_RPL5_

The entire thread is pure gold. If you scroll down, he writes:

> The Chair [of the Columbia Film program] reached out and said he was confident that we could work out an arrangement to get my degree. But he insisted that I fly out to NYC immediately for an in-person meeting. So I spent a fortune on a last-minute flight in order to hear his proposal.

> But instead of telling me what I need to do to satisfy the requirements to get my degree, the Chair began pitching me his idea for a TV pilot. In excruciating scene-by-scene detail. I nodded along, waiting to get back to the terms of me getting my degree.

> But to my horror... I slowly begin to realize this IS the deal. He made it pretty clear if I wanted my degree, I needed to help him sell his tv pilot. Yep, the Chair of Columbia’s prestigious graduate film program tried to shake me down in order to jump-start his own stalled out career.

Here is a list of 200 most popular books sold at Ozon, the biggest on-line retailer in Russia:

https://www.ozon.ru/highlight/top-200-knig-po-mneniyu-chitat...

Of the Top-12, 6 to 8 are some form of a self-help book:

* 1st: The Subtle Art of Not Giving a Fck.

2nd: Say Yes To Life, a self-help book from an Austrian Holocaust surviver.

* 4th: Ben Graham's Intelligent Investor.

* 5th: A Russian-author book on the art of "convincing" & "influencing" people (sound familiar?).

* 6th: Another American book, "Radical Forgiveness: A Guide to Spiritual Healing"

* 8th: Seven Habits of Highly Effective People.

* 11th: Women Who Love Too Much: If Love is Causing Suffering. Also a US book.

* 12th: Atlas Shrugged. I suppose it's not a self-help book, but it's very much in line with the spirit of "open-your-eyes" literature.

* If you go down the list, there is a bunch of other titles like Rich Dad Poor Dad, the full set of Nassim Taleb's quasi self-improvement books, etc.

We can sort of argue whether some of these books are self-help adjacent or not (like Ben Graham or Nassim Taleb), but the trend is clear: self-improvement literature is very popular in Russia.

This shows that the self-help cottage industry is not limited to the US. I think people just like the idea of self-improvement.

edit: formatting

Have you? What's your impression?

My pet theory is that it takes more effort with a Russian keyboards to type the smiley face.

With English, you do "SHIFT + ;" for the colon and then "SHIFT+0" for the bracket. When I type it, I hold down SHIFT with my right pinkie, and hit ";" with my middle finger. I then outstretch my pointer to hit 0. It's mostly a single fluid motion.

With Russian, you have to engage two hands instead of one. We have more letters in our alphabet so the ";" key is occupied by "ж" (a "zh" sound as G in Gerome), and the colon gets moved to "SHIFT+5". So now, to make the colon you have to first find "5" on the keyboard with your left hand, while holding down SHIFT with your right. Then you have to disengage your left hand, and reach for the 0 with your right to make the bracket. Rather than do all that, you can just place ")" and the context is enough to understand it's a smile.

It could also just be tradition. My earliest memories of the Russian internet are from the late 1990s, and the convention of "))))" was already in place.

>People don’t really have organizations they can go to to advocate their political positions.

>[People] need an outlet to push the ideas and causes they care about

>I like that people want to express themselves and improve the world we live in, but they need to find a better place to do that.

Political parties & elections are instruments designed to do just that. It's actually mildly alarming that people are so jaded they forget about these (?).

>I don't want to leave the determination of what constitutes a bad health-related decision to the vote.

The answer is to maintain both private and public insurance systems. As is already the case with Medicare. I don't think anyone is stopping you from buying your own private insurance if you are 65+.

And that's the thing I find strange about the healthcare debate in the US. There is already a huge public insurance system in place - Medicare. Yet people pretend that a public system like that is some sort of ideological novelty in the US. It's not.

The primary goal of the penitentiary system is to reform criminals and to reintegrate them into society as productive members. A reformed criminal is worth infinitely more than a person who you sent away and who is now contributing zero. We should focus on improving this high-level function of prisons, rather than look for new australias.

But as far as NYC goes: yes, 500k is a lot. NY taxpayers should look into why it costs so much.

I find their hypothesis suspect.

> According to the researchers, the decrease in the size of game and the need to hunt small, swift animals forced humans to display cunning and boldness—an evolutionary process that demanded increased volume of the human brain.

There are plenty of predators that hunt fast small prey, but don't have advanced cognitive capacities. Cats, for example, are adapted to hunting rodents, but have pretty small brains.

