trentmkelly/LessWrong-43k huggingface dataset:
An argument against excessive consumption of coffee from 1674
by M. Y. Zuo — 2022-02-12T18:39:19.593Z

One every now and then discovers the most amusing writings buried in the dusty and forgotten past. The following was apparently in response to the dramatic increase in coffee consumption of renaissance England, from an eccentric’s curiosity to a widespread trend.

The beginning and ending of the essay alone are sufficient to demonstrate the type of arguments employed:

“

THE WOMEN’S PETITION AGAINST COFFEE
REPRESENTING TO PUBLICK CONSIDERATION THE GRAND INCONVENIENCIES ACCRUING TO THEIR SEX FROM THE EXCESSIVE USE OF THAT DRYING, ENFEEBLING LIQUOR.

Presented to the Right Honorable the Keepers of the Liberty of Venus.
By a Well-willer

London, Printed 1674.

To the Right Honorable the Keepers of the Liberties of Venus; The Worshipful Court of Female Assistants, &c.

The Humble Petitions and Address of Several Thousands of Buxome Good-Women, Languishing in Extremity of Want.

…

Wherefore the Premises considered, and to the end that our Just Rights may be restored, and all the Ancient Priviledges of our Sex preserved inviolable; That our Husbands may give us some other Testimonial of their being Men, besides their Beards and wearing of empty Pantaloons: That they no more run the hazard of being Cuckol’d by Dildo’s: But returning to the good old strengthening Liquors of our Forefathers; that Natures Exchequer may once again be replenisht, and a Race of Lusty Here’s begot, able by their Atchievements, to equal the Glories of our Ancesters.

We Humbly Pray, That you our Trusty Patrons would improve your Interest, that henceforth the Drinking COFFEE may on severe penalties be forbidden to all Persons under the Age of Threescore; and that instead thereof, Lusty nappy Beer, Cock-Ale, Cordial Canaries, Restoring Malago’s, and Back-recruiting Chochole be Recommended to General Use, throughout the Utopian Territories.

In hopes of which Glorious Reformation, your Petitioners shall readily Prostrate themselves, and ever Pray,&c.

FINIS.

“

From https://quod.lib.umich.edu/e/eebo/A66888.0001.001?rgn=main;view=fulltext

Although the writer purports to be the representative of a certain cohort of women the amusing phrasing does make me wonder. Such as:

Nor is this (though more than enough!) All the gr

...
Designing environments to select designs
by pchvykov — 2022-02-12T17:54:29.203Z

In the last few posts we reasoned that “growing cars” would mean somehow creating them without a human planning out all the details of the design. We then saw that any such “automated” design process will involve a feedback loop where some computation determines whether a candidate design is “good” or “bad.” If we try specifying this computation “by hand,” it may be quite challenging to remember about all the design criteria we care about. This is actually one of the key worries in AI-risks research: that one such design criterion we might forget to specify is “don’t kill all humans while you’re designing a better toaster.”

In contrast, this illustrates why natural selection in biology is so cool — instead of comparing a design against a long list of performance metrics, we simply throw it into its target environment and see how well performs. Here by “how well” we usually mean something like “how robustly is its genetic code propagated.” In this same sense, we might try to use stability or robustness in the face of environmental mayhem as a “natural selection” principle for designing artificial things (hehe, the irony). The key with such approach is to construct such an environment, where the only way to be stable is to exhibit the behaviors and performances that we want from our design. To give a biological metaphor, if we want to find an animal that performs well at cold temperatures, we look for it in environments that are cold. If we could wait a bit, we might even design the appropriate target environment, and wait for animals to evolve to survive there. The cool thing then is that the design that this “natural selection” will pick out may be unlike anything we could have ourselves planned for, and yet will achieve the performance goals our environment set for it.

