Then they would've softened their blow and perhaps delayed their relative demise until the rise of smartphones. But I do think it's fair to say that the transition was going to be a bloodbath of layoffs and declining revenue in any case.
2,805 karma · joined May 1, 2017
Why keep insisting on proxy variables and anecdotes?
:-)
I tend to prefer "engineering ethics" courses that focus on the second question because the first question is completely parametric in more general ethical and even political considerations.
It's true that engineers should take a course of study in pure ethics to learn how to think through questions of the first variety, but I'm not sure if engineering departments are the right ones to house/teach that particular course.
This is an interesting point in-between zero intervention and over-bearing puritanism codified in law. I'm having trouble finding more information. Could you share a link?
These traits 1) help soften the downside of "losing" the "startup lottery" (making it non-analogous to the actual lottery in pure mathematical terms). And 2) perhaps also might help explain any measured difference in outcomes between startup founders and lottery winners.
But the "People who are attracted to it..." portion of your post doesn't seem necessarily true and is a pretty harsh/puritan attitude. Do you have any hard empirical data supporting these claims?
Defore testing this claim, and for your own sake, we should investigate parent's original question: do we see a similar pattern with startup "winners"? Because if so, your reasoning here might have disturbing entailments for the entrepreneurial set ;-)
I assume parent meant "beyond the standard discrete/calc required of any reasonable cs major".
I thnk you're doing it wrong because that's exactly why there's so much demand for CS PhDs right now.
Rewind to mid-2000's when the CS postdocs/phds graduating over the past couple of years were choosing to major in CS. They were warned that everything was being outsourced to India and advised to choose a "real" engineering field, or perhaps finance/physics/math. It's hard to imagine today, but lot of smaller colleges/universities were killing CS majors back in the mid 00's!
So not only is there a shortage of CS PhDs in the pipeline, but the ones that made it through came in with a burning passion for the science (as opposed to the money/hotness). This combination of input bias and restricted supply is what makes the current labor market so damn hot.
In 3 years, that will invert, and some major struggling to justify its existence because it's hard but has "no future" will blow up. Rinse and repeat.
Revenue is not profit, and "price per hour for labor" != "hourly rate I pay my laborers". This is even true in owner-operator businesses. Furthermore, in bursty markets, "annual income amortized over career" != "my hourly rate multiplied by 40-60 multiplied by the number of weeks I want to work".
The numbers from my links are INCOME, not "what the business charged the client".
https://money.usnews.com/careers/best-jobs/plumber/salary
When the data disagrees with your mental model, consider re-evaluating your mental model.
The amount a client pays per hour for labor != the takehome pay of the person whose time you're ostensibly being charged for.
Yes, do consider.
In many, many cases the appropriate answer is "whatever stack you're familiar with".
In maybe 10% of cases the answer will boil down to technical requirements and you'll need familiarity with "Postgres, SQLite, Redis, LevelDB, ..."
And that's where the bimodal distribution comes from.
> There are "law schools" and "med schools" to teach you all the knowledge required to become a lawyer or a doctor. There is no "programming school", every programmer is self-taught
I don't know a lot about the law.
But if you think that MDs are not "self-taught" in the same way bootcamp graduates or CS graduates are "self-taught", you should talk to an MD some time.
Anyone != everyone (literally the point of my post)
And "qualified" != anyone who can squeeze out a 20 on the ACT. Certainly far south of 70+%.
> labor market doesn’t pay you for the useless subjects you master ...Girard-style mimetic crises
Well hell, maybe try less Girard and more Fourier?
No. Discovering those correlations is completely legal. Making certain decisions based upon those correlations is illegal. Mostly because by making a decision, you make a tacit assumption about causation that's borderline impossible to prove and can have a huge impact on people's lives.
Like I said, if it's just you in your home office having fun, go at it. But if you then bake that model into certain products, you have a very serious bug.
> Well, that's precisely "polite fiction".
Except in this case, it's not even that!!!
The observation that certain crime statistics are highly correlated with race, and that race is correlated with zip code, are not new observations. ML did not usher in some brave new world here. Just because we call it "AI in 2018" instead of "John from the Actuarial dept. in 1960" doesn't change the moral, ethical, or legal landscape. (the article literally makes exactly this point.)
And despite your characterization, this particular truth (race correlates to crime correlates to zip) isn't even a politically incorrect observation!!! It's something everyone already knows, and I've never seen someone attacked for pointing out this correlation. In fact, it's a favorite talking point of social justice types! Pointing out this correlation is not impolite.
The impolite assertion is that there's a causative link between race and crime. That's an assertion that models (tacitly) make when their users shift from truth-seeking to decision-making. See the "question you thought you asked vs. question you actually asked" portion of the essay.
Now, if ML algorithms discovered some genetic, racial, causal theory of crime, then you might have a point about ML exposing polite fictions in this case. But they didn't, so you don't. COMPAS isn't being censored from sharing a politically incorrect truth. It's being prevented from ruining people's lives with a really, really lazy application of statistics.
I often joke that racial discrimination laws are one of the few examples where "being bad at math" is not just criminal, but unconstitutional.
> ...laws are not designed as truth-seeking tools
Again, in cases where bias becomes illegal, these models are NOT just being used to seek truth. They're being used to make decisions.
You seem to have misconstrued the fundamental thesis of the article. The author isn't calling for death to polite fictions. And there's a concrete example in the article of this point of departure between your perspective and his. Namely, his response to people-as-gorillas was not "fuck you, the math is right". And the crescendo of the piece is "AI is just a tool, not a divine oracle, and there's nothing new under the sun".
