trentmkelly/LessWrong-43k huggingface dataset:
ToL: Foundations
by Hazard — 2019-12-10T20:15:09.099Z

(These are the touched up notes from a class I took with CMU's Kevin Kelly this past semester on the Topology of Learning. Only partially optimized for legibility)

Possible World Semantics

Let W be the set of all possible worlds. The nature of your inquiry is going to shape what sorts of worlds are in W .

Now we consider some true or false proposition A concerning W. A could be "There are more than 15 people in North America". The key idea in possible world semantics is that every proposition A is represented by the set of all worlds where A is true.

Def: Proposition A={w∈W:A is true in w}

We'll still refer to propositions by their English sentence description, but when we start doing math with them you should think of them as sets of worlds. Here are some consequences of defining logical propositions as such:

A∨B=A∪B
A∧B=A∩B
¬A=Ac
A→B=A⊆B
⊤=W
⊥=∅

Convince yourself that the above are true. If you're wondering, "Hmmm, the logic of set containment and classical logic seem eerily similar" you're right, and might want to ponder if that has anything to do with how we decided what the rules of classical logic should be in the first place.

Information Basis

Now that we have our world, the next thing we want is our information basis I. I is made up of information states, and each information state is a proposition. This means that they follow all the same union and intersection rules that other propositions do, and that an info state is a set of possible worlds. However, your info basis can't just be any old set of proposition. We are trying to capture the set of propositions that you could know about the world. Upfront, I want to acknowledge that this "could" might cause some confusion. How do we know what you could or couldn't know? Isn't that what we're trying to figure out? For now, we will resolve that with the following distinction. When we're talking about possible worlds and information states, they are not defined by some requirement that a particular person could access them. Later when we talk about methods, then we'll be talking about, "What conclusions could someone with XYZ method reach?"

The information basis, just like the possible worlds, will be shaped by how we construct the setup to our inquir...
Applications of Economic Models to Physiology?
by johnswentworth — 2019-12-10T18:09:43.494Z

Applying economic models to physiology seems really obvious. For instance:

Surely the body uses price signals to match production to consumption of various metabolites. Insulin as a price signal for glucose is one example.
Presumably such price signals coordinate between spatially-separated organs with specialized roles in various physiological "supply chains". That should lead to general equilibrium models, and questions of convexity and stability.
Can we back out an implied discount rate for the body's long-term energy stores?

Yet when I run a google search for the obvious phrase "econophysiology", I get back five results, most of which appear to be misspellings. (I feel like I ought to write something right now just to call dibs on the name.)

Does anyone know of sources on this sort of thing? Is there a name for it?
Many Turing Machines
by Logan Zoellner — 2019-12-10T17:36:43.771Z

Consider an idealized Turing machine. It has two parts, a "tape" which contains an infinite series of finite states: S0,S1,S2,... and a "head" which sits at a particular index i and stores a single value H.

At each step, the Turing machine reads the state at it's current index. The Turing machine then looks up the combination (Si,H) in its instructions. The table contains instructions, which describe how i,Si,H should be updated.

Now, if the Church Turing Hypothesis is true, then this metaphorical tape is sufficiently powerful to simulate not only boring things like computers, but also fancy things like black-holes and (dare I say it) human intelligence!

However, I have pulled a fast one on you.

For, you see, as I have described it there is not one Turing machine, but an infinite sea of possible Turing machines, many of which are simulating your current consciousness at this very moment.

Now, based off of the reasoning here, we all know that it would be completely unparsimonious and silly to imagine that an infinite number of Turing machines simply "collapse" into the one that actually describes the reality you are currently inhabiting. Rather, all of the possible Turing machines exist and you merely observe the branch of reality in which the Turing machine happens to be simulating your current existence.

Now I know what some people will say. The will tell me to "shut up and calculate". They will explain that Turing machine theory exists to predict observations, and that I can do this without worrying about whether or not the other branches of the Turing machine exist. They will tell me that the existence of the other branches of the Turing machine is a metaphysical question that science can know nothing about.

