trentmkelly/LessWrong-43k huggingface dataset:
A misconception about immigration
by limerott — 2019-08-19T22:37:17.875Z
Link: https://limerott.com/a-misconception-about-immigration/

Edit: I am not arguing for or against immigration. My intent was to explain why the common reaction "Immigrants are robbing us of our jobs" is flawed.

In discussions about immigration, there is a crucial aspect about its economic viability that is often left unsaid: Immigrants create their own demand.

When somebody immigrates to a new country, most things about him remain the same. His set of skills stays the same, so do his traditions, norms and culture. But more importantly, since he is still a human being, there is a long list of services and commodities that he demands: groceries, cloths, a home, a barber, entertainment to name a few of them.

Just by entering another country, he does not suddenly become a one-dimensional economic agent who can take a job but who never consumes anything. Quite the contrary: As he adapts to a new culture, his demand will eventually match that of the native population.

Why is this important? Because for the job that he takes away from the economy, he compensates by increasing aggregate demand. This eventually leads to more jobs. For example, since there is a rise in demand for produce, the supermarket is forced to create a new position. Since the immigrants want to enjoy their native cuisine, a nifty business man starts a restaurant, creating multiple jobs as a result. To get to work, they may commute via bus, which may eventually lead to the opening of an entirely new bus connection. So, just by being a human being, an immigrant creates jobs.

At this point, it is important to note that immigration poses a wide range of challenges: from different social norms to xenophobia to language barriers. But for clarity, we will restrict ourselves to its economic side.

Short and long term

Imagine that there is a town A with 10 people and a town B with 1000 people. Both of them have functional economies.

In one scenario, the political leaders of the two towns decide to unite the two towns. Nothing except the name will change. So, there is now a town C that has 1010 inhabitants. Will town C have a functional economy? Of course! After all, all that was done was subsuming two already functional economies.

In another scenario, a catastrophic event makes town B uninhabitable and forces everyone to leave for town A. In the beginning, it is completely overwhelmed by the foreigners. If there were any open jobs, there will be massive competition for ...
Do We Change Our Minds Less Often Than We Think?
by Raemon — 2019-08-19T21:37:08.004Z

In "We Change Our Minds Less Often Than We Think", Eliezer quotes a study:

Over the past few years, we have discreetly approached colleagues faced with a choice between job offers, and asked them to estimate the probability that they will choose one job over another. The average confidence in the predicted choice was a modest 66%, but only 1 of the 24 respondents chose the option to which he or she initially assigned a lower probability, yielding an overall accuracy rate of 96%.

—Dale Griffin and Amos Tversky

Eliezer then notes that this radically changed the way he thought:

When I first read the words above—on August 1st, 2003, at around 3 o’clock in the afternoon—it changed the way I thought. I realized that once I could guess what my answer would be—once I could assign a higher probability to deciding one way than other—then I had, in all probability, already decided. We change our minds less often than we think. And most of the time we become able to guess what our answer will be within half a second of hearing the question.

[...]

But we change our minds less often—much less often—than we think.

But a) this seems like it's pre-replication crisis, b) regardless, a sample size of 24 is not nearly high enough for me to be very confident in this.

"How often people change their mind" seems like a fairly important question. Anyone know of further work in similar space here? Ideally asking the question from a few different angles.
Classifying specification problems as variants of Goodhart's Law
by Vika — 2019-08-19T20:40:29.499Z

There are a few different classifications of safety problems, including the Specification, Robustness and Assurance (SRA) taxonomy and the Goodhart's Law taxonomy. In SRA, the specification category is about defining the purpose of the system, i.e. specifying its incentives. Since incentive problems can be seen as manifestations of Goodhart's Law, we explore how the specification category of the SRA taxonomy maps to the Goodhart taxonomy. The mapping is an attempt to integrate different breakdowns of the safety problem space into a coherent whole. We hope that a consistent classification of current safety problems will help develop solutions that are effective for entire classes of problems, including future problems that have not yet been identified.

The SRA taxonomy defines three different types of specifications of the agent's objective: ideal (a perfect description of the wishes of the human designer), design (the stated objective of the agent) and revealed (the objective recovered from the agent's behavior). It then divides specification problems into design problems (e.g. side effects) that correspond to a difference between the ideal and design specifications, and emergent problems (e.g. tampering) that correspond to a difference between the design and revealed specifications.

