trentmkelly/LessWrong-43k huggingface dataset:
Are explanations that explain more phenomena always more unlikely than narrower versions?
by FinalFormal2 — 2021-12-01T18:34:34.219Z

The classic example of a hypothesis explaining more being less likely would of course be conspiracy theories, where adherents add more and more details under the false assumption that this makes the theory more likely rather than less likely.

However, when we have multiple phenomena that follow a similar pattern, isn't it simpler and more likely that there's only one cause for both situations?

Is it possible that in some circumstances it could be more unlikely that the pattern is completely coincidental?

It seems like the problem with conspiratorial thinking isn't that they explain more with less, but that they can selectively pull their facts from a wide range of fact space. Similar to how you can take advantage of people's tribe-brain and narrative thinking to make them think that surgeons are evil, if you want to tell a story about how sugar companies are taking over the world, you can probably find some number of world leaders with ties to Big Glucose.
Taking Clones Seriously
by Raymond Douglas — 2021-12-01T17:29:08.759Z

What this is not: an exhortation to dig up Von Neumann and clone him

What this is: an exhortation to think seriously about how to decide whether to dig up Von Neumann and clone him

Having tried to clarify that I am not presently advocating for cloning, and am far more concerned with the meta level than the object level, let me now sink to the object level.

The argument for cloning

Suppose everything Eliezer Yudkowsky said in his recent discussions on Artificial General Intelligence (AGI) is entirely literally true, and we take it seriously.

Then right now we are a few decades (or perhaps years) away from the development of an unaligned AGI, which will swiftly conclude that humans are an unnecessary risk for whatever it wants.

The odds are not great, and it will take a miracle scientific breakthrough to save us. The best we can do is push as hard as we can to increase the possibility of that miracle breakthrough and our ability to make use of.

The claim I really want to draw attention to is this particular chestnut:

Paul Christiano is trying to have real foundational ideas, and they're all wrong, but he's one of the few people trying to have foundational ideas at all; if we had another 10 of him, something might go right.

Well, why not? 10 of him, 10 Von Neumanns, and an extra Jaynes or two just in case. If we have a few decades, we have just enough time.

Leave aside all the obvious ways this could go horribly wrong, the risks and the costs: if what Eleizer says is true, it is conceivable that cloning could save the world.

The argument for the argument about clones

I am not saying this is a good idea, but I am saying it could be a good idea. And if it is a good idea, it would be really great to know that.

There are obvious problems, but I don't feel well-placed to judge how high the benefits might be, or how easily the problems might be resolved. And I am sufficiently uncertain in this regard that I think it might be valuable to become more certain.

A modest proposal?

I think OpenPhil, or somebody else in that sphere, should fund or conduct an investigation into cloning. Most basically, how much would it actually cost to clone somebody, and how much impact could that potentially have?

It may become immediately clear that this is prohibitively expensive relative to just hiring more of the very smartest people in the world, but if the limiting factor is not money then I think ...
Hypotheses about Finding Knowledge and One-Shot Causal Entanglements
by J Bostock — 2021-12-01T17:01:44.273Z

Epistemic status: my own thoughts I've thought up in my own time. They may be quite or very wrong! I am likely not the first person to come to these ideas. All of my main points here are just hypotheses which I've come to by the reasoning stated below. Most of it is informal mathematical arguments about likely phenomena and none is rigorous proof. I might investigate them if I had the time/money/programming skills. Lots of my hypotheses are really long and difficult-to-parse sentences.

What is knowledge?

I think this question is bad.

It's too great of a challenge. It asks us (implicitly) for a mathematically rigorous definition which fits all of our human feelings about a very loaded word. This is often a doomed endeavour from the start, as human intuitions don't neatly map onto logic. Also, humans might disagree on what things count as or do not count as knowledge. So let's attempt to right this wrong question:

Imagine a given system is described as "knowing" something. What is the process that leads to the accumulation of said knowledge likely to look like?

I think this is much better.

We limit ourselves to systems which can definitely be said to "know" something. This allows us to pick a starting point. This might be a human, GPT-3, or a neural network which can tell apart dogs and fish. In fact this will be my go-to answer for the future. We also don't need to perfectly specify the process which generates knowledge all at once, only comment on its likely properties.

