trentmkelly/LessWrong-43k huggingface dataset:
Are PS5 scalpers actually bad?
by willbradshaw — 2021-05-18T12:27:16.544Z

Lately, I've been trying to get my hands on a PlayStation 5. These are in pretty short supply in the UK these days – thanks to the chip shortage, and exacerbated by Ever Given, demand currently far outstrips supply. To get a PS5 from any retailer, you need to subscribe to and religiously follow stock alerts, read up on various site-specific tricks, and generally sink a whole lot of time and mental effort.

One thing that a lot of people believe makes this much harder is the presence of "scalpers": resellers who buy up large amounts of PS5 stock at every drop and sell it for large markups on eBay[1]. Since these resellers, unlike most retail consumers, are trying to acquire many PS5s across multiple stock drops, they can invest in various tools (chiefly, various kinds of bots) to make them more effective at winning these drops. As a result, an unknown-but-probably-substantial proportion of the consoles available at each drop go to these resellers.

If you have any experience of interacting with humans, it will not surprise you that a lot of people are very angry about this. My natural libertarian-ish response to this is to say these people are being silly – that this is just the kind of thing that happens when retailers are foolishly prevented (by public opinion, if not by law) from charging the true market price. But I don't like feeling like a dogmatic libertarian, so I'd like to dig into this a little more.

Even in the complete absence of resellers, I'm pretty confident that getting ahold of a PS5 right now would still be a difficult and costly proposition, requiring large investments of time and mental effort. For those who are willing and able to pay the marked-up price, resellers are thus providing a valuable service, allowing these consumers to pay money to avoid these costs. Having spent a few fruitless hours trying to win PS5 stock drops, the idea of paying someone else to do this is pretty attractive to me, and I'm sure I'm not the only one.

The main counter to this is that, by buying up large amounts of PS5 stock at every drop, resellers are making things (even) harder for those who are not willing or able to pay the marked-up prices. If you have strong egalitarian instincts, this might be enough to condemn these resellers as harmful. But even assuming that this would be bad if it were true, I'm not sure it is true – at least if you consider a slightly l...
Knowledge Neurons in Pretrained Transformers
by evhub — 2021-05-17T22:54:50.494Z
Link: https://arxiv.org/abs/2104.08696

This is a link post for the Dai et al. paper “Knowledge Neurons in Pretrained Transformers” that was published on the arXiv last month. I think this paper is probably the most exciting machine learning paper I've read so far this year and I'd highly recommend others check it out as well. Edit: Maybe not; I think Paul's skeptical take here is quite reasonable.

To start with, here are some of the basic things that the paper demonstrates:

BERT has specific neurons, which the authors call “knowledge neurons,” in its feed-forward layers that store relational facts (e.g. “the capital of Azerbaijan is Baku”) such that controlling knowledge neuron activations up-weights/down-weights the correct answer in relational knowledge prompts (e.g. “Baku” in “the capital of Azerbaijan is <mask>”) even when the syntax of the prompt is changed—and the prompts that most activate the knowledge neuron all contain the relevant relational fact.

Knowledge neurons can reliably be identified via a well-justified integrated gradients attribution method (see also “Self-Attention Attribution”).

In general, the feed-forward layers of transformer models can be thought of as key-value stores that memorize relevant information, sometimes semantic and sometimes syntactic (see also “Transformer Feed-Forward Layers Are Key-Value Memories”) such that knowledge neurons are composed of a “key” (the first layer, prior to the activation function) and the “value” (the second layer, after the activation function).

The paper's key results—at least as I see it, however—are the following:

Taking knowledge neurons that encode “the r of h is t” and literally just adding t′−t to the value neurons (where t, t′ are just the embeddings of t, t′) actually changes the knowledge encoded in the network such that it now responds to “the r of h is <mask>” (and other semantically equivalent prompts) with t′ instead of t.