> As humans moved into permanent settlements and became farmers, their brain size decreased to its current volume of 1300-1400cc. This happened because, with domesticated plants and animals that don't take flight, there was no more need for the allocation of outstanding cognitive abilities to the task of hunting.

There are active hunter-gatherer societies out there (or at least societies that lean heavily that way along the hunter-farmer axis). Do these societies have larger brains than everyone else? Seems like a simple way to test their hypothesis.

Thank you for the detailed response. It makes things a lot clearer.

I notice that you mention "Machine Learning Engineer" as a separate role. If in the idealized world, data scientists do analytics and train models, and data engineers take care of data, then what do Machine Leaning Engineers do? Are they, basically, software engineers who specialize in putting other peoples' models into production?

And you are right in sensing my confusion. There seems to be an abundance or data-related titles, which seem to overlap in their functions a lot, but are also very different when you examine them closely. So thank you again for your responses, they are very helpful.

Thank you for your comments! They are very insightful. To piggyback a bit:

Assuming you are a competent data "analyst" who wants to become a data engineer, how would you go about it? Is "go back to school and get a CS degree" the answer? I suppose this question is very broad, but I am curious if a practitioner like you has an opinion.

---

To give some context:

I recently graduated with a STEM PhD, and looking to move into data science. Reading the comments, I feel like I fall into the "pointless data scientist" cohort derided in this thread. Eg: I am very comfortable doing typical analytical work & occasionally training models inside a notebook, but I am neither a cutting-edge theoretical statistician nor a data engineer.

I've been trying to improve on the engineering side. For example, I did a project recently where I set up a rudimentary pipeline that continuously pings an API, uploads the data to a cloud database, then serves up the analysis via a Flask app. For me this was a big step up from just doing notebooks on a csv file :)

But moving beyond the basics, I am not sure what to study next. Hence my question. If you have any suggestions, I would greatly appreciate it!

I sure hope so. There were dudes inside Congress waving rebel flags. Trump and friends probably didn't count on that when they were posturing on Twitter.

Hopefully, that scares them straight and cools some heads. And not just Trump, but the entire political class who've been playing loose with divisive/identity politics for the last decade.

To me the DNC leak stood out as more damaging than Facebook ads / twitter bots.

That being said, if anyone has an unbiased academic paper that looked in-depth at Russia's Twitter/Facebook shenanigans, please cite. I'd like to read it.

I am curious how something like this would have been handled 50, 100, 200 years ago.

Let's say you have a newspaper spreading misinformation that is hurting an important social institution. What do you do? Close it down? Sue the editor for defamation, then close it down? There must have been precedent.

You aren't wrong. I got caught up making the comparison between structural biologists and taxi drivers being ran out of business by AI, so I ended up exaggerating the work load that's addressed by AlphaFold. I should been more precise.

The industry process will not change. You still need industrial biologists to generate and validate AphaFold structures, interpret the results as part of the bigger picture, and to finally design the drugs. And, then, of course you still need to validate the drugs in experimental systems (first the test tube, then mice, then humans).

So your second guess is correct - one of the steps is much cheaper now, which marginally improves the entire pipeline. As a result, drugs should now arrive to the market faster.

As a side note, I am curious what happens to the field of structural biology in 10 to 15 years from now. Every research university has a large structural biology department with super expensive Xray/NRM/Cryo-EM machines, and armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein. If AlphaFold works as advertised, NIH will gradually shift funding to other problems.

(It was predicted that it'd be taxi drivers, not professors, that AI got first. Ironic.)

Hah, that's true about Bornholm. Now that you mention it, I feel like I've read about it recently, somewhere. Thanks for reminding me.

Your argument is that EU countries will not stand up to the US, because they are afraid of losing American protection.

However, in case of Germany you linked evidence that goes directly against your argument:

>German Chancellor Angela Merkel on Tuesday called for creating a European Union army, stressing that Europeans can no longer merely rely on the U.S. for their security.

Given the context of the thread, this quote reads like the Germans want to build their own army to spite the US. For example, because the US spied on them.

---

As far as Denmark goes, their feelings regarding the US are probably genuine, but I very much doubt that the Danes see Russia as an existential threat.

The last country to invade and occupy Denmark was Germany, not Russia. If you read between the lines you find that the main security benefit of US/NATO is keeping Europeans from fighting each other rather than defending them from some external threat.

I that sense, I completely agree that US and NATO are the cornerstone of European security. And that's why industrial espionage against them is a bad idea. It makes European countries suspicious of the US and suspicious of each other. The resulting fragmentation leads to insecurity and then we are back to 1939 eyeing each other as our next meal.

> I don't see how it could have played out any other way.

