trentmkelly/LessWrong-43k huggingface dataset:
Submit comments on Paxlovid to the FDA (deadline Nov 29th).
by DirectedEvolution — 2021-11-27T18:44:27.615Z

1,000 Americans are dying daily from COVID-19. Paxlovid is 89% effective against it. Pfizer submitted an EUA on Paxlovid to the FDA on Nov. 16th. The FDA has yet to schedule a meeting to review this EUA request, 11 days later. Thousands of lives are at stake over this delay.

For molnupiravir, they only took 3 days to schedule a meeting, but they scheduled that meeting for 7 weeks out, on Nov. 30th. Here is a link to the announcement.

They accept public comment at the meeting.

And as far as I can tell, it doesn't have to be about molnupiravir. It can be about Paxlovid.

Last I checked, they had 23 comments, including mine.

From their website,

Interested persons may present data, information, or views, orally or in writing, on issues pending before the committee.

The deadline to have comments considered by the committee has already passed, but "comments received after that date but by November 29, 2021, will be taken into consideration by FDA."

This might be one method to express any thoughts you might have about the review of Paxlovid's EUA, as well as on molnupiravir.

The FDA still hasn't scheduled a meeting to review Paxlovid. It scheduled its meeting for molnupiravir 7 weeks out. But it scheduled its review of the vaccines just 3 weeks after the submission. One suggestion, if you submit a comment, is to urge the FDA to schedule its review ASAP and to make the timeline 3 weeks (or less) rather than 7.

Note that Dr. Marty Makary of Johns Hopkins, who routinely conducts and evaluates clinical trials, has said that this review should take no more than a few hours, even for the 43,000 cases in the vaccine trials. This trial is less than 2,000 cases.

Timeline:

Nov. 9th, 2020: Pfizer announces preliminary data on its vaccine showing high efficacy, and plans to seek EUA.
Nov. 20th, 2020 (12 days later): Pfizer and Moderna vaccines submitted for EUA to the FDA
Dec. 11th (3 weeks later): Comirnaty (Pfizer-BioNTech COVID-19 Vaccine) issued EUA by the FDA
It also took Pfizer 2 weeks from preliminary data announcement on effectiveness of Paxlovid to submit for an EUA. If it takes 3 weeks to approve, then we should have an EUA issued in about 10 days, perhaps the end of the first week of December.
Oct. 11th, 2021: Merck submits EUA for molnupiravir.
Oct. 14th, 2021 (3 days later): FDA announces meeting scheduled to discuss EUA for molnupiravir on Nov. 30th, 2021.
Nov. 5th, 202

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I currently translate AGI-related texts to Russian. Is that useful?
by Tapatakt — 2021-11-27T17:51:58.766Z

I live in Saint-Petersburg, Russia, and I want to do something useful to AI-Alignment movement.

Several weeks ago I started to translate "Discussion with Eliezer Yudkowsky on AGI interventions". Now it's complete and linked to in couple thematic social network groups, and I currently translate old metaphorical post almost as a rest, after which I plan to follow the list from Rob Bensinger's comment.

My English is not very good, I know a lot of people with better language proficiency, but it seems to me that I am on Pareto Frontier on "translation skills + alignment-related knowledge + motivation to translate". Otherwise, I think, there would be more than one alignment-related translation on lesswrong.ru over the past two years!

I would like to know if this is helpful or if I should focus on something completely different.
Finding the Central Limit Theorem in Bayes' rule
by Maxwell Peterson — 2021-11-27T05:48:06.161Z

I feel like there's something in the basic Bayesian update formula that suggests that, as the amount of input data grows, almost every Bayesian posterior will tend toward a Gaussian. This post is me trying to scrape at that vague intuition and get a handle on it. I'm going to list four observations, then put them together at the end to explain why I think this might be true.

#1: The central limit theorem can be viewed as a statement about convolutions

I covered this here, but a quick summary: a decent form of the Central Limit Theorem goes like this:

If you have a bunch of distributions fi (say, n of them), and you convolve them all together into a distribution F∗:=f1∗f2∗f3...∗fn, then the larger n is, the more F∗ will resemble a Gaussian (AKA Normal) distribution.

This is true for most component distributions, even if they're not originally Gaussian, and it's pretty robust to the shapes of the input distributions, even if those shapes are different. One example from that link:

Convolving three Beta distributions of somewhat different shape converges to Gaussian right away.

(Although the CLT is often taught in the context of taking means of samples drawn from a population, in order to estimate the population mean, that's not an important part of how I'm using it here, and that view of things is best forgotten while reading this post.)

#2: Convolutions have a relationship to the Fourier transform

The Fourier transform of a convolution of two functions is the pointwise product of their Fourier transforms. The inverse is true too, and more relevant to this post: convolving the Fourier transforms of two distributions is the same as multiplying them in the original domain. To say it shorter:

Convolution in one domain is equivalent to multiplication in the other.

