trentmkelly/LessWrong-43k huggingface dataset:
Is growth linear, not exponential?
by jasoncrawford — 2022-04-20T18:46:42.186Z
Link: https://rootsofprogress.org/is-growth-linear-not-exponential

This week Thomas Philippon posted a paper (PDF) claiming that TFP growth is linear, not exponential. What does this mean, and what can we conclude from this?

A few people asked for my opinion. I’m not an economist, and I’m only modestly familiar with the growth theory literature, but here are some thoughts.

Background for non-economists

Briefly: what is TFP (total factor productivity)? It’s basically a technology multiplier on economic growth that makes capital and labor more productive. It isn’t measured directly, but calculated as a residual by taking capital and labor increases out of GDP growth. What remains is TFP.

In neoclassical growth theory, TFP matters because it enables economic growth. Without increases in TFP, growth would plateau at some per-capita income level limited by technology. We can increase per-capita output if and only if we continue to improve the productivity multiplier from technology.

What does the paper say?

Philippon’s core claim is that a linear model of TFP growth fits the data better than an exponential model. Over long enough time periods, this is actually a piecewise linear model, with breaks.

To demonstrate this, the two models are subjected to various statistical tests on multiple data sets, mostly 20th-century, from the US and about two dozen other countries. In a later section, the models are tested on European data from 1600–1914. The linear model outperforms on pretty much every test:

Model D is linear; Model G is exponential

One theoretical implication of linear TFP growth is that GDP per capita can continue to grow without bound, but that growth will slow over time. Depending on the assumptions you make, growth will converge either to zero or to some positive constant rate.

What to think?

First, I think the evidence Philippon presents at least for the 20th century is compelling. It really does seem that TFP is growing linearly, at least in the last several decades. This is a mathematical model of the Great Stagnation.

Over longer periods of time, however, a pure linear model doesn’t work. TFP, and GDP, clearly grow faster than linear over long periods:

Our World in Data

In fact, our best estimates of very long-run GDP growth grow faster than exponential:

Our World in Data

(Note that both of these charts are on a log scale.)

Philippon deals with this by making the model piecewise linear. At certain points, the slope of the line discr...
How to place a bet on the end of the world
by DirectedEvolution — 2022-04-20T18:24:18.212Z

Edit: This post is about a practical method for structuring a bet. It's not on how to extract information about people's true confidence levels based on the betting odds.

Bryan Caplan and Eliezer Yudkowsky figured out a way to place a meaningful bet on the end of the world[1]. Bryan described their simple approach in his April 2022 80,000 Hours podcast interview.

In brief, Bryan assigns a lower probability than Eliezer to the world ending by January 1, 2030. Of course, if the world ends by that date, Eliezer can't collect his winnings. So Bryan prepays Eliezer now, and Eliezer pays him back if he loses the bet.

This works for friendly bets. Bryan points out that they need to account for interest. To that, I'd add both risk and opportunity cost. Commenters have changed my mind, however, about whether we can really learn much about the participants' confidence levels from such a bet. I elaborate on this in the comments.

Here's the relevant part of the transcript, under "Bryan's Betting Record" starting at 1:48:19 in the podcast episode.

Bryan Caplan: Let’s see. Well, I’ve already done one huge [bet against the effective altruism community], and this is that machines are going to kill us — or at least do something terrible to us — in the medium term. So I literally have an end-of-the-world bet with Eliezer Yudkowsky.

Rob Wiblin: Oh wow. I didn’t know that.

Bryan Caplan: Yeah. Which many people believe cannot be made, but it’s super easy. The person who disbelieves in the end of the world just pays the money now. And then if the world does not end, the loser pays back with whatever the odds are.

Rob Wiblin: I can’t believe I didn’t think of that.

Bryan Caplan: Yeah. So Eliezer, we actually have a bet. It takes a little effort to understand the bet, because his view is so specific. He said, “Look, I want a bet on there will no longer be any human beings on the surface of the Earth on January 1, 2030.” I was willing to give him, like, “How about all of human extinction?” — “No, no, no, no, no. There could still be humans in mine shafts. That’s OK. But not the surface of the Earth.” And I’m like, “All right. If that’s such a big deal to you, fine, we’ll make it the surface of the Earth, whatever.” But yes.

Bryan Caplan: So anyway, we have a bet, where I don’t remember the exact odds. It might be just like two-to-one. And I prepaid, so implicitly there’s interest. So it’s not

...
If everything is genetic, then nothing is genetic - Understanding the phenotypic null hypothesis
by tailcalled — 2022-04-20T16:25:26.323Z

This is a cross-post from my Substack.

The first law of behavioral genetics states that “All human behavioural traits are heritable.” This sounds like quite a win for behavioral genetics, yet Eric Turkheimer, the man who came up with the laws of behavioral genetics, is an avid critic of heritability. How can this be?

