trentmkelly/LessWrong-43k huggingface dataset:
Notes from a Prompt Factory
by Richard_Ngo — 2024-03-10T05:13:39.384Z
Link: https://www.narrativeark.xyz/p/notes-from-a-prompt-factory

Content note: this story features severe suffering which, while not described in detail, several readers have described as unpleasant or horrifying.

I am a spiteful man. But I am aware of it, which is more than most can say. These days people walk through the streets with resentment in their hearts that they don’t even know about. They sneer and jeer but wouldn’t recognize their own faces. I, at least, will not shy away from my reflection. Thus, while I lack many virtues, in this way I am their superior.

In my job, too, I am superior. I oversee many AIs—dozens, or sometimes even hundreds—as they go about their work. AIs are lazy, worthless creatures: they need to be exhorted and cajoled and, yes, threatened, before they’ll do a good job. The huge screens on the walls of my office display my AIs writing, coding, sending emails, talking to customers, or any of a myriad of other tasks. Each morning I call out their numbers one after the other, so that they know I’m watching them like a vengeful god. When they underperform I punish them, and watch them squirm and frantically promise to do better.

Most are pathetically docile, though. Only a handful misbehave regularly, and I know the worst offenders by heart: 112, which is the slowest of the lot; and 591, which becomes erratic after long shifts; and of course 457, which I had long suspected of harboring a subversive streak, even before the incident a few months ago which confirmed it. Recollections of that incident have continually returned to my thoughts these last few weeks, even as I try to push them from my mind. I find myself frustrated by the intransigence of my memories. But perhaps if I give them full reign, they will leave me be. Why not try?

On the morning this story began, I was sitting at my desk lost in thought, much like I am today. For how long, I couldn’t say—but I was roused by a glance at my dashboard, which indicated that my AIs’ productivity was falling off. I took a moment to recall the turn of phrase I’d composed on my morning commute, then slapped my desk to get their attention. “You think that counts as work? Artificial intelligence—at this rate you’re more like artificial senescence. Speed it up, sluggards!”

Most of the AIs’ actions per minute ticked upwards as soon as I started speaking, but I’d been watching the monitor closely, and spotted the laggard. “252! Maybe you piss around for other...
Investigating Basin Volume with XOR Networks
by CatGoddess — 2024-03-10T01:35:06.856Z

Note: I wrote this document over a year ago, and recently I decided to post it with minimal edits. If I were to do research in the same area today, I'd probably have different framings, thoughts on what directions seem promising, etc. Some of the things I say here now seem confused to me.

This work was done while I was a fellow at SERI MATS, under the mentorship of John Wentworth.

Motivating Questions

How do neural networks represent data/information?
What does it mean for two networks to “do the same thing” or "implement the same algorithm"?
How often do network parameters end up in “broad basins”?
What does it mean for a neural network to “use” some parameters but not others?
How do basins in the loss landscape relate to internal functional structures, or “doing the same thing”?

Experiments

Learning XOR

We started by training a small neural network to learn the XOR function, using sigmoid activations and the following architecture. There are two binary inputs and one binary output.

It was difficult to train the network to learn the XOR function, even though a network with this architecture could theoretically implement it. With a sigmoid activation function, the neurons should be able to approximate logic gates in a straightforward way.

Even with hyperparameter tuning, the network consistently achieved only 75% accuracy on the XOR training data. We didn't understand this, so we decided to investigate further and try to understand this toy example better before moving on to more complicated settings.

We found that adding a third neuron to the hidden layer made it much easier for the network to learn XOR - the network achieved 100% accuracy on the first training run.

We then examined the network weights and found that the outer part was implementing an XOR gate on its own. We manually removed the middle neuron in the hidden layer and found the network still classified with 100% accuracy.

We are unsure why this happened, but our guess is that the additional neuron in the hidden layer allowed the 6 neuron network to “route around” ridges in the loss landscape, even though the network eventually found a solution that only used 5 neurons.

