throwawayjava

> You're assuming bad faith.

On the contrary, I misinterpreted your meaning precisely because I was being charitable.

Your original post could've been interpreted in two ways:

1. Charitably: you simply didn't know that these people were citizens of the USA, and are reasonably suspicious of someone who built their career and fortune here but who doesn't want to obtain citizenship.

2. Uncharitably: you distrust people because of where they were born.

I chose the charitable interpretation.

MSFT sells ICE a lot of software and regularly sells software/services to the DoD as well as other parts of the USG.

Google was opposed to working on military tech for years before Sundar Pichai became CEO.

> I realize this won't be a popular view here

Blatant factual inaccuracies and ignorance of extremely obvious alternative explanations should be unpopular. Both are avoidable and both derail potentially productive conversations.

Facebook, Google, and some game companies definitely do hire lots of human-computer interaction Ph.D.s who definitely are trained in exactly this sort of thing.

There's a reason the title is "computer science researches" and not "computer programmers".

RALPH, a 1990 Pontiac Sport minivan, drove across the US 98% autonomously. In 1995.

Much of the control theory, robotics, and AI work that enabled the current self-driving gold rush was invented decades ago.

The Dartmouth workshop was in 1956. Dearth of choices? Please! Those days provided an enormous surplus of choices, almost all of which were good ones! People from that older age invented reinforcement learning, image classification, natural language processing, OOP, hell, even the notion of a pointer! And the list goes on.

Today there are far fewer choices than there were back then because so much has already been done.

That is, of course, assuming you're in the business of "doing things no one else has done before" as opposed to the business of "following a well-trodden path".

So, I guess if your view of the world is confined to "using things other people already invented and explained to me", you might consider the 1950s and 1960s a bleak period when no one knew how to do anything. As opposed to the cusp of a century-long period of continuous innovation...

Again, as the article says, I guess it boils down to what you want to get out of a lifetime of work.

> Funny, you mention graphics, what do graphics usually involve?

shapes, views, raytracing, texture mapping, lighting, color, filtering, scaling, reconstructing, visual surfaces, grids and voxels, octrees, kd-trees, partition trees, polygonal rendering, ...

And that's just the undergrad stuff, not the cutting edge.

Oh yeah, knowing C/CUDA/OpenCL is nice. But when compared to deep expertise, it's a rather trivial time investment and is completely orthogonal: an implementation detail, not the fundamental content.

> Operating systems?

Kernels, scheduling, device drivers, caching, distributed systems, energy models, timing attacks, and the list goes on.

Of course, knowing C is essential, but that's the easy stuff when compared to wrapping your head around a modern OS, or even a tiny piece of a modern OS

> Robotics?

SLAM, sensor fusion, filters, actuation for various types of novel actuators, PDEs and ODEs, optimal control, stability and robustness, system identification, model-predictive control, motors, servos, simulation, etc. And that's just the software side.

Oh yeah, knowing C is nice. But when compared to deep expertise in robotics, it's a rather trivial time investment and is completely orthogonal: an implementation detail, not the fundamental content. Many of the fundamental ideas at techniques in robotics pre-date C by decades.

> On the other hand, how often do you hear about a Java expert? Someone who knows the intricacies of the GC? All about the JVM? Not that often. They exist. But it's not hip. They chose "poorly".

I know a few true, honest-to-god Java experts. They all make insane amounts of money (even by SFBA SE standards) and love their work. Turns out Google has quite a bit of Java code and a metric shitload of money.

You think graphics, robotics, and OSes are just "C and POSIX". That's not true. C and POSIX aren't even table stakes. They're the thing you pick up in a few weeks or maybe a semester so that you can spend several years obtaining the table stakes -- see the list above. Then you need to build true expertise on top of that.

The path from "I know C" to "robotics expert" or "graphics expert" is at the very least a multi-year path. And that's assuming you're bright and have your full work day (and then some) to dedicate to following advances and building your own.