For a given relation (e.g. “place of birth”), if all knowledge neurons encoding that relation (which ends up being a relatively small number, e.g. 5 - 30) have their value neurons effectively erased, the model loses the ability to predict the majority of relational knowledge involving that relation (e.g. 40 - 60%).

I think that particularly the first of these two results is pretty mind-blowing, in that it demonstrates an extremely simple and straightforward procedure for dire...
What Would You Store to Maximize Value in 100 Years? A Thought Experiment
by JohnGreer — 2021-05-17T20:46:18.933Z

This is cross-posted from my blog.

When I was a kid, I used to want to not take my action figures out of the packaging because I knew it would mess up the collector’s value of it. After all, it might be worth a lot in the future! Beanie Babies, football cards, Pokemon cards - these were precious commodities to childhood John and to plenty of adults in the 90s.

How wrong I was. This is obviously not worth doing. You’d get more utility from just playing with them.

https://www.youtube.com/watch?v=TF2zPfwMF3c

First, there’s the cost of storing the damn things for years taking up space in a tub in your attic. Second, it’s very unlikely they’ll be worth any real money in the future. For every one that’s worth hundreds of dollars, there are thousands that are only worth $5. You’ve likely paid hundreds of dollars for them in the first place. Now you have to do the work of listing and selling them, and you’ve made at best triple your money. The returns just aren’t worth it. If you just cared about making money, there are way better opportunities elsewhere (currently cryptocurrency).

If you wanted to store something that would be worth the most value in 100 years, what would you store?

Let’s say you had a budget of $50,000, or $100,000, or $1,000,000.

The most straightforward thing to do is to store $100 bills. But will we use physical currency in a hundred years? Will we even use currency in a hundred years? Even if we still used USD, inflation would just depreciate its value. I doubt any of the cryptos today will be the main one used. Maybe they would be a collectible, though?

Gold, gems, and other precious metals are the most lindy but I don’t know how valuable they’ll still be besides their use in manufacturing.

If I had to pick a precious metal to store, I’d probably pick rhodium which is going for a cool $16,650 to gold’s $1,907.10 and platinum’s $1,059.00. Funnily enough, in looking this up, I just learned platinum is currently worth less than gold. I always thought platinum was worth more than gold but apparently its price fluctuates more. It’s often worth more but not always.

You could store rare comic books or trading cards. Famous paintings. But if the world moves towards VR and such, then maybe people will care much less unless physical copies are still status items.

You could buy Action Comics #1 and hope the superhero franchises keep chugging alon...
Love on Cartesian Planes
by bksilv3r — 2021-05-17T19:19:02.840Z

Introduction

Note: this was originally published on Medium, since I'm putting a bunch of other related posts here I thought it best to migrate the original as well.

For over a decade now I’ve had a concept milling around my head that has been subtly influencing my behaviours. Perhaps formulating a Theory of Love is not something you’d expect coming from a a university student studying macroeconomics and drawing too many AD-AS curves, but its staying power and adaptability has continued to surprise me over the years and in the spirit of Tyler Cowen’s advice on writing a book I felt like I had to get this out of my system.

In essence the concept dwells on identifying causal components of the feeling of love, assessing how their relationship to love maps on a two-dimensional plane, and then assessing their movement over time in a way to allow myself to make more optimised decisions in relation to love experienced. Like all good data-nerds I love a good XY plot, and applying their power to the concept of love itself has been a fun, enlightening and rewarding exercise.

Initially I developed this framework based on my own strong internal preferences toward the remembering self, expressing “love experienced” as the Area-Under-Curve for a chart expressing love felt over time. A notable effect of this is the overwhelming impact over time of shared experiences as the primary actionable component of love.

Love in a given relationship over time

Note: all charts included in this piece were fabricated in Microsoft Word, badly. I could have scripted up something in Python to draw nicer pictures, but this would not have accurately captured my (much more visual) thought process.