Properties of "Learning"

Say we have a very general system, with parameters θ, with t representing time during learning. Let's say they're initialized as θ0 according to some random distribution. Now it interacts with the dataset which we will represent with X, taken from some distribution over possible datasets. The learning process will update θ0, so we can represent the parameters the parameters after some amount of time as θ(θ0; X; t). This reminds us that the set of parameters depends on three things: the initial parameters, the dataset, and the amount of training.

Consider θ(θ0; X; 0). This is trivially equal to θ0, and so it depends only on the choice of θ0. The dataset has had no chance to affect the parameters in any way.

So what about as t→∞? We would expect that θ∞(θ0; X)=θ(θ0; X; ∞) ...
The Limits Of Medicine - Part 1 - Small Molecules
by George3d6 — 2021-12-01T15:51:54.126Z
Link: https://www.epistem.ink/p/the-limits-of-medicine-part-1-small?justPublished=true

Much of medicine relies on what is dubbed "small molecules".

"Molecules" since they are atoms tightly bound together, forming what is easily seen as a unitary whole, as opposed to e.g. lipoproteins, which are synergistic ensembles held together by much weaker forces (pun not intended).

"Small", in that they are, well... lightweight. But for all intents and purposes, size and weight don't separate quite the same way at the molecular level. So whatever.

This class includes drugs such as all NSAIDs, all antibiotics, all antihistamines, almost every single drug a psychologist or even a psychiatrist is allowed to prescribe, virtually all supplements, sleep aids, wakefulness aids, every single schedule I through III drugs (i.e. fun and insightful drugs), almost all anesthetics, and almost all anti-parasitic drugs, etc.

Unless you are old or chronically sick, it's likely that the only drug you've ever taken that hasn't been a small molecule is a vaccine. Indeed, drugs that aren't small molecules are so strange and rare we usually don't think of them as "drugs" but as a separate entity: vaccines, monoclonal antibodies, anabolic steroids.

But, like, the vast majority of molecules found in our body, and in all of organic life, are not classified as "small". And the vast majority of the things doing something interesting are not really molecules, but more so fuzzy complexes of molecules (ribosomes, lysosomes, lipoproteins, membranes).

So why are virtually all drugs small molecules? Prima facei we'd expect most of them to be complexes made up of dozens to thousands of very large molecules.

The answer lies in several things:

Easy to mass-produce
Simple to administer
Cheap to store
Homogenous in effect
Quick to act

Let's look at each of these aspects. None are unique to small molecules and not all small molecules bear all of them, but they are all traits significantly more likely to be found in small molecules.

Easy to produce

Producing a protein is hard, you have to get a gene sequence for the protein you want to produce, create some genetically modified organism (usually yeast) with a zillion copies of that genes, let it breed, extract the protein, makes super-duper-sure all potentially dangerous compounds are separated.

Along this process, you will have issues at every step, from errors in creating the DNA, to errors in creating the proteins to potential "errors" (mainly due to enviro...
FDA Votes on Molunpiravir
by Zvi — 2021-12-01T15:00:00.482Z

While Paxlovid Remains Illegal and is expected to remain illegal for at least several weeks, the FDA did manage to finally meet to discuss whether or not to legalize the other Covid-19 treatment pill, Merck’s Molunpiravir. While later data reduced effectiveness estimates from 50% to 30%, that’s still much better than 0% and it uses a unique mechanism that can probably be profitably combined with other treatments, so one might naively think that after sufficient stalling for appearances this would be easy.

One would be wrong. The vote was 13-10, was restricted to those at high risk, and could easily have failed outright.

I may want to later refer back to this, so it’s splitting off into its own post.

High Level Summary

Usually we get live-blogs from Helen Branswell and Mattew Herper. They held off and only issued a summary post later on this time, perhaps because the meeting was too painful. Luckily, my commenter Weekend Editor is excellent at summarizing such meetings, so I’ll quote their summary in full, link goes to their full post. After the summary, I’ll note the salient other details in the full post.

Today the FDA’s AMBAC meeting voted to recommend molnupiravir get an emergency use authorization. But just barely: 13 yes, 10 no, 0 abstentions. And with a lot of caveats among the yes votes.

Some issues:

(a) The efficacy vs hospitalization was “wobbly”: the interim report had 48.3% (CL: 20.5% – 66.5%) efficacy, but when they added the rest of the data it was only 30.4% (CL: 1.0% – 51.1%). People thought this might mean there were responder/nonresponder populations, and nobody knew a biomarker to distinguish them.