Self-driving cars (controls, perception, planning, safety, sensor design, localization, mapping, integration, security, etc.), human-competitive NLP/image classification, advanced robotics (repeat list from cars here but for things in the air, off-road, on the water, in the water, in orbit, in deep space, ...).

Those are all examples of real things that real people get to work on every day.

Operating systems topics don't even scrape the top 20 of stuff I think of when I think of deep expertise.

And even if we limit ourselves to the sort of things you mention, most people who work deeply on languages, tools, and standards view these as manifestations of their deep exploration rather than the focus or subject of the dive.

For example, TensorFlow. The framework very much is the product. But even if some other framework won the day tomorrow, the people who worked on TensorFlow would not have "wasted" their time thinking deeply about how to build the system.

This is why researchers whose original contributions were made in the 70s and 80s none-the-less continue to establish themselves as desired experts in new technology trends (e.g., Leslie Lamport and cloud computing or Martin Abadi and ML frameworks). Because they were focused on ideas and fundamental problems. The problems never disappeared, just changed form. And ideas have a lot more staying power than their manifestation in code.

Most people working deeply on systems today are not "revolving around POSIX in some way". See the proceedings of OSDI. And most deep experts choose other topics, most of which your post doesn't mention: graphics, programming languages (making them and analyzing programs written in them), compilers, security, robotics, user interfaces, NLP, ...

Your definition of "expert" seems to revolve around using things, mostly things based on ideas and techniques that were well-understood already 20 years ago and that are related to building a particular type of software system. Which, if anything, seems to deepen the author's point.

Someone gets to fill the AI research labs, staff the self-driving car companies, work at NASA, build core infra at large tech companies, and build the foundation for the next 20 years of trendy growth areas.

It's possible to get to those places without a degree, of course, but a degree is by far the path of least resistance. And in most of these cases, learning the material from the degree isn't optional; you're probably going to have hard time doing that controls engineering job at a self-driving car company if you never made your way through a calc sequence, some physics, and an algorithms course.

It's also worth noting that very often, building wordpress plugins pays more than doing all of those things I mentioned. I guess it's all about what you want to spend your life doing, which is exactly what the author says at the end of the article.

The paragraph hits on so many ways in which the popular (press) conception of science and academia is toxic, and sexism is only one part of that toxicity.

Yes, the disparity in how female scientists are described in a problem. But that fact that these attributes are absent from descriptions of male academics is also troubling. How are there so many profiles of Professors that don't mention teaching or mentoring even once?!

When new phd students ask for advice on choosing a Ph.D. advisor, I give them three strictly ordered criteria:

1. Is this person a good mentor? (Where are their former students? Do those students speak positively and also candidly about their advisor?)

2. Does this person have a work-life balance? (Family? Intense hobby? Good friendships outside of the department? Encyclopedic knowledge of their favorite TV series? An inappropriate percentage of their net worth invested in cars/wines/whatever? Literally, any non-trivial time invested in something other than work most weeks.)

3. Are you excited about the research agendas that they currently have (relatively fresh) money to explore, and do you think this person has the expertise needed to help you push those agendas in the way you want?

Those three questions, in that order. If you can't find someone who meets some minimum reasonable threshold for all three, maybe consider expanding your research interests or finding a new department.

Profiles of male academic scientists should go beyond individual contributions to discuss teaching/mentoring and work/life balance. After all, it's a profile of a person who is a professor! Teaching/mentoring is a huge chunk of the job, and the person is more than their work.

If there's nothing to say about the person other than some brilliant results? If their former students resent them and their family life is in shambles? That's a tragic personal interest story. If the person has a normal relationship with their students and a normal personal life? Then there's no personal interest story at all and the article should maybe just talk about the results instead of profiling the person.

