trentmkelly/LessWrong-43k huggingface dataset:
Why did we wait so long for the threshing machine?
by jasoncrawford — 2021-06-29T19:55:38.883Z
Link: https://rootsofprogress.org/why-did-we-wait-so-long-for-the-threshing-machine

When ripe wheat is harvested, the edible seed is encased in an outer husk. Before the seed can be ground into flour, or boiled into porridge, or planted in the field to produce next year’s harvest, it must be removed from the husk. This process is called threshing.

As the husk is quite hard, threshing is a violent process. Traditionally, it was done with a tool called a flail, which is simply a short stick attached by a cord to a longer handle. The grain was spread out on the ground (yes, disgusting) and beaten with the stick to open the casings.

The Growth of Industrial Art, 1892

Other methods included “treading”, in which livestock trampled the grain with their hooves (yes, even more disgusting) or dragged a sledge over the grain (the Latin word for this sledge is tribulum, from which we get the world “tribulation”).

Horse treading. The Growth of Industrial Art, 1892

Occasionally the grain would be rubbed against a wire screen, or placed in a sack and beaten with rocks. It’s no coincidence that the word “thrashing” is similar: it is an archaic spelling of the same term.

As one of the more labor-intensive stages of wheat farming, threshing was a natural candidate to be automated by machinery. And the threshing machine was a relatively simple device: like the cotton gin, the flying shuttle, or the bicycle, it was a mechanical invention that did not depend on any scientific discoveries. Still, threshing machines were not used to any significant degree until the late 1700s in the UK and the early 1800s in the US.

Once again, the question arises: why did we wait so long?

Early claims and concepts

First, it’s not because no one had the idea to mechanize the threshing process. Although we think of the Industrial Revolution as a uniquely inventive age, and the era before it seems blind to the possibility of innovation by comparison, we must remember that there were inventions prior to 1700, many of which were broadly adopted, especially when they helped with essential economic activities. The loom and the printing press predated the threshing machine by centuries, and neither seems far less complex.

The threshing machine was referred to in an English patent as early as 1636 (for historical context, this is not long after the major works of Galileo and Francis Bacon). Sir John Christopher van Berg, a knight of Moravia (part of the present-day Czech Republic), fleeing religio...
Is there a "coherent decisions imply consistent utilities"-style argument for non-lexicographic preferences?
by Tetraspace — 2021-06-29T19:14:21.436Z

The post Coherent decisions imply consistent utilities demonstrates some situations in which an agent that isn't acting as if it is maximizing a real-valued utility function over lotteries is dominated by one that does, and promised that this applies in general.

However, one intuitively plausible way to make decisions that doesn't involve a real-valued utility and that the arguments in the post don't seem to rule out is to have lexicographic preferences; say, each lottery has payoffs represented as a sequence (u1,u2,u3,…) and you compare them by first comparing u1, and if and only if their u1s are the same compare u2, and so on, with probabilities multiplying through by each un and payoffs being added element-wise. The VNM axioms exclude this by requiring continuity, with a payoff evaluated like this violating it because (0,0,…)≺(0,1,…)≺(1,0,…) but there is no probability p for which a p probability of a (1,0,…) payoff and a 1−p probability of a (0,0,…) payoff is as exactly as good as a certainty of a (0,1,…) payoff.

Are there coherence theorems that exclude lexicographic preferences like this also?
Trying to approximate Statistical Models as Scoring Tables
by Jsevillamol — 2021-06-29T17:20:11.050Z

For my PhD research, I have been tinkering with the idea of approximating complex, non-linear statistical models (Bayesian networks) as simple, quasi-linear models.

I am making some headway into the problem, but I feel I need yet to clarify what exactly I want to do, and why this is important.

I am writing this blogpost to 1) spell out the idea better (and get questions and pointers on how to explain it better) and 2) get feedback from the community on which parts of this feel interesting / useful, and which parts I could drop.

First I will explain why I am working on approximating Bayesian networks. Then I will explain the solution I came up with. Finally I will discuss issues with my current approach and next steps.

If you want something to play with, try out

this public prototype

. It showcases a (clunky) solution to the problem, and might help understand what I am trying to do.

Requisites: you should be familiar with Bayesian Networks, d-separation, belief propagation and vector logodds to understand the gist of this post. Shapley values and double crux are also mentioned in passing, though you should be able to skip the parts where they are mentioned and still follow the post. If there is anything else that seems unclear, you should probably let me know, because it would help me understand what do I need to explain better.

