trentmkelly/LessWrong-43k huggingface dataset:
Scraping training data for your mind
by Henrik Karlsson — 2022-09-21T16:27:48.499Z
Link: https://escapingflatland.substack.com/p/training-data

2432 pages into Karl Ove Knausgaard’s autobiographical My Struggle comes a pivotal plot point: the publication of a new Proust translation in Norwegian.

Knausgaard at this point, in his mid-twenties, has spent nearly ten years learning to write. Without success, to put it mildly. His best friend, Tore Renberg, having read the results, in one scene comes over to Knausgaard’s flat, looking a little as if he has been drinking before he arrived to work up his nerve.

“But Karl Ove”, Renberg says about his writing, “there is… nothing there”.

This isn’t the first time we’ve seen how people react to Knausgaard’s prose. Earlier in the book, when he is working as a teacher in a remote fishing village in northern Norway, Knausgaard comes home to find his colleagues laughing while reading a sex scene he’s written. Knausgaard—still a virgin—walks straight through the kitchen into his study, where he downs a full bottle of wine in one go and proceeds to throw up all over the bookcase.

But Renberg’s criticism cuts deeper. Renberg, who is younger than Knausgaard, has already become an accomplished writer and knows what he’s talking about. There really is nothing there.

So Knausgaard stops writing. When the new translation of Prouts’s In Search of Lost Time is published he has not written for two years. In the spring light, he reads Proust’s memoirs, all seven of them, in one big gulp like “drinking a glass of water”. He has said it was like “visiting a wood you have been in before, a long time ago . . . and when you start walking, the memories start coming back”.

After that epiphany . . . he spends another two years not writing. That is about 200 pages of his autobiography.

Then, for inexplicable reasons, an editor at Tiden, a subsidiary of Norway’s biggest publishing house, an editor who, like everyone else, is unconvinced by Knausgaard’s writing, decides that, well, why not give him a book deal anyway. Knausgaard abandons everything, moves back to his mother’s town, Arendal, and sets out to write a debut novel. He doesn’t know what to write about. He overhears a conversation in the library, writes it down, and then wings it from there.

The novel turns into a story about a 26-year-old teacher, Henrik Vankel. Like many debuts, it is hard to not read as autobiographical—which becomes all the tenser as the plot centers on the sexual relationship Van...
Trends in Training Dataset Sizes
by Pablo Villalobos — 2022-09-21T15:47:41.979Z
Link: https://epochai.org/blog/trends-in-training-dataset-sizes

Summary: We collected a database of notable ML models and their training dataset sizes. We use this database to find historical growth trends in dataset size for different domains, particularly language and vision.

Key takeaways

We collected over 200 notable ML models and estimated their training dataset sizes in number of data points.
Vision and language datasets have historically grown at 0.1 and 0.2 orders of magnitude (OOM) per year, respectively.
There seems to be some transition around 2014-2015, after which training datasets became much bigger and (in the case of language) smaller datasets disappeared. This might be just an artefact of our small sample size.
We also provide trends for games, speech, recommendation and drawing, but since our sample size is very small in these domains we would advise some level of scepticism.

Figure 1: Training datasets for language (left) and vision (right).

Domain

Scale

(data points)

Yearly growth

(OOM)

Yearly growth

(OOM) (95% CI)

#systems
Language	1e2- 2e12	0.22	[0.18 ; 0.28]	79
Vision	2e3 - 3e9	0.09	[0.08 ; 0.11]	55
Speech	9e2 - 3e12	0.21	[0.17 ; 0.30]	13
Games	7e5 - 4e11	0.09	[0.08 ; 0.15]	12
Recommendation	1e8 - 1e10	0.05	[0.00 ; 0.47]	11
Drawing	6e4 - 4e9	0.43	[0.17 ; 0.64]	10

Table 1: Summary of trends for each domain. Scale is the maximum and minimum observed dataset size, and yearly growth is the slope of the best exponential fit (and 95% CI).

Introduction

Data is one of the main ingredients of Machine Learning (ML) models. To understand the progress of ML over time, it is necessary to understand the evolution of training datasets.