But those people are schmucks.

I demand to know whether or not my "Many Turing Machines" hypothesis is true or false. And I demand that science have an objective opinion on whether it is true or false. And I demand that they agree with me that it is indeed true, since it avoids the nonlinear "collapse" operator which I find so distasteful.

Basically, I don't understand why people think this is a rational position to take when it comes to quantum mechanics.

P.S. I know about things like Delayed Choice Quantum Eraser and Quantum Bomb Testers. But none of those things change the fact ...
Predictive coding = RL + SL + Bayes + MPC
by Steven Byrnes — 2019-12-10T11:45:56.181Z

(Update much later (2021-06): I put in some minor updates and retractions below. Also, if I were writing this today I would probably have framed it as "here's how I disagree with predictive coding" rather than "here's a version of predictive coding that I like". Also, wherever you see "predictive coding" below, read "predictive processing"—at the time that I wrote this, I didn't understand the difference, and used the wrong one. By the way, I wrote this near the very earliest stages of my learning about the brain. Having learned a lot more since then, I guess I'm not too embarrassed by what I wrote here, but there are certainly lots of little things I would describe differently now.)

I was confused and skeptical for quite a while about some aspects of predictive coding—and it's possible I'm still confused—but after reading a number of different perspectives on brain algorithms, the following picture popped into my head and I felt much better:

This is supposed to be a high-level perspective on how the neocortex[1] builds a predictive world-model and uses it to choose appropriate actions. (a) We generate a bunch of generative models in parallel, which make predictions about what's going on and what's going to happen next, including what I am doing and will do next (i.e., my plans). The models gain "prominence" by (b) correctly predicting upcoming sensory inputs; (c) correctly predicting other types of input information coming into the neocortex like tiredness, hormonal signals, hunger, warmth, pain, pleasure, reward, and so on; (d) being compatible with other already-prominent models; (e) predicting that a large reward signal is coming, which as discussed in my later article Inner Alignment in the Brain, includes things like predicting that my goals will be fulfilled with minimal effort, I'll be eating soon if I'm hungry, I'll be sleeping soon if I'm tired, I'll avoid pain, and so on. Whatever candidate generative model winds up the most "prominent" wins, and determines my beliefs and actions going forward.

(Note on terminology: I'm calling these things "generative models" in all cases. Kurzweil calls them "patterns". They're also sometimes called "hypotheses", especially in the context of passive observation (e.g. "that thing I see is a bouncy ball"). Or they're called "subagents"[2], especially in the context of self-prediction (e.g. "I am about to eat").

Before...
[Review] Meta-Honesty (Ben Pace, Dec 2019)
by Ben Pace — 2019-12-10T00:37:43.561Z

[Edit: I'm going to re-write this at some point, I don't think I managed to say much of anything very clearly. I've removed the first section.]

The essay takes the same starting assumption as I do. Honesty is deadly important and something you work hard at. There are some edge cases though, and Eliezer tries to make a simple rule that captures a lot of them.

Eliezer's general approach is not case-based as mine is, but more law-based, where he looks for general rules, and considers individual cases insofar as they provide him with forced-moves from the rule-based perspective. The things that motivate him aren't individual experiences, but are:

The fact that it’s easier to always say things that are technically true if you’ve got high verbal intelligence, and that this means requiring absolute honesty is unfairly easier for some than for others.
The massive number of counterfactual versions of you who would like you to keep plausible deniability when answering questions like “What did you do last night?”.
The case of hiding Jews in the attic and answering to Nazis who ask if you are hiding Jews.
Robin Hanson's problem of automatic norms, where people judge others for not immediately knowing their own norms. This induces (amongst many things) some self-doubt about whether your norms are as obviously correct as you’ve been thinking.

Instead of coming up with bits of advice here and there, he comes up with a new rule.

