trentmkelly/LessWrong-43k huggingface dataset:
No Really, There Are No Rules!
by J Bostock — 2021-10-07T22:08:13.834Z

All video games are made by programmers, but Baba Is You was made by programmers. Also the title screen contains a link to givingwhatwecan.org, which I feel obliged to mention.

The First Level

The premise of the game is simple. There are objects on the screen, and rules governing how those objects behave. Each object takes up a tile. "Baba Is You" means that when you press the arrow keys the little rabbit (or whatever it is?) called Baba moves around. Having this rule and "Flag Is Win" means that when Baba reaches the flag, you win. "Rock Is Push" means the rock is a physical object you can push around. "Wall Is Stop" means you can't walk through the wall tiles.

If instead "Flag Is You" and "Baba Is Win" then you have to move the flag around and reach the (unmoving) Baba. The objects have no properties other than what the rules give them. Some objects which are important in certain levels are just decoration in others, as they have no rules pointing to them to give them properties. The tile squares in the level above act in that way.

But these rules are built from words which are themselves movable objects. "Baba Is You" is a rule which exists because the "Baba", "Is", and "You" tiles are all in a row. If you (as Baba) push the word "Rock" in place of the word "Baba" in "Baba Is You", then you control all the rocks on screen at once. A lot of the puzzles rely on your limited supply of tiles to manipulate.

The hardest thing to keep in mind is that, no really, there are no rules! except those that are written on the screen. No matter how many times you've pushed rocks around in previous levels, if this level doesn't have "Rock Is Push" (or you've disrupted that rule by moving things around) then you can't push it, you might even walk right through it if there aren't any rules!

Realizing there are no rules! is often difficult. You have to put in some conscious effort to not expect rules to be followed. Because so many of the objects in Baba Is You are "loaded" in some way (like keys, doors, boxes) you often find yourself forgetting that, no, this time the key doesn't unlock the door because keys don't unlock doors unless the rules specify that.

The "ghost in the machine" type thinking is an example of this. There is no mind which looks over code to evaluate it, the mind is the code. Programming a computer might seem like giving instructions to a person, but it require...
What Do GDP Growth Curves Really Mean?
by johnswentworth — 2021-10-07T21:58:15.121Z

Gross domestic product (GDP) is a

monetary

measure

of the market value of all the

final goods

and services produced in a specific time period. - Wikipedia,

GDP

Due to inflation, GDP increases and does not actually reflect the true growth in an economy. That is why the GDP must be divided by the inflation rate (raised to the power of units of time in which the rate is measured) to get the growth of the real GDP. - Wikipedia,

Real GDP

The two quotes above reflect how I used to think about real GDP growth: it’s roughly the growth in economic production (as measured by dollar worth of outputs), discounted for inflation. This picture turns out to be extremely misleading, especially when using GDP as a growth measure. Forget complaints about how GDP doesn’t measure happiness, or leisure time, or household work, or “

the health of our children, the quality of their education or the joy of their play

”. Even if we accept the dollar value of goods as a proxy for whatever purpose we have in mind, GDP (as we actually calculate it) is still a wildly misleading measure of growth. In particular, it effectively ignores major technological breakthroughs.

A Puzzle

Here’s real GDP of the US for the last ~70 years, from

FRED

:

According to this graph, real GDP has grown by roughly a factor of 6 since 1960. That seems… way too low, intuitively. Consider:

I’m typing this post on my laptop (which conveniently has a backspace button and everything I type is backed up halfway around the world and I can even insert images trivially)...
while listening to spotify…
through my noise-canceling earbuds…
and there’s a smartphone on my desk which can give me detailed road maps and directions anywhere in the US and even most of the world, plus make phone calls…
and oh-by-the-way I have an internet connection.

I’d expect the equivalent of any one of these things in 1960 would have cost at least a hundred times the annual income of an average person if it was even possible at all. Just from these five things alone, it seems like real GDP ought to have grown by a factor of hundreds.

… and yet, whatever formula we’re using for real GDP says it's only grown by a factor of 6. What gives? How the heck is real GDP computed that makes it so low? What exactly is it measuring?

Real GDP Is Not Nominal GDP Divided By Inflation

First things first: real GDP is not calculated by dividing nominal GDP by inflation. It’...
Book summary: Selfish Reasons to Have More Kids
by Optimization Process — 2021-10-07T20:19:58.265Z

Bryan Caplan's Selfish reasons to have more kids: why being a great parent is less work and more fun than you think was recommended to me as a strongly-science-supported take on childrearing. I found it valuable; here are my take-aways.

TLDR: Variation in parenting (within reason) has no significant long-term effects. So take it easy, and maybe invest some of that saved energy in another kid.

