trentmkelly/LessWrong-43k huggingface dataset:
The Story of the Reichstag
by Martin Sustrik — 2021-02-05T05:51:59.243Z

When the Red Army was conquering Berlin, capture of Reichstag was seen as an important symbolic act. For Russians, the Reichstag was a symbol of the Nazi Germany. Nazis, however, never used the building, seeing it as a symbol of the despised democracy.

When the building was finally captured Soviet soldiers flew a flag from the roof of the building. The iconic photograph of the moment is not entirely truthful though. Rising smoke in the background has been added a few days later. The editor has also noticed that the soldier securing the soldier who was holding the flag had watches on both wrists. He corrected the error using a needle.

Soviet soldiers, as they were progressing westward through the Eastern Europe, were notorious for looting watches. Some of them were wearing multiple on each wrist.

As the old joke goes: What did marshal Konev say when they were dragging the Prague astronomical clock away? "Часы тяжелые." ("The times are hard." or, alternatively, "The clock is heavy.")

When a footage from Yalta conference was shown in cinemas in Eastern Europe, the sequence where Roosevelt shook Stalin's hand invariably elicited cries of "Mind your watch!"

To veer off in completely random direction: When George W. Bush visited Albania, the footage of him shaking hands with the crowd shows a watch on his wrist at one moment but no watch few seconds later. Immediately, conspiracy theories sprang up. They were, however, quickly debunked by the White House spokesperson: "The President has not been robbed. He took his watch off."

Anyway, back to the Reichstag.

Red Army Soldiers covered the building with graffiti.

These were simple inscriptions saying "Kiew - Berlin. Krasotkin." "From Moscow to Berlin. Major Yakovlev." "Sergey was here." and similar.

When the building of Reichstag was being repaired after the war it turned out that cleaning the walls would be too expensive. Those were poor times. The builders therefore decided to build clean hallways just by raising a thin smooth walls covering the original walls.

After the unification of Germany, when the Reichstag was being repurposed to become the seat of the parliament once again the builders drilled a hole into the outer wall and discovered that the old Russian inscriptions were still there, untouched.

It was decided to remove the wall and leave the graffiti at different locations in place, on public display.

I think the fac...
Networking and Expert Consulting As Rationality Bottlenecks
by DirectedEvolution — 2021-02-05T05:41:07.723Z

Introduction

John Wentworth is on fire writing about the importance of gears-level models and the challenge that novices face in evaluating the claims of experts. Let's call the latter "consulting."

It's an absolutely basic problem of everyday life. How do I know the salesman, or the plumber, or the mechanic, is giving me a fair deal? When a friend invites you on a technical climbing trip, and tells you not to worry and that they'll plan everything out safely, how do you know whether to trust them or decline? Should you get a second opinion from another doctor, and if so, why not get a third, and even a fourth?

It's also an issue in politics. Julia Galef and Matthew Yglesias just put out a scintillating Rationally Speaking episode about their mutual mistake in initially supporting the Iraq War. Matt thought at the time that he should trust the experts in Washington, who had access to classified information and top experts in the field, over the opinions of the anti-war pundits.

This issue feeds back into itself in the form of credentialism. After all, there's little to stop people from speaking beyond their specific domain of expertise. As Julia and Matt discuss in this same episode, a textile manufacturer probably knew more about whether or not we could rapidly get cloth masks out to the population than Dr. Fauci. If the initial decision to tell people that masks were ineffective was motivated by a desire to preserve PPE for healthcare providers, did they think to ask textile manufacturers whether this was a serious concern? If not, why not?

In general, how do issues come to be "owned" by a certain profession or specialty? How did "bioethicist" become something you could be an "expert" in? Do I have to defer to Peter Singer on moral issues, or can I think for myself?

And this is a meta-issue as well. After all, who's qualified to have opinions on how specialty topics should be divided up? Who can claim to be an expert in distinguishing issues where we should defer to experts, and where we can trust our own judgment? On what basis do they claim that expertise?