In this document, we characterise the historical trends in training dataset size for different domains, using a custom dataset of ML models. In the Methods section, we explain our method for quantifying training dataset size, as well as the inclusion criteria for our database. In the Dataset size trends section, we present the trends in dataset size for different domains.

Methods

Measuring dataset size

There is no canonical way to quantify the amount of data in a given dataset. Below are some ways of quantifying dataset size that we have considered, each with its own problems:

Size in bytes (uncompressed): The problem with this approach is that some modalities of data have very high redundancy, like videos or high-resolution images. A 4K image has ten times more pixels than a HD one but probably does

...
Can you define "utility" in utilitarianism without using words for specific human emotions?
by SurvivalBias — 2022-09-21T03:29:34.261Z

I'm trying to get a slightly better grasp of utilitarianism as it is understood in rat/EA circles, and here's my biggest confusion at the moment.

How do you actually define "utility", not in the sense of how to compute it, but in the sense of specifying wtf are you even trying to compute? People talk about "welfare", "happiness" or "satisfaction", but those are intrinsically human concepts and most people seem to assume non-human agents at least in theory can have utility. So let's taboo those words, and all other words referring to specific human emotions (you can still use the word "human" or "emotion" itself if you have to). Caveats:

Your definition should exclude things like AlphaZero or a $50 robot toy following a lights spot.
If you use the word "sentient" or synonyms, provide at least some explanation of what do you mean by it.

If the answer is different for different flavors of utilitarianism, please clarify which one(s) your definition(s) apply to.

Alternatively, if "utility" is defined in human terms by design, can you explain what is the supposed process for mapping internal states of those non-human agents into human terms?
"Infohazards" The ML Field's Greatest Excuse.
by Puffy Bird — 2022-09-21T03:19:29.496Z

1.Intro

Picture this, you’re the parent of your extremely smart 12-year-old son. This 12-year-old had some plans before his 13th birthday to successfully create a nuclear reactor, making him the youngest child ever to do so. Given that you have supported his efforts this far you most likely are an extremely prideful parent, your kid just built something that most people could only dream of making as a hobby. Would you consider this a problem? Possibly.

The conjecture is as follows: if everyone has access to world destroying information, what will they do with it? World destroying information A.K.A infohazards pose the threat to destroy entire worlds and economies. We have the DNA sequences of the worst modern viruses that exist. Why hasn’t an ordinary disgruntled scientist caused the next pandemic? It is because of the fact that right at this moment the common household does not have the equipment needed to replicate DNA. However, this faith that technology won’t catch up in the common household is faulty at best. We neglect the prospect of both previous tech and new tech coming into our houses. These new advances will allow for an infohazard’s effect to be executed.

Imagine if you were able to create any mRNA sequence in your house? You could now easily cure all illnesses that you have or will have. You won’t have to go to the pharmacy for medicine if your own equipment is able to replicate the same service. For example, now the necessity for going to a computer lab has become largely irrelevant. This would be a net good thing for society.

Well at least supposedly… Hackers have been around since the beginning of toolmaking, and with this tool will come more furious and harmful exploits. This hacker will go back to that same research lab documentation described earlier and create the worst known at that time. There goes the whole of humanity because of some unsuspecting lab work. But what does this have to do with AI?

2.The Problem with Gatekeeping Infohazards

The most important infohazards have already been leaked. If a child can create a nuclear reactor, why wouldn't a state organization? Capital for these ventures is becoming easier and easier to obtain in the modern landscape of business. As more organizations learn how to make world ending technologies the more collective points of failure get a lot higher. Hacktivists always want to obtain these infoha...
Case Rates to Sequencing Reads
by jefftk — 2022-09-21T02:00:02.053Z

In thinking about how you might

identify future pandemics by
sequencing wastewater, you might have a goal of raising an alert
before some fraction of people were currently infected. What you're
actually able to observe, however, are

sequencing reads, several steps removed
from infection rates. Can we use covid data to estimate how the
fraction of people currently infected with some pathogen might
translate into the fraction of wastewater sequencing reads that match
the pathogen?