In the Goodhart taxonomy, there is a variable U* representing the true objective, and a variable U representing the proxy for the objective (e.g. a reward function). The taxonomy identifies four types of Goodhart effects: regressional (maximizing U also selects for the difference between U and U*), extremal (maximizing U takes the agent outside the region where U and U* are correlated), causal (the agent intervenes to maximize U in a way that does not affect U*), and adversarial (the agent has a different goal W and exploits the proxy U to maximize W).

We think there is a correspondence between these taxonomies: design problems are regressional and extremal Goodhart effects, while emergent problems are causal Goodhart effects. The rest of this post will explain and refine this correspondence.

The SRA taxonomy needs to be refined in order to capture the distinction between regressional and extremal Goodhart effects, and to pinpoint the source of causal Goodhart effects. To this end, we add a model specification as an intermediate point between the ideal and design...
Unstriving
by Jacob Falkovich — 2019-08-19T14:31:56.786Z

Cross-posted from Putanumonit

Successometer

Since I was a kid, I have built my self-esteem on a feeling of “forthcoming greatness”. Whatever I actually accomplished I never paused to be proud of, and I never sweated the failures either. It was all just a stepping stone to something completely different and undeniably awesome, a “life mission” that will be important and meaningful and finally confer on me the title of #SuccessfulPerson. Until that moment came, I just needed to know that I was growing, progressing, improving, optimizing.

But upon entering my second gigasecond I’m starting to realize that this mindset makes little sense going forward, and was perhaps delusional in retrospect as well. But if I give up on rapid improvement and impending awesomeness, I don’t know what can possibly replace them.

By all objective metrics, I’m as successful today as I could hope to be a decade ago. I’m happily married, well inside the richest 1% globally, have found my tribe and earned some respect in it. I should be able to relax and take some satisfaction in my current situation. And yet the thought that in 5 years my life will look exactly like it does today fills me with dread.

What’s the problem? Jordan Peterson’s fourth rule says: compare yourself with who you were yesterday, not with who someone else is today. I do both, and both are a problem.

I seek to be inspired by awesome people, but it is then inevitable that I compare myself to them. There could be many blogs that are worse than Putanumonit, but I don’t have the time to read them. I read SlateStarCodex and the very best curated articles from elsewhere, and in comparison to those Putanumonit seems quite shabby. Offline too, one of the main perks of success is associating with successful people. Being involved in the rationality community in NYC means I hang out with Spencer Greenberg, who runs an Effective Altruism startup foundry while doing social science research and innovative meetups in his spare time — making me feel less than impressive.

But that’s not even the main issue. I’m never jealous of other people’s success for one, and I know that the people I look up to probably feel inadequate when they read about Elon Musk. It’s the comparison to who I was yesterday that’s more insidious.

Comparing myself to my old self means that my internal successometer measures only the derivative of my life’s trajectory...
Goodhart's Curse and Limitations on AI Alignment
by Gordon Seidoh Worley — 2019-08-19T07:57:01.143Z

I believe that most existing proposals for aligning AI with human values are unlikely to succeed in the limit of optimization pressure due to Goodhart's curse. I believe this strongly enough that it continues to surprise me a bit that people keep working on things that I think clearly won't work, though I think there are two explanations for this. One is that, unlike me, they expect to approach superhuman AGI slowly and so we will have many opportunities to notice when we are deviating from human values as a result of Goodhart's curse and make corrections. The other is that they are simply unaware of the force of the argument that convinces me because, although it has been written about before, I have not seen recent, pointed arguments for it rather than technical explanations of it and its effects, and my grokking of this point happened long ago on mailing lists of yore via more intuitive and less formal arguments than I see now. I can't promise to make my points as intuitive as I would like, but nonetheless I will try to address this latter explanation by saying a few words about why I am convinced.

Note: Some of this borrows heavily from a paper I have out for publication, but with substantial additions for readability by a wider audience.

Goodhart's Curse

Goodhart's curse is what happens when Goodhart's law meets the optimizer's curse. Let's review those two here briefly for completeness. Feel free to skip some of this if you are already familiar.