Eliezer's rule is fairly straightforward. He acknowledges that, yes, there are situations where even a very honest person will fail to be honest. In line with what I've said above, there are many edge cases.

And so he simply suggests that on top of trying hard to be honest throughout life, you should be absolutely honest about where you'll likely be honest and dishonest.

Here’s how he puts it.

Be at least as honest as an unusually honest person. Furthermore, when somebody asks for it and especially when you believe they're asking for it under this code, try to convey to them a frank and accurate picture of the sort of circumstances under which you would lie. Literally never swear by your meta-honesty that you wouldn't lie about a hypothetical situation that you would in fact lie about.

In many of the edge cases I listed above, I have genuinely sometimes communicated falsehoods to someone (wittingly and unwittingly). The important thing that Eliezer adds i

...
[Review] On the Chatham House Rule (Ben Pace, Dec 2019)
by Ben Pace — 2019-12-10T00:24:57.206Z

This is a brief review of On the Chatham House Rule by Scott Garrabrant.

I tend to be very open about my thoughts and beliefs. However, I naturally am still discrete about a lot of things - things my friends told me privately, personal things about myself, and so on.

This has never been a big deal, figuring out the norms around secrecy. For most of my life it's seemed pretty straightforward, and I've not had problems with it. We all have friends who tell us things in private, and we're true to our word. We've all discovered a fact about someone that's maybe a bit embarrassing or personal, where they don't know we know, and so we've e.g. carefully moved a group conversation around not putting pressure on that person to explain why they were unavailable last Tuesday. Discretion is a tool we all use implicitly, and often successfully.

And yet, in the last two years, I've had to think very carefully about norms around secrecy, and found increasingly difficult problems that have substantially changed my behaviour.

I think the main change is that I much more regularly start interactions with 5-25 minutes of conversation about the expected disclosure norms. And furthermore, I regularly decline information that comes with strong nondisclosure norms attached, after spending a minute talking about the terms and conditions of secrecy.

Now, why do I do this? And what has changed? I'm not certain.

I think I've not enjoyed the weird politicisation of language that happens behind secrecy, where people use abstract words a lot, and then don't correct you when you misuse them, because they can't tell you the thing they were actually thinking of. I especially don't like talking with people who aren't even telling me that they're hiding secret information that informs their opinions, sometimes this feels outright deceptive.

More than either of these though, I don't like being in that position myself. I like to just say my thoughts out loud in conversation. It's very, very limiting to not be allowed to just answer when someone asks me why I believe what I believe. Note that there's a big difference between not letting certain information out, and recomputing how you would reply and what thoughts you would have if you had never heard that information in the first place.

Riffing off of meta-honesty: it's very hard to be meta-open about openness norms.

"I see, so you don'

...
Were vaccines relevant to 20th century US mortality improvements?
by jasoncrawford — 2019-12-10T00:13:46.593Z
Link: https://rootsofprogress.org/vaccines-and-20th-century-mortality-improvements

I heard a vaccine skeptic claim that “90% of the decline in infectious disease mortality in the 20th century in the US was due to factors other than vaccines.” I wondered, is that right?

My guess is yes—but at the same time, I think this is very misleading. That statistic makes it sounds like vaccines just aren’t very important to health—sort of a sideshow in the fight against infectious disease. But here’s what the stat leaves out, and why vaccines still matter:

First, the greatest victory of vaccines was over smallpox. Smallpox vaccination was invented in 1796, and other immunization techniques were in use in England and America as early as 1721, so by 1900, immunization had already been fighting smallpox for well over 100 years. Smallpox is also the only disease we have ever completely eradicated—wiped off the face of the earth—and it was only possible because of vaccines. But by the 20th century, most of what remained to be done here was outside the US. So starting the clock in 1900, and restricting to the US, carves out most of the progress against smallpox.