(The Fourier transform is a rich topic, and I myself don't understand most of its behavior. If the same is true for you, don't worry - all you need to know about it for this post is that it moves numbers from one space into another, it has an inverse, and the stuff in this section and the next.)

#3: The Fourier transform of the Gaussian is Gaussian

A table of some transforms. The left column is the original function g, the right is the Fourier transform F(g).

The Fourier transform of most functions looks pretty different tha...
Tentative Anger
by Adam Zerner — 2021-11-27T04:38:28.087Z

Epistemic status: Exploratory. Trying to figure it out.

There is a feeling I often find myself feeling. It is an uncomfortable feeling, and the fact that I have trouble labeling it makes the discomfort even worse. I think I may have arrived at a solid label though: tentative anger.

Examples

Let me start by giving some examples of what I mean.

Mike Tomlin

Yesterday was Thanksgiving, so let's talk about football. I am a Steelers fan. However, I'm not really a fan of their head coach, Mike Tomlin. Why? Because he does stupid things.

Here's one example. We always end up in these situations where linebackers are covering slot receivers. Linebackers are generally about 250lbs or so, whereas slot receivers are more like 200 pounds. Which means those receivers are almost always faster and more agile, making it really hard for linebackers to cover them. It's like asking a boxer to fight outside of his weight class. Usually you'd have ~200lb cornerbacks guarding those receivers instead.

No one, including Tomlin, actually thinks that it is a good strategy to put linebackers on receivers. It's just that... I'm not sure actually. For whatever reason, sometimes these mismatches happen. But Tomlin doesn't seem to mind very much. Compared to other coaches, he doesn't seem to try as hard to avoid them. And when they do happen, his response is something like:

Tough luck. The standard is the standard. You've gotta step up and meet the challenge. I don't care that he's 50 pounds lighter than you.

What makes more sense to me would be something like:

I got outcoached. My bad. I've got to put my players in a position to succeed, and I didn't do that. I'll hit the drawing board and try to prevent this from happening in the future.

But here's the thing: he's an ultra successful football coach, and I'm not. Maybe he knows better than me. Maybe there's a Bayesians vs Barbarians angle to this.

That's plausible. But then why doesn't this happen to the other football coaches? And why do all the other fans get angry about this too? And why do journalists who seem to know football make the same points I make?

Ultimately, all things considered, I do suspect that Tomlin is being dumb here, and that I am justified in being angry. However, I'm only something like 80% sure. There is still a very reasonable chance that I am wrong and that my anger would be misplaced.

So what is the appropriate res...
Comment on "Deception as Cooperation"
by Zack_M_Davis — 2021-11-27T04:04:56.571Z

In this 2019 paper published in Studies in History and Philosophy of Science Part C, Manolo Martínez argues that our understanding of how communication works has been grievously impaired by philosophers not knowing enough math.

A classic reduction of meaning dates back to David Lewis's analysis of signaling games, more recently elaborated on by Brian Skyrms. Two agents play a simple game: a sender observes one of several possible states of the world (chosen randomly by Nature), and sends one of several possible signals. A receiver observes the signal, and chooses one of several possible actions. The agents get a reward (as specified in a payoff matrix) based on what state was observed by the sender and what action was chosen by the receiver. This toy model explains how communication can be a thing: the incentives to choose the right action in the right state, shape the evolution of a convention that assigns meaning to otherwise opaque signals.

The math in Skyrms's presentation is simple—the information content of a signal is just how it changes the probabilities of states. Too simple, according to Martínez! When Skyrms and other authors (following Fred Dreske) use information theory, they tend to only reach for the basic probability tools you find in the first chapter of the textbook. (Skyrms's Signals book occasionally takes logarithms of probabilities, but the word "entropy" doesn't actually appear.) The study of information transmission only happens after the forces of evolutionary game theory have led sender and receiver to choose their strategies.

Martínez thinks information theory has more to say about what kind of cognitive work evolution is accomplishing. The "State → Sender → Signals → Receiver → Action" pipeline of the Lewis–Skyrms signaling game is exactly isomorphic to the "Source → Encoder → Channel → Decoder → Decoded Message" pipeline of the noisy-channel coding theorem and other results you'd find beyond the very first chapter in the textbook. Martínez proposes we take the analogy literally: sender and receiver collude to form an information channel between states and actions.