Theory of research

My (post-hoc) reasoning to have been focusing on this problem is something like this:

AI models are increasingly embedded in society, and are going to determine an important part of the future of society
In order to increase the benefits of AI, we need to ensure they are robustly aligned with human values
One way of going about this is designing ways of making the AI more transparent to users - so they can oversee if the AI is eg not making hiring decisions based on prejudice or making medical diagnosis based on faulty logic
It is not clear how to do this, but one way of doing it might be 1) identifying how concepts are encoded in AI models, 2) identifying the ways those models process information through the lens of these concepts
Even if we somehow solve 1), we have that 2) is largely an unsolved problem. Concretely, we have Bayesian models as an example where 1) is solved (by design specially in expertly design networks) but there are no clear concepts and abstractions for 2)
We do have some transparent, but ineffi

...
How will OpenAI + GitHub's Copilot affect programming?
by smountjoy — 2021-06-29T16:42:35.880Z

https://copilot.github.com/

https://github.blog/2021-06-29-introducing-github-copilot-ai-pair-programmer/

GitHub Copilot is powered by OpenAI Codex, a new AI system created by OpenAI. OpenAI Codex has broad knowledge of how people use code and is significantly more capable than GPT-3 in code generation, in part, because it was trained on a data set that includes a much larger concentration of public source code.

Will Copilot or similar systems become ubiquitous in the next few years? Will they increase the speed of software development or AI research? Will they change the skills necessary for software development?

Is this the first big commercial application of the techniques that produced GPT-3?

For anyone who's used Copilot, what was your experience like?
Opinionated Uncertainty
by DirectedEvolution — 2021-06-29T00:11:32.183Z

Real Life: It's More Than Mere Algebra!

The expected value (E) of an event is the product of its value (V) and its probability (P):

E = VP.

Considered algebraically, both V and P play equal roles in determining E. It wouldn't make sense to say that "the magnitude of the consequences of an event are more important than its probability." However, the E = VP equation draws attention away from other questions where this statement makes more sense. For example, we will always have uncertainty in estimating the values of V and P. If it is cheaper to reduce our uncertainty of V than P by a certain percentage through further study, then it's more important to study V.

Let's use two examples, one to show when we might be agnostic about whether it's easier to study V or P, and another where we might have strong intuitions that we can gain more by studying one than the other.

For an example of the "agnostic" situation (we have no strong intuitions about whether V or P will be easier to study), imagine that your friend is going to drive to the pub to have a couple beers. His plan is to sober up, then drive home. This is based on his intuition that he won't drink more than a couple beers, and that even if he did, he'd just call a cab. Fortunately, your friend is very responsive to rational argument, and very open to answering your questions honestly. He understands that his risk/benefit calculation is subject to uncertainty, and would change his mind if further investigation changed the result.

You are afraid that he's making a flawed risk/benefit calculation, but you only have five minutes to do your research before he heads out the door. Would you spend that five minutes trying to research the pros and cons of driving himself vs. taking a cab (V)? Or would you try to better estimate the likelihood that your friend gets into an accident on the way home, despite his prediction that he'll have "only a couple beers" (P)?

Determining which is easier to research might depend on the specific situation. But it's easy to see that they're not likely to be equally easy. Your brain might first quickly consider the various factors you might research. Is your friend concerned about factors beyond the cost of cab fare? Is he perhaps hoping that driving himself will act as a commitment device to prevent himself from drinking too much? Will he have to drive any notoriously dangerous roads alo...
Intro to Debt Crises
by Connor_Flexman — 2021-06-28T23:50:15.634Z

In 2018 Ray Dalio wrote a book called Principles of Navigating Big Debt Crises. He argues that we are at least near enough to being in a debt crisis that it is important to start thinking about how to most gracefully deal with them. After reading it, I feel similarly. And yet, I didn’t know what a debt crisis was before his book, and many people I talk with don’t understand why bad debts can not only be hard on their owners but on the whole economy. This is an intro to what a debt crisis is and why specifically it is bad, to be followed up with some posts on their causation and how they relate to the current US situation.

Disclaimer: Dalio is hard to interpret on some central points of causation. I am relaying my best guess as to the truth after looking at some other sources, but I didn't know much about macroeconomics until the last year so this is decidedly not Lindy.

Basics of a debt crisis

In short, debt crises are when a country ends up with too much bad debt, and then its economy suffers. For example, the recent government-debt crisis in Greece was a debt crisis. This was part of a larger and slower debt crisis often called the European sovereign debt crisis. The crash in 2008 was a banking crisis, a type of debt crisis where the debt is held by banks. The Great Depression was a debt crisis (and became a banking crisis). Hyperinflation in the German Weimar Republic in the 20s was a debt crisis, because they owed the Allies a lot of money in war reparations.