The main thrust of the book is:

Nature >> Nurture

I mean, Caplan says it with somewhat more caveats, but that's the bottom line from his chapters on behavioral genetics. He cites a big pile of twin studies and adoption studies, and tries to answer, for various attributes:

If you're 80th-percentile on the scale, what percentile should you expect your adopted sibling to be in? How about your long-lost identical twin?

The answers, summarized:

Attribute
Adopted sibling's percentile
Identical twin's percentile

Life expectancy
~50
58

Health (self-reported)
~50
56-65

Health (objective)
~50
56-65

Dental problems
~50
56-65

Height (6in above average)
~50
5in above average

Weight (20lb above average)
~50
15lb above average

IQ
51-55
70-80

Misc intelligence
~50
70+

Happiness
~50
67

Education
51-55
75

High school GPA
~50
71

Income
51-55
"about twice as similar as fraternal" (like you'd expect from pure genetics)

Conscientiousness, agreeableness
~55
"unusually large effects"

Political/religious behavior[1]
56
(not given)

Political/religious labels
73
(not given)

Filial appreciation
~60
(not given)

And a couple of attributes that don't fit neatly in to that table:

If a kid is born to parents without criminal records, then being adopted by parents with criminal records (vs adoptive parents with no records) raises their chance of getting a criminal record by 1.2% (from 13.5% to 14.7%).

Smoking/alcoholism/drug use have mixed results. One study shows a moderate-to-large nurture effects on alcohol use, one shows none.

Note that the world is really complicated, and each of these traits has several studies measuring slightly different things, and the whole thing is riddled with quirks and caveats that don't fit into this table. Does it make sense to separate out marijuana use from amphetamines/cocain/sedatives, or is that p-hacking? What about subgrouping adopted children based on whether their biological parents have criminal records? I dunno, ...
Safety-capabilities tradeoff dials are inevitable in AGI
by Steven Byrnes — 2021-10-07T19:03:22.334Z

A safety-capabilities tradeoff is when you have something like a dial on your AGI, and one end of the dial says “more safe but less capable”, and the other end of the dial says “less safe but more capable”.

Make no mistake: safety-capabilities tradeoff dials stink. But I want to argue that they are inevitable, and we better get used to them. I will argue that the discussion should be framed as “Just how problematic is this dial? How do we minimize its negative impact?”, not “This particular approach has a dial, so it’s automatically doomed. Let’s throw it out and talk about something else instead.”

(Recent examples of the latter attitude, at least arguably:

here

,

here

.)

1. Background (if it’s not obvious): why do safety-capabilities tradeoff dials stink?

The biggest problem is that the world has ambitious people and competitive people and optimistic people and people desperate to solve problems (like climate change, or the fact that their child is dying of cancer, or the fact that their company is about to go bust and they’ll have to lay off thousands of employees who are depending on them, etc. etc.). Also, we don’t have

research liability insurance

that internalizes the societal cost of catastrophic accidents. And even if we did, I don’t think that would work for catastrophes so bad that no humans would be left to sue the insurer, or so weird and new that nobody knows how to price them, or if the insurance is so expensive that AGI research shifts to a country where insurance is not required, or shifts underground, etc.

So I expect by default that a subset of people would set their dial to “less safe but more capable”, and then eventually we get catastrophic accidents with out-of-control AGIs self-replicating around the internet and all that.

2. Why do I say that these dials are inevitable?

Here are a few examples.

Pre-deployment testing. Will pre-deployment testing help with safety? Yeah, duh! (It won’t catch every problem, but I think it’s undeniable that it would help more than zero.) And will pre-deployment testing hurt capabilities? You bet! The time and money and compute and personnel on the Sandbox Testing Team is time and money and compute and personnel that are not training the next generation of more powerful AGIs. Well, that’s an oversimplification. Some amount of sandbox testing would help capabilities, by helping the team better understand how things a

...
Blog Post Day IV (Impromptu)
by Daniel Kokotajlo — 2021-10-07T17:17:39.840Z

I decided to run another Blog Post Day. It's happening this Saturday the 9th. Sorry for the short notice!

Why do it? See this post.

If you are interested, you can join us online via this special-purpose discord channel, or you can join us in person at the CLR office lounge in London. Email me if you are coming in person and I'll give you my phone number. Or you can use the discord.

The event will run from about 10am to about midnight. Come whenever and leave whenever. Mostly we'll just be on our computers writing, but at various points if desired we can take breaks and chat and discuss our planned posts etc.
Covid 10/7: Steady as She Goes
by Zvi — 2021-10-07T12:10:01.434Z

For the second week in a row little has changed. The biggest news is that Merck has a new anti-viral pill that looks to be somewhat effective as a treatment. Otherwise, it should be a quick week. Which is good, since I spent the week moving (to New York City! to Manhattan! Woo-hoo!) and am quite exhausted. There were a few non-Covid things worth discussing given the lack of Covid news, but due to the time crunch I’m at least pushing them to next week.