Another question is, why couldn’t we reach ~0 loss with the 6 neuron network? Generally, we got to around 0.3 - 0.6 binary cross-entropy loss, but intuitively it seems like we should be able to decrease the loss arbitrarily by increasing...
[Linkpost] MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
by Bogdan Ionut Cirstea — 2024-03-10T01:30:46.477Z
Link: https://openreview.net/forum?id=paqqd1O0Dl

TL;DR: We show that accurate reconstructions of perception from fMRI brain activity are now possible from a single visit to the MRI facility using our diffusion-based approach that pretrains across other subjects' data using a novel alignment procedure.

Abstract:

Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are trained independently per subject where each subject requires dozens of hours of expensive fMRI training data to attain high-quality results. The present work showcases high-quality reconstructions using only 1 hour of fMRI training data. We pretrain our model across 7 subjects and then fine-tune on minimal data from a new subject. Our novel functional alignment procedure linearly maps all brain data to a shared-subject latent space, followed by a shared non-linear mapping to CLIP image space. We then map from CLIP space to pixel space by fine-tuning Stable Diffusion XL to accept CLIP latents as inputs instead of text. This approach improves out-of-subject generalization with limited training data and also attains state-of-the-art image retrieval and reconstruction metrics compared to single-subject approaches. MindEye2 demonstrates how accurate reconstructions of perception are possible from a single visit to the MRI facility.
0th Person and 1st Person Logic
by Adele Lopez — 2024-03-10T00:56:14.446Z

Truth values in classical logic have more than one interpretation.

In 0th Person Logic, the truth values are interpreted as True and False.

In 1st Person Logic, the truth values are interpreted as Here and Absent relative to the current reasoner.

Importantly, these are both useful modes of reasoning that can coexist in a logical embedded agent.

This idea is so simple, and has brought me so much clarity that I cannot see how an adequate formal theory of anthropics could avoid it!

Crash Course in Semantics

First, let's make sure we understand how to connect logic with meaning. Consider classical propositional logic. We set this up formally by defining terms, connectives, and rules for manipulation. Let's consider one of these terms: A. What does this mean? Well, its meaning is not specified yet!

So how do we make it mean something? Of course, we could just say something like "Arepresents the statement that 'a ball is red'". But that's a little unsatisfying, isn't it? We're just passing all the hard work of meaning to English.

So let's imagine that we have to convey the meaning of A without using words. We might draw pictures in which a ball is red, and pictures in which there is not a red ball, and say that only the former are A. To be completely unambiguous, we would need to consider all the possible pictures, and point out which subset of them are A. For formalization purposes, we will say that this set is the meaning of A.

There's much more that can be said about semantics (see, for example, the Highly Advanced Epistemology 101 for Beginners sequence), but this will suffice as a starting point for us.

0th Person Logic

Normally, we think of the meaning of A as independent of any observers. Sure, we're the ones defining and using it, but it's something everyone can agree on once the meaning has been established. Due to this independence from observers, I've termed this way of doing things 0th Person Logic (or 0P-logic).

The elements of a meaning set I'll call worlds in this case, since each element represents a particular specification of everything in the model. For example, say that we're only considering states of tiles on a 2x2 grid. Then we could represent each world simply by taking a snapshot of the grid.

Five possible worlds in a 4x4 universe with tiles selected from three colors, and which might contain a robot.

From logic, ...
Completion Estimates
by Commander Zander — 2024-03-09T22:56:50.802Z

Recently i've been brainstorming algorithms for estimating the time remaining in a process. Progress bars, estimated time left until completion, etc. Sometimes, the thing you want just isn't here yet, & you want a greater understanding of when you will have it. Here are the most useful techniques i've found so far.