More generally, though, climate science makes me queasy. I'm more than happy to accept the dominant theories and sort of understand that there's a lot of people who understand enough of the related science to have an informed opinion, but I feel like it would take at least a Ph.D. and a postdoc before I could really get to that point. There are just so many complex systems in play.

In reality, it would probably be more effective to dedicate nearly $0 to infrastructure and $N,000.00 to an endowment for 1-3 hours of engineering time each year.

Figuring out how to host a static site for free and moving the site to that service requires almost no time for a competent engineer. But finding a solution that is guaranteed to exist in 100 years with zero human intervention would probably cost millions...

If you really believe this, pressure USC's department to start publishing their pass rates each year and pressure your peers at other institutions to do the same. IME departments outright lie on visit days and/or change their definition of "reasonable" any time they accidentally over-admit.

E.g., Berkeley -- one of the few places to actually reveal information of any sort on prelim pass rates -- describes their pass rate as "reasonable" and in the same sentence goes on to clarify that they typically pass 2/3rds of students but of course don't make any promises or anything.

Maybe us Ph.D.s are so damn battle-scarred we don't realize that a 1/3rd attrition rate on a poverty salary is actually really unacceptable and absolutely not normal? Can you name a single software company that works like that?

Fraud at 18? -> Stupid kid (obv., fraud is never ok)

Fraud at 45 with three dependents on the verge of bankruptcy? -> poor bloke, the choice between a big personal risk vs. failing your family really sucks (obv., fraud is never ok)

Fraud at 27 with no real responsibilities in life? -> WTF?

E.g., one year:

Me: requests A and B

Friend: requests A and non-honors B

hour 4: Me in non-honors B; friend in gym.

hour 5 (only hour honors B is offered): Me in A; friend in honros B.

Based on a few years of observations, I'm almost certain courses were filled using some variant of this algorithm:

    for each student s sorted by student number:
        for each non-filled course c in hour 1..n:
           if(s wants c and not in c): assign s to c and continue to next hour.
        for each non-filled course c in hour 1..n:
           if(s wants c' and not in c' and c is like c'): assign s to c and continue to next hour.

Things got better each year but never in a way that would suggest someone finally decided to pick up an algorithms textbook.

I suspect by now they've purchased something that works or else managed to prove P=NP and the solution is billions of if statements fixing special cases the teachers/students complained loudly enough about...

So. Much. Fucking. Carbon. Trillions is nothing if it means no consumer-level caps on carbon output for the next generation or two.

1. It's not like those math Ph.D.s are sitting around reading textbooks with their advisors.

They are TA'ing service courses. A lot of TA'ing. In addition to TA'ing, they are prepping for a high-stakes exam.

The exam will wash out the 50-70% of people who are needed as temp TA labor but whom the department has no intention of advising through to a Ph.D.

After 2 or 3 years, you pass the exam. Yay! But wait. Reading textbooks with your advisor? No! you need to take a whole slew of courses. Why? Because course enrollment == money and easy/interesting teaching assignments. And good luck getting tons of research done in-between your TA'ing and courses...

Now you're done with courses! Yay! After TA duties, you can spend 100% of your time on research (aka 50% of a 40 hour work week aka 80% of a grad student work week). Except you only have 2 years left :-(

So now it's on to 1-3 postdocs so that you can start doing, after 5 years, what that CS phd student was doing when they "hit the ground running": build a research agenda that resonates with a large enough set of researchers that it translates into a research job.

--

Also, the "one singular result" style of research is unique not just to mathematics, but to a particular subset of mathematics. It's just not a good fit for most research problems, even in Mathematics. I've seen tons of well-composed dissertations focused on a singular, pointless problem.

The hodgepodge dissertations don't have a "beautiful singular result" feel, but almost invariably contribute at least one piece of useful, new knowledge.

Finally, worth noting that the best and brightest don't leave research; they might leave academia, but typically not research.