Executive Summary

Merck has a new anti-viral pill that’s promising but not a full solution.
Cases continue to decline.

Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: 630k cases (-10%) and 13,700 deaths (-1%).

Results: 621k cases (-11%) and 12,405 deaths (-13%).

Prediction for next week: 560k cases (-10%) and 11,500 deaths (-8%).

Often it’s tricky to make a prediction, but this is not one of those weeks. I expect only a small error here.

Deaths

A dramatic decline outside of the Northeast indicates we have turned the corner. The Northeast number is quite unexpectedly bad and does not seem to be an obvious data error, but I’d presume it’s mostly a fluke in some non-obvious way anyway, although the accurate death count shouldn’t peak there for another week or two.

Cases

Progress continues, slightly ahead of predicted pace. There’s no reason to expect that to stop for at least another few weeks, and a good chance it keeps going for a lot longer.

Vaccinations

There’s been a clear uptick in vaccinations, presumably due to mandates having an effect, but it’s also possible a lot of this is disguised booster shots.

Johnson & Johnson gives data overwhelmingly supporting booster shots, files for boosters. Those boosters remain technically forbidden.

Moderna plans to build a plant in Africa to produce 500 million vaccine doses per year. That’s excellent long term news (it will take 2-4 years to build because there’s no rush or anything) and reinforces that yes we could have built more capacity and decided not to.

Vaccine Effectiveness

New study out of the United States shows immunity to infection (but not hospitalization or death) waning over time (paper).

Between Dec 14, 2020, and Aug 8, 2021, of 4 920 549 individuals assessed for eligibility, we included 3 436 957 (median age 45 years [IQR 29–61]; 1 799 395 [52·4%] female and 1 637 394 [47·6%] male). For fully vaccinated individuals, effectiveness ag

...
Is Legal Reasoning Fundamentally Irrational? 
by ThomasMore — 2021-10-07T08:22:49.052Z

And, if so, is this a bad thing?

This is a short essay on modern law; specifically, how it is possibly irrational, and how this irrationality could in fact be necessary for large legal systems to operate. I conclude with some observations on potential wider significance of this phenomenon.

Modern Legal Reasoning

It is fairly well known that you cannot reason your way around breaching a contract's terms. Regardless of what is rational or commonsensical, you will be forced to pay on the basis 'the law is the law.' This literal-mindedness is known in legal circles as 'Legal Positivism.'

Legal positivism is essentially the view that 'the law' (the set of rules considered legally valid in a jurisdiction) is determined ultimately by social facts. Thus, when we want to work out the law, we look at historical processes, such as legislation going through the correct procedure, or judgements being issued from recognised tribunals. When you ask the small claims court 'why is the law the law' they can respond 'it says so according to the Invoices Act 1998, which was passed by Parliament.'

If the official takes the law as a non-defeasible reason for action independent from its practical or moral reasonableness, this can lead to irrational results. This is a symptom of law's reliance on arguments from authority: 'But why? Because X said so.'

Rationalist Counterargument

Such rigid thinking is distasteful, and is met with the riposte 'just because something is the law doesn't mean you ought to follow it.'

The rational account of rule-following has a different approach. It goes something along the lines of: "in general, it is best to defer to legal rules, all things being equal, but should a legal rule prove demonstrably net pointless or harmful then it should be ignored."

Thus, behaviour should be oriented according to the law only if it 'makes sense', taking into a wide range of factors, such as:

Deferring to the expertise of the rule-maker
The benefit from consistent rule-adherence by all its addressees (ie, driving on the right side of the road)
A meta-principle about when it is rational to follow a rule subject to 'exceptions' or to entirely revise it.
The coordination benefit of using and following clear simple rules, even if on the face it would not benefit the rule-follower and, hypothetically, a collective of rational agents

...
Towards a Bayesian model for Empirical Science
by Yair Halberstadt — 2021-10-07T05:38:25.848Z

The aim of this post is to explore if we can create a model for doing Empirical Science that better incorporates Bayesian ideas.

The Current Model Of Doing Science

Imagine you are a Good Scientist. You know about p-hacking and the replication crisis. You want to follow all best practices. You want to be doing Good Science!

You're designing an experiment to detect if there's a correlation between two variables. For instance height and number of cigarettes smoked a day. You want to follow all best practices, so you write a procedure that looks something like this (taken from https://slatestarcodex.com/2014/04/28/the-control-group-is-out-of-control/):

You find a large cohort of randomly chosen people. You use the SuperRandomizerV3 to teleport 10000 completely randomly chosen people into your laboratory, and refuse to let them out till they answer your questionnaire about their smoking habits, and allow you to measure their height.
You consider a p value of 0.001 as significant.
You calculate the correlation between their height and the number of cigarettes they smoke a day. By pure chance it's almost certain not to be 0.
You calculate the the chance they would get this correlation assuming the true correlation was 0 (the p value).
If p > 0.001 you conclude the experiment is a dud. No evidence of correlation. Better luck next time.
If p < 0.001 you're golden! It's now been scientifically proven that height correlates with smoking habits.
You preregister the experiment in detail, including the exact questions you ask, the p value you will consider significant, the calculations you will do, whether the test is two tailed or one tailed etc.