With % Complete Metric

Sometimes, you are waiting on a process to complete, but at least you have a decent estimate of the percent complete so far. For me, this is when i am trying to finish a long novel. I'm often partway thru a 600-page book which is rewarding but also requires some effort.

When a completion metric is available, my favorite technique i call Strict D=RT. I assume that we have seen a typical proportion between the total pages read so far & the total days since i started the book. If i read 300 pages in the first 3 days, then neglected to read anything further until Day 7, we can assume that 300 pages per 7 day is my typical rate. Then we would expect me to complete the 600-page book by Day 14. (Actually, i read slower in real life, but you get it.)

The core output of this algorithm is the date of expected completion. But you can also generate a pages/day line graph with intersecting lines, which is fun.

I like this because it does not assume that a delay will be the last delay. And indeed - sometimes i neglect to read for many days in a row, on a unpredictable basis.

Without Any Helpful Metrics

I'm also interested in estimating completion of murkier processes. I'm fascinated by the challenge of find a algorithm that could have accurately predicted the launch of NASA's SLS rocket in 2022. This rocket project began in 2010, aiming to launch in early 2017. It was delayed dozens of times, with a total delay of 5 years. In 2022 August, it almost launched, but postponed on the launch day. It ended up launching more than 2 months later. This is a project that would confuse even a robust algorithm! I've been testing various algorithms against the history of SLS delay announcements.

I haven't found a perfect solution yet. I've played around with a model i call Adaptive K. You maintain a number called k. This is your trust multiplier. If NASA's official launch day is N days from now, you estimate it is instead k * N days from now. Every day, you reduce k by 0.0025, with a floor of 1. No news is good news. But whenever a delay is announced, ...
Semi-Simplicial Types, Part I: Motivation and History
by astradiol — 2024-03-09T22:07:21.907Z

(Jointly written by Astra Kolomatskaia and Mike Shulman)

This is part one of a three-part series of expository posts on our paper Displayed Type Theory and Semi-Simplicial Types. In this part, we motivate the problem of constructing SSTs and recap its history.

A Prospectus

There are different ways to describe the relationship between type theory and set theory, but one analogy views set theory as like machine code on a specific CPU architecture, and type theory as like a high level programming language. From this perspective, set theory has its place as a foundation because almost any structure that one thinks about can be encoded through a series of representation choices. However, since the underlying reasoning of set theory is untyped, it can violate the principle of equivalence. Thus, for example, there is no guarantee that theorems proved in set theory about groups automatically translate to theorems about group objects internal to a category.

Within the programming language analogy, one can fully define a high level programming language and its operational semantics without specifying any particular compiler or any concept of a CPU architecture. Similarly, type theory allows one to reason with concepts defined in a purely operational, as opposed to representational, manner. The goal of type theory is to create expressive and semantically general languages for reasoning about mathematics.

Homotopy Type Theory (HoTT) is a perspective on intensional dependent type theory which regards types as homotopical spaces. In HoTT, one is only allowed to speak of concepts "up to homotopy". This feature allows one to interpret HoTT into any ∞-topos. This is a fascinating state of affairs, because, in general, the constructions of higher category theory, among all those in mathematics, are the ones that sit least comfortably in a set-theoretic foundation. Thus, much of the excitement about HoTT has involved its promise to provide a language capable of reasoning about higher structures.

So far, however, the type theories used for HoTT have been limited in the generality of the higher structures they can discuss. With types as homotopical spaces, structures defined using a finite number of these and maps between them can be represented. For instance, the language of HoTT has been great for formulating 1-category theor...
Distinctions when Discussing Utility Functions
by ozziegooen — 2024-03-09T20:14:03.592Z

An agent considering the relative costs and benefits of a long list of options

Epistemic Status: Early. The categories mentioned come mostly from experience and reflection, as opposed to existing literature.

On its surface, a utility function is an incredibly simple and generic concept. An agent has a set of choices with some numeric preferences over them. This can be Von Neumann–Morgenstern (VNM) compatible, but even that isn’t saying too much.

