trentmkelly/LessWrong-43k huggingface dataset:
The innocent gene
by Joe Carlsmith — 2021-04-05T03:31:29.782Z

(Cross-posted from Hands and Cities. Content warning: violence, spoilers for King Lear)

“With this and that I tried to keep the bucket together,
and then the bottom fell out.
Where water does not collect,
the moon does not dwell.”

— Chiyono

This post re-imagines Richard Dawkins’s description of evolution, with genes not as selfish agents, but as passive, innocent patterns, that don’t want to survive, and that know not what they do. In a sense, this is just the standard story. But in the context of Dawkins’s gene-centered (rather than organism-centered) framing, I find it evocative regardless.

Lady Chiyo (Nyodai) and the Broken Water Bucket.
(Source here. Author: Yoshitoshi).

I. Microscopic Machiavellis

“Is man no more than this?”

– King Lear, Act 3, Scene 4

In The Selfish Gene, Richard Dawkins presents evolution from a “gene’s-eye” point of view. The gene, he argues, is the fundamental unit of replication. Evolution does not select, directly, over organisms, groups, species, or ecosystems. It selects over genes; and what we get, as a result, is a biological world structured, in important part, by the genes that caused their own survival.

Dawkins’s scientific picture has been subject to (sometimes spicy) debate (see e.g. the “Darwin Wars,” and this book), which I won’t wade into here (and it’s not actually clear to me how much the central premises are in dispute). My interest is in how we imagine the picture in question.

The central, titular conceit of the book is that genes are helpfully thought of as “ruthlessly selfish” (in the introduction to the 30th edition, Dawkins acknowledges the possibility of emphasizing, instead, their immortality, or their cooperativeness, or the altruism of the organism they create; but the text itself focuses on selfishness, and I will, too). That is: Dawkins suggests that we do well, in predicting and explaining the effects of evolution, to personify genes, and to think of them as “trying” to survive, with no intrinsic regard for the interests of anything else.

In particular, genes build themselves elaborate “survival machines” — the giant, lumbering robots we think of as individual organisms — which they use to perpetuate and replicate themselves. They program these robots to prey upon, parasitize, manipulate, and cooperate with the survival efforts of other genes. But they will also happily harm or discard their robots, when doing so...
Open & Welcome Thread – April 2021
by niplav — 2021-04-04T19:25:09.049Z

Can anyone start these? Let's find out!

If it’s worth saying, but not worth its own post, here's a place to put it.

If you are new to LessWrong, here's the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are invited. This is also the place to discuss feature requests and other ideas you have for the site, if you don't want to write a full top-level post.

If you want to explore the community more, I recommend reading the Library, checking recent Curated posts, seeing if there are any meetups in your area or online, and checking out the Getting Started section of the LessWrong FAQ. If you want to orient to the content on the site, you can also check out the new Concepts section.

The Open Thread tag is here. The Open Thread sequence is here.
What do you think would be the best investment policy for a cryonics trust?
by Mati_Roy — 2021-04-04T19:20:22.705Z

Assume this policy will be followed by someone else, potentially for centuries, unless your cryonics revival timeline is shorter.

You can answer differently for different trusts; notably:

Trust with the goal of maintaining a patient in stasis indefinitely, and providing the fund for revival

Trust with money that you will gain access to again once you're reanimated
About Me: Background and Biases
by tkpwaeub — 2021-04-04T14:19:31.220Z

I've been a civil servant working in state government for almost ten years. I've worked at several regulatory agencies, I take lots of civil service tests to (among other things) manage my nagging impostor syndrome, and as a result I've also had the opportunity to interview for jobs in this leviathan I expect to be connected to for the foreseeable future. Even when I didn't get the jobs I interviewed for, it's always given me an opportunity to "pull back the curtain" on a lot of the under-appreciated things that government does (sometimes unbeknownst to average citizens).
Currently, I work as a paralegal, but I dabble with actuarial science (I passed Exam P and I'm currently studying for Exam FM). Before I started in state government I temped at an insurance company. In what now seems like prehistoric times I pursued a PhD in math, managed to pass my qualifying written and oral exams, completed a master's degree, but never completed a dissertation. Cognoscenti refer to this as "ABD" ("all but dissertation"). I'm fortunate enough to be represented by a union that negotiated very good educational benefits, and that allows me to take courses that nourish my mind.
This gives me some insight that others might not have, but inevitably it may also bias me somewhat. Specifically, I may have higher standards than most for what "good" government systems ought to look like, particularly those that leverage information technology. Thus, while I think we might all broadly agree on Gehm's contrapositive of Clarke's Third Law, to wit:

Any technology that does not appear magical is insufficiently advanced.