You then follow this procedure exactly, publish your results in a journal no matter what they show, and demand replication.

And that's pretty much the procedure for how to do good science under the current empirical model.

The great thing about this procedure is it doesn't require much subjective decision making. So long as your honest about it, and go carefully, you don't actually have to be very clever to do this - anyone who's capable of following steps and doing some simple statistics can carry out this procedure. It's reproducible, and the procedure can be easily checked for design flaws simply by reading it. This is important given how many people work in science - you want a technique that doesn't require brilliant practitioners...
Cheap food causes cooperative ethics
by lsusr — 2021-10-07T01:52:54.212Z

Nobody has ever fought a war over the oxygen in the air because there's plenty of oxygen to go around. We fight over things that are scarce and valuable. Historically, most wars have been fought over land and people. If you win you get to force your subjects to collect raw materials from the land. Sometimes these raw materials are mineral resources. Historically, most slaves have been forced to cultivate food.

It's hard to comprehend how important food staples used to be. In Edo Japan, wealth was measured in koku (石). One koku is (in theory) enough rice to feed one man for one year. The amount of koku a daimyo controlled was basically how many people he owned because a region's food staple production determined its carrying capacity and the human population grew until it hit carrying capacity. In other words, we bred until we were on the edge of starving to death. Most wars have ultimately been fought over land because land determines food production and food production was a matter of life and death.

My grandfather, who grew up in Taiwan before the green revolution, was too poor to afford rice. He ate sweet potatoes instead.

The green revolution of the 1950s and the 1960s increased food production faster than our population growth. On the Chinese version of TikTok[1] there's a video of a guy eating a bowl of white rice with a spoonful of soy sauce. The comments are all of people feeling sorry for him. We have so much food these days that eating nothing but rice makes you surprisingly poor even by Chinese standards.

World War II ended in 1945—right before the green revolution. There has been no direct war between major world powers between then and now. I don't think this is a coincidence. Wars are usually about land and land is usually about food production and food production skyrocketed in the decades after World War II. (Birth control became widely adopted around the same time too.)

We still have small wars. We fight over oil and ideologies. But cars and capitalism aren't as important as food production. Fighting wars over food is stupid when food is cheap. Less fighting over food means less fighting overall. Countries being at peace with each other set the conditions for us to build more complex, interdependent trade networks. I think the idea that we're all human beings, regardless of our race, religion, sex and country of origin gained increasing power bec...
Automated Fact Checking: A Look at the Field
by Hoagy — 2021-10-06T23:52:53.577Z

Intro / Motivation

This post is the write-up of a few days reading the automated fact-checking literature.

As AIs become more powerful, it would be very valuable to be able to train an agent to be able to tell us, not just whether particular statements are true, but why, and to explain their sources and reasoning as best as possible, especially in slow-takeoff scenarios. Fact-checking represents the present-day, practical end of the spectrum of possible work in this area, and I was curious what what had been done on it, the challenges the field faces, and whether it might shed light on the wider alignment problem.

Ultimately, I don't think there are any major positive surprises or insights generated by what I've read of the field. Instead the tale is more about the difficulties of getting high-quality data, and (especially given the current difficulty of the task) the need to carefully combine automated tools with natural human workflow in order to make something useful. Nonetheless, you might find value in some background on the field, its datasets and challenges.

If you're just interested in a general survey of the field, I would recommend reading the recent summary of the field Automated Fact-Checking for Assisting Human Fact-Checkers which talks through largely the same material from a more practical perspective. I'm instead looking more closely at the generation of the datasets and the techniques employed in solving them.

Summary

The few fact-checking datasets generally consist of tens to hundreds of thousands of sentences hand-curated by Mechanical Turk or similar workers who are told to create 'facts' from structured sources like Wikipedia. The core bottleneck is that generating explicit 'explanations' is both difficult and incredibly time-consuming at a size large enough to form a useful training set.

There has been some movement towards the use of large, pre-trained models with the use of a fine-tuned BERT model, but there's not much visible activity to apply the big beasts of language modelling to integrated fact-checking systems (though work may well be ongoing at Facebook and elsewhere). These machine learning systems are therefore some way behind the cutting edge of language understanding and I expect large leaps would result from greater attention and firepower. At the moment, state-of-the-art fact checking systems are piecemeal and machin...