RNA Viromics of Southern California Wastewater and Detection of
SARS-CoV-2 Single-Nucleotide Variants (Rothman
et al 2021) is the closest study I know in this area,
though it wasn't exactly what they were trying to answer. They took
many samples of municipal wastewater, extracted the RNA, filtered it
to increase the fraction of the RNA corresponding to viruses, and then
sequenced it.

(They also did several other things, including enriching some samples
for respiratory viruses, but here I'm only looking at the unenriched
data.)

They got 795M sequencing reads, 337 of which they identified as covid.
This means the fraction of all reads that were covid (the
"proportional abundance") was 4e-7. There's a typo in the paper where
they say this is "0.0004%", but I wrote to the author to ask about it
and they confirmed it was a missing zero.

How many people were infected during this period? The first tricky
thing in answering this question is that the sequencing quantity
wasn't uniform over this period:

Rothman
2021 SF4_sample_metadata: xlsx

And neither were covid cases:

LA County Covid
Dashboard

The paper accounted for the variability in confirmed cases by looking
at the relationship between the number of cases in the county served
by each water treatment plant and the amount of covid they measured in
their samples using qPCR. Because qPCR gives you much more precise
estimates for a given cost, they were able to quantify the covid
composition of 85 of their samples across seven plants:

(You might be wondering: if qPCR is cheap and precise why not use it
directly in looking for new pandemics? Why invest so much effort and
money in sequencing? The issue with qPCR is that it's a method for
checking how much of a specific sequence is present in a
sample. That means you have to already know what you're looking for,
and probably wouldn't be ab...
Towards deconfusing wireheading and reward maximization
by leogao — 2022-09-21T00:36:43.244Z

TL;DR: A response to “

Reward is not the optimization target

,” and to a lesser extent some other shard theory claims. I agree with the headline claim but I disagree with some of the implications drawn in the post, especially about wireheading and the "executing behaviors downstream of past reinforcement" framing.

My main claims:

Not only does RL not, by default, produce policies which have reward maximization as their behavioral objective, but in fact I argue that it is not possible for RL policies to care about “reward” in an embedded setting.
I argue that this does not imply that wireheading in RL agents is impossible, because wireheading does not mean “the policy has reward as its objective”. It is still possible for an RL agent to wirehead, and in fact, is a high probability outcome under certain circumstances.
This bears a clean analogy to the human case, viewing humans as RL agents; there is no special mechanism going on in humans that is needed to explain why humans care about things in the world.

A few notes on terminology, which may or may not be a bit idiosyncratic:

I define an RL policy to refer to a function that takes states and outputs a distribution over next actions.
I define an RL agent to be an RL policy combined with some RL algorithm (i.e PPO, Q-learning) that updates the policy on the fly as new trajectories are taken (i.e I mostly consider the online setting here).
The objective that any given policy appears to optimize is its behavioral objective (same definition as in Risks from Learned Optimization).
The objective that the agent optimizes is the expected value the policy achieves on the reward function (which takes observations and outputs reals).
A utility function takes a universe description and outputs reals (i.e it cares about "real things in the outside world", as opposed to just observations/reward, to the extent that those even make sense in an embedded setting).
I mostly elide over the

mesaoptimizer/mesacontroller distinction

in this post, as I believe it’s mostly orthogonal.

Thanks to AI_WAIFU and Alex Turner for discussion.

Outside world objectives are the policy’s optimization target

First, let us think about the mechanics of an RL agent. Each possible policy in policy-space implements a behavioral objective, and as a result has some behavior which may or may no...
What key nutrients are required for daily energy?
by trevor — 2022-09-20T23:30:02.540Z

I recently found out that I was constnatly tired every day because I was getting virtually zero Vitamin D in my diet.

In addition to Vitamin D, sleep, and Fiber, what other key nutrients are necessary to have energy and a healthy brain?

Linking me to a previous masterpost is fine, I'm asking because I have no idea where to find such a thing.
Quantified Intuitions: An epistemics training website including a new EA-themed calibration app
by Sage Future — 2022-09-20T22:25:44.973Z

Crossposted to EA Forum

TL;DR

Quantified Intuitions

helps users practice assigning credences to outcomes with a quick feedback loop. Please leave feedback in the comments, join our

Discord

, or send thoughts to

aaron@sage-future.org

.

