trentmkelly/LessWrong-43k huggingface dataset:
PrinciplesYou - Seems to be a new personality assessment tool
by jmh — 2021-04-22T19:20:46.384Z

https://principlesyou.com

I saw a story that mentioned this tool and took the test. I probably didn't give the answers as much thought as perhaps I should have (but wouldn't that also fit with a good personality tool's analysis?) but found some of the results both interesting and informative.

Has anyone else seen this and have any comments? I think of it more as alternative way of letting others be the mirror showing you what you look like.
Have an idea for an interactive learning module? Consider applying for a ClearerThinking.org micro grant!
by spencerg — 2021-04-22T18:13:44.050Z

If you have an idea for an interactive learning module (on topics such as rationality, psychology, self-improvement, statistics, probability, effective altruism, philosophy economics, math, science, behavior change, etc.) that you think would be valuable to a wide audience, we hope you'll consider applying to our second annual ClearerThinking.org micro grants program! Our program last year was a huge success, resulting in 15 new interactive modules, so we decided to do it again.

Applying to stage 1 is very fast and simple. Using the step-by-step process we provide, and feedback from our team plus a study we run on your work, winners will end up producing full-blown interactive learning modules like those on ClearerThinking.org. The top programs will also be featured on our website and sent out to our 130,000 email list subscribers!

Applications are due by May 17, 2021. All of the details of the program can be found here.

Some benefits you may get from applying to the program:

Helping other people improve their lives by creating a fun, educational tool that can be used by anyone, anywhere in the world (who speaks English and has internet access).
Gaining experience developing an idea into a complete, high-quality, interactive learning module with help from experienced professionals.
Receiving a monetary prize for each stage of the program you advance to (as long as you’re successful in the first stage).
The possibility of having your work published on ClearerThinking.org, and shared with our mailing list of 130,000+ people.
An easy application process - all you have to do for stage 1 is describe your idea for a beneficial learning module in around 150 to 400 words.

Go here to learn more and apply.
Two Designs
by SatvikBeri — 2021-04-22T16:32:26.425Z

Here's a concrete example of two approaches to a software problem, each with different advantages, and circumstances that would lead me to choose one over the other.

Recently I wrote a job to create and save datasets that are used for downstream work. The code is run through a scheduler that will spin up a worker and execute, so it has to be decoupled from any upstream or downstream jobs.

The first design I considered is pretty standard: we tell the job which datasets we want, validate that the parameters needed for the dataset are present, and then build those datasets.

def main(make_dataset1: bool, make_dataset2: bool, make_dataset3: bool, a = None, b = None, c = None, d = None):
if make_dataset1:
assert a is not None and b is not None
build_dataset1(a, b, d)
if make_dataset2:
assert c is not None and d is not None
build_dataset2(c)
if make_dataset3:
assert b is not None and d is not None
build_dataset3(b, d)

In the second approach, instead of telling the job which datasets to build, we pass it all the parameters we have, and the job builds whatever datasets it can.

def main(params: Dict):
for dataset_builder in [build_dataset1, build_dataset2, build_dataset3]:
try:
dataset_builder(**params)
except TypeError: # Error we get if arguments are missing
print(f"Skipping job {job.__name__} due to missing parameters")

These are pretty different approaches, representing two poles of design – let's call them explicit and pass-through. (In between versions exist too, but let's ignore them for now.) What are some tradeoffs between them?

In the explicit version, code calling the job needs to know about the different datasets and parameters. This makes it tightly coupled to the details of the build_dataset functions, or at least their signatures. In the pass-through approach, the calling code can just submit the job to a scheduler – it doesn't know anything about those functions, and doesn't need to change when we modify them.
Similarly, the build_dataset functions all have different signatures, making it hard to abstract over them and requiring us to change the job code if the functions ever change. In the pass-through approach, we can call them all in the same way, and only need to change the job code if we add or remove a dataset.
In the first version, it's easier to be explicit about exactly which datasets we want, and to fail loudly if one or more

...
Covid 4/22: Crisis in India
by Zvi — 2021-04-22T13:40:01.676Z

The United States appears to have turned the corner. Despite our determination to sabotage vaccination efforts, they have taken only minor damage, and we are starting to see declines again in the number of cases. Unless a new strain more dangerous than the English one reverses things once again, we should soon start to see steady declines in cases.

Other places without our access to vaccines are not as lucky, and in particular India is in crisis. Things there are worse than they’ve ever been and rapidly getting worse, with the hospitals on the verge of collapse. This is likely to be the biggest human cost of the entire pandemic, plausibly by a very large margin given how many people live in India, and it is entirely the our responsibility for not accelerating vaccine production in time to help them. Even when we have vaccine we are unwilling to use, we refuse to use it to help where it is needed.