The trouble is that people want to interpret heritability as being a measure of how “genetically coded” or “biological” a variable is. But the first law is very broad, and so it also applies to obviously nonbiological variables like “how much education has this person received?” (educational attainment).

While there can be no doubt that much of the variation in educational attainment ultimately originates in biology, the variable itself is obviously not biological; it’s socially constructed, and in our society it is based on membership in school institutions and the degrees they hand out.

The fact that a socially constructed variable can be genetic (or at least, have a heritable component) is an important fact, and once we look closer, it tells us something about both the advantages and the limitations of behavioral genetics. (The title of this post, “then nothing is genetic”, is somewhat dismissive because it is primarily aimed at informing the people who can be excessively into genetics; however, I do think that the sense in which everything is genetic is meaningful, as you will see.)

Why is educational attainment heritable?

A variable is heritable if variation in genes act as a root cause for variation in that variable. So for instance, people differ from each other in height, and most of those differences ultimately arise because people have different genes, which influence things like skeletal growth that ultimately influence height.

Simplified model of the heritability of height.

Note an important thing here: Heritability is transitive. Genes influence skeletal growth, and skeletal growth influences height, so therefore genes influence height, and therefore height is heritable. As a general rule, there will be several intermediate variables that genes pass through before they influence a variable of interest. This is because the direct effects of genes are very simple, coding for proteins, and most traits that we are interested in are much more complex than just proteins.

This sets the stage for educational attainment. Why is educational attainment...
Judge Overturns Transportation Mask Mandate
by Zvi — 2022-04-20T13:50:01.747Z

A federal judge has overturned the transportation mask mandate, saying the CDC overstepped its authority. The Biden administration is adhering to this ruling. It intends to appeal, but is not being loud about that.

Mask mandates are lifted on planes. This happened in-flight. Reactions from passengers and crew in flight were highly supportive.

Public health advocates naturally took a somewhat different view of events, this being a central reasonable version of their reaction.

There’s that ‘don’t change things people won’t like it’ principle again, and also citing whatever invokes the availability heuristic this week. Most importantly he collected data from this experiment, and remember that connecting to Boston via JetBlue should be an unusually liberal set of passengers.

Lifting the mandate during flights led to some great moments.

As a small point yes I do think this could have waited a few hours given that people boarded the planes with an expectation?

In future, I’d urge such folks to get P100 or superior masks, which should do a much better job on you alone than the KN95s did on everyone combined. Which was the right play before but almost no one did it, and probably almost no one will do it in the future either. In general, there are two reasons to wear a mask – you either want to be someone wearing a mask, or you want actual protection for yourself or others. Now that we no longer need to do the first one, those who want protection can do the second, which means P100 or better.

Uber has lifted its own mandate in response, as has Lyft. Some local authorities like the MTA in NYC (which includes the Subway) and NJ Transit are leaving their mandates in place, others are not.

What should we make of all this?

The Details Of This Decision Imply Bad Faith

The transportation mask mandate was already hanging by a thread. The CDC had multiple times come close to letting it expire. The CDC’s latest extension of the mandate was only for two weeks. The Senate voted 57-40 to repeal the mandate. Airlines, who have the most skin in the game, called for lifting the mandate.

The Biden administration so far held firm, as part of its ‘trust the science’ branding. This was a no-win situation for them, with inevitable heat on them no matter their decision.

Having a judge take it out of their hands does them a favor, letting them dodge blame fo...
Axis oriented programming
by Donald Hobson — 2022-04-20T13:22:44.935Z

I was reading some APL when I realized I had invented a new style of programming language. It hasn't been implemented as far as I know. The syntax is just any old syntax. I am trying to be non-cryptic, but if you are planning to implement this, you probably want better, more consistent syntax. This is supposed to be a cool new idea, some bits haven't been fully thought through. If you are looking for inspiration in designing a programming language, this might be it.

Suppose you are dealing in numpy, and some of your arrays have like 10 or 20 dimensions. And you are finding it really hard to keep track and remember that axis 4 of array foo represents the same thing as axis 7 of array bar. Then array oriented programming is for you.

Conceptually, there are axis and there are data. If you see something like

Axis foo=Axis([5])
Int a=1
Axis bar=Axis([6])
Float b=bar.index.as_type(float)

Every variable like a and b can be imagined as actually being an array, with a dimension for every axis. There is no such thing as a constant, just an array that happens to be constant. [5] represents the type of numbers that are non-negative integers < 5. Ie 0,1,2,3,4.

bar.index is an array of type [6], which can be typecast to float.

A naive compiler/ interpreter for this language would soon find every object becomes an exponentially large array as soon as more than a few Axis are created. Its the compilers job not to be that naive. It shouldn't store fast grids of the same number over and over. A good compiler might use all sorts of tricks, like lazy evaluation, and https://en.wikipedia.org/wiki/Matrix_chain_multiplication to reorder operations. These are implementation details.