Quantified Intuitions

Quantified Intuitions

currently consists of two apps:

Calibration game

: Assigning confidence intervals to EA-related trivia questions.

Question sources vary but many are from

Anki deck for "Some key numbers that (almost) every EA should know"

Compared to

Open Philanthropy’s calibration app

, it currently contains less diversity of questions (hopefully more interesting to EAF/LW readers) but the app is more modern and nicer to use in some ways

Pastcasting

: Forecasting on already resolved questions that you don’t have prior knowledge about.

Questions are pulled from

Metaculus

and

Good Judgment Open

More info on motivation and how it works are in the

announcement post

Please leave feedback in the comments, join our

Discord

, or send it to

aaron@sage-future.org

.

Motivation

There are huge benefits to using numbers when discussing disagreements: see “3.3.1 Expressing degrees of confidence” in

Reasoning Transparency

by OpenPhil. But anecdotally, many EAs still feel uncomfortable quantifying their intuitions and continue to prefer using words like “likely” and “plausible” which could be interpreted in many ways.

This issue is likely to get worse as the EA movement attempts to grow quickly, with many new members joining who are coming in with various backgrounds and perspectives on the value of subjective credences. We hope that Quantified Intuitions can help both new and longtime EAs be more comfortable turning their intuitions into numbers.

More background on motivation can be found in Eli’s forum comments

here

and

here

.

Who built this?

Sage is an organization founded earlier this year by

Eli Lifland

,

Aaron Ho

and

Misha Yagudin

(in a part-time advising capacity). We’re funded by the

FTX Future Fund

.

As stated in the grant summary, our initial plan was to “create a pilot version of a forecasting platform, and a paid forecasting team, to make predictions about questions relevant to high-impact research”. While we build a decent beta forecasting platform (that we plan to open source at some point), the pilot for forecasting on questions relevant to high...
The Redaction Machine
by Ben — 2022-09-20T22:03:15.309Z

On the 3rd of October 2351 a machine flared to life. Huge energies coursed into it via cables, only to leave moments later as heat dumped unwanted into its radiators. With an enormous puff the machine unleashed sixty years of human metabolic entropy into superheated steam.

In the heart of the machine was Jane, a person of the early 21st century.

From her perspective there was no transition. One moment she had been in the year 2021, sat beneath a tree in a park. Reading a detective novel.

Then the book was gone, and the tree. Also the park. Even the year.

She found herself laid in a bathtub, immersed in sickly fatty fluids. She was naked and cold.

The first question Jane had for the operators and technicians who greeted her with a warm towel was "where am I?''. They brought back the dead a hundred times a day, and knew that it was best to answer when, not where.

Jane's second question was "how did I come to be in the year 2351?''.

The answer begins in the year 2021. Jane sat beneath a tree. She read her book. She walked home. She attended university. She played her guitar. Went to pubs and festivals with her friends. She studied philosophy as her course, but there wasn't much work going in analysing Plato so she got a job in marketing.

She married, had children. Lived a normal enough life.

She followed the scientific news no more than most. She did watch the much-hyped documentary about the possibility of restoring objects to a previous state. She found it a disappointment. So they could get a single molecule to unreact? Maybe some chemist somewhere cared.

But technology's march was unceasing. The egg was unboiled only two decades later.

The redaction machines, as they came to be known, restored an object to a previous state. Set the machine for 10 minutes and insert your alarm clock. Ping! The machine takes no time at all to operate. Within find alarm clock, time as it read 10 minutes ago.

Smash said alarm clock with hammer. Insert the pieces into the machine. Activate. Find alarm clock restored, exactly, to state 10 minutes prior. No longer smashed. It is not now a repaired alarm clock. If something has been repaired then one supposes that it was once broken. A repaired thing still possesses the history of being broken and then fixed. But that clock, as taken from the machine, is the clock from earlier, before it ever suffered damage. Its histo...
You Are Not Measuring What You Think You Are Measuring
by johnswentworth — 2022-09-20T20:04:22.899Z

Eight years ago, I worked as a data scientist at a startup, and we wanted to optimize our sign-up flow. We A/B tested lots of different changes, and occasionally found something which would boost (or reduce) click-through rates by 10% or so.

