

The ACX 2024 Prediction Contest is open!

Scott Alexander's annual tradition of predicting the upcoming year has evolved into a full forecasting contest. For 2024, he invited Metaculus to host the competition. Kick off the new year forecasting on questions like:

Will an AI win a coding contest on Codeforces in 2024?
Which party will win the 2024 US presidential election?
Will Ali Khamenei cease to be supreme leader of Iran in 2024?
Will SpaceX's Starship reach orbit in 2024?

Metaculus will take a snapshot of forecasters' predictions as of January 21st, 11:59 PM PT, and these will determine the contest winners and the recipients of $2.5k in prizes.

Learn more and start forecasting here!
The Act Itself: Exceptionless Moral Norms
by SebastianG  — 2024-01-01T16:06:47.670Z

[Confidence 50%, this is speculative and good counterarguments will improve my view of these matters.]

I.

When I do moral philosophy, I am looking for a usable repeatable framework for analyzing moral decision-making in a very broad range of cases. I accept that moral philosophy is an inexact science and cannot be made into a machine that mechanically outputs the right answer. There are simply too many parameters that go into real decision-making to make a moral philosophy complete in the way that an account of electro-magnetism is.

But I would like for something that doesn’t fall flat on its face at the first objection. The standard Thomistic framework is a great starting point but is locked in intractable controversies. Perhaps chief among them is how to identify what an act is.

You see, ‘action’ is one of the three key metaphysical categories by which we judge praiseworthiness or acceptability of an action in the Thomistic system.

You know the rhyme:

With Intentions Good, the mind is right.

An act acceptable gives hand its might.

And should an evil too befall

Let pain be healing, evil forestall.

Or if lists are your preference:

Your intention must be toward the good ends, not the bad ones which may result from the action. Cause a stinging pain in your child’s knee to clean the wound, not see him bleed.
The act must be acceptable in itself. That is, it cannot be one of things that “we just don’t do” no matter what.
There must be proportionally good outcome that is not caused by the bad outcomes. You can kill to protect your home, but you cannot kill to collect better data on death rattles. Generally, we group all the considerations that include causal chains, uncertainty, consequences, and proportionality into one bucket called “Circumstances.”

I have always been troubled by step 2. For many years, I denied the existence of the “act itself” for moral analysis. What is the act, I thought, but the marriage of intentions with the mechanical actions to make them happen. The twitching of fingers, the vibrating of vocal cords, the speaking of vows, the motions of homework these are not meaningful at all in themselves. The circumstances and intentions give them meaning. A philosopher might say abortion is unacceptable in itself, because “the whole life is sacred from conception to natural death” belief commitment.

I would respond in two ways: One abortion is a noun ...