This is an interesting mix of good and bad thinking. Yes, it’s possible that the 50% vs. 30% thing wasn’t random, but why is that an issue here?

If you have a drug with 30% efficacy, that’s good. If you have a drug that is 50% effective half the time and 10% effective the other half, depending on who it is used on, and you can’t tell who is who, then you still have a 30% effective drug.

The difference is that because of the way you are now stacking your coin flips, it feels like you are now giving a 10% effective drug to some people without knowing it? Or there’s the ‘problem’ that if you knew more you could differentiate between the two populations, so now you have an imperfect procedure and it wouldn’t be ‘ethical’ somehow to proceed,...
How do you write original rationalist essays?
by lumenwrites — 2021-12-01T08:08:03.330Z

I really enjoy Scott Alexander's and Paul Graham's essays. How can I practice to learn to write as they do?

I'm getting pretty okay at writing tutorials, where I just walk people through the process of completing some project. I'm also okay at research-based posts - it's not that difficult to gather information from the internet and compile my own summary that is hopefully useful to other people.

But I don't understand how PG and SSC create such insightful essays seemingly by making them up (I'm talking about the SSC posts where he just shares his thoughts, not the ones where he analyzes studies or teaches you more than you ever wanted to know about x).

I can share things I have learned from experience, I can try to explain complicated subjects in more accessible ways, but none of these approaches will lead to essays that I see so many of at Less Wrong. People seem to just sit down, and generate interesting, original, and unique thoughts through the process of writing itself, by "thinking on paper". Or maybe not, I don't know.

Aside from just being born naturally very smart and gifted, is there anything that can be done to learn to write like this?

How do people like Scott Alexander, Paul Graham, Eliezer Yudkowsky, etc, just think of all these unique and original things? It seems like they just have a boundless source of ideas, every paragraph is insightful, and most of these thoughts are something they just came up with, not something they have learned elsewhere. Not just that, it seems that they come up with them as they are writing the post, not through collecting random epiphanies a person has now and then. Like, they can generate these epiphanies intentionally, on demand.

I guess the more general question is - how to I get better at creative, original thinking?

I have spent years practicing "creative" skills - traditional and digital art, writing, gamedev, programming, fiction. I've made a lot of things, I'm pretty proud of some of my projects, I'm getting pretty decent at some of these skills. But gun to my head - I can't seem to just sit down and make up an original non-fiction essay worthy of Less Wrong (or even my personal blog), even a simple one. What's wrong with me?
Experience with Cue Covid Testing
by jefftk — 2021-12-01T02:50:05.274Z

Several months ago my work started offering at-home covid-19 rapid
molecular testing via

Cue.
Now that it's possible to

buy these kits
as an individual I wanted to write some about our experience and who
it might be a good fit for.

These tests offer sensitivity comparable to PCR in an at-home ~25min
timeframe, and if you choose to take the test with a video call you
get an official result. The main downside is that they are expensive.

The system is two parts: a reusable reader and disposable cartridges
(with nasal swabs).

You connect to the reader with your phone over bluetooth, and there's
an app that walks you through all the steps. You can choose to have
your test "proctored", in which case you have a video call with Cue to
verify your identity and watch that you test correctly. It takes
about 25 minutes and to end, with an extra ~10min if proctored.
Occasionally a test fails and you need to retake, so there's some risk
of twice as long.

In terms of cost, while there are several options it looks to me like
if this is worth it for you at all you would choose Cue+ Complete,
at $149 + $90/month for 20 annual tests plus $60/test for each
additional test (in packs of ten).

That is quite a bit more than the $12/test you'd pay for an antigen
test like BinaxNOW,
so is this worth the extra cost?

In most cases, I think it isn't. While antigen tests are less
sensitive, the cases they miss are generally cases where people are
less infectious: lower viral concentration in the sample is correlated
with lower viral shedding in general.

The place where I think the Cue is potentially worth it is in
interacting with institutions that require a molecular test. For
example, it is common for schools and daycares to require that
children who have potential covid symptoms stay home pending a
negative test. In our experience this means missing two or three
days: if you go get a PCR test on the first day you don't get the
results in time for the second, and you only sometimes get the results
back in time to attend on the third day. With the Cue you get results
soon enough that you don't have to miss any days. If your kid missing
school/daycare means you missing work, $60/test may actually be a very
good deal. Same goes for people in jobs with a similar policy for
employees.