Second, if we look just at the US in the 20th century, one of the greatest victories of vaccines was over polio. But the devastation of polio wasn’t just death—it was paralysis. Ten to twenty times more people were paralyzed by polio than died from it (especially after the “iron lung”). Unlike some other diseases, we weren’t able to fight polio with better sanitation or hygiene—in fact, it is believed that improved cleanliness caused the polio epidemics of the late 1800s and early 1900s. (Basically, before good sanitation, most people were exposed to polio in infancy, when they still had leftover immunity from their mothers, and when the disease is less likely to cause paralysis. Cleaner water led to a first exposure later in life, which led to a much worse disease.) So a vaccine was really our only weapon.

Third, in general, pharmaceuticals have been a bit ahead of vaccines. For some diseases, such as diphtheria and tuberculosis, an antitoxin or antibiotic was available before a vaccine was. This is also basically the story with influenza/pneumonia. Influenza is a viral disease that often results in an opportunistic infection of bacterial pneumonia. The pneumonia is what kills you. So antibiotics for pneum...
Bayesian examination
by Lê Nguyên Hoang — 2019-12-09T19:50:53.348Z

A few months ago, Olivier Bailleux, a Professor of computer science and reader of my book on Bayesianism, sent me an email. He suggested to apply some of the ideas of the book to examine students. He proposed Bayesian examination.

I believe it to be a brilliant idea, which could have an important impact on how many people think. At least, I think that this is surely worth sharing here.
tl;dr Bayesian examinations seem very important to deploy because they incentivize both probabilistic thinking and intellectual honesty. Yet, as argued by Julia Galef in this talk, incentives seem critical to change our thinking habits.

Let's take an example

Where is the International Olympic Committee?
1. Geneva
2. Lausanne
3. Zurich
4. Lugano

Quite often, students are asked to select one of the four possible answers. But this is arguably pretty bad, for several reasons:
- It makes impossible to distinguish a student who has a hunch from a student who really studied and knew the answer.
- It gives students the habit of self-identifying with a single answer.
- It normalizes deterministic question answering.
- It motivates students to defend the answer they gave (which encourages the motivated reasoning fallacy...).
Instead, Bayesian examination demands that students provide probabilistic answers. In other words they will have to provide percentage for each answer.
In our case, a student, call her Alice, might thus answer
1. 33%
2. 33%
3. 33%
4. 1%
Alice would essentially be formalizing the sentence "I really don't know but I would be very surprised if Lugano was the right answer".
Another student, let's call him Bob, might answer
1. 5%
2. 40%
3. 50%
4. 5%
Bob might be having in mind something like "I know that FIFA and CIO are in Zurich and Lausanne, but I don't remember which is where; though Zurich is larger so it would make sense for CIO to be in Zurich rather than Lausanne".
Spoiler: the answer turns out to be Lausanne.

Why naive notation is bad

Now, how would such an exam be scored? One intuitive idea could be that Alice should thus get 0.33 points, while Bob should get 0.4 points. Denoting qi the probability assigned by a student to answer i, and i∗ the right answer, this would correspond to giving the student a score equals to S=qi∗.
This would not be a great idea though. The reason for this has to do with incentives. Indeed, it turns out that...
Is Rationalist Self-Improvement Real?
by Jacob Falkovich — 2019-12-09T17:11:03.337Z

Cross-posted from Putanumonit where the images show up way bigger. I don't know how to make them bigger on LW.

Basketballism

Imagine that tomorrow everyone on the planet forgets the concept of training basketball skills.

The next day everyone is as good at basketball as they were the previous day, but this talent is assumed to be fixed. No one expects their performance to change over time. No one teaches basketball, although many people continue to play the game for fun.

Snapshots of a Steph Curry jump shot. Image credit: ESPN.

Geneticists explain that some people are born with better hand-eye coordination and are thus able to shoot a basketball accurately. Economists explain that highly-paid NBA players have a stronger incentive to hit shots, which explains their improved performance. Psychologists note that people who take more jump shots each day hit a higher percentage and theorize a principal factor of basketball affinity that influences both desire and skill at basketball. Critical theorists claim that white men’s under-representation in the NBA is due to systemic oppression.