I am also quite willing to concede that some things that might appear magical to you, dear reader, may not seem quite as magical to me - and vice versa. In some cases the best thing we can do is let everyone else catch up.
Hope this helps.
What if we could use the theory of Mechanism Design from Game Theory as a medium achieve AI Alignment?
by farari7 — 2021-04-04T12:56:05.263Z

I watch some nice conferences from the MIRI team about the challenges in the area of AI Alignment (specifying the right utility function,

making an AI reliable for the task, ways to teach an AI etcetera). While looking to these information, and given the reward and utility functions

paradigm they used, it came to my mind, what if we could use some of the theory developed in Mechanism Design to incentive desirable Social Choice Functions for AI agents?

and guarantee nice properties like knowing the agent won't lie about private information (maybe intentions, source code, etc.) or other axiomatic principles like Pareto, Non Dictatorial etc.

I am wondering if anyone has given a though to this and if they are willing to share what their analysis is?

(for anyone interested in this area I leave a link with a good introduction).
How do scaling laws work for fine-tuning?
by Daniel Kokotajlo — 2021-04-04T12:18:34.559Z

The scaling laws, at least according to the interpretation used in Ajeya's framework (and this seems to be basically endorsed by tons of people I respect on this matter) say basically that if you increase parameter count by an order of magnitude, you also need to increase training steps/data points by about an order of magnitude, or else you are wasting your compute and could get the same performance with a smaller parameter count. For example, for a 10^14 parameter model (the size of the human brain, basically) we'd need 10^13 training steps/data points.

Now we have papers like this one claiming that pre-trained transformers can be fine-tuned to do well at completely different tasks (incl. different modalities!) by only modifying 0.1% of the parameters.

Does this mean that this fine-tuning process can be thought of as training a NN that is 3 OOMs smaller, and thus needs 3 OOMs fewer training steps according to the scaling laws? I'm guessing the answer is no, but I don't know why, so I'm asking.

(If the answer is yes, how does that not contradict the scaling laws for transfer described here and used in this calculation by Rohin?)
By which mechanism does immunity favor new Covid variants?
by anorangicc — 2021-04-04T10:24:20.691Z

I'm not a biologist, but I'm trying to get something like a gears-level understanding of the situation we are in. There are phenomena that seem superficially clear to me, but if I'm honest, I do not really understand what's going on.
For example, take the new Covid variants from Great Britain, South Africa and India. It is often said that the danger for such mutations is higher if a population already has some immunity because it increases the selection pressure. First, I thought "that's obvious, it's just how evolution works". But, how exactly does this work?
Here is my current understanding:
* Viruses do not have a metabolism, which is why I suspect that there is no strong competition in the human body among different virus variants. (Please correct me if this is wrong -- I asked some friends doing biology but they also could not tell me whether this is true.)
* Immunity essentially disadvantages the "old" variant, but because of the first point, it does not give any advantage to the "new" variants.
* Although the share of the more contagious, new variants are higher with some existing immunity, in absolute numbers, the new variants are not more successful than without immunity.
* It even seems plausible (and for some variants, there's evidence) that immunity agains the old variant also gives partial immunity agains the new variants.
* I conclude that the presence of the new variants is mainly driven by the mere number of viruses, which is proportional to the number of mutations. When the number of viruses increases, the chance of a dangerous mutation also increases. However, immunity seems to be a good thing to mitigate mutations in every case.
Unfortunately, I must have made a mistake somewhere because experts say otherwise.
I'd be grateful for suggestions how this mechanism "immunity -> selection pressure -> more creepy variants" really works!
A Cruciverbalist’s Introduction to Bayesian reasoning
by Davidmanheim — 2021-04-04T08:50:07.729Z

Status: Hopefully a nice introduction to some of the basics of Bayesian reasoning for newcomers.

Clue: Mathematical methods inspired by an eighteenth century minister (8)

“Bayesian” is a word that has gained a lot of attention recently, though my experience tells me most people aren’t exactly sure what it means. I’m fairly confident that there are many more crossword-puzzle enthusiasts than Bayesian statisticians — but I would also note that the overlap is larger than most would imagine. In fact, anyone who has ever worked on a crossword puzzle has employed Bayesian reasoning. They just aren’t (yet)aware of it. So I’m going to explain both how intuitive Bayesian thinking is, and why it’s useful, even outside of crosswords and statistics.

But first, who was Bayes, what is his “law” about, and what does that mean?