Now, it might be true that most or all of these questions can be answered by learning how to think for ourselves on every issue. Building our own complete set of gears level models.

But let's assume for a moment that there's a reason we've had roles and hierarchies of experts all around the world, since time i...
Looking at RSS User-Agents
by jefftk — 2021-02-05T02:10:06.551Z

An RSS reader sends periodic requests to get the latest feed. This
includes a User-Agent field, identifying which fetcher is running:

Feedbin feed-id:1242010 - 38 subscribers

This fetcher is nicely passing along statistics, saying how many
readers it represents.

I took one day of logs, with 5,962 requests for my RSS feed:

$ sudo grep '"GET /news.rss ' \
/var/log/nginx/access.log.1 \
| awk -F'"' '{print $6}' \
| wc -l
5962

There were 162 unique User-Agents:

$ sudo grep '"GET /news.rss ' \
/var/log/nginx/access.log.1 \
| awk -F'"' '{print $6}' \
| sort \
| uniq \
| wc -l
162

Of the 5,962 requests, 932 (16%) gave stats:

$ sudo grep '"GET /news.rss ' \
/var/log/nginx/access.log.1 \
| awk -F'"' '{print $6}' \
| grep 'subscriber\|reader' \
| wc -l
932

They sent 21 distinct User-Agents:

$ sudo grep '"GET /news.rss ' \
/var/log/nginx/access.log.1 \
| awk -F'"' '{print $6}' \
| grep 'subscriber\|reader' \
| sort \
| uniq \
| wc -l
21

Some sent multiple requests with different numbers of subscribers:

Feedbin feed-id:1242010 - 38 subscribers
Feedbin feed-id:372940 - 11 subscribers
Feedbin feed-id:382 - 1 subscribers

I suspect this comes from people using old URLs that then get
redirected to my current URL. For example, now it's

https://www.jefftk.com/news.rss, but it used to be

http://www.jefftk.com/news.rss, and even longer ago it
was an

sccs.swarthmore.edu address. Summing subscriber
counts, I see:

Feedly: 573

inoreader.com: 87

NewsBlur: 62

Feedbin: 50

theoldreader.com: 34

Dreamwidth Studios: 7

BazQux: 5

Bloglovin: 2

Feed Wrangler: 2

pine.blog: 1

While this only tells us about users who are subscribed to my blog, it
seems like Feedly is the biggest player here by a lot.

Different services fetched at different intervals. Taking the
shortest interval for each distinct User-Agent:

Feedly: 7min

Feedbin: 15min

Bloglovin: 30min

Dreamwidth Studios: 30min

Feed Wrangler: 30min

NewsBlur: 30min

BazQux: 40min

inoreader.com: 1hr

theoldreader.com: 2hr

pine.blog: 24hr

Looking through the requests that don't list subscribers, several do
seem to be services. I'll try reaching out to them to see if they're
interested in adding subscriber counts to their User-Agents.
Why are clinical trials so expensive?
by ChristianKl — 2021-02-05T00:10:21.735Z

While I know that clinical trials such as the trials for vaccines cost hundreds of millions of dollar, I have a hard time understanding why they are so expensive. I read about clinical trials for a vaccine costing 10,000/study participant. Given that the trials are so expensive they seem to be the bottleneck for medical progress. If we find a way to run them cheaper that would massively accelerate medical progress.

What resources do a good job at documenting the costs of clinical trials and how those high costs come about?
Covid 2/4: Safe and Effective Vaccines Aplenty
by Zvi — 2021-02-04T14:50:01.552Z

The vaccine data is in. It’s pretty great.

We now have six known safe and effective Covid-19 vaccines: Pfizer, Moderna, AstraZeneca, Novavax, Johnson & Johnson and Sputnik.

