throwawaymath

If not, it seems like we should operate on our best hypothesis with evidence for it, not speculate about unknown unknowns.

Nope! We shouldn't. Speculation is precisely what we should do! That's what a critical review of research involves. We should take this as excellent motivation for further research. It could be true, but we haven't done nearly enough work to conclude as much. I'm not saying it's wrong, I'm saying it's uncertain, and it's strictly unscientific to proceed by assuming something uncertain is the truth just because it's intuitively compelling. Intuitively compelling narratives are very dangerous in science, because they make you believe you have a shortcut to the truth and they're vindicated right up until they aren't. It's not a good habit.

Happiness is incredibly complicated. Contradicting evidence and results abound across studies of life satisfaction. Measuring the impact and success of advertising itself is highly complex; the researchers have shown this correlation under their current methodology. What are we to conclude if someone uses a different methodology to study the same topic, equally as valid, and comes away with a different conclusion? That's very possible, and we can't dismiss it. The researchers themselves hedge their claims and don't come out and say they've found causation.

I don't really have a dog in the race with advertising. But I really despise this kind of reporting by HBR, because the result is threads like this one where people walk away with unproven "truths" that become part of the popular zeitgeist because they just seem to vindicate obvious beliefs. Today it's advertising, tomorrow it'll be something else. Given the replication crisis we're undergoing, we shouldn't take anything away from studies like this except that further research is required.

I wish yours wasn't the only comment talking about this. It saddens me to see articles like this, where causation is imposed on correlation. Everyone "knows" correlation doesn't imply causation, but they only seem to know it in the sense of a student reciting it. They don't apply that knowledge critically.

A single study of two things as complicated as advertising measurement and happiness/satisfaction, and the intuitively compelling conclusion is immediately embraced as the truth because it's "obvious."

Edit: Again with the downvotes...come on HN. I'm not defending advertising, I'm criticizing the reporting on a study in a world which has a rampant replication crisis.

I can see a few compelling arguments for it, but personally I'd be very wary of concluding causation from this relationship. The two things are very complicated and have many confounding variables involved. I see it just as likely that the correlation is spurious, and people exposed to advertising have lower life satisfaction for a variety of complex reasons incidental to the ads themselves.

Capitalism affords many opportunities for someone to be unhappy and unsatisfied; advertising may cause this or simply exacerbate what is already there.

I'm disappointed with the way HBR reported this. The researchers are explicitly quoted as saying they found a "negative connection", which, yes, is an inverse correlation. No matter how intuitively compelling, you can't just extrapolate that to a conclusion of causation as the author of this article proceeded to do right in the introduction.

Edit: Why is this being downvoted? Do you disagree with my point that we can't extrapolate causation from correlation, or do find my comment to be off topic?

That raises profound and fascinating philosophical questions about the nature of knowledge and happiness. I wonder how many of those people would choose to forego the knowledge of what they don't have if it meant they would be happy again. On the other hand, is that even a meaningful question to ask, given it's not possible?

Which of course leads to the ethical question: is it right for people to live in ignorance if it makes them happy, it it's not their choice? Is it fundamentally better for people to be happy rather than aware of massive inequality (up to and including significant poverty)? How much would be appropriate to hide, for how much additional happiness? Is it better in the long run for some to be unhappy if it brings attention to inequality?

I don't have any of those answers, but they're interesting and challenging questions.

Apple actually does. Like I said, that icon is in the article, right after the paragraph I quoted. Look at the images the author included.

The author is making the point that this shouldn't take much effort because Apple already uses different icons.

Apple has icons for SSDs. They are shown in the article right after this:

So what should Apple do? Customized icons for different types of drives would be a good start. Time Machine drives get custom icons automatically, as do many other types of storage devices that were more common in the past, so surely Apple could design different icons for SSDs and Fusion Drives, and then display them appropriately based on the type of drive.

Apple could repurpose one of the existing SSD icons pretty easily.

Usually because the strategies which have genuine alpha and consistently outperform are capacity constrained, and so cannot be scaled to handle both.