TL;DR: If you can find an advisor who isn't a slave driver (the majority of them at good places), the CS model is 1000% better than the Math model. You might be hired as a researcher for someone else's research agenda, but at least you don't spend the first 1/3rd of the phd as temp teaching labor stressing over high-stakes exams.

Okay, granted.

> Anyone working there needs to be called out.

I'll take this seriously when you start "calling out" IBM employees for working for a company that in the past enabled genocide.

I'm unsure how research on "visual and robot learning" or on "geometric 3D reasoning" is supposed to be enabling genocide. Those seem more akin to "green energy researchers taking money from big oil cos". Which is very different, ethically, from actively helping build and maintain the core business. Unless you want to suss out the link between geometric reasoning and genocide for me?

And in the case of Facebook, your opinion is worse than just hyperbolic. There is an entire research field that focuses on detecting speech sentiment. Your position, as stated, is that those people should not work with Facebook, even if 100% of their time is spent designing algorithms and processes to detect and remove hate speech/calls to violence during active genocides.

I think that advice is actively dangerous because I don't believe in a magical world where Facebook disappears tomorrow.

IDK. Maybe -- just possibly -- there do exist researchers who have their choice of funding spigots and are choosing to work with Facebook. Either that or HN has some damn high standards for what it means to be "worth their salt".

There's nothing intrinsically wrong with industry collaboration, even when it involves companies whose impact on the world you might not like. The big oil companies are, unlike FB, an actual existential threat to humanity. But I wouldn't fault renewable energy researchers for taking research dollars from those companies.

The question is: will the funded research agendas push science forward in the direction it was headed anyways, or will this money distort the type of research being done?

In any case, in a week we'll be back to our regular programming bemoaning the fall of the industry research lab and the paltry salaries offered to phd students...

A set of images designed by advertising and consumer goods companies are not optimized for expressing the full range of the human experience? I'm shocked! /s

The real critique: we've had <img> since the 1990s, and plenty of more than usable chat systems/protocols/markup languages that allow inline images for about that long as well. Why are standardized emojis so popular, and why do people even give a fuck about them when there are obvious workable alternatives?

Emojis are not "radical" or "new", as the author puts it. They are a crippled instantiation of an old and obvious idea, an idea that has itself been implemented in better ways thousands of times prior to the rise of emojis.

(I guess you could say that didn't see this coming, but the parallels with MSFT and IE are pretty hard to not see...)

The fine is going to hurt a lot more than any remedial action.

You'd either fire them on the spot. Or, if that's not an option [1], quietly figure out a longer-term strategy for eventually firing them.

Or, if you're a Nice Guy, you'd offer to let them stay on if they return the gratuitous waste of capital and/or pay the company back.

I'll never quite understand why founders are treated as an exceptional case by capital. Especially in hard tech fields like self-driving where the idea is literally obvious to a five-year-old but the execution requires courting `the best humans available.

--

[1] e.g., an engineer with mostly undocumented knowledge of key parts of your software or sales rep with close relationships to a client during a critical time period.

Let's assume that the technology and mapping becomes a Solved Problem.

Then the competition with humans boils down to the cost of sensors and compute (plus recouping R&D). I expect prices for both of those to plummet whenever self-driving capabilities are clearly demonstrated because the massive anticipated demand will justify huge capex in improving efficient manufacturing of these often bespoke sensors.

The market for computing machines 1950 - 1990 is instructive.

It's not a $5 daily coffee, it's 2 or 3 $2 coffees. 2 reasonably priced coffees isn't so insane if your workplace doesn't have a machine and won't let you donate one.

> One final thought: often the people who say this have zero experience with actually living close to the poverty line. A cheap, unhealthy, delicious indulgence on the daily can make living with less feel much more palatable - or at least, suck less hard.

THIS. I spent five years in poverty. Sometimes actually, sometimes just above the line. Always worried about making rent.

Eschewing a twice-weekly visit to the coffee shop is a LOT easier, psychologically, when it's not "my only reprieve from eating shitty food in a shitty apartment and still worrying about how we'll pay the grocery bills and rent".