The "channel" story draws our attention to different aspects of the situation than the framing focused on individual signals. In particular, Skyrms wants to characterize deception as being about when a sender benefits by sending a misleading signal—one that decreases the re...
Almost everyone should be less afraid of lawsuits
by alyssavance — 2021-11-27T02:06:52.176Z

One sad feature of modern American society is that many people, especially those tied to big institutions, don't help each other out because a fear of lawsuits. Employers don't give meaningful references, or ever tell their rejected interviewees how they could improve their skills. Abuse victims keep silent, in case someone on their abuser's side files a defamation case. Doctors prescribe unnecessary, expensive tests as "defensive medicine". Inventions don't get built, in case there's a patent lawsuit. I'm not an attorney myself, but my best guess is that letting litigation fears stop you is often a mistake, and I've given this advice to friends several times before. Here's why:

Almost all lawsuit threats never happen

Threats are easy - anyone can threaten to sue anyone else, with two minutes of time and a smartphone. Actually suing is much harder. Outside of small claims court, hiring an attorney will usually cost tens of thousands of dollars, at least. Litigating a case takes months or even years, while angry feelings often go away after a few weeks. The person suing will have to give up a lot. Instead of playing games or taking a vacation or putting in extra hours at work, they will have to do legal research and give testimony. Most people are distracted by their families, their career, their hobbies, and their lives, and will (often rationally) eventually give up, rather than remaining obsessed with whatever the case was about.

Lawsuit mitigation can be expensive

Doctors as a profession are traditionally concerned with legal risk. But the total value of medical malpractice claims is around $5 billion per year in the US - compared to healthcare spending of $3,800 billion, malpractice lawyer fees of $3 billion, and "defensive medicine" costs of $45 billion according to one study. Likewise, the cost to media companies and journalists of not publishing articles to stop defamation suits surely exceeds that of the few dozen defamation cases against them every year (stats from the UK, but British defamation law is widely considered plaintiff-friendly). The cost of things like rape victims staying silent because of defamation threats is hard to quantify, but clearly also bad. Some of these costs will fall on you directly, but often many more are externalized; eg., the cost to a patient of not giving them the right treatment out of fear can be enormous.

Many leg

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larger language models may disappoint you [or, an eternally unfinished draft]
by nostalgebraist — 2021-11-26T23:08:56.221Z

what this post is

The following is an incomplete draft, which I'm publishing now because I am unlikely to ever finish writing it.

I no longer fully endorse all the claims in the post. (In a few cases, I've added a note to say this explicitly.) However, there are some arguments in the post that I still endorse, and which I have not seen made elsewhere.

This post is the result of me having lots of opinions about LM scaling, at various times in 2021, which were difficult to write down briefly or independently of one another. This post, originally written in July 2021, is the closest I got to writing them all down in one place.

-nost, 11/26/21

0. caveat

This post will definitely disappoint you.

Or, anyway, it will definitely disappoint me. I know that even though I haven't written it yet.

My drafts folder contains several long, abandoned attempts to write (something like) this post. I've written (something like) this post many times in my head. I just can't seem to get it right, though. The drafts always sprawl out of control.

So, if I can't do it right, why not do it wrong? Here's the disorganized, incomplete, brain-dump version of the better post I wish I were writing. Caveat lector.

1. polarization

The topic of this post is large language models (LMs) like GPT-3. Specifically, what will happen as we make them larger and larger.

By my lights, everyone else seems either too impressed/scared by the concept of LM scaling, or not impressed/scared enough.

On LessWrong and related communities, I see lots of people worrying in earnest about whether the first superhuman AGI will be a GPT-like model. Both here and in the wider world, people often talk about GPT-3 like it's a far "smarter" being that it seems to me.

On the other hand, the people who aren't scared often don't seem like they're even paying attention. Faced with a sudden leap in machine capabilities, they shrug. Faced with a simple recipe that can make those machines even better -- with eerie, physics-like regularity -- they . . . still shrug. I wrote about the most infamous of these detractors here.

Meanwhile, I'm here in the middle. What do I think? Something like:

The newer (i.e. large transformer) LMs really are a huge advance in NLP over the prior state of the art
The prior state of the art was really bad, though. Bef

...
Kriorus update: full bodies patients were moved to the new location in Tver
by avturchin — 2021-11-26T21:08:47.804Z

Yesterday Valery Pride got a decision from police that she is an acting director and owner of the dewar vessels with full bodies patients in Sergiev Posad (Semhoz) which she unsuccessfully tried to take in September. Based on that decision she arranged the transportation of the three large vessels with bodies into a new (but still unfinished) building of the Kriorus in the Tver region, around 170 km from Moscow. A driver was injured in hand during a transportation accident, not related to the conflict.

Danila Medvedev, her former husband and now rival commented on the event relatively peacefully: "It seems to me that this whole story also has a positive side. At the very least, we get an interesting answer to the objection "who will need these cryopatients in the future?" If even now, when, in the opinion of ordinary people, this is just a bunch of frozen bodies; there are those who are ready to fight for cryopatients. Thus, over time, as the reality and the likelihood of revival gradually grows, I think, and the value of cryopatients will also grow." (In Telegram chat "Open cryonics").