Of course, at RenTech in particular the employee-only fund is the good fund. But that's not always the case, so the parent commenter has a point. Deferred/locked up compensation can really suck.

Usually top ~20 schools with a bachelors in math or CS. Sometimes Masters or PhD, but those end up working directly in research. The new grads I know aren't actually quants.

They work in what's usually termed the "front office." Quant, sure, but also research development (read: trading algorithm implementation).

That's not really how it works for software engineers/quants. But in general, yes.

Some funds share bonuses across the entire firm, while others operate as internally competing pods/teams. I know people at Jump Trading doing spectacularly well because they're on a high performing team. Not everyone does as well. Millennium and Citadel have a similar model.

RenTech and TGS had a spectacular year as always. But even if you don't work at a firm like that, being on the right team at a strong second tier firm can be extremely lucrative.

You're talking about banking. I think the context of the original question is actually the buy side, because that's the comment you're replying to.

I have not seen any of the typical sell side grind common to investment banking among hedge funds. New grads hired to reputable hedge funds typically work comparable hours to their friends who work at Google/Facebook. They might earn 20 - 100% more though.

Important note: the person you're responding to is talking about the buy side. It's not uncommon to find a work life balance resembling tech in the buy side. For example, it's a popular meme that Citadel has a horrible work life balance. But I know two devs there who earn in excess of $500k each year and only work about 45 - 50 hour weeks.

I also know another new grad recently hired at Hudson River Trading, and another new grad recently hired at Jump Trading. Both are earning over $400k per year, but they work 45 hour weeks. And since RenTech has been mentioned in this thread: the people I know there have wonderful work life balance.

I have found that most popular conceptions of bad work life balance in finance apply to banking and sell side, not to the buy side. Working as a developer at a bank usually sucks, both financially and culturally, in my experience. Not always, but commonly. Working at a hedge fund can be very nice both financially and culturally.

Yeah that definitely seems off. I worked at a tech company where approximately half our employees were customer support, and each was paid closer to $30/hour. I don't see how we could have ever paid $15/hour/head for our support team.

This is a great comment, but I just want to point out:

Bcrypt is not a KDF and not a hash function

This is true, but it's also a good example of what I was saying in my other comment. bcrypt is an example of a password hashing function which is not itself a KDF, but which can be used to construct a KDF.

All password hashing functions can be used to construct key derivation functions or simply are key derivation functions. But not all password hashing functions are key derivation functions. Whether or not it would be advisable to use a given password hashing function as a KDF depends, of course. In bcrypt's case you can construct a reasonable KDF. For example: https://github.com/pyca/bcrypt/blob/master/README.rst

I don't know of a good source beyond textbooks or papers which focus precisely on the low level crypto details you (rightly) want to avoid. There just isn't much of a need for that kind of nuance most of the time. I also (gently) reject the premise; as far as practical security is concerned, if your team is recommending Argon2/scrypt/bcrypt to developers then that's far more important than being able to explain the difference between key derivation and keyless password hashing.

It's essentially like rectangles versus squares. You can create a key derivation function out of anything which passes all the criteria of a password hashing function. But it won't be a particularly performant or useful key derivation function. Likewise you can create a password hashing algorithm out of a dedicated key derivation function, but that's insufficient on its own.

There's no need to get bogged down in the details, just continue recommending a reputable implementation of these algorithms. On the other hand, if you'd like to learn more out of intellectual curiosity, Boneh & Shoup's textbook is good (work in progress) [1]. Galbraith's textbook includes chapters which cover the topic to a depth that's beyond what you're looking for, but you'll learn whatever it is you want to know [2].

Finally, more accessible, informal answers that get the basic idea across are [3], [4].

1. https://toc.cryptobook.us/

2. https://www.math.auckland.ac.nz/~sgal018/crypto-book/main.pd...

3. https://security.stackexchange.com/questions/95410/what-is-t...

4. https://crypto.stackexchange.com/questions/70716/key-derivat...

That's a good point; I was just giving you a handy chart with an explicitly called out platform :)

Unfortunately, not all password hashing algorithms are key derivation functions. That's just a common design and closely related.