Goodhart's Law

As originally formulated, Goodhart's law says "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes". A more accessible expression of Goodhart's law, though, would be that when a measure of success becomes the target, it ceases to be a good measure. A well known example of Goodhart's law comes from a program to exterminate rats in French-colonial Hanoi, Vietnam: the program paid a bounty for rat tails on the assumption that a rat tail represented a dead rat, but rat catchers would instead catch rats, cut off their tails, and release the rats so they could breed and produce new rats so their tails could be turned in for more bounties. There was a similar case with bounties for dead cobras in British-colonial India that intended to incentivize the reduction of cobra populations that instead resulted in the creation of cobra farms. And of course w...
Raph Koster on Virtual Worlds vs Games (notes)
by Raemon — 2019-08-18T19:01:53.768Z

Raph Koster is a game designer who's worked on old-school MUDs, Ultima Online, and Star Wars Galaxies among others. His blog is a treasure trove of information on game design, and online community building.

The vibe I get is very sequences-like (or, perhaps more like Paul Graham?). There's a particular genre I quite like of "Person with a decades of experiences who's been writing up their thoughts and principles on their industry and craft. Reading through their essays not only reveals a set of useful facts, but an entire lens through which to view things."

I'll most likely use the comments of this post to braindump thoughts or summarize things as I work my way through his corpus.

He has two major books I'm aware of:

A Theory of Fun – An illustrated book who's central thesis here is "Games are about learning. When you've learned all you can learn from a game, it becomes boring." (this has a vibe very similar to Scott McCloud's Understanding Comics, which is a similar kind of braindump that teaches a lens to view things). [PDF of the first few pages here]

Postmortems – This is a collection of essays Raph wrote across his career, starting from text-based Multi User Dungeons, then Ultimate Online, and eventually Star Wars Galaxies. (I think most of this is available online as blogposts but I purchased it to read on kindle)

And meanwhile, his blog itself, divided into:

Recommended Posts from 1998 - 2012, with topics including:
Recommended Posts from 2012 - 2017, topics including
Most popular posts (all time, I think?)

With posts clustered into topics like:

Theory of Fun (cognition and games
Game Grammar articles
Game Development Process
Experience/Narrative Design
Games as Art
General Game Design
Game Economies
Community Design
Star Wars Galaxies articles/anecdotes
Ultimate Online Articles/ancedotes
Misc game postmortems
Ethics in Game Design
Player Rights (i.e. treating players ethically)
Gamification
Games as Business
Community and Marketing

Virtual Worlds vs Games

A central question he keeps revisiting is the difference between a virtual world, and a game. I think this is well encapsulated in "Designing a Living Society in SWG, part one and part two."

A game is "something with rules, where your actions have consequences, and you can achieve some kind of mastery." Whereas a world is more like, well, a world – ...
"Can We Survive Technology" by von Neumann
by Ben Pace — 2019-08-18T18:58:54.929Z
Link: http://geosci.uchicago.edu/~kite/doc/von_Neumann_1955.pdf

"The great globe itself" is in a rapidly maturing crisis —a crisis attributable to the fact that the environment in which technological progress must occur has become both undersized and underorganized. To define the crisis with any accuracy, and to explore possibilities of dealing with it, we must not only look at relevant facts, but also engage in some speculation. The process will illuminate some potential technological developments of the next quarter-century.

Added: see orthonormal's comment below for a summary.
Prokaryote Multiverse. An argument that potential simulators do not have significantly more complex physics than ours
by mako yass — 2019-08-18T04:22:53.879Z

Definitions

"Universe" can no longer be said to mean "everything", such a definition wouldn't be able to explain the existence of the word "multiverse". I define universe as a region of existence that, from the inside, is difficult to see beyond.

I define "Multiverse" as: Everything, with a connoted reminder; "everything" can be presumed to be much larger and weirder than "everything that you have seen or heard of".

What this argument is for

This argument disproves the simulation argument for simulators hailing from universes much more complex than our own. Complex physics suffice much much more powerful computers (I leave proving this point as an exercise to the reader). If we had to guess what our simulators might look like, our imagination might go first to universes where simulating an entire pocket universe like ours is easy, universes which are as we are to flatland or to conway's game of life. We might imagine universes with more spacial dimensions or forces that we lack.

I will argue that this would be vanishingly unlikely.

This argument does not refute the common bounded simulation argument of simple universes (which includes ancestor simulations). It does carve it down a bit. It seems to be something that, if true, would be useful to know.

The argument

The first fork of the argument is that a more intricate machine is much less likely to generate an interesting output.

Life needs an interesting output. Life needs a very even combination of possibility, stability, and randomness. The more variables you add to the equation, the smaller the hospitable region within the configuration space. The hospitable configuration-region within our own physics appears to be tiny (wikipedia, anthropic coincidences) (and I'm sure it is much tinier than is evidenced there). The more variables a machine has to align before it can support life, the more vanishingly small the cradle will be within that machine's spaces.