Ray Dalio documents ~100 debt crises in various countries from the last century in the end of his book, and I think all of them led to recession. Not every economic crisis can be thought of in this manner—sometimes there are currency crises, commodities crises, or pandemic crises—but nearly every big recession I know of was caused by bad debt (the main counterexample was probably the dot-com bubble, which was in equity and not debt and barely caused a GDP drop). To give you a sense of scale, Dalio’s ballpark for big crises was that 20% of debts got written down 40%, and with a 2x debt-to-GDP ratio this meant that an amount equivalent to 16% of GDP was lost by that country in wealth (before follow-on effects).

People often think of debt issues as stemming from public/government debt (both national and state/municipality).

But this isn’t necessarily true

—about half the crises listed above were about privat...
A quantitative introduction to churning
by electroswing — 2021-06-28T22:21:09.028Z

Credit card companies offer sign up bonuses and other benefits to incentivize you to open a credit card with them. Churning is the art of going after these benefits systematically. It's surprisingly lucrative.

This post serves two purposes. One, I will describe common churning strategies. Two, I will do some rough cost-benefit analysis on these strategies so you can decide for yourself whether churning is worth it. Comments, critiques and corrections are welcome.

Disclaimer

I am a knowledgeable, but not expert churner. Fact check me before making personal financial decisions. A good resource is /r/churning. This is not financial advice.
This advice works best if you're in the US and have a decent credit score (or no credit history).

The big picture

Say you're planning on making a $1000 purchase.

One way to do this is with your debit card or cash. Spend $1000, get the $1000 item.

You can pay a bit less by using a credit card instead. For example, the Chase Freedom Unlimited offers 1.5% cash back on all purchases. You spend $1000, get the $1000 item, and get 1500 Ultimate Rewards Points which can be redeemed as a $15 statement credit.

You can do even better by signing up for a new credit card. For example consider this American Express card:

So you can make the $1000 purchase and get an extra $200 from the signup bonus. All while paying no annual fee. (Yes, this card also earns 1.5% cash back, but you would have counterfactually earned this on a card like the Chase Freedom Unlimited so this isn't super relevant here.)

The Cash Magnet card isn't a once in a lifetime opportunity. There are dozens of stellar signup bonuses offered by various credit providers.

Effective churning is basically a 20% coupon on everything you purchase. The cost? A little time and a bit more attention to detail.

Really? 20%?

Yes, really. To estimate the benefit of churning, I've gone through the credit cards offered by Chase, American Express, Bank of America, Citi, Barclays, and US Bank. I kept track of each card's costs and benefits.

Costs:

Minimum spend requirement (MSR): amount you need to spend on it in order to unlock the sign-up bonus.
Annual fee (AF) for the first year.

Benefits:

The sign-up bonus (SUB). This can come in the form of statement credits, credit card points, airline miles, and hotel points.
Other relatively liquid benefits. Miles a

...
How much interest would there be in a fringe theories wiki?
by Matthew Barnett — 2021-06-28T21:03:16.298Z

I've been exploring a concept for a wiki recently. The idea would be that people contribute fringe theories and present the best evidence for that theory, perhaps by contrasting it with mainstream interpretations of the data. Some examples of pages on the wiki could include,

CellBioGuy's theory that the paleocene-eocene thermal maximum was caused by an industrial civilization of birds living in Antarctica.
Robin Hanson's theory that a panspermia sibling to Earth hosts an ancient advanced alien civilization, whose world government experienced complex system rot in the process of developing an Earth-monitoring system, resulting in bizarre UFO encounters with them that we sometimes hear about on Earth.
The theory from various cryonicists, following Eric Drexler, that future nanotechnology will be sufficient to repair damage from vitrification and subsequent cryopreservation.
Robin Gardiner's theory that the ship called "The Titanic" was in fact the ship Olympic, and was purposely sunk as part of an elaborate insurance scam.
Scott Alexander's pseudo-religious theory that "There is an all-powerful, all-knowing logically necessary entity spawning all possible worlds and identical to the moral law."

The purpose would not be to make a determination to whether each theory was true or false, but rather just present the evidence.