Clue: Sound of a Conditional Reverend’s Dog (5)

“Bayes” of statistical fame, is the Reverend Thomas Bayes. He was a theologian and mathematician, and the two works he published during his lifetime dealt with the theological problem of happiness, and a defense of Newton’s calculus — neither of which concern us. His single posthumous work, however, was what made him a famous statistician. The original title, “ A Method of Calculating the Exact Probability of All Conclusions founded on Induction,” clearly indicates that it’s meant to be a very inclusive, widely applicable theorem. It was also, supposedly, a response to a theological challenge posed by Hume — claiming miracles didn’t happen.

Clue: Wonders at distance travelled without vehicle upset (8)

“Miracles”, Hume’s probabilistic argument said, are improbable, but incorrect reports are likely— so, the argument goes, it is more likely that the reports are incorrect than that the miracle occurred. This way of comparing probabilities isn’t quite right, statistically, as we will suggest later. But Bayes didn’t address this directly at all.

Clue: Taking a risk bringing showy jewelry to school (8)

“Gambling” was a hot topic in 19th century mathematics, and Bayes tried to answer an interesting question; when you see something happen several times, how do can you figure out, in general, the probability of it occurring? His example was about throwing balls onto a table — you aren’t looking, and a friend throws the first ball. After this, he throws more, each time, telling you whether the bal...
Tales from Prediction Markets
by ike — 2021-04-03T23:38:22.728Z
Link: https://misinfounderload.substack.com/p/tales-from-prediction-markets

Prediction markets are fun, at least if you're making money. I've only been into them for a few months, but have already collected a bunch of interesting tales. Note: I may have been involved with some of these, but I'm telling these tales from a third person perspective.

One general point: all of these took place on Polymarket, a crypto prediction market. You can track which accounts place each bet, and so you can see their history of bets, but you can't tie it to an actual person unless they've chosen to identify themselves. You can look at the bet history at Polymarketwhales.info, although there's a ton of bets so it's easier if you know what you're looking for.

The Tesla market. Polymarket had a market on whether Tesla would announce a Bitcoin purchase by Mar 1, 2021. On January 27, an unknown user bet $60k on Yes. This was their only trade on the site, before or after. They won $180k, or 120k in profit. Odds are pretty good it was an insider. Is this insider trading? I asked Matt Levine but he didn't respond. Anyway, there’s another user that lost $242k betting that Tesla would not announce a Bitcoin purchase. This user is affectionately called the "Tesla whale” on the Polymarket discord. They're also notable for losing $92k on the super bowl the day before Tesla made the announcement, and they get honorable mention for having lost the most money on the 100 million vaccine market: see below. As of this writing, the Tesla whale is down nearly $500k.

Watch out for slippage: there was a market on whether Joe Biden would still be president as of Mar 1, 2021. Someone owned around 200k shares of Yes. The market price was very close to $1 each on the morning of Mar 1st, and they apparently decided to sell all their shares instead of waiting for it to resolve; however, there wasn't enough liquidity to sell them all at market price, and they ignored the warning about the slippage the order would encur. Their order ended up executing at an average price of 2 cents, and someone else scooped up those cheap shares a minute later, spending $1k to make $155k; talk about being in the right place at the right time! The initial user ended up with a total loss of $156k on this market. However, even taking that into account, as of this writing they're still up $175k, so don't feel too bad for them. (Note: it's possible they were trying to sell No shares to make a few cents an...
An exploration of exploitation bias
by Eric Neyman — 2021-04-03T23:03:22.773Z
Link: https://ericneyman.wordpress.com/2020/07/05/an-exploration-of-exploitation-bias/

This is a map of Nassau Street, the northern edge of Princeton University.

It’s a very standard sort of street; I imagine one quite like it exists in most college towns. It has lots of great places to eat, shown on the map in orange.

During my senior year, because of Princeton’s absurdly expensive meal plan, it made financial sense for me to eat dinner at these restaurants. And so, over my time at Princeton, I visited these places around 200 times in total, often by myself.

And yet, I don’t recall ever once deciding to try out a new place by myself. Whenever I went somewhere for the first time, it was always because of a friend’s initiative. Over 90% of my visits to Nassau Street have been to one of just four places: Mamoun’s, Ajiten, Panera, and Tacoria, each of which I’ve been to maybe 50 or so times.

Moreover, when I went to one of these restaurants, I would always get the same thing. The first time I went to Panera I tried their creamy tomato soup, liked it, and never tried anything else. The first time I went to Ajiten, I tried their chicken baitan ramen, liked it, and never tried anything else (well, except the one time I worked up the willpower to try one of their sushi plates). Same story for Mamoun’s and Tacoria.

This is, from what I understand, quite unusual. And it’s not that I’m a particularly picky eater. So what’s going on here?