The second fork of the argument is that complex physics are simply the defining feature of a theory that fails kolmogorov's razor (our favoured formalisation of occam's razor).

If we are to define some prior distribution over what exists, out beyond what we can see, kolmogorov complexity seems like a sensible metric to use. A universe generated by a small machine is much more likely a-priori - perhaps we should assume it occurs with much greater frequency - than a ...
Neural Nets in Python 1
by  — 2019-08-18T02:48:54.903Z

Introduction

This post is an attempt to explain how to write a neural network in Python using numpy. I am obviously not the first person to do this. Almost all of the code is here adapted from Michael Nielsen's fantastic online book Neural Networks and Deep Learning . Victor Zhou also has a great tutorial in Python . Why am I trying to do the same? Partially, it's for my own benefit, cataloging my code so I can refer back to it later in a form more captivating than a mere docstring. Also partially, I think I can share a few intuitions which make the backpropagation equations a lot easier to derive.

Okay, so here's a typical picture of a neural network:

If you're new to all this: A neural network is a function that takes in an input vector (or matrix) and outputs another vector (or matrix). The input starts at the leftmost vertical layer of nodes and then gets transformed, via a series of operations, to the rightmost vertical layer of nodes. Each layer is a linear combination of the layer before it, followed by an activation function, which is applied to each node. In other words, a neural net is parameterized by a series of weight matrices W1,W2,.., a series of bias vectors, b1,b2,..., and an activation function a (typically a nonlinear function like tanh(x) which is applied element-wise).

The typical picture, while good for representing the general idea of a neural net, does not do a good job of showing the different operations being performed. I prefer representing a neural net as a computational graph, like below:

Here, it's clearer to see how each node is a function of the step before it. A normal three-layer neural network is given by the following composition of functions:
f0=X=input
f1=W1⋅f0+b1

f2=a(f1)
f3=W2⋅f2+b2
f4=a(f3)=^Y=predicted output
This recursive definition will make it easy to derive the backpropagation algorithm, which we'll use to train our network. It also allows us to easily unroll the function, if we want to see what's going on in on line, by substituting until we get back to the input:
f4=a(W2⋅(a(W1⋅f0+b1)+b2))
And of course, if our neural network has more than three layers, we just add more recursively defined functions.

Forward Pass

The process of taking an input and going through the process of matrix multiplications, vector additions, and activation functions to get the output is referred to as the **forward...
Inspection Paradox as a Driver of Group Separation
by Shmi — 2019-08-17T21:47:35.812Z

Have you ever noticed that anybody driving slower than you is an idiot, and anyone going faster than you is a maniac? -- George Carlin

The Inspection Paradox, where the reported results are heavily observer-dependent, has been mentioned here a couple of times before:

https://www.lesswrong.com/posts/tqEAiScpz7g2xLHtE/the-inspection-paradox-is-everywhere

https://www.lesswrong.com/posts/HW2fbbGM8B6y7pkDb/the-just-world-hypothesis#8r2uaA6Mb25k2vF9Z

An example of it that is familiar to everyone is that, when driving with an average speed, you see all other cars separating into two categories: slow drivers and fast drivers, because you naturally encounter more cars who are faster or slower than you are, and none that move at the same speed. So a normal distribution of speeds becomes bimodal, from something like this:

to something like this:

Another familiar example, from the newer post by the original author, is that for an average Facebook user,

Your friends have more Facebook friends than you do.

Because, naturally, members with more connections are more likely to have a connection with you, among others.

(For the record, the inspection paradox can be classified as subset of the sampling bias, which, in turn, is a form of the oft discussed selection bias.)

But back to the apparent multi-modality. You encounter is whenever unusual events have a higher availability, sometimes properly measured like in the average driver case above, and sometimes perceived, like in the availability heuristic. This doesn't have to be about the frequency of the observations, it could be about their emotional impact. If the more "out there" the observations are, the more they affect you, then their distribution would appear bimodal, and you might instinctively recoil from them, and seek the comfort of the in-group. In this case the inspection-induced multi-modality may turn into an actual one, a case of perception becoming reality. (I had attempted to model it numerically some months ago, in two companion posts, sadly not written well enough to attract much interest, but showing that the effect described may well be real.)