Simple functions using axis

Lets look at a few functions.

Sum:[Axis] -> Float -> Float

Takes the sum of the inputted array along the Axis. Places this value in every position in the array. The axes used must implement the summable characteristic. (Which pretty much means they must be finite)

Sort:Axis -> [Axis] -> Float -> Float

Takes an axis to sort along. It also takes a list of axis to take lexical priority over.

Sort foo [] is the standard sorting. Sort foo [bar] means to slice the array up into lists of length 6. Consult the first item in the list, if thats a tie, consult the second item.

All axes used here (oh and the type) must imple...
[Intro to brain-like-AGI safety] 12. Two paths forward: “Controlled AGI” and “Social-instinct AGI”
by Steven Byrnes — 2022-04-20T12:58:32.998Z

(Last revised: July 2024. See changelog at the bottom.)

12.1 Post summary / Table of contents

Part of the “Intro to brain-like-AGI safety” post series.

Thus far in the series,

Post #1

defined and motivated “brain-like AGI safety”; Posts #2–#7 focused mainly on neuroscience, painting a big picture of learning and motivation in the brain; and Posts

#8

–

#9

spelled out some implications for the development and properties of brain-like AGI.

Next,

Post #10

discussed “the alignment problem” for brain-like AGI—i.e., how to make an AGI whose motivations are consistent with what the designers wanted—and why it seems to be a very hard problem.

Post #11

argued that there’s no clever trick that lets us avoid the alignment problem. Rather, we need to solve the alignment problem, and Posts #12–#14 are some preliminary thoughts about how we might do that, starting in this post with a nontechnical overview of two broad research paths that might lead to aligned AGI.

[Warning: Posts #12–#14 will be (even?) less well thought out and (even?) more full of bad ideas and omissions, compared to previous posts in the series, because we’re getting towards the frontier of what I’ve been thinking about recently.]

Table of contents:

Section 12.2 lays out two broad paths to aligned AGI.

In the “Controlled AGI” path, we try, more-or-less directly, to manipulate what the AGI is trying to do.
In the “Social-instinct AGI” path, our first step is to reverse-engineer some of the “innate drives” in the human

Steering Subsystem (hypothalamus & brainstem)

, particularly the ones that underlie human social and moral intuitions. Next, we would presumably make some edits, and then install those “innate drives” into our AGIs.

Section 12.3 argues that at this stage, we should be digging into both paths, not least because they’re not mutually exclusive.
Section 12.4 goes through a variety of comments, considerations, and open questions related to these paths, including feasibility, competitiveness concerns, ethical concerns, and so on.
Section 12.5 talks about “life experience” (a.k.a. “training data”), which is particularly relevant for social-instinct AGIs. As an example, I’ll discuss the perhaps-tempting-but-mistaken idea that the only thing we need for AGI safety is to raise the AGI in a loving human family.

Teaser of upcoming posts:

The next post (#13) will dive into a ...
GPT-3 and concept extrapolation
by Stuart_Armstrong — 2022-04-20T10:39:29.389Z

At the latest EAG in London, I was challenged to explain what concept extrapolation would mean for GPT-3.

My first thought was the example from this post, where there were three clear patterns fighting each other for possible completions: the repetition pattern where she goes to work, the "she's dead, so she won't go to work" pattern, and the "it's the weekend, so she won't go to work" pattern.

That feels somewhat like possible "extrapolations" of the initial data. But the idea of concept extrapolation is that the algorithm is trying to cope with a shift in world-model, and extend its goal to that new situation.

What is the world-model of GPT-3? It consists of letters and words. What is its "goal"? To complete sentences in a coherent and humanlike way. So I tried the following expression, which would be close to its traditional world-model while expanding it a bit:

ehT niar ni niapS syats ylniam ni eht

What does this mean? Think of da Vinci. The correct completion is "nialp", the reverse of "plain".

I ran that through the GPT-3 playground (text-davinci-002, temperature 0.7, maximum length 256), and got:

ehT niar ni niapS syats ylniam ni eht teg dluoc I 'segaJ niar ni dna ro niar ni eht segauq ,ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ,ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni eht segauq ro niar ni eht dna ro niar ni e

I think we can safely say it broke GPT-3. The algorithm seems to have caught the fact that the words were spelt backwards, but has given up on any attempt to order them in a way that makes sense. It has failed to extend its objective to this new situation.
A very quick analogy regarding "opinions"
by Magnus — 2022-04-20T02:28:12.541Z

Say I were to tell you "I have a large treasure chest full of gold doubloons" and you were to say "Really, let me see it?", so we went to my house to look at my chest.

We enter my living room, where there is a fireplace with a mantel. I say "Look, it's right on top of the mantel", but there are only picture frames.