***

The multi-arm bandit problem is a classic problem in computer science. Imagine you have a machine with ten buttons, each of which gives you some (possibly random) amount of money when you press it. These buttons are different — maybe one gives you a random reward between 5 and 7 dollars, and another one gives you nothing with 90% probability and 100 dollars with 10% probability — but you don’t know what they do ahead of time. You are allowed to press whatever buttons you want, but can only press buttons 100 times in total. What should your strategy be if you want to maximize your reward?

The precise answer depends on the assumptions you make about the distributions of the rewards, but the basic idea is always the same. At each time step, you should do one of two things:

(1) Explore: choose a button you haven’t pressed all that much — so you’re pretty uncertain about how good it is — and press it. The value of doing this isn’t so much the reward you expect to get from the button, but rather the information and potential for m...
What are all these children doing in my ponds?
by dominicq — 2021-04-03T20:16:58.994Z

(this is not criticism of effective altruism, only one analogy that's used as an argument)

Peter Singer writes in the The Drowning Child and the Expanding Circle:

To challenge my students to think about the ethics of what we owe to people in need, I ask them to imagine that their route to the university takes them past a shallow pond. One morning, I say to them, you notice a child has fallen in and appears to be drowning. To wade in and pull the child out would be easy but it will mean that you get your clothes wet and muddy, and by the time you go home and change you will have missed your first class.

I then ask the students: do you have any obligation to rescue the child? Unanimously, the students say they do. The importance of saving a child so far outweighs the cost of getting one’s clothes muddy and missing a class, that they refuse to consider it any kind of excuse for not saving the child. Does it make a difference, I ask, that there are other people walking past the pond who would equally be able to rescue the child but are not doing so? No, the students reply, the fact that others are not doing what they ought to do is no reason why I should not do what I ought to do.

Once we are all clear about our obligations to rescue the drowning child in front of us, I ask: would it make any difference if the child were far away, in another country perhaps, but similarly in danger of death, and equally within your means to save, at no great cost – and absolutely no danger – to yourself? Virtually all agree that distance and nationality make no moral difference to the situation. I then point out that we are all in that situation of the person passing the shallow pond: we can all save lives of people, both children and adults, who would otherwise die, and we can do so at a very small cost to us: the cost of a new CD, a shirt or a night out at a restaurant or concert, can mean the difference between life and death to more than one person somewhere in the world – and overseas aid agencies like Oxfam overcome the problem of acting at a distance.

Singer's analogy is incomplete because it doesn't capture the essence of the drowning child scenario. The following does.

You're walking somewhere, and you see a child drowning in a shallow pond. You naturally decide that you ought to save this child despite your other obligations, so you rush in, get your clothes wet and muddy, a...
The Holy Algorithm
by Malmesbury — 2021-04-03T18:09:53.117Z
Link: https://www.telescopic-turnip.net/hogwash/the-holy-algorithm/

As it will surely not have escaped your insight, this weekend is Easter. Why now? The date of Easter is determined by a complicated process called the Computus Ecclesiasticus. I will just quote the Wikipedia page:

The Easter cycle groups days into lunar months, which are either 29 or 30 days long. There is an exception. The month ending in March normally has thirty days, but if 29 February of a leap year falls within it, it contains 31. As these groups are based on the lunar cycle, over the long term the average month in the lunar calendar is a very good approximation of the synodic month, which is 29.53059 days long. There are 12 synodic months in a lunar year, totaling either 354 or 355 days. The lunar year is about 11 days shorter than the calendar year, which is either 365 or 366 days long. These days by which the solar year exceeds the lunar year are called epacts. It is necessary to add them to the day of the solar year to obtain the correct day in the lunar year. Whenever the epact reaches or exceeds 30, an extra intercalary month (or embolismic month) of 30 days must be inserted into the lunar calendar: then 30 must be subtracted from the epact.

If your thirst of knowledge is not satisfied, here is a 140-page document in Latin with more detail.

As far as I understand, during the Roman Era the Pope or one of his bureaucrats would perform the computus, then communicate the date to the rest of Christianity and everybody could eat their chocolates at the same time. Then, the Middle-Ages happened and communication became much harder, so instead they came up with a formula so people could compute the date of Easter locally. Of course, the initial formulas had problems – with the date of Easter dangerously drifting later and later in the year over centuries, and don’t even get me started on calendar changes. Eventually Carl Friedrich Gauss entered the game and saved humanity once again with a computationally-efficient algorithm (I am over-simplifying the story so you have more time to eat chocolate).

But now is 2021, and I’m wondering how they run the algorithm now, in practice. I looked up “how is the date of Easter calculated” but all the results are about the algorithms themselves, not about their practical implementation. I have a few hypotheses:

There are responsible Christians everywhere who own printed tables with the dates of Easter already computed for

...