This might seem somewhat sci-fi, but actually it roughly describes what our recent paper in Science magazine is about! [also check out some videos] In it, we used the above idea to get a swarm of simple robots to achieve collective behaviors we wanted. Designing a specific behavior for a swarm using traditional methods is notoriously hard (e.g., it will often fail if any one of the robots is slightly off for any reason). Here we used precisely such a “natural selection” perspective described above (which we termed the “low-rattling selection principle”) to get the swarm to spontaneously organiz...
Most likely is not likely
by Malmesbury — 2022-02-12T17:27:27.120Z

Cross-posted from Telescopic Turnip.

“And I stop at every mirror just to stare at my own posterior.” – Megan Thee Stallion

I love bayesian statistics just as much as you do. However, there is a trap that people keep falling into when interpreting bayesian statistics. Here is a quick explainer, just so I can link to it in the future.

You roll a 120-faced die. If it falls on one, you stop. If it gives any other number, you re-roll until you get a one. How many times do you think you will have to roll the die? Let’s do the math: you have a 1/120 chance of rolling the die only once, a 119/120 × 1/120 chance of rolling it twice, and so on. So a single roll is the most likely outcome, any other number of rolls will happen less often. Yet, a 1/120 chance is not even 1%. In this case, “most likely” is actually pretty unlikely.

While this problem is pretty obvious in a simple die-roll game, it can become very confusing for more complicated questions. One of them, a classic Internet flamewar topic, is the doomsday argument. It’s a calculation of the year humanity is the most likely to go extinct, using Bayesian statistics. Briefly, it goes like this:

We have no idea when or how humanity can possibly go extinct, so we start with no strong a priori beliefs about that, aside from the fact that it’s in the future. However, we have one observation: we know we are living in the 21st century.
Bayes’ theorem says that the probability of an hypothesis is proportional to your prior beliefs times the likelihood (that is, the probability of observing what we observe under the hypothesis).
If humanity goes extinct in 3 billion years, it means you were born in the very, very beginning. If we pick a human life at random, it’s quite unlikely to live in the 21st century.
If humanity goes extinct in a few centuries, then you were born somewhere around the middle of the crowd, and living in the 21st was pretty likely. The prior is roughly the same, but the likelihood is much higher.

The exact parameters can change, but the conclusion is usually that humanity will most likely go extinct within a few millenia.

And that’s where the flamewars begin: how can you know that such a huge disaster will happen in the next millenia? I think the problem with this reaction is that is conflates “most likely” with “likely”, just like the example above with the dice. The calculation says that the year 9000 A...
D&D.SCP: Anomalous Acquisitions
by aphyer — 2022-02-12T16:03:01.758Z

STORY (somewhat longer than usual, still skippable)

When you graduated top of your class from Data Science School, you didn't care where you ended up working, you just wanted to find the highest-paid job possible. (Your student loans may have had an impact on this decision).

(Yes, I know a past scenario background has also begun this way. It's a versatile backstory. And one that I think may resonate with many of my players.)

Somewhat surprisingly, at the last minute Goldman Sachs was outbid by a firm you'd never heard of before called "Marshall, Carter and Dark Ltd." They hired you (for a substantially higher salary than anyone else was offering) to analyze sales data for "our ultra-exclusive chain of clubs, facilities, and banking services, catering to the finest gentlemen since 1952".

You've been working for them for several years now, in the course of which you've seen a variety of...very strange objects pass through their firm. It seems that, in addition to their upper-class business, they acquire and sell a variety of items that don't strictly seem to obey the laws of physics.

In the course of the last few years, you have excelled beyond your employers' expectation at all the technical aspects of your job...while perhaps performing less well on the social aspects.

It might have been turning down an invitation to drinks with your boss Mr. Alden because you don't like alcohol...

...or spending forty-five minutes explaining a particularly interesting piece of your data methodology to a client who had asked an innocuous question (how were you supposed to know he would be such a philistine as to find that boring?)...

...or asking a few too many questions about the various strange items that passed through the firm...

...or (what seemed based on your colleagues' reactions to be worst of all) pointing out that when you acronym "Marshall, Carter and Dark" to "MC&D" it looks very like "McDonald's".