Papers are published, tenure is awarded.

New scientific disciplines emerge and begin studying jump shots more systematically. Evolutionary physiologists point out that our ancestors threw stones more often than they tossed basketballs, which explains our lack of adaptation to that particular motion. Behavioral kinesiologists describe systematic biases in human basketball, such as the tendency to shoot balls with a flatter trajectory and a lower release point than is optimal.

When asked by aspiring basketball players if jump shots can be improved, they all shake their heads sadly and say that it is human nature to miss shots. A Nobel laureate behavioral kinesiologist tells audiences that even after he wrote a book on biases in basketball his shot did not improve much. Someone publishes a study showing that basketball performance improves after a one-hour training session with schoolchildren, but Shott Ballexander writes a critical takedown pointing out that the effect wore off after a month and could simply be random noise. The field switches to studying “nudges”: ways to design systems so that players hit more shots at the same level of skill. They recommend that the NBA adopt larger hoops.

Papers are published, tenure is awarded.

Then, one day, a woman sits down t...
Who are some people you met that were the most extreme on some axis?
by Mati_Roy — 2019-12-09T16:49:42.054Z

Characteristics like:

person that had the most imaginary friends

person that was the most extroverted

person that lived the most online

person that recorded their life the most

person that asked the most questions online

person that was the most secure

person that was the most caring

person that was the most frugal

person that was the most transparent

person that was the most productive

person that was the most minimalist

person that spent the least

person with the most automated life

person with the most artificial/mindful way to speak

person that ate the same thing the most

person that was the most (emotionally) empathetic

person that was the most emotional

person that was the most expressive

etc.

I'm curious about a general description of them (you should probably avoid outing anyone without their consent).

I also value a lot interacting with such people in general. I find them interesting and they help me understand a wider range of human experience:)
What are some things you would do (more) if you were less averse to being/looking weird?
by Mati_Roy — 2019-12-09T16:10:07.535Z

I had forgotten I had asked this question already, and I asked it again here: https://www.lesswrong.com/posts/mengYutEGfzyeA6Xi/what-would-you-do-differently-if-you-were-less-concerned
Long Bets by Confidence Level
by jefftk — 2019-12-09T14:20:02.076Z

If you want to make a

long-term bet
one of your options is to register your bet with the Long Now
Foundation as a Long Bet. They have some rules, which are

roughly:

Both parties put up the same amount, at least $200/each.

Long Bets effectively runs a donor-advised fund (DAF).

When the bet concludes the winner chooses a charity to receive the
money.

The charity gets the initial stakes, plus half the investment income.

While people have all sorts of reasons why they might want to use Long
Bets, one question is: how confident do you need to be for placing a
long bet to result in more money going to your preferred charity than
just putting the money in a DAF now?

Let's say I claim we'll have talking horses ten years from now, and
you're skeptical. You consider betting $1000 against my $1000 via
Long Bets. If you win you'll get your $1000 back, my $1000, and half
the investment income which (figuring the stock market returns a
nominal 7%) will be ~$967, for a total of ~$2967. On the other hand,
if you had just put your $1000 in a DAF you'd have ~$1967. Is this a
good deal?

Provided putting the money in a DAF for at least that long would
otherwise be your best option, if you're 100% confident that (a)
you'll win and (b) Long Bets will still be around, then it's a solid
deal. You're up about 50%. On the other hand, the less confident you
are the worse the deal looks:

(sheet
you can copy and play with)

For example, at 60% confidence you're neutral at 6 years, and negative
after that. At 75% you're down to neutral at 16 years. At 90%, 32
years. At 99%, 75 years. For an organization trying to promote
long-term thinking, it's surprising they would choose a fee structure
that penalizes long-term bets so heavily.

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