And I had the benefiting of knowing that the situation was temporary and it could all be over the moment I decided I had had enough. And a great parental safety net that saved me from the credit card debt treadmill more than once. I can't imagine how bleak things are existing like that with no safety and knowing you don't have an out.

The latter two sound like business uses. The article is about personal use of personal vehicles.

> Kids, gear

Even then, a sedan or utility/mini van can do the job at much lower costs.

> boats, ATVs

Boats and ATVs are also good examples of money pits that prevent people from saving for retirement.

(Especially boats. I looked into buy one, and quickly noticed that I'd have to be on the lake like half of in-season weekends and/or use all my vacation during the summer before ownership beat renting. But I'm only on the lake a few weekends a year, and even then only need/want something other than a kayak maybe every other visit.)

And again, the conclusion of the article is "But if you’re one of the many people who are woefully unprepared for retirement or any of your other saving goals, a good place to start would be cutting back on any unnecessary spending on transportation."

If you're on track to retire, go ahead and buy that nice car/boat/etc. But if not, maybe consider cheaper alternatives.

Also, consider the possibility that you are wrong about the fad being stupid precisely because you've already accepted the fundamental premise behind the koolaid.

The original instantiation of devops is actually the "fad" that made me realize this particular truth. I had only ever worked in situations where all the infra people sling scripting languages along with the best of 'em and the software developers are as comfortable with iptables and apt and load balancers as they are with their editor/ide/compiler of choice.

So to me, devops sounded like a lot of ridiculous hot air -- it's just a new name for how things have always been; how can all these promises about productivity improvement make any sense to anyone?! And besides, the stuff that wasn't obvious was often ridiculous cargo culty stuff.

What I didn't know was the sheer number of shops where the IT people didn't have any programming skills and the programmers had never become competent with CLIs! In many shops, IT folks knew some recipes in the Windows Server GUIs and the devs knew how to use one IDE. When I first encountered a shop like that, I suddenly realized devops isn't hot air, even if for me the core tenets are just a description of how you should obviously do thins and how they've always been done...

This is not really relevant. The author of this piece suggests that the path to affordable higher ed in the US is elite institutions doubling their full-rides. But that's a drop in the bucket; "everyone go to an ivy on a full ride" is not a sustainable or realistic way of decreasing the cost of college.

> Just for the sake of round numbers, let's say there are enough quarter-million-dollar-a-year jobs for 1% of the population, but 10% of the population, with a bit of training, could step in and do those jobs.

This is also not a problem. Most people who go to college would be perfectly happy with a stable 80k. Or even 60k. The problem is not that folks can't get 250k. The problem is the massive amount of money they have to spend to get certain jobs that pay those wages, even at lower-tier universities. And more Harvard scholarships aren't going to solve that problem.

A counter-argument to the "you should hate tech people" thesis might be the pronounced support among tech workers for fixing the problems their industry helps create. The author argues that this is actually yet another reason why we should have a negative view of tech companies and tech workers, because UBI won't work and because we could fix college affordability by forcing Harvard to fund a few more full rides.

Harvard matriculates a teeny, tiny fraction of all university students. Doubling that number is not a solution. Even doubling that number at all ivies and elite universities is not even remotely close to a solution.

UBI may not be the correct solution, but even UBI isn't this out of touch.

point is, most people funded by gofai money weren't doing agi. they were building pls, dbs, networks, etc.

Either one. But certainly not "Java Programming".

Good thing, too -- I doubt many of the UT Austin students Dijkstra was teaching back in the day ended their careers still pounding out punch cards...

GOFAI, as a 10ish year era of easy money for CS research agendas, was a relatively cheap investment and ultimately a tremendous financial, economic, and scientific success. If you're using functional languages, sql databases, or networked computers then you're benefitting from GOFAI. ATM you're using all three.