Actions have consequences. In particular, a lot of death.

Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: Positivity rate of 5.8% (up 0.2%) and deaths unchanged.

Result:

The number of tests continues to crater, and also the numbers continue to not make much sense as reported here, since if tests fall 26.5% and cases fall 11.9%, that should imply the positive test percentage is higher rather than lower. I still don’t get how that one keeps happening.

Johns Hopkins has the rate declining from 5.2% to 4.7%, so I suppose that decline is real, and the 26.5% number isn’t real and I should chalk this kind of thing up to systematic reporting delays.

Can you tell that I really, really miss the Covid Tracking Project?

The trick as always is whether this is a data error that self-corrects, or the start of a trend which in this case would be the beginning of the final phase barring a new strain worse than the English one. I’m going to cautiously say it’s more likely to be mostly real, but likely got a little ahead of itself because graphs should be smooth.

Deaths should continue to slowly decline as vaccinations work, but it’s a slow process.

Prediction: Positivity rate of 5.1% (down 0.2%) and deaths decline by 4%.

Deaths

Date	WEST	MIDWEST	SOUTH	NORTHEAST	TOTAL
Mar 4-Mar 10	2595	1775	3714	1539	9623
Mar 11-Mar 17	1492	1010	3217	1402	7121
Mar 18-Mar 24	1823	957	2895	1294	6969
Mar 25-Mar 31	1445	976	2564	1262	6247
Apr

...
Löb's theorem simply shows that Peano arithmetic cannot prove its own soundness
by Adrià Garriga-alonso — 2021-04-22T09:17:07.096Z

EDIT: substituted "consistency" for "soundness". Thanks to Oscar_Cunningham for the correction!

Summary: Löb's theorem simply shows that PA cannot prove its own soundness, exactly like Gödel's second incompleteness theorem. I am very confident that this is not new, but it was new to me. Previously the theorem seemed circular and baffling.

Löb's theorem states that, in Peano Arithmetic or any system that contains it:

if PA⊢(Provable(p)→p), then PA⊢p.

I am using this notation instead of its more familiar modal logic rendition □(□p→p)→p because it yields the interpretation more easily.

The inner statement □p→p states that "if p is provable in PA, then it is a theorem of PA". It is the notion of soundness: that if the statement p can be proven, then it is true; that the PA system would not allow you to prove p if it were false.

With this in mind, we revise the statement of Löb's theorem, replacing p→q with the equivalent ¬p∨q:

PA⊢¬(Provable(p)→p)∨p.

That is: either p is true, or you cannot prove its soundness. You can thus only prove soundness of p (so (Provable(p)⟹p) is true) in settings where it is completely trivial, because you know that p is true already.

Even if Löb's theorem applied to only one false proposition p, it would show that PA is incomplete: it cannot determine its own soundness in this case. As it is stated, the situation is even worse: PA cannot determine its own soundness for any statement, unless soundness is trivial.
NTK/GP Models of Neural Nets Can't Learn Features
by interstice — 2021-04-22T03:01:43.973Z

Since people are talking about the NTK/GP hypothesis of neural nets again, I thought it might be worth bringing up some recent research in the area that casts doubt on their explanatory power. The upshot is: NTK/GP models of neural networks can't learn features. By 'feature learning' I mean the process where intermediate neurons come to represent task-relevant features such as curves, elements of grammar, or cats. Closely related to feature learning is transfer learning, the typical practice whereby a neural net is trained on one task, then 'fine-tuned' with a lower learning to rate to fit another task, usually with less data than the first. This is often a powerful way to approach learning in the low-data regime, but NTK/GP models can't do it at all.

The reason for this is pretty simple. During training on the 'old task', NTK stays in the 'tangent space' of the network's initialization. This means that, to first order, none of the functions/derivatives computed by the individual neurons change at all; only the output function does.[1] Feature learning requires the intermediate neurons to adapt to structures in the data that are relevant to the task being learned, but in the NTK limit the intermediate neurons' functions don't change at all. Any meaningful function like a 'car detector' would need to be there at initialization -- extremely unlikely for functions of any complexity.
This lack of feature learning implies a lack of meaningful transfer learning as well: since the NTK is just doing linear regression using an (infinite) fixed set of functions, the only 'transfer' that can occur is shifting where the regression starts in this space. This could potentially speed up convergence, but it wouldn't provide any benefits in terms of representation efficiency for tasks with few data points[2]. This property holds for the GP limit as well -- the distribution of functions computed by intermediate neurons doesn't change after conditioning on the outputs, so networks sampled from the GP posterior wouldn't be useful for transfer learning either.