Pfizer and Moderna are amazingly safe and effective. They have been approved and are being distributed. The problem is that due to our unwillingness to properly fund the process of scaling up, we are stalling out at about 1.3 million doses per day for a population of 330+ million people each of whom needs two doses. That won’t do, the vaccine rollout is going about the way you would expect if you’ve been paying attention so far, and the English and other variant strains are rising fast.

The other vaccine approvals are not in. It’s pretty terrible.

Johnson & Johnson and Novavax are also safe and effective, far better than we need them to be. They both held onto their data longer than they would have in a sane world, and took longer to collect it than they would have in a sane world, but there’s nothing other than bureaucratic delays now standing in our way. Johnson & Johnson is expected to get its approval at some unspecified date, presumably this month. Yet there is no sense of urgency.

AstraZeneca is even more egregious. There are doses ready to go, it has been approved by major regulatory agencies elsewhere, and the main objections seem to be “the data isn’t from Americans” and “we’re mad at you about previous mistakes so we need to punish that somehow.” We are still going to wait until April or so for the American data, plus the delays after that, unless something changes and the new administration and/or the FDA see themselves as sufficiently blameworthy for not approving the vaccine. This badly needs more outrage. There’s an increasing amount, but so far it’s nowhere near enough.

Sputnik is the least egregious. Not using it is going to get a lot of people killed, but it’s a Russian project largely motivated by national pride, that never held out any hope of convincing us to trust it and always planned to distribute its doses in Russia and then the third world, so I can sort of understand this one. I’ll settle for the other five.

The short term situation is clearly headed in a positive direction. Death counts are being remarkably stubborn, but are on their way down along with hospitalizations, case counts and positive test percentages. By all metrics, every day we are a ...
Early career choices to maximise learning?
by Evzen — 2021-02-04T13:37:22.505Z

I'm a student in a subfield of CS and biology, getting my bachelor's degree later this year and planning on continuing to masters (and maybe beyond). My ultimate goal is to work on the edge between biology, CS and datascience, preferably in an industry-backed research centre (think: Microsoft labs, or Deep Mind).

I decided that when it comes to my current career possibilities, I will prioritise learning before anything else. The question is, how do I do that? The process is two-fold: first I have to choose a workplace, and once I'm there I have to do certain things in order to reach my goal. The "best path" will then be a combination of those two things.

I'm lucky enough to be a very good student, so with some amount of effort I can get to most places I'd like. The possibilities are roughly:

a big pharma corp, in which I've worked already, and that has some really brilliant people in it
a research centre, in which I work at the moment — this would be probably the only place to do proper scientific research in, because I think they don't open student positions in industry R&D labs
a big non-pharma corp (FAANG?)

Which one of those do you think should fit my goals the best? And what do I do once I'm there?
OpenAI: "Scaling Laws for Transfer", Hernandez et al.
by Lukas Finnveden — 2021-02-04T12:49:25.704Z
Link: https://arxiv.org/pdf/2102.01293.pdf

OpenAI has released a paper on scaling laws for transfer learning from general English text to python code. Author's tweet summary.

Abstract: "We study empirical scaling laws for transfer learning between distributions in an unsupervised, fine-tuning setting. When we train increasingly large neural networks from-scratch on a fixed-size dataset, they eventually become data-limited and stop improving in performance (cross-entropy loss). When we do the same for models pre-trained on a large language dataset, the slope in performance gains is merely reduced rather than going to zero. We calculate the effective data "transferred" from pre-training by determining how much data a transformer of the same size would have required to achieve the same loss when training from scratch. In other words, we focus on units of data while holding everything else fixed. We find that the effective data transferred is described well in the low data regime by a power-law of parameter count and fine-tuning dataset size. We believe the exponents in these power-laws correspond to measures of the generality of a model and proximity of distributions (in a directed rather than symmetric sense). We find that pre-training effectively multiplies the fine-tuning dataset size. Transfer, like overall performance, scales predictably in terms of parameters, data, and compute."