I recall seeing a curve like this (sans AuC) on a webcomic like XKCD or SMBC around 2004–2005, but when I went looking for it years later I was unable to find it. That one basic diagram gave me a foundation for everything that follows, and helped inform my relationship decisions greatly. So whoever made it, thanks!

Choosing a definition of love

Articulating the concept of love is, to be honest, too difficult a job for me to try to fully explore. The cop out way to express what is love would be to say “you know it when you feel it”, but as evidenced by numerous works of art over the ages, this is really bad advice (the most pertinent example to come to mind is Romeo & Juliet). Instead I will si...
Announcing the UK Covid-19 Crowd Forecasting Challenge
by nikos — 2021-05-17T17:29:26.282Z

Next Monday (May 24th 2021) is the official begin of the UK Covid-19 Crowd Forecasting Challenge. Over the course of 12 weeks, human forecasters will be asked for their prediction of Covid-19 case and death numbers. The (aggregated) forecasts will then be submitted to the European Forecast Hub and compared to models submitted by other research institutions.

How to sign up

In order to sign up, checkout this post that explains everything in detail. You can also go directly to app.crowdforecastr.org or rt-app.crowdforecastr.org where you can directly make a forecast. Forecasts can be made every week from Sunday 10am to Monday 10pm UK time.

Why is this interesting

This challenge is part of an ongoing research project by the Centre for Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine in collaboration with Imperial College London and Public Health England.

At the moment, the UK is at a critical juncture: on the one hand we see a very successful vaccination campaign. On the other hand, new variants are emerging and spreading and easing restrictions is not without risk. Many of these things are hard to model and hard to predict. The next weeks may therefore hold important lessons for other countries going out of lockdowns.

In the past we submitted forecasts to the European Forecast Hub as well as the German and Polish Forecast Hub - with very promising results. The crowd forecasts usually were among the top performers, beating most other computer-based prediction models. Another thing that we found is that humans had a comparative advantage when it came to forecasting cases. Case numbers are extremely hard to model and depend on chance as well as shifting policies. Deaths, on the other hand, are a lagged quantity that depend on previous cases through some complicated mechanism. Models often were slightly better at capturing this.

The main goal of the Crowd Forecast Challenge is to

learn more about how well humans (as opposed to computer models) are able to foresee these future developments
provide valuable insight to policy makers through the European Forecast Hub
Investigate an exciting new way of making forecasts. In addition to making a direct forecasts, participants can forecast the trajectory of Rt (i.e. the average number of people each infected person will infect in turn). The trajec

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A.D&D.Sci May 2021: Interdimensional Monster Carcass Auction
by abstractapplic — 2021-05-17T15:54:28.974Z

You are the most successful butcher in your tiny, snow-swept village. You’re just about to close up shop when a man appears from an interdimensional portal and offers to sell you carcasses from a distant timeline.

You’d be more perturbed, but you live and work in a village frequented by Adventurers; this is maybe the second- or third-weirdest thing you’ve had to deal with this month. Instead of panicking, you calmly and logically inquire as to how you could possibly evaluate the value of such oddities.

The stranger smiles, tells you you always say that, and offers you a record (written in what is unmistakably your handwriting) telling you how much money your parallel-universe doppelgangers made when he sold them a batch of dead things.

Finding this to be in order, you prepare to begin negotiations. The man smiles again, confesses he’s found himself to be a much worse salesman than you are a haggler, and instead proposes a sealed-bid auction, like the ones you remember from your apprenticeship. When you accept, he conjures three versions of you from neighbouring timelines and tells you all to get bidding.

(In case the subtext isn’t clear: yes, this challenge will be pitting you against your fellow LessWrongers.)