Photos here.
Economist Irene Ng on Market design, entrepreneurship, and innovation.
by fowlertm — 2021-11-26T19:00:38.659Z

At the Futurati Podcast we recently recorded an interview with Irene Ng on market design, modularity and legibility in systems of behavior, innovation, game theory, privacy, the blockchain, and a few other subjects.

Though it's far removed from the AI alignment and Bayesian epistemology that LWers normally discuss, I know that there's enough interest in economics and related disciplines for me to think it'd be worth posting the episode and the show notes from it.

Show Notes

Irene began her journey as an entrepreneur in the 1990's at age of 26, where she successfully turned around a cruise ship company before selling it at 33.
After this she enrolled in a PhD program to study economics, industrial design, and market design.
'Market design', which occupied much of our conversation, centers around designing systems of transactions which are safe and efficient.
These can often be used by public sector entities as a way of handling problems when public funds don't do the trick.
We spent some time discussing the connection to repugnant markets. An example might be a market for kidneys.
On the one hand, we want to do whatever we can to shorten the time required to get someone a kidney. On the other, we don't want to incentivize people to steal each other's kidneys.
Dr. Ng cites the work of other economists in successfully using market design to create markets which solve this problem without repugnant side effects.
I take a step back and ask Dr. Ng what it even means to design a market. Most economists treat markets as spontaneous orders which emerge when humans engage in certain kinds of activity, so how can one be designed.
Dr. Ng's answer centers around the idea of transactions, which can involve exchanges of money or favors or almost anything else.
Transactions can be thick or thin, and in a well-designed market there will be thin transaction points where new resources can enter. This depends on the modularity of the system.
An example might be two new parents. In the first month of baby's life every transaction is thick, direct, hands on. But if they break their routine into modules a think point may emerge where a nanny can enter the picture and help.
I tie this back to the idea of legibility, with thick transactions being exactly the sort which can't be read by an outsider.
Modularization makes systems more legible, and therefore easier to manage and optimize.
I

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Omicron Variant Post #1: We’re F***ed, It’s Never Over
by Zvi — 2021-11-26T19:00:00.988Z

The last day has seen the imposition of new travel restrictions and spreading alarm about the Omicron variant. It sure looks like a repeat of what happened with Alpha and Delta, as well as the original strain back in early 2020, and that we are probably doing this again.

How will this play out this time? It’s very early. There’s tons of unknowns. Yet there is a lot we can say already, and many actions need to happen now to have any hope of doing much good. So it’s time to move fast, see what we can say right now and try to capture the speed premium.

I’ll start with a summary of what we know about the game board based on previous experience, then look at what we know about Omicron and what moves have been played so far in this round.

I was almost finished with this post when the WHO decided to go with Omicron instead of Nu (or even Xi) and then I had to go through and replace Nu with Omicron about 25 times. We could have just said ‘the Nu variant’ a lot but the WHO hates both efficiency and fun even more than it hates freedom, it seems. Sad.

The First Three Times

In early 2020, we got warnings about a new potential pandemic. Almost all reactions were too little too late. What warnings I offered were less too late than most, but still too tentative and too late.

About a year ago, as the Alpha variant was spreading, it seemed like the same pattern was happening again. It was clear Alpha would take over. I extrapolated into the future, did the math as best I could, wrote the post We’re F***ed, It’s Over, noted the unknowns, and predicted a 70% chance of a large wave between March and May.

As I noted in the later update, it was not over. There was no big wave between March and May of 2021. Alpha wasn’t as additionally infectious as I expected, but it wasn’t that much less additionally infectious than I expected, and I didn’t have enough respect for several factors including seasonality. In hindsight the 70% prediction was somewhat overconfident, and a prediction of about 45% would have been better.

Then, as it looked like things would have otherwise died down and normal life resumed, Delta arrived. It was quickly clear it would not be contained for long and Delta would take over. The situation in India seemed super scary, with hospitals overwhelmed and the serious possibility Delta would sweep through the entire population, first in India and then around much of the worl...
Responding to common objections to the FDA unbanning Paxlovid *right now*
by delton137 — 2021-11-26T17:18:51.499Z
Link: https://moreisdifferent.substack.com/p/the-fda-has-blood-on-their-hands

Here I respond to a number of objections to the FDA making Paxlovid available right now that came up in the comments section on Scott's post "When will the FDA Approve Paxlovid?". I also saw many of these objections on Twitter too in addition to the usual "but what about Thalidomide?".

I'm taking some time off from work right now and had been planning to some researching and writing on AI forecasting via scaling laws, but this seemed way more important!

Procedural notes: I only plan to share posts from my Substack here that are relevant to discussions going on in the LessWrong/Rationalist community. If you want to be notified of all my future posts, please subscribe. I shared this as a link post, but if people think a cross-post for this type of thing would be better in the future, please let me know.