Then one week I was puzzling over a discrepancy in the variance of our daily signups. Eventually I scraped some data from the log files, and found that during traffic spikes, our server latency shot up to multiple seconds. The effect on signups during these spikes was massive: even just 300 ms was enough that click-through dropped by 30%, and when latency went up to seconds the click-through rates dropped by over 80%. And this happened multiple times per day. Latency was far and away the most important factor which determined our click-through rates. [1]

Going back through some of our earlier experiments, it was clear in hindsight that some of our biggest effect-sizes actually came from changing latency - for instance, if we changed the order of two screens, then there’d be an extra screen before the user hit the one with high latency, so the latency would be better hidden. Our original interpretations of those experiments - e.g. that the user cared more about the content of one screen than another - were totally wrong. It was also clear in hindsight that our statistics on all the earlier experiments were bunk - we’d assumed that every user’s click-through was statistically independent, when in fact they were highly correlated, so many of the results which we thought were significant were in fact basically noise.

Main point of this example: we were not measuring what we thought we were measuring. We thought we were testing hypotheses about what information the user cared about, or what order things needed to be presented in, or whether users would be more likely to click on a bigger and shinier button. But in fact, we were mostly measuring latency.

When I look back on experiments I’ve run over the years, in hindsight the very large majority of cases are like the server latency example. The large majority of the time, experiments did not measure what I thought they were measuring. I’ll call this the First Law of Experiment Design: you are not measuring what you think you are measuring.

Against One-Bit Experiments

A one-bit experiment is an experiment designed to answer a yes/no question. It’s the prototypical cas...
What happened to the idea of progress?
by jasoncrawford — 2022-09-20T19:56:37.102Z
Link: https://bigthink.com/progress/a-new-philosophy-of-progress-jason-crawford/

Big Think magazine has a special issue on progress out today, featuring writers including Tyler Cowen, Charles Kenny, Brad Delong, Kevin Kelly, Jim Pethokoukis, Eli Dourado, Hannah Ritchie, Alec Stapp, Saloni Dattani, and yours truly.

My piece is a revised and expanded version of “We need a new philosophy of progress,” including material from “Why do we need a NEW philosophy of progress?” and from recent talks I’ve given. Here’s an excerpt from the opening:

The title of the 1933 Chicago World’s Fair was “A Century of Progress”; the 1939 fair in New York featured “The World of Tomorrow,” and people came back from it proudly sporting buttons that said “I Have Seen the Future.” In the same era, DuPont unironically used the slogan “better things for better living… through chemistry.”

In the 1950s and ‘60s, people looked forward to a future of cheap, abundant energy provided by nuclear power; Isaac Asimov even predicted that by 2014, appliances “will have no electric cords, of course, for they will be powered by long-lived batteries running on radioisotopes.” A 1959 ad in the Los Angeles Times sponsored by a coalition of power companies referred to “tomorrow’s higher standard of living”—without explanation, as a matter of course—and illustrated the possibilities with a drawing of a flying car.

Today, the zeitgeist is far less optimistic. A 2014 editorial in The Atlantic asked “Is ‘Progress’ Good for Humanity?” Jared Diamond has called agriculture “The Worst Mistake in the History of the Human Race.” Economic growth is referred to as an “addiction”, a “fetish”, a “Ponzi scheme”, or a “fairy tale.” Some even advocate a new ideal of “degrowth”.

We no longer assume that tomorrow will bring a higher standard of living. A 2015 survey of several Western countries found that only a small minority think that “the world is getting better.” The most optimistic vision of the future that many people can muster is one in which we avoid disasters such as climate change and pandemics. Young people are not even that optimistic: in a recent survey of 16- to 25-year-olds in ten countries, more than half said that “humanity was doomed” from climate change.

What happened to the idea of progress?

Read the whole thing at Big Think.