Whatever the reason, when you were called into the office of Mr. Marshall for a private interview, you assumed you were going to be fired.

(You weren't too worried. You're a data scientist. You can find another job in less time than it'll take you to leave the building. And given the severance package in your contract, firing you would basically amount to paying you a large bonus.)

With your usual level of social a...
Chaos and Stability
by lsusr — 2022-02-12T10:19:59.628Z

Human lifespan, athletic performance and the number of children a woman can gestate are short-tailed. We operate within hard physical limits. Wealth, power, fame, pandemics, religions, pleasure and pain are long-tailed. They are Pareto-Distributed. Less than 1% of the world population owns more than 99% of the private jets.

What determines whether a distribution is short-tailed or long-tailed? Leverage, information and recursive growth.

Leverage

Leverage is the potential for a single decision to have wide-ranging effects. Leveraged systems are chaotic.

Japan's Sengoku Jidai (Warring States Period) was a chaotic era. Chaos favors the underdog. The Sengoku Jidai was so chaotic the underdog sandal-bearer Toyotomi Hideyoshi became Daimyo of Japan. Hideyoshi was succeeded by Tokugawa Ieyasu who founded the Tokugawa shogunate.

The opposite of leverage is stability. The Tokugawa shogunate enforced a period of stability from 1603 to 1868. Social mobility was outlawed[1]. No longer could a farmer become a lord.

Mathematically, a chaotic system is one in which a small change to the initial state has a large change to the later state. Weather, politics and double-pendulums are all chaotic systems.

Image from this website via Creative Commons

The opposite of a chaotic system is a stable system. A stable system is one in which a large change to the initial state produces a small change to the later state. The flat(ish) surface of the ocean is stable. If you make a big splash, the surface of the ocean will quickly revert to the usual wave pattern as if you had never been there.

Many of the systems we interact with are quasi-stable. A quasi-stable system is stable up to the point it suddenly becomes chaotic. Financial markets are quasi-stable. The value of mortgage-backed securities was reliable until 2008 when suddenly it wasn't. Governments are quasi-stable too because allies will flock to the winning side. Small rebellions are crushed by the government but big rebellions become the government. Your human body is good at maintaining internal homeostatis until it isn't and you die.

Image from the (completely real) Ladder Association

Whether a system is "stable" or "chaotic" often depends on the scale at which you observe it. A brick wall appears stable if you observe it with the macroscopic human eyes but appears chaotic if you look at the movement of individual atoms. If there is a...
A Nice Representation of the Laplacian
by DanielFilan — 2022-02-12T03:20:00.918Z

Epistemic status: some proofs here may well contain a small bug.

The Laplacian of a function from n-dimensional space to the real line is a generalization of the second derivative of a normal function: specifically, it’s the sum of second derivatives of the function along each dimension. One day, I recall coming across a theorem that said that the Laplacian of a function was equal to something like the limit of the average function value on a shell around a point, minus the function value at that point, divided by the squared radius of the shell. This is a nice theorem, because it connects the Laplacian to one of my favourite mathematical objects, the graph Laplacian. However, I later couldn’t find this theorem anywhere. Yesterday, I decided to re-derive the theorem, and today I decided to write it up. You can see the theorem and its proof here. Do let me know if there are any errors I should fix.
Full Non-Indexical Conditioning Also Assumes A Self Sampling Process. 
by dadadarren — 2022-02-12T01:46:42.005Z

In a previous post, I argued for the distinction between the first-person and physical person. I purpose the root cause of paradoxes in anthropics is considering it as an Observation Selection Effect. I.E. treating the first-person perspective as a sample. SSA and SIA disagree on the correct selection process. But in my opinion, indexicals such as "I" and "now" shouldn't be treated as a random sample in the first place.

I didn't touch on another camp, Full Non-Indexical Conditioning (FNC). Unlike SSA and SIA, FNC does not state anything in the like of "treat oneself as randomly selected from such and such". Nevertheless, I am against FNC for the exact same reason. Because in my opinion, FNC still implicitly assumes a particular sampling process and considers indexicals the outcome of said process.