The available lots are as follows:

Lot Number	Species	Days Since Death
#1	Red Dragon	1
#2	Jungle Mammoth	1
#3	Mild Boar	5
#4	Jungle Mammoth	5
#5	Mild Boar	1
#6	Green Dragon	2
#7	Mild Boar	2
#8	Mild Boar	5
#9	Mild Boar	8
#10	Mild Boar	6
#11	Mild Boar	8
#12	Blue Dragon	8
#13	Jewel Beetle	1
#14	Mild Boar	1
#15	Jungle Mammoth	4
#16	Jungle Mammoth	2
#17	Mild Boar	5
#18	Red Dragon	6
#19	Mild Boar	5
#20	Jungle Mammoth	1

You have 300 silver pieces to hand. So does the competition. How will you bid?

Notes:

Payments are collected in lot order; if you’re unable to pay your bid by the time a given lot comes up, you lose your claim to that lot but incur no penalty.
Matching bids will be resolved randomly.
One entry per player, please.
If I get more than four entries, I’ll split the game into multiple universes; and if I get a number of entries not divisible by four, I’ll fill the empty spots with NPCs: no matter what happens, you’ll definitely be bidding against three opponents.
As a prize, the player who makes the most profit gets to specify one non-ridiculous thing about July’s D&D.Sci challenge. (Note that by my standards, “it must centre on a talking penguin called Steve” is much less ridiculous than “it must not involve any sele

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SpaceX should provide IPFS proxies with Starlink
by ChristianKl — 2021-05-17T15:05:28.521Z

If everything goes according to plan for SpaceX, SpaceX will start permanent bases on moon and mars in the next decade. One challenge for moon and mars bases will be that a good chunk of the software we have on earth stops working when there's no internet connection.

Package managers, whether they are Ubuntu's Snap or programming tools like npm download their packages from the internet and fail when there are minutes of delay for traffic between earth and mars. The same goes for App stores and operating system updates.

If those services would use IPFS instead of TCP/IP they would still work on mars if there's an IPFS bridge between earth and mask that transfers IPFS packages that are requested on mars but currently unavailable from earth to mars.

While it's possible to write custom software for Mars that works without TCP/IP it would benefit a Mars base a lot when as much of normal software as possible that can use IPFS to transfer data uses IPFS.

One reason why IPFS currently isn't used more is that every node that normally requests data from the IPFS network has to broadcast it's IP address openly together with the data it wants to access. In that configuration an attacker can gather knowledge about which software is run within a company network which is undesirable.

From the privacy perspective it would be advantageous if a user only has to reveal to his ISP which package they want to access via IPFS. If the user only would have to reveal to his ISP the content they want to access they would have better privacy then they currently have with TCP/IP.

This setup would be advantageous for the ISP as well because content that's downloaded by many users doesn't have to be requested multiple times from its original source which saves both the ISP and the original data source bandwidth costs.

Legacy ISPs profit a bit from this setup by being able to bill Netflix, Youtube and Amazon prime for hosting servers on their premises to do a similar job, but this predatory setup is bad for the open internet. It means that only companies that can afford servers at every ISP can get comparable performance for their video service while if this job would be done by the ISP, the ISP would pay for the servers on their network.

IPFS to TCP/IP being superior for a lot of content is a reason for SpaceX to provide IPFS proxies to their Starlink customers. Providing IPFS proxies will be an ...
How to determine the value of optionality?
by sphericaltree — 2021-05-17T02:19:05.263Z

How does one determine the value represented by the ability to choose from many options, and to switch between them over a period of time?

Intuitively, it makes sense that getting to choose from Choice A or Choice B over a time period t, is more valuable than selecting one of A or B and sticking with it for that time period. But how would one go about calculating that premium?

For example, owning a house presents the optionality of living in it, selling it, or renting it out. Imagine a world where you could move in to that house whenever, rent it out quickly for any period of time (AirBnb?), or sell it instantly (Opendoor?).

Without having the choice and just selecting some option, you just calculate the value of house over a time period t as max(value(Live, t), value(Rent, t), value(Sell, t)).

What is the premium for having the choice between these options and the ability to switch between them, over the single decision?
How concerned are you about LW reputation management?
by DirectedEvolution — 2021-05-17T02:11:38.718Z

I've been considering why I sometimes vote, but don't comment on posts, even though I'd like to. Sometimes, I delete substantial comments that I've already written.