This also makes me skeptical of the Mingard et al. result about SGD being equivalent to picking a random neural net with given performance, given that picking a random net is equivalent to running a GP regression in the wide-width limit. In particular, it makes me skeptical that this result will generalize to the complex mod...
What books are for: a response to "Why books don't work."
by Aaron Bergman — 2021-04-21T23:18:09.393Z
Link: https://aaronbergman.substack.com/p/what-books-are-for

...and a follow up to my recent post “

Recommended Readings

.”

Introduction

Andy Matuschak

’s 2019 essay “

Why books don’t work

” has been on my mind for a while now. I highly recommend you give it a read. The piece’s central claim is that books fail at their fundamental implicit task: conveying information to their reader. Let Matuschak speak for himself:

Picture some serious non-fiction tomes. The Selfish Gene; Thinking, Fast and Slow; Guns, Germs, and Steel; etc. Have you ever had a book like this—one you’d read—come up in conversation, only to discover that you’d absorbed what amounts to a few sentences? I’ll be honest: it happens to me regularly…
I know I’m not alone here. When I share this observation with others—even others, like myself, who take learning seriously—it seems that everyone has had a similar experience…

Why? Because

books have no carefully-considered cognitive model at their foundation, but the medium does have an implicit model. And like lectures, that model is transmissionism. Sequences of words in sequences of lines in sequences of pages, the form of a book suggests people absorb knowledge by reading sentences.

But transmissionism is basically false. Matuschak concludes that

as a medium, books are surprisingly bad at conveying knowledge, and readers mostly don’t realize it.

And the answer, he thinks, is not to change how we read but rather to replace books with a medium more conducive to understanding.

We agree so far

I basically buy the central thrust of Matuschak’s argument. A typical nonfiction book contains probably many thousands of individual statements and propositions, the vast majority of which the vast majority of us will never remember. And I agree that changes to the medium can help. For example, Spencer Greenberg’s excellent post “

How You Can Gain Self Control Without ‘Self-Control’

” includes built-in, interactive flashcards to help readers absorb and remember its content.

Books do work, sort of

Matuschak acknowledges that books aren’t useless*:*

I’m not suggesting that all those hours were wasted. Many readers enjoyed reading those books. That’s wonderful! Certainly most readers absorbed something, however ineffable: points of view, ways of thinking, norms, inspiration, and so on. Indeed, for many books (and in particular most fiction), these effects are the point.

But I do think that h...
Faerie Rings
by hamnox — 2021-04-21T21:47:33.581Z

This post describes "Faerie rings", a copiable form of community that forms a building block for capturing the upsides: learning from each other, deeper relationships, and support to solve problems that are beyond one's abilities. I believe it counts as a nascent prototype for an Iterated Trust Kickstarter.

I copied most of this description outright from @mixmix on the scuttlebutt platform. Citation details:

@ye+QM09iPcDJD6YvQYjoQc7sLF/IFhmNbEqgdzQo3lQ=.ed25519
%YrKbs+z2CmLt2+VtaEvUCDY+DUQAqQgB7vuUh3kNsdk=.sha256

The form

A group of up to 8 people
A call once every 2 weeks, for 2-3 months. The schelling days for this are Full Moon and New Moon.

Everyone has a turn checking in

how are you
how is it going

There's a host (who changes)

They open & close the circle
They make sure everyone gets to speak

The rest of the format grows to fit the group. It might include things like

a focus question
asks / offers

do you have something you’d like to get help with / to offer ?

freestyle

(I have some ideas of my own, but this post is for getting the main concept across.)

Every 4th meeting, one person from a faerie ring temporarily swaps with a person from another ring. This encourages networks to graft together in microsolidarity and cross-pollinate good format ideas.

Check out this doc for coordinating a ring.
In Defence of Spock
by Zac Hatfield-Dodds — 2021-04-21T21:34:04.206Z

[Epistemic status: tongue-in-cheek]

Spock has traditionally had a poor reputation
among the aspiring rationalists of the early 21st century; for his poorly calibrated
(even anti-calibrated)
probability estimates, labelling of actions and emotions "logical" or "illogical",
and difficulty with emotional and interpersonal relationships.
I argue that such condemnation, based on televised and non-representative
events, is ill-founded.

First, all credible sources agreee that the Enterprise did in fact survive
innumerable and hitherto-unknown hazards, which makes it
hard to believe
the the First Officer (and Science officer) could be so incompetent.
Observing the strong effect of selecting events for dramatised television,
our confusion is explained away by
Berkson's so-called Paradox:
Spock's probability estimates appear to be anti-correlated with subsequent
events, but this is only because the many cases where he correctly identifies
an event as likely or unlikely make for dull entertainment and are therefore
ommited from the record.