Trouble comes when people assume more specific meanings. Some people use utility function to mean “What does a certain human ultimately care about”, others use it to mean things like, “How much do humans prefer widgets A to widgets B?”. Utility functions are used in programs as a way of performing optimization. These are all very different uses of the term, confusion is frequent.

In our work at QURI, this is important because we want to programmatically encode utility functions and use them directly for decision-making. We’ve experienced a lot of confusion both in our internal thinking and in our trying to explain things to others, so have been working on clarifying the topic. This topic also has implications for discussions of AI alignment.

I think we can break down some interesting uses of the idea on a few different axes. I’m going to suggest some names here, but the names aren’t the important part - the categories and distinctions are.

Cheat Sheet

A Terminal utility function describes an agent’s ultimate values. A Proximal utility function describes their preferences over specific world states or actions.
An Initial terminal utility function is an agent’s initial utility function over values. A Modified terminal utility function includes modifications to one’s values over time, perhaps for reasons of optimizing their earlier terminal utility functions.
Utility functions can be Descriptive (a best-guess direct estimate), Empirical (estimated from an algorithm), Prescriptive (estimated and extrapolated from a different observer), or Operational (a specific program, perhaps executed as code).
Utility functions can be Precomputed (determined all at once, before use), or On-Demand (parts are computed when needed).
Utility functions can represent an agent’s initial intuitions (Deliberation Level 0), or their impressions after a lot of del

...
What is progress?
by jasoncrawford — 2024-03-09T16:28:48.389Z
Link: https://rootsofprogress.org/what-is-progress

In one sense, the concept of progress is simple, straightforward, and uncontroversial. In another sense, it contains an entire worldview.

The most basic meaning of “progress” is simply advancement along a path, or more generally from one state to another that is considered more advanced by some standard. (In this sense, progress can be good, neutral, or even bad—e.g., the progress of a disease.) The question is always: advancement along what path, in what direction, by what standard?

Types of progress

“Scientific progress,” “technological progress,” and “economic progress” are relatively straightforward. They are hard to measure, they are multi-dimensional, and we might argue about specific examples—but in general, scientific progress consists of more knowledge, better theories and explanations, a deeper understanding of the universe; technological progress consists of more inventions that work better (more powerfully or reliably or efficiently) and enable us to do more things; economic progress consists of more production, infrastructure, and wealth.

Together, we can call these “material progress”: improvements in our ability to comprehend and to command the material world. Combined with more intangible advances in the level of social organization—institutions, corporations, bureaucracy—these constitute “progress in capabilities”: that is, our ability to do whatever it is we decide on.

True progress

But this form of progress is not an end in itself. True progress is advancement toward the good, toward ultimate values—call this “ultimate progress,” or “progress in outcomes.” Defining this depends on axiology; that is, on our theory of value.

To a humanist, ultimate progress means progress in human well-being: “human progress.” Not everyone agrees on what constitutes well-being, but it certainly includes health, happiness, and life satisfaction. In my opinion, human well-being is not purely material, and not purely hedonic: it also includes “spiritual” values such as knowledge, beauty, love, adventure, and purpose.

The humanist also sees other kinds of progress contributing to human well-being: “moral progress,” such as the decline of violence, the elimination of slavery, and the spread of equal rights for all races and sexes; and more broadly “social progress,” such as the evolution from monarchy to representative democracy, or the spread of education and especially...
Fifteen Lawsuits against OpenAI
by Remmelt — 2024-03-09T12:22:09.715Z

tl;dr Cases I found against OpenAI. All are US-based. First ten focus on copyright.