Naturally, I'm more interested in theories that (1) have some sort of technical argument favoring it (regardless of credibility), (2) aren't merely moral or political theses in disguise, and (3) aren't already covered in sufficient detail in other places (unlike the JFK conspiracy theory). Wikipedia is a terrible place to do this, given their rules disallowing original research. Of course, without such constraints, there is a large risk that a Fringe Theories Wiki would attract bad editors, so some strict editing rules would still probably be required on the site.
Revive Meetups?
by goose000 — 2021-06-28T15:26:06.726Z

I moved to the DC area about a year ago, and now that case levels are extremely low and vaccinations have been readily available for months, I'd love to find a chance to meet up in person (I would've been happy to meet outside earlier, but it seems others didn't really feel the same). Happy to join anything SSC (well, ACX now) or lesswrong focused, or really anything from this crowd.
I'd offer to host, but I'm afraid my location is not generally accessible.
How teams went about their research at AI Safety Camp edition 5
by Remmelt — 2021-06-28T15:15:12.530Z

AI Safety Camp connects new collaborators worldwide to discuss and decide on a concrete research proposal, gear up online as a team, and try their hand at AI safety research* during intensive coworking sprints.

Six teams formed at our recent 5-month virtual camp. Below are their explanations.
Each team has summarised their analysis and experiments, and presented their findings at our final online weekend together. Some published a paper or post since. Most are continuing work, so expect a few more detailed and refined write-ups down the line.

Modularity Loss Function

Team members: Logan Smith, Viktor Rehnberg, Vlado Baca, Philip Blagoveschensky, Viktor Petukhov
External collaborators: Gurkenglas

Making neural networks (NNs) more modular may improve their interpretability. If we cluster neurons or weights together according to their different functions, we can analyze each cluster individually. Once we better understand the clusters that make up a NN, we can better understand the whole.

To that end, we experimented with pairwise distances according to the neuron’s jacobian correlation, coactivations, and estimated mutual information. These metrics can be plugged into spectral clustering algorithms to optimize for modules in the network; however, having a modular NN does not equate to a more interpretable one. We investigated task-based masking methods to test for modularity as well as neuron group activation (via Google Dream) in order to test for these modules being more interpretable than an equivalent amount of neurons. We ran out of time before fitting all the pieces together, but are intending on working on it more over the summer.

Presentation on final weekend (slides)

Cooperativity & Common Pool Resources

Team members: Quinn Doughtery, Ben Greenberg, Ariel Kwiatkowski

In environments with common pool resources, a typical failure mode is the tragedy of the commons, wherein agents exploit the scarce public resource as much as possible. An every-man-for-himself dynamic emerges, further increasing scarcity. This behavior results in conflict, inefficient allocation, and resource depletion.

Even if an individual would prefer to harvest the resource sustainably, they are punished for doing so unilaterally. What’s missing is an institution that will incentivize the group to “cooperate”. In this project, we study such interventions for avo...
Eponymous Laws Part 2: Laws of Programming and Software Development
by rogersbacon — 2021-06-28T12:46:21.718Z

(See

Eponymous Laws Part 1: Laws of the Internet

)

Though ostensibly about programming and software development, most of these laws refer to fundamental aspects of human psychology (and folly). Enjoy.

Flon’s Law – “There does not now, nor will there ever, exist a programming language in which it is the least bit hard to write bad programs.”

Postel’s Law (aka Robustness Principle) – “Be conservative in what you send, be liberal in what you accept”… “In other words, programs that send messages to other machines (or to other programs on the same machine) should conform completely to the specifications, but programs that receive messages should accept non-conformant input as long as the meaning is clear.”

Bradley’s Bromide – “If computers get too powerful, we can organize them into a committee — that will do them in.”

Weinberg’s Law – “If builders built buildings the way programmers wrote programs, the first woodpecker that came along would destroy civilization.”

Hartree’s Law – “Whatever the state of a project, the time a project-leader will estimate for completion is constant.”

Hoare’s Law of Large Programs – “Inside every large program is a small program struggling to get out.”

Jakob’s Law of the Internet Use Experience – “Users spend most of their time on other sites. This means that users prefer your site to work the same way as all the other sites they already know.”

Ninety-Ninety Rule – "The first 90% of the code takes the first 90% of the time. The remaining 10% of the code takes the remaining 90% of the time."

Knuth’s optimization principle – “Premature optimization is the root of all evil.”

Linus’ Law – “Given enough eyeballs, all bugs are shallow.”

Kerchkhoff’s Principle – “In cryptography, a system should be secure even if everything about the system, except for a small piece of information – the key – is public knowledge.”

Wirth’s law – “Software gets slower faster than hardware gets faster.” (as a counterpoint to Moore’s Law)

Brooks’ Law – “Adding manpower to a late software project makes it later.”

Conway’s Law – “Any piece of software reflects the organizational structure that produced it.” Or…”Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations.”

Eagleson’s Law – “Any code of your own that you haven’t looked at for six or more months might as well have been written by...