Second, while the destruction of Vulcan makes reliable sources difficult to
find, Spock's World
documents the philosophical/religious idea of cthia, translated as
"reality-truth — seeing things the way they really are,
instead of the way we would like to see them".
It is unfortunate that, being a scientist and officer but not a linguist,
Spock resorts to the concise "(il)logical" when he has trouble translating
such nuanced concepts into Federation Standard. Recall that this is a language
which conflates (epistemic) rationality with (instrumental) rationality,
requires lengthy explanation of foundational concepts such as
curiosity, relinquishment, or lightness,
and cannot safely dereference the
void*.

Third, we might accept as the premise
that Vulcans do have emotions, but (like humans) also some difficulty safely expressing them
in ways acceptable to both Vulcan and most twentieth-century human cultures.
It is therefore unsurprising that the sources where we tend to find evidence
of Spock's richer expressions of emotion and deeper relationships are
often disputed or dismissed by historians.

In summary, we can understand Spock as a competent officer disadvantaged by
cultural and language barriers and unfairly maligned by the televised medium.
I propose that we refer to this half-Vulcan,
half-steelman as Spock′.
A Crackpot Physics: Time, Special Relativity, and Why Bother?
by ACrackedPot — 2021-04-21T20:17:45.614Z

Previous Post: A Crackpot Physics: Issues

In reverse order. As I've written the last couple of posts, I think I've perhaps found another way of expressing the fundamental ideas here.

Why Bother?

I didn't, for a long time. The original idea involved here is something I came up with when I was thirteen or fourteen, and over time I'd occasionally have little "epiphanies", like when I realized that the idea was fundamentally a wave - prior to that I would have expressed it as an infinite sum (which in retrospect was a Euler series, but I lacked the mathematical experience to recognize it as such). I'd say most of that time was destructive, in regards to the idea, which started off very complicated, and has over time become quite simple. Originally the idea had two kinds of charge; an electrical charge, and another kind of charge which was related to whether particles annihilated or created space-time (don't ask). It also had a minimum of six dimensions; now I hesitate to even say it has four. (Depending on what you mean by a dimension).

The original idea was to try to recover particles, get rid of a particular notion of quantization, and get rid of uncertainty. At this point, I'd say it is, respectively, a failure, a success, and a mu. So, needless to say, a lot of the little epiphanies were disappointments.

These aren't the ideas I set out to create, and I don't actually like them very much. This is very much the product of churning away on an entirely false theory of reality until it started to approximate reality.

That said - why am I bothering?

Part of it is that I think I have some useful insights to contribute, and I should put forward at least a minimum effort towards conveying them to the rest of the world, given the effort other people have put forward into conveying their insights. I don't joke when I say I believe all this, nor am I joking when I describe myself as a crackpot - the problem is it is very difficult to convey what I mean by "all this".

So let's try this from another logical direction: What am I doing here?

I guess I'm advocating for something completely different. Stop trying to find the theory of everything, and instead assume one already exists, and try reasoning about it in the abstract. What do you expect of a theory of everything? What criteria should we expect it ...
[AN #147]: An overview of the interpretability landscape
by Rohin Shah — 2021-04-21T17:10:04.433Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer.

HIGHLIGHTS

Opinions on Interpretable Machine Learning and 70 Summaries of Recent Papers (Peter Hase and Owen Shen) (summarized by Rohin): This is basically 3 months worth of Alignment Newsletters focused solely on interpretability, wrapped up into a single post. The authors provide summaries of 70 (!) papers on the topic, and include links to another 90. I’ll focus on their opinions about the field in this summary.

The theory and conceptual clarity of the field of interpretability has improved dramatically since its inception. There are several new or clearer concepts, such as simulatability, plausibility, (aligned) faithfulness, and (warranted) trust. This seems to have had a decent amount of influence over the more typical “methods” papers.

There have been lots of proposals for how to evaluate interpretability methods, leading to the problem of too many standards. The authors speculate that this is because both “methods” and “evaluation” papers don’t have sufficient clarity on what research questions they are trying to answer. Even after choosing an evaluation methodology, it is often unclear which other techniques you should be comparing your new method to.

For specific methods for achieving interpretability, at a high level, there has been clear progress. There are cases where we can:

1. identify concepts that certain neurons represent,

2. find feature subsets that account for most of a model's output,

3. find changes to data points that yield requested model predictions,

4. find training data that influences individual test time predictions,

5. generate natural language explanations that are somewhat informative of model reasoning, and

6. create somewhat competitive models that are inherently more interpretable.

There does seem to be a problem of disconnected research and reinventing the wheel. In particular, work at CV conferences, work at NLP conferences, and work ...