In our personal situation, because we already need to have an ...
Infra-Bayesian physicalism: proofs part II
by Vanessa Kosoy — 2021-11-30T22:27:04.744Z

This post is an appendix to "Infra-Bayesian physicalism: a formal theory of naturalized induction".

Proposition 2.16: Consider some Γ0, Γ1,
Φ and Θ∈□c(Γ0×Γ1×Φ).
Define π:elΓ0×Γ1→elΓ0
by π(y,z,α):=(y,{y′∈Γ0∣(y′,z)∈α}).
Then,
(π×idΦ)∗BrΓ0×Γ1(Θ)⊆prelΓ0×ΦBrΓ0(Θ)⊆BrΓ0(prΓ0×ΦΘ)

We can prove the second subset inclusion directly as a corollary of Proposition 2.10, just let the t of Proposition 2.10 be the projection function Γ1×Φ→Φ, so that just leaves the first subset inclusion direction. If you've seen the proofs so far, you know we do a thing where we try to show subset inclusion with expectations of functions and inequalities instead. And that the proofs all proceed by transforming the expectations until we get a maximum over contribution expectation values, and that's always where the hard part of proving the inequalities shows up. So, let's just get that part over with, an interested reader can work it out with previous proofs as a guide. Unusually, we'll be keeping track of identity functions here.

Plugging in some f, and doing our usual activities to get every term into the appropriate form, we can get this result if we manage to show that
maxθ′∈BrΓ0×Γ1(Θ)(π×idΦ)∗θ′(λy0α0x.f(y0,x,α0))
≤maxθ∈BrΓ0(Θ)prelΓ0×Φθ(λy0α0x.f(y0,x,α0))
So, to establish this, we'll show that, given some θ′∈BrΓ0×Γ1(Θ), we have
(π×idΓ1×Φ)∗(θ′)∈BrΓ0(Θ), and that
prelΓ0×Φ((π×idΓ1×Φ)∗θ′)=(π×idΦ)∗θ′
because, if we show that, then it means that BrΓ0(Θ) is a rich enough set for the right-hand side of the equation to match anything the left-hand-side can put out.

First off,
prelΓ0×Φ((π×idΓ1×Φ)∗θ′)=(π×idΦ)∗θ′
is pretty trivial to show. The only difference between the two processes is that the Γ1 coordinate of θ′ is discarded immediately on the right-hand-side, and it's preserved for one step and then discarded on the second step for the left-hand-side.

Now for our inequality of interest. Let θ′∈BrΓ0×Γ1(Θ), and we're trying to show that
(π×idΓ1×Φ)∗(θ′)∈BrΓ0(Θ)
First off, showing the support condition for (π×idΓ1×Φ)∗(θ′) which is somewhat nontrivial this time around. We start off with a guarantee that (y0,y1)∈α. This happens iff
y0∈{y′0|(y′0,y1)∈α}=π(y0,y1,α)2Γ0
And so, we get that y0∈α0 is guaranteed for that pushforward, support condition established.

endofunction condition time. It's important to remember that we want to treat elΓ0 as the computation side of things, and Γ1×Φ ...
Infra-Bayesian physicalism: proofs part I
by Vanessa Kosoy — 2021-11-30T22:26:33.149Z

This post is an appendix to "Infra-Bayesian physicalism: a formal theory of naturalized induction".

Lemma 1: prΓ×Φ(χy∈α(s∗(θ)))∈Θ occurs for all s:Γ→Γ iff, for all g:Γ×Φ→[0,1] and s:Γ→Γ, θ(λyαx.χs(y)∈αg(s(y),x))≤Θ(λyx.g(y,x))

This lemma will be implicitly used all over the place, in order to deal with the "presence in the bridge transform" condition algebraically, in terms of function expectations. The bulk of the conditions for a contribution to lie in the bridge transform is this endofunction condition (the support condition is trivial most of the time), so it's advantageous to reformulate it. First-off, we have that
prΓ×Φ(χy∈α(s∗(θ)))∈Θ
iff, for all g:Γ×Φ→[0,1], we have
prΓ×Φ(χy∈α(s∗(θ)))(λyx.g(y,x))≤Θ(λyx.g(y,x))
By LF-duality for ultradistributions, proved in Less Basic Inframeasure theory. A contribution lies in an ultracontribution set iff its expectations, w.r.t. all functions, are less than or equal to the ultracontribution expectations.