I suppose we are getting technical here, but at some point someone working for these governments (an actual person with a calculator) has had to sit down and do the math to figure out how much money needs to be set aside in order to achieve the advertised pension payout.

Who is that "person" in most cases, and why was their math so off? The math must have been very optimistic on the ability of the pension funds to compound wealth.

And so the facetious answer to your remark would be "things would have played out much better for these governments if the person doing the math was more realistic about expected returns." Not sure how realistic that goal is in itself.

For groups 1 and 2, who gets to decide if a homeless person is to be committed? This seems like a non-trivial question, or am I missing something?

You are getting a lot of critical responses, and I hate to dog pile, but:

>Amazon/Google/etc happily pay ten times that salary

In the thread, they say Helsinki juniors start out at ~$3k/mo after taxes. In the Bay Area, let's say you are a junior at a FAANG and are getting $150k/year. This works out to $8k/mo after taxes. So the difference between a FAANG and Helsinki is more like 3x. Maybe, 4X if you account for bonuses/options. But it's not 10x.

Obviously, 3-4X is still a lot. If it's a choice between Helsinki and SF, SF wins hands down.

Problem is that most of the engineers working in the US are not working for a FAANG company in the Bay Area. If you look at what engineers are getting paid in places like Houston or Atlanta, you will find that juniors start at $60k/year, which is $4k/mo after taxes. That's very close to the reported $3k/mo rate for Helsinki.

There is this weird trend I noticed on HN. Whenever the subject of money comes up, people start casually quoting Bay Area numbers like they are representative of the industry as a whole. I don't think they are.

I don't understand what Foxconn's endgame is.

Did they really have a good-faith plan to build a mega factory going into this? And then they failed because of befuddling incompetence as the article suggests? Or did they do the whole thing as a Potemkin village to curry favor with the Trump administration? If so, where did they cash it in? Seems convoluted.

Occam's razor says it was incompetence, but how does Foxconn get on in the world if they are so dysfunctional? Shouldn't they be bankrupt by now?

The first table in the report says that FB's effective tax rate in 2020 was 4%. The same chart says that in 2019 their effective tax rate was 17%.

Can someone with insight explain this discrepancy? Is this some accounting artifact, did FB get a COVID tax break, or is it something else?

Thank you!

The author makes a point that I relate to. I've been on the receiving end of a couple 'Statistical Learning 101' courses. These courses go roughly as he describes it in his blog post: they first teach you how to multiply two matrices, then launch into linear and logistic regression, then classification via clustering, then decision trees and SVMs, then CNNs and deep learning. Along the way, they do a lecture or two on reinforcement learning and HMMs.

In the end, I ended up with a thin smear of half-baked knowledge in my head, where I stop understanding the math once we are half-way through the material.

So how to you achieve this level of deep/intuitive understanding:

> Without understanding the mathematical underpinnings of key models and techniques in full detail, students aren’t able to quickly choose the right models for certain scenarios.

Does anyone have a good study plan with MOOCs and so on? If you have any practical advice, I would appreciate it!

Here is a source in Russian [0] saying that Apple is asking Telegram to remove messages that de-anonymize Belorussian police members, rather than blocking channels outright. I don't know whether their source is reliable, but here is the relevant snippet (google translate):

> Apple says it didn’t require any channels to be removed. But they demanded to immediately remove any information that discloses someone's data on the Internet without the consent of these persons, as well as content aimed at specific people in accordance with the rules of the App Store.

This refers to the efforts of the Belorussian opposition to 'unmask' members of the riot police by posting their personal details online. Without going into the moral weeds, from the terms-of-service point of view that's basically griefing, no?

So this is a very curious quandary Apple finds itself in. Let's assume that griefing Belorussian law enforcement is a good thing. But at the same time griefing people (regardless of whether they are bad/good/chaotic neutral) is against the TOS.

So what do you do if you are Apple?

[0] https://tjournal.ru/tech/221326-apple-zayavila-chto-ne-trebo...

The top comment in this thread (from someone who speaks the language) says that he openly encouraged people to spam meaningless PRs in order to get "free swag." I think the rebuke he is getting is deserved.

When I first heard about Hacktoberfest a few days ago, I thought it was neat. I am relatively new to programming, and as a newbie I got excited that there was an event like this. It prompted me to read about how pull requests work, and to research several open source projects that I could contribute to. I haven't made any contributions yet, but it's on my to-do list for October.

I suppose you can already count me as one of the beneficiaries of this program, simply because it got me to learn new things. I think Hacktoberfest is a good idea, speed bumps like this one notwithstanding.