​

The power law they find is DT=k(DF)α(N)β with coefficients
A Gentle Introduction on How to Use Anki to Improve Your Memory
by simbyotic — 2021-02-04T09:59:26.690Z

This article was initially published in Superpowered Self, but after reading, and very much enjoying, TurnTrout's Lessons I've Learned from Self-Teaching I figured people would find this introduction I've written to Anki for complete beginners valuable.

Where would you be in life if you did not forget?

You would have done better in school, for starters. Instead of turning in your bed unable to sleep terrified of the exam coming the next day, you would soundly sleep with the knowledge that you know everything you need to know to ace the exam. And ace it you would indeed.

You would have spent fewer hours studying. How many times have you opened the textbook only to find that you’ve forgotten all that you’d studied the day before? If you did not forget things then those hours you spent studying would always amount to something, instead of leaving you feeling like you’re swimming against the current. School might even have been fun if you did not forget.

What would your career look like if you did not forget?

Forgetfulness affects us all. There is no one that has not grappled with this problem before. Our lives would be better if we did not forget.

Unfortunately, forgetting is inescapable. There is no such thing as a perfect memory. I am not here to sell you on a magic pill that will turn you into Bradley Cooper in the movie Limitless.

However, that doesn’t mean there aren’t things you can do to massively reduce the speed at which you forget things, because there are.

Science has known about what it takes to get memories to stick around in your memory for a long time. It has known about it for a while now, in fact. It’s just that it has done a terrible job so far at making sure that you know about it, you whose life would massively benefit from that knowledge.

My intent for writing this is to correct these wrongs and introduce you to spaced repetition, the more than established method that will put you in control of your memory once and for all, and Anki, the software that will help you do so.

So, let’s start at the beginning: What is spaced repetition?

The Centuries Old Science They Don’t Tell You About

More than a hundred years ago, the German psychologist Hermann Ebbinghaus not only started the scientific study of memory, but he also made its most impactful discovery.

After all, what discovery could be more impactful than what makes memories stick?

Ebbinghaus created var...
Introduction to Heel-Toe Drumming
by jefftk — 2021-02-04T02:00:05.915Z

Now that I've been playing heel-toe for

a couple months I have some idea
about how to do it. I wanted to write something up in case other
people are interested in learning. Since each foot can play two
pedals, a basic set up is

four beaterless
pedals:

I'm using the Yamaha KU-100 pedal, left to right as:

Left toe: snare

Left heel: kick

Right heel: cymbal

Right toe: hi-hat

Here's what I have them set to sound like, with my DTX-500:

In teaching myself to play, I've worked on figuring out specific
patterns. Each is a nice concrete step, and once I get a pattern into
my muscle memory I can play it without conscious effort. As I've
learned more, I've felt my fluency increase, where progressively more
complex patterns will just come out, fitting whatever I happen to be
playing on the piano. Often I will get to a place where there's
something that feels like it should go with the music is too hard, so
I'll stop and work it out, and repeat it until it's solid.

Here are a set of patterns that you might find a useful starting place
if you are teaching yourself:

1. Kick on the downbeat

Tap your left heel in time with the music. Work on getting it
completely steady.

2. Kick on the downbeat, hi-hat on the upbeat

Continue tapping your left heel on the downbeat, but now add your
right toe on the upbeat.

3. Kick on 1 + 3, snare on 2 + 4

On successive downbeats, alternate between tapping your left heel and
left toe.

4. Combine both

This is the two previous ones together: kick on 1 + 3, snare
on 2 + 4, and high hat on every upbeat.

5. Add cymbal

Go back to kick and snare, but add the cymbal on the upbeat at the very
end of the phrase.

6. Kick on 1 + 2&, snare on 2 + 4

This is the same as kick and snare on successive downbeats, but the
second kick is slightly earlier, on the upbeat.