A Simplified Use of FNC

For the Sleeping Beauty Problem, FNC's approach is as follows. Once waking up in the experiment, there is no need to assume anything like "today is randomly selected from all days/all awakened days." Just make observations. For example, say I find the weather is cloudy. Then without using any indexicals, it can be stated "Beauty is awake on a cloudy day".

Now, "Beauty is awake on a cloudy day" is more likely to be true if there are two awakenings instead of just one. Given two awakenings (tails), as long as there is one cloudy day during the experiment the statement is true. If there is only one awakening (heads) then it has to be cloudy on that exact day. So this seemingly independent information about the weather would bump up the probability of Tails.

That alone would not make the Tails twice likely as Heads. Because there is a chance the two days during the experiment are both cloudy. In which case the observation would not favour either head or tail. However, if I consider ALL my experiences after waking up (the feeling of airflow on the skin, the sequence of things I see, etc) then it is practically impossible for the two awakenings to be identical. So the probability of Tails should be nearly (equals to in any practical sense) twice that of Heads. Numerically, FNC would give the same answer as SIA.

I think FNC still implicitly assumes a self-sampling process. Because a non-indexical statement such as "Beauty is awake on a cloudy day" alone is not enough for the purposed probability update. This is best sh...
If you want to live longer, become President
by Bezzi — 2022-02-11T23:14:58.453Z

The Queen of England (age 95) recently celebrated her 70th anniversary on the throne. The average life expectancy in the UK is 81.3 years[1], and we all know that her longevity is the subject of many jokes. But is she that much of an odd outlier compared to other Heads of State?[2]

In this post I will consider only people born after 1895 who served as Heads of State (as indicated by Wikipedia). How many of them reached old age?

Let's start from the US. We could note that President Biden (age 79) has already reached the average life expectancy in the US (78.9 years[1]). What about his predecessors? Trump is 75. Obama is 60. George W. Bush is 75 too. Also Clinton is 75. All of them are still alive. Then we reach George H. W. Bush, died at age 94. Before him, Reagan died at 93. Jimmy Carter is still alive at 97. His predecessor Gerald Ford died at 93. We have to go back to Nixon (died at 81) to find a President died before 90. The only one who ruins the average is Lyndon Johnson, having died at 64. The last President born after 1895 was JFK, but since he was assassinated I'll exclude him from the statistics.

Fine, let's move somewhere else. What about Italy, with its 83.5 years[1] life expectancy? The current President Sergio Mattarella was just reconfirmed in charge for a second mandate, and he's already 80. His predecessor Giorgio Napolitano is 96, and he's still healthy enough to sit in the Senate (in Italy, former Presidents become Senator for life by default). His predecessor, Carlo Azeglio Ciampi, died at 95. And before Ciampi there was Oscar Luigi Scalfaro, died at 93. Before him there was Francesco Cossiga, died at 82. His predecessor Sandro Pertini died at 93. Before him there was Giovanni Leone, also died at 93. His predecessor Giuseppe Saragat died at 89. The last president born after 1895 was Antonio Segni, died at 81.

Oh, and just to remain in Rome, Pope Francis is 85, and the former Pope Benedict XVI is still alive at 94. His predecessor John Paul II died at 84. John Paul I died at 65 (after only 33 days of pontificate), and Paul VI at 80.

More monarchs

King Felipe of Spain is 54; his predecessor Juan Carlos I is still alive at 84.

King Willem-Alexander of the Netherlands is 54 too. Before him there was Queen Beatrix (also still alive at 84), and before her there was Queen Juliana, died at 94.