I think the reason is something like reputation management. Ideally, we'd all feel safe to debate and consider ideas here, without fearing looking stupid. After all, that's how we learn.

Yet I want to be able to signal "I've put a lot of work into this post, believe it really offers some value, and am prepared to stand behind it" in some cases. I fear that commenting a lot, or posting imperfect posts, puts me at risk of losing some of that credibility. The more I comment or post (or at least the more above average I do so), the more I risk accumulating a track record of incorrect statements. Also, even participating in discussions that aren't my area of writerly focus makes me perceive a credibility risk, analogous to how some scientists carefully avoid speaking about topics beyond their specialty.

A side issue is that public comments can sometimes activate my "called out in public" social sense. For that reason, I have started to use PMs and zoom calls more, both to de-confuse myself with someone else's post, and to have informal discussions off the public record. Those discussions often seem much more alive and productive than the ones I have through the comment section here.

While it seems beneficial to the community to have people willing to publicly list their mistakes and risk a free and open dialog, I wonder how much this reputation management concern is felt by others?

Also, if it's not just me, what would be the best way to solve this problem? I can think of a few:

Deciding that this is a feature, not a bug, and continuing to treat reputation as a fragile and valuable thing.
LW developers create an official and convenient anonymizing option, where posters can choose not to have their profile name attached to a comment/post but can reveal it later if they choose.
Actively taking steps to overcome this phobia, perhaps by creating a "ways I've been wrong" page, or making an effort to treat the comment section of posts more as a casual and informal space.
Creating two accounts, one public and one pseudonymous.
Using private messages, zoom calls, and chat for more informal conversation.
Looking into guidance aimed at academics for managing their online presence.
What will 2040 probably look like assuming no singularity?
by Daniel Kokotajlo — 2021-05-16T22:10:38.542Z

I'm looking for a list such that for each entry on the list we can say "Yep, probably that'll happen by 2040, even conditional on no super-powerful AGI / intelligence explosion / etc." Contrarian opinions are welcome but I'm especially interested in stuff that would be fairly uncontroversial to experts and/or follows from straightforward trend extrapolation. I'm trying to get a sense of what a "business as usual, you'd be a fool not to plan for this" future looks like. ("Plan for" does not mean "count on.")

Here is my tentative list. Please object in the comments if you think anything here probably won't happen by 2040, I'd love to discuss and improve my understanding.

Energy is 10x cheaper. [EDIT: at least for training and running giant neural nets, I'm less confident about energy for e.g. powering houses but I still think probably yes.] This is because the cost of solar energy has continued on its multi-decade trend, though it is starting to slow down a bit. Energy storage has advanced as well, smoothing out the bumps. [EDIT: Now I think fusion power will also be contributing, probably. Though it may not be competitive with solar, idk.]
Compute (of the sort relevant to training neural nets) is 2 OOMs cheaper. Energy is the limiting factor.
Models 5 OOMs more compute-costly than GPT-3 have been trained; these models are about human brain-sized and also have somewhat better architecture than GPT-3 but nothing radically better. They have much higher-quality data to train on. Overall they are about as much of an improvement over GPT-3 as GPT-3 was over GPT-1.
There's been 20 years of "Prompt programming" now, and so loads of apps have been built using it and lots of kinks have been worked out. Any thoughts on what sorts of apps would be up and running by 2040 using the latest models?
Models merely the size of GPT-3 are now cheap enough to run for free. And they are qualitatively better too, because (a) they were trained to completion rather than with early stopping, (b) they were trained on higher-quality data, (c) various other optimized architectures and whatnot were employed, (d) they were then fine-tuned on loads of data for whatever task is at hand, and (e) decades of prompt programming and prompt-SGD has resulted in excellent prompts as well that fully utilize the model's knowledge, (f) they even have custom chips specialized to run specific models.
The biggest

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