7. Add hi-hat

Now add the hi-hat on the upbeat.

8. Make things up and keep exploring!

Listen to patterns in music you like, listen to what the music you're
playing seems to be saying should come next, focus on learning
things just on the edge of what you can do.

Comment via: facebook
Making Vaccine
by johnswentworth — 2021-02-03T20:24:18.756Z

Back in December, I

asked

how hard it would be to make a vaccine for oneself. Several people pointed to

radvac

. It was a best-case scenario: an open-source vaccine design, made for self-experimenters, dead simple to make with readily-available materials, well-explained reasoning about the design, and with the name of

one of the world’s more competent biologists

(who I already knew of beforehand) stamped on the whitepaper. My girlfriend and I made a batch a week ago and took our first booster yesterday.

This post talks a bit about the process, a bit about our plan, and a bit about motivations. Bear in mind that we may have made mistakes - if something seems off, leave a comment.

The Process

All of the materials and equipment to make the vaccine cost us about $1000. We did not need any special licenses or anything like that. I do have a little wetlab experience from my undergrad days, but the skills required were pretty minimal.

One vial of custom peptide - that little pile of white powder at the bottom.

The large majority of the cost (about $850) was the peptides. These are the main active ingredients of the vaccine: short segments of proteins from the COVID virus. They’re all <25 amino acids, so far too small to have any likely function as proteins (for comparison, COVID’s spike protein has 1273 amino acids). They’re just meant to be recognized by the immune system: the immune system learns to recognize these sequences, and that’s what provides immunity.

Each of six peptides came in two vials of 4.5 mg each. These are the half we haven't dissolved; we keep them in the freezer as backups.

The peptides were custom synthesized. There are companies which synthesize any (short) peptide sequence you want - you can find dozens of them online. The cheapest options suffice for the vaccine - the peptides don’t need to be “purified” (this just means removing partial sequences), they don’t need any special modifications, and very small amounts suffice. The minimum order size from the company we used would have been sufficient for around 250 doses. We bought twice that much (9 mg of each peptide), because it only cost ~$50 extra to get 2x the peptides and extras are nice in case of mistakes.

The only unusual hiccup was an email about customs restrictions on COVID-related peptides. Apparently the company was not allowed to send us 9 mg in one vial, but could send us two ...
[AN #136]: How well will GPT-N perform on downstream tasks?
by Rohin Shah — 2021-02-03T18:10:03.856Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer.

HIGHLIGHTS

Extrapolating GPT-N performance (Lukas Finnveden) (summarized by Asya): This post describes the author’s insights from extrapolating the performance of GPT on the benchmarks presented in the GPT-3 paper (AN #102). The author compares cross-entropy loss (which measures how good a model is at predicting the next token) with benchmark performance normalized to the difference between random performance and the maximum possible performance. Since previous work (AN #87) has shown that cross-entropy loss scales smoothly with model size, data, and FLOP requirements, we can then look at the overall relationship between those inputs and benchmark performance.

The author finds that most of the benchmarks scale smoothly and similarly with respect to cross-entropy loss. Three exceptions are arithmetic, scramble (shuffling letters around the right way), and ANLI (a benchmark generated adversarially against transformer-based language models), which don't improve until the very end of the cross-entropy loss range. The author fits linear and s-shaped curves to these relationships, and guesses that:

- Performance improvements are likely to slow down closer to maximum performance, making s-curves a better progress estimate than linear curves.

- Machine learning models may use very different reasoning from humans to get good performance on a given benchmark, so human-level performance on any single benchmark would likely not be impressive, but human-level performance on almost all of them with few examples might be.

- We might care about the point where we can achieve human-level performance on all tasks with a 1 token "horizon length"-- i.e., all tasks where just 1 token is enough of a training signal to understand how a change in the model affects its performance. (See this AI timelines report draft (AN #121) for more on horizon length.) Achieving this milestone is likely to be more difficult than getting t...