King Philip of Belgium is currently 61, and his sti...
Question 2: Predicted bad outcomes of AGI learning architecture
by Cameron Berg — 2022-02-11T22:23:49.937Z

Architecture-independent risks: human and AGI misalignment

Let’s say as AGI safety researchers, we’ve done our research and soul-searching, and we’ve placed our personal bets about the most likely learning architecture of AGI. What then? Given the overarching goal of AGI-induced existential risk reduction, the most obvious next step is to ask where we might expect existential risks—or, if we’re thinking more generally, plain-old bad outcomes—to crop up from an AGI built with the learning architecture we think is most likely. (If you consider yourself to be one of these more general thinkers, then you should read every use of ‘existential risks’ in the following paragraphs as ‘bad outcomes’.)

Because the framework I am putting forward in this sequence is hierarchical—i.e., each subsequent research question takes the output of the previous question as input—the relevant problem spaces (e.g., the ‘existential-risk-space,’ the ‘control-proposal-space’, etc.) will explode combinatorially. In other words, this section would get far too long (and far too error-ridden) if I now attempted to spell out all of the conceivable existential risks associated with all of the conceivable learning architectures. So instead, I will address some important questions that seem necessary for ultimately achieving our goal of correctly anticipating the existential risks that are most likely to emerge from an AGI with our predicted learning architecture.

As was briefly mentioned at the end of the last section, the first—and perhaps most important—question to ask is the degree to which we expect certain AGI-related existential risks to be learning-architecture-independent. That is, maybe whether an AGI learns in way X, Y, or Z won’t change the fact that, say, it will exhibit tendencies towards instrumental convergence. I think that architecture-independent existential risks are plausible—but that their architecture independence necessitates their being formulated in fairly general, low-resolution terms. It is also worth noting immediately that even if particular existential risks are architecture-independent, it does not necessarily follow that the optimal control proposals for minimizing those risks (discussed in Question 3) would also be architecture-independent! For example, just because an SL-based AGI and an RL-based AGI might both hypothetically display tendencies towards instrumen...
Does needle anxiety drive vaccine hesitancy?
by Matthew Barnett — 2022-02-11T19:35:21.730Z

Yesterday, Katja Grace asked, "Why do people avoid vaccination?" I suggested that the answer might be anxiety over getting stabbed with a needle. The main idea is that concern over bodily autonomy is common—indeed, it forms the basis of much of our legal system—but people are perhaps too embarrassed to talk about needle anxiety publicly, so they self-deceive themselves about the real reason why they don't want to get their shots.

Since commenting, I've looked into the issue a little bit, and have decided to share some of my findings.

First, although not strong evidence, it is striking to note that there is a strong relationship between age and vaccine uptake. The young, by and large, are more vaccine hesitant than the old, despite being generally more liberal politically.

This is probably explained to a large degree by the fact that Covid-19 is far more dangerous to older people, and older people are on average more trusting of their physicians.

But here's another fact that could help explain the data: needle anxiety is concentrated in the young, and declines sharply with age. From one meta-analysis, "The results of meta-regression indicated that, for every decade increase in age (years), there was an 8.7% (95% CI: 6.0%, 11.4%) decrease in the prevalence of needle fear (p<0.001)."

The same pattern can be observed across the genders. Women are both more likely to be needle phobic and more likely to be vaccine hesitant. The same meta-analysis concludes, "For needle fear, the pooled female:male prevalence ratio was 1.4 (95% CI: 1.1, 1.8) with I2 of 89.8% and τ2 of 0.067. For needle phobia, the pooled female:male prevalence ratio was 1.7 (95% CI: 1.3, 2.1) with I2 of 63.4% and τ2 of 0.038."

By comparison, one study that surveyed a "sample of almost six thousand adult Poles, which was nationally representative in terms of key demographic variables" asked about vaccine hesitancy. Here were their main results,

However, these results may not be robust cross-nationally. In the US, the gender gap looks smaller to me, with FiveThirtyEight even reporting that men were less likely to get the vaccine in June 2021. By September, however, those numbers might have reversed with Pew reporting rates of 74% and 71% having received at least a single dose for adult men and women respectively.

One source just authoritatively states,

Women were sig

...