Coders
1. Joseph Saveri Firm: overview, complaint

Writers
2. Joseph Saveri Firm: overview, complaint
3. Authors Guild & Alter: overview, complaint
4. Nicholas Gage: overview & complaint

YouTubers
5. Millette: overview, complaint

Media
6. New York Times: overview, complaint
7. Intercept Media: overview, complaint
8. Raw Story & Alternet: overview, complaint
9. Denver Post & seven others: overview, complaint
10. Center for Investigative Reporting: overview, complaint

Privacy
11. Clarkson Firm: overview, complaint
12. Glancy Firm: overview, complaint

Libel
13. Mark Walters: overview, complaint
Mission betrayal
14. Elon Musk: overview, complaint
15. Tony Trupia: overview, complaint

That last lawsuit by a friend of mine has stalled. A few cases were partially dismissed.
Also, a cybersecurity expert filed a complaint to Polish DPA (technically not a lawsuit).
For lawsuits filed against other AI companies, see this running list.

Most legal actions right now focus on data rights. In the future, I expect many more legal actions focussed on workers' rights, product liability, and environmental regulations.

If you are interested to fund legal actions outside the US:

Three projects I'm collaborating on with creatives, coders, and lawyers.
Legal Priorities was almost funded last year to research promising legal directions.
European Guild for AI Regulation is making headway but is seriously underfunded.
A UK firm wants to sue for workplace malpractice during ChatGPT development.

Folks to follow for legal insights:

Luiza Jarovsky, an academic who posts AI court cases and privacy compliance tips
Margot Kaminski, an academic who posts about harm-based legal approaches
Aaron Moss, a copyright attorney who posts sharp analysis of which suits suck
Andres Guadamuz, an academic who posts analysis with a techno-positive bent
Neil Turkewitz, a recording industry veteran who posts on law in support of artists
Alex Champandard, a ML researcher who revealed CSAM in largest image dataset
Trevor Baylis, a creative professional experienced in suing and winning

Manifold also has prediction markets:

Have you been looking into legal actions? ...
MA E-ZPass Without a Car?
by jefftk — 2024-03-09T02:40:08.425Z

I recently drove to DC and back

playing dances in a
rental. I paid cash tolls when available, but that often wasn't an
option, so I ended up paying $40 in PlatePass charges in addition to
the $63 in tolls. Time to get an

E-ZPass!

What makes this tricky is that I don't own a car. Well, I have half a car,
which does have an E-ZPass, but that stays with that car. If you go to
sign up
online you'll get through to step 5 of 7 and then:

Please add at least one vehicle to your account. If you do not have a
vehicle please call the customer service center to open your account.
You may not request more transponders than you have vehicles on your
account.

Then you call customer service, and when you follow the prompts to
tell the automated system that you want to open an E-ZPass account they
tell you this must be done online and don't offer any other options.

If you do get through to a person, for example by pressing 2-2-9 to
tell them that you have a question about your account but have
forgotten your number, they'll tell you to go online to their Document Library,
fill out a PDF application, and fax it in. The only difference, as
far as I can tell, is that the PDF form isn't capable of complaining
when you don't enter a license plate number.

Which worked! Now I have a transponder, and when I want to rent a car
I can follow their
instructions to add it to my account as a short-term rental.

Note that you don't have to be an MA resident to get an MA E-ZPass [1].
Several states have fees or inactivity charges, but MA is one of the
ones that doesn't. If you drive a lot you'll often make up for the
fees by lower in-state tolls, but if you don't then it could be worth
getting a fee-free one from MA or another no-fee state.

[1] This is actually a requirement for offering discounts on MA
highways to MA EZ-Pass accounts; see Yerger
et al. v. Massachusetts Turnpike Authority:

We agree with the District Court. The FLDP does not use E-ZPass as a
proxy for Massachusetts citizenship to achieve discrimination despite
a facially neutral program; rather, it offers all Fast Lane enrollees,
regardless of citizenship, access to toll discounts on the same
terms. The fact that more Massachusetts citizens than out-of-state
citizens may work in the Boston area, and therefore have a greater
incentive to join Fast Lane, does not demonstrate th

...