You approach the fireplace confused. "Where is it?" you finally ask.

"Don't you see, right here." I say, walking towards the fireplace and picking up one of the picture frames atop the mantel.

"Isn't it beautiful, my chest full of gold?" I say, handing the picture to you.

You look at the image in the frame. It is not just a picture, but a picture of a picture of a chest full of gold.

So, clearly, I am out of my mind.

With material objects, it's quite obvious to any sane person when they truly have them or not. Even if it is not obvious, it will be obvious to some other sane person.

With immaterial objects, like opinions, it is not so obvious. Many people's "opinions" on a subject are truly images of images of an opinion. This is not so easily felt, nor can it be easily identified by the person who is being told the opinion is legitimate.

You should be careful to make sure when holding a belief "Do I have this, or do I have an image?"
Why No *Interesting* Unaligned Singularity?
by David Udell — 2022-04-20T00:34:55.582Z

Putting two matching ideas I've encountered together.

The Question:

agentofuser:

Considering all humans dead, do you still think it's going to be the boring paperclip kind of AGI to eat all reachable resources? Any chance that inscrutable large float vectors and lightspeed coordination difficulties will spawn godshatter AGI shards that we might find amusing or cool in some way? (Value is fragile notwithstanding)

Eliezer Yudkowsky:

Yep and nope respectively. That's not how anything works.

Yudkowsky's confidence here puzzled me at first. Why be so sure that powerful ML training won't even produce something interesting from the human perspective?

Admittedly, the humane utility function is very particular and not one we're likely to find easily. And a miss in modeling it along plenty of value dimensions would mean a future optimized into a shape we see no value in. But what rules out a near miss in modeling the humane utility function, a miss along other value dimensions, that loses some but not all of what humanity cares about? A near miss would entail some significant loss of value and interestingness from our perspective -- but it'd still be somewhat interesting.

The Answer:

Scott Alexander:

The base optimizer is usually something stupid that doesn’t “know” in any meaningful sense that it has an objective - eg evolution, or gradient descent. The first thing it hits upon which does a halfway decent job of optimizing its target will serve as a mesa-optimizer objective. There’s no good reason this should be the real objective. In the human case, it was “a feeling of friction on the genitals”, which is exactly the kind of thing reptiles and chimps and australopithecines can understand. Evolution couldn’t have lucked into giving its mesa-optimizers the real objective (“increase the relative frequency of your alleles in the next generation”) because a reptile or even an australopithecine is millennia away from understanding what an “allele” is.

Even a "near miss" model is impossibly tiny in simplicity-weighted model space. A search algorithm with a simplicity bias isn't going to stumble all the way over to that particular model when so many other far-simpler models will perform just as well.

But isn't there some chance that ML training will happen to find a near-miss model anyways?

No. That forlorn hope is something you couldn't ever expect given those premises, like, e.g., hop...
Another argument that you will let the AI out of the box
by Garrett Baker — 2022-04-19T21:54:38.810Z

Suppose there exist some non-consequentialist moral philosophies which the right arguments could convince you of, with sufficient strength that you would (temporarily, for at least an hour) become a fanatic. This seems a likely assumption, as I know many people (including myself) have experiences where they are argued into a particular belief during a conversation, only to later reflect on this belief (either in conversations with others, or after going for a walk) and come up with a relatively simple reason why it cannot be the case. Often this is attributed to that person's conversation partner being a better argument-maker than truth-seeker.

We also have many such examples of these kinds of arguments being made throughout the internet, and already the YouTube algorithm learned once before how to show people videos to convince them of extreme views (this paper doesn't support the conclusion I thought it did. See this comment thread for more info. Thanks to Pattern for catching this mistake!). A powerful AI could put much more optimization power toward deceiving humans than happens in these examples.

Many non-consequentialist philosophies are sufficiently non-consequentialist so as to make it very easy for an adversary to pose a sequence of requests or other prompts which would cause a fanatic of the philosophy to give some of their resources to the adversary. For instance, any fanatic of a philosophy which claims people have a moral obligation not to lie or break promises (such as Kantianism), is subject to the following string of prompts:

1. Adversary: Will you answer my next question within 30s of my asking only with "yes" or "no"? I will give you <resource of value> if you do.

2. Fanatic: Sure! Non-consequentialism is my moral opinion, but I'm still allowed to take <resource of value> if I selfishly would like it!

3. Adversary: Will you answer this question with 'no' <logical or> will you give me <resource of value> + $100

4. Fanatic: Well, answering 'no' would be lying, but answering yes would make me lose $100. However, my moral system says I should bear any cost in order to avoid lying. Thus my answer is 'yes'.

This example should be taken as a purely toy example, used to illustrate a point about potential flaws in highly convincing moralities, of which many include not-lying a central component[1].

More realistically, there ...