I'm not sure I'm following you. I'm also not arguing for or against the results here. I'm just giving you the background to understand the author's point; it's not something they just came up with, it's been under study for quite a while in constructivist mathematics.

I also don't really think you're using the right definition of computable here. You make it sound as though we're estimating, or truncating uncomputable numbers to make them computable when you say:

From that definition, it's quite obvious that everything you compute has to be continuous, because you are never sure of what other decimals may be coming up, so whatever you compute has to be close enough.

It's not about being close enough or estimating, they're categorically different things. You can't obtain an uncomputable number, even by estimating, to any meaningful precision with a finite amount of time. So what are you saying here?

There is a comparison of Blake3 to various hash functions (including SHA-2 and SHA-3 families) on AWS c5.metal; see the chart here: https://github.com/BLAKE3-team/BLAKE3

That's because the author is using constructive mathematics (i.e. computable analysis instead of standard real analysis). It is true that all computable functions are continuous. Likewise discontinuous real functions aren't computable.

The continuum hypothesis is tangentially related to this topic and makes for fun reading.

Which post-quantum cryptosystems are more efficient than RSA?

I successfully started a 501(c)(3) private foundation for precisely the charitable purpose of "supporting (scientific) open source software" in 2017. The IRS granted it with no questions asked.

Also not legal advice, but I literally did it so...

What's the point being driven at here? Is it minimizing the deaths of Australian wildlife, or is it making a quip about the sheer scale of cat hunting activity?

If it's the former, that seems like a bad comparison. The Australian wildfires are destroying ecosystems on a scale cats don't really do. And a much greater variety of wildlife is dying than what cats will typically kill.

Finally, wildfires don't support life. They're naturally occurring things, but they are not an activity occurring in the support of other life. Cats kill to hone hunting skills (or simply to hunt, if they're actually hungry). With that observation we can impose a reasonable normative, which is that we don't need to accept wildfires the same way we accept predator activity.

Put simply: I don't see that these two things belong to the same category of destruction.

Please tell me a few of these objective reasons why someone who puts "country music" on their resume as an interest isn't sending a good signal.

As a resume screener you should calibrate your recruiting process to be empirical. What you're describing doesn't sound empirical at all.

Are you being facetious? I don't think the candidate's favorite music genre is being explicitly used as a job qualification. They're not filtering on it because it's relevant to the job.

Link without paywall: http://archive.is/qDalg

Considering the people they're explicitly looking for, according to that page:

People who have done well at math competitions (USAMO, IMO, PUTNAM), competition programming (ACM, USACO, codejam, topcoder), science fairs (ISEF, STS), or capture the flag (DEFCON, secuinside, GITS). Those competitions don't just select for ability, they also select for quickness. We are in a very competitive space.

...it's absurdly low. Companies like Google hand out close to $200k total, liquid compensation to new grads who haven't placed in any of those competitions. The people who have ranked in any of those (especially the math ones, and doubly so the Putnam) can easily write their ticket to a job paying double $130k right out of college.

Anyone with that kind of competitive math/programming experience and real world machine learning engineering experience could earn triple that range if they wanted to. That's a ridiculously small and competitive set of candidates to be targeting. It's also not necessary, because strong performance on the e.g. IMO doesn't a priori map to outperformance, on a per dollar basis, writing autonomous driving logic.

Basically: no it's not competitive for San Diego, Comma is asking for wildly overqualified people to sacrifice significant amounts of money to work there, and it's not clear they should be using those kinds of qualifications as a filtering criteria in the first place.

This kind of cargo culting does not inspire confidence in their recruiting.

Hotz tweeted from the official Comma.ai account that rather than providing the requisite response, the company would instead be cancelling Comma One entirely, and turning its attention to “other products and markets,” since Hotz says that the prospect of a life “dealing with regulators and lawyers… isn’t worth it.”

...Wow. Maybe don't develop autonomous driving tech then?

In my opinion the central thesis of this article is obscured by its framework of first vs higher order facts and thinking. The article states its real point quite simply:

You must temper your desire to jump to conclusions, explore the search space of possible conclusions, and generate probabilities of truth.