Now, we just need to unpack the left-hand-side into our desired form. Start off with
prΓ×Φ(χy∈α(s∗(θ)))(λyx.g(y,x))
Apply how projections work
=χy∈α(s∗(θ))(λyαx.g(y,x))
Now, we can move the indicator function into the function we're taking the expectation again, because there's no difference between deleting all measure outside of an event, or taking the expectation of a function that's 0 outside of that event. So, we get
=s∗(θ)(λyαx.χy∈αg(y,x))
Then we use how pushforwards are defined in probability theory.
=θ(λyαx.χs(y)∈αg(s(y),x))
And that's our desired form. So, we've shown that for any particular s:Γ→Γ, we have prΓ×Φ(χy∈α(s∗(θ)))∈Θ iff, for all g:Γ×Φ→[0,1],
θ(λyαx.χs(y)∈αg(s(y),x))≤Θ(λyx.g(y,x))
And so, we get our desired iff statement by going from the iff for one s to the iff for all s. ■

Proposition 2.1: For any Γ, Φ and Θ∈□c(Γ×Φ),
Br(Θ) exists and satisfies prΓ×ΦBr(Θ)=Θ.
In particular, if Θ∈□(Γ×Φ) then Br(Θ)∈□(elΓ×Φ).

Proof sketch: We'll show that for any particular contribution in θ∈Θ, there's a contribution θ∗ which lies within Br(Θ) that projects down to equal θ. And then, show the other direction, that any contribution in Br(Θ) lands within Θ when you project it down. Thus, the projection of Br(Θ) must be Θ exactly.

For the first direction, given some θ∈Θ, let θ∗:=θ⋉(λy.{y}). Note that since θ∈Δc(Γ×Φ), this means that θ∗∈Δc(Γ×2Γ×Φ), so the type signatures line up. Clearly, projecting θ...
Infra-Bayesian physicalism: a formal theory of naturalized induction
by Vanessa Kosoy — 2021-11-30T22:25:56.976Z

This is joint work by Vanessa Kosoy and Alexander "Diffractor" Appel.
For the proofs, see 1 and 2.

TLDR: We present a new formal decision theory that realizes
naturalized induction.
Our agents reason in terms of infra-Bayesian hypotheses, the domain
of which is the cartesian product of computations and physical states,
where the ontology of "physical states" may vary from one hypothesis
to another. The key mathematical building block is the "bridge transform",
which, given such a hypothesis, extends its domain to "physically
manifest facts about computations". Roughly speaking, the bridge
transforms determines which computations are executed by the physical
universe. In particular, this allows "locating the agent in the
universe" by determining on which inputs its own source is executed.

0. Background

The "standard model" of ideal agency is Bayesian reinforcement
learning, and more specifically, AIXI.
We challenged
this model before due to its problems with non-realizability, suggesting
infra-Bayesianism as an alternative. Both formalisms assume the "cartesian
cybernetic framework", in which (i) the universe is crisply divided
into "agent" and "environment" and (ii) the two parts interact
solely via the agent producing actions which influence the environment
and the environment producing observations for the agent. This is
already somewhat objectionable on the grounds that this division is
not a clearly well-defined property of the physical universe. Moreover,
once we examine the structure of the hypothesis such an agent is expected
to learn (at least naively), we run into some concrete problems.

The modern understanding of the universe is that no observer plays
a privileged role[1]. Therefore, the laws of
physics are insufficient to provide a cartesian description of the universe,
and must, to this end, be supplemented with "bridge rules"
that specify the agent's location inside the universe. That is, these
bridge rules need to translate the fundamental degrees of freedom
of a physical theory (e.g. quantum wavefunction) to the agent's observations
(e.g. values of pixels on a camera), and translate the agent's actions
(e.g. signal to robot manipulators) in the other direction[2].
The cost of this is considerable growth in the description complexity
of the hypothesis.

A possible retort is something like "how many bits can it really take to pick out the computer ...