Yes. but in my limited experience with ML, "the data is good" usually isn't the
>> most likely
explanation :-)
> This is what happens with loan or crime data sets, and it made plenty of noise in the news recently - but only the kind of noise in which people say it's obviously the algorithm that's broken, because it doesn't fit the "polite fiction" they'd like to believe.
People say the algorithm is broken because it's illegal to discriminate on the basis of race, and these algorithms were sneaking "discriminate by race" "in through the back door".
Calling the law a polite fiction won't get you very far when the judge issues an injunction against using your product because it's racially biased.
And the judge doesn't care how machine learning researchers define bias. He cares how the law defines bias.
So, if you're diddling around on your computer in your own time, then I guess the algorithm isn't broken. But if you're building a product you want to sell to courts or insurance companies, the algorithm very much is broken.
That is, unless you think "the law is a polite fiction and bias means only what ML researchers say it means" would be a winning argument to lift an injunction against your product. If you're going to make those arguments in front of a real judge, let me know; I want to see the bulge in that judge's forehead :-)
No. People say the algorithm is broken because it obviously is broken.
Many papers list authors' email addresses.
Scrape PDFs on the services you support for email addresses. If a domain name occurs in enough author email addresses and isn't on a "known generic" list (gmail et al), consider it "safe".
Here's my take on the purpose of math courses in college: any college graduate should be able to teach themselves something "like" calculus or linear algebra upon graduation. So you should teach calculus and especially linear algebra in such a way that a student who has never seen e.g. graph theory can pick that up a bit of graph theory their own using the thinking skills they acquired by studying linear algebra or calculus. Ditto for dynamic programming or combinatorics or basic probability or...
So if you're not teaching proofs, wth are you teaching all semester? A few conceptual underpinnings that take about a week to explain, and then a whole bunch of crap Mathematica can do for you anyways.
> computational mathematics like calculus and (linear) algebra.. move proof-based mathematics into a targeted graduate degree
Memorizing symbolic calculations and/or understanding (how to use) numerical algorithms do not endow students with the skills required to learn new mathematics. Proof-based calculus and algebra courses do teach those skills.
The recent post on HN about PID control comes to mind. I'd expect someone with a bit of practice at writing proofs to be able to teach themselves why PID controllers work and avoid pitfalls. But I would not expect your average human-meat-based-derivative-and-integral-calculator to be able to understand the same.
To put it in terms of a folk saying about fishing: if you teach a man to calculate, he can perform a specific calculation. If you teach a man how to write proofs, he can learn any calculation he might need throughout his lifetime.
Distinguishing between rental markets and less pathological applications of market mechanisms is not exactly novel economic doctrine. And in fact, you'll have a hard time finding a tradition of economics without its own giant calling out landlords.
People who don't empathize with you aren't always misinformed. Sometimes there are fundamental moral incongruities.
There are apt analogies of "the system is fucked and I'm just working in it" throughout US history.
More seriously, what you're supposed to do is undermine yourself. Support efforts to substantially decrease the market rate of housing.
As a very, VERY purposeful non-landlord who has never missed a rent check or mortgage payment, I don't have any sympathy for land lords. I understand your financial and moral viewpoint, but I don't find it redeemable.
So. That's the analog of your viewpoint. Not deadbeats, but your financial equals who don't see anything redeemable about lordship.
1. https://en.wikipedia.org/wiki/List_of_countries_by_prevalenc...
This data isn't up-to-date and the list is for opiates, not opioids But this still gives you a good sense for which countries to look up. IMO opiates are closer to marijuana or cocaine than to heroin -- i.e., an otherwise healthy, happy, intelligent, and drug-educated person using in a purely recreational context is unlikely to die or even become severely addicted. Different from opioids in that respect.
2. https://en.wikipedia.org/wiki/Opioid_epidemic#Outside_North_...
Not much info, but we get pointers to some of the same countries at the top of the opiates list.
C.f. SF Bay and rural Oklahoma.
In policy, outcomes are at least as important as intentions. The question is whether welfare has this effect, not whether the original/current proponents of welfare intend for this effect.
There's the kernel of a reasonable idea here [1]. Unfortunately for your parent post, even the studies most favorable to this conclusion find only a modest effect and cite factors like relationship quality and access to decent paying jobs as much better predictors of marriage success.
> People on unemployment benefits still look for a job, because they value their dignity over money.
Even the studies that are used to justify your parent post's viewpoint ultimately conclude that this is true for the vast majority of people. Interestingly, those studies also find that the poorer you are, the less likely you are to consider loss of welfare benefits when making a decision about marriage.
Aggressive means-testing is a compromise outcome, as attributable to conservatives as to liberals/socialists.
> This encourages an anti-marriage, anti-stable family pattern.
Most of the think-tank pieces pushing this assertion cite the same sociologist, W. Bradford Wilcox, whose work is supported by AEI's Institute for Family Studies. Here's what Wilcox says about what his empirical work demonstrates [1]:
> one of the study’s co-authors, said the study’s findings show that welfare is “not the most important factor in explaining why we see a pretty marked increase in single parenthood and unmarried childbearing in the last 40 years.”
Discussed at greater length in [2], reaching the conclusion that
> Taken together, these results suggest that marriage penalties associated with American social-welfare programs play only a modest role in shaping the marriage decisions of contemporary couples with new children
So even the primary academic proponent of this theory, when pressed, admits his own empirical studies don't justify his favored causative hypothesis -- that the effect is mild at best.
It's unsurprising, then, that these think thank pieces focus on logical deductions about homo economicus rather than empirical datasets and statistical tests. Social science at its best ;-)
Now, none of this is to say that means testing shouldn't be reformed in order to encourage marriage! And in fact, I agree with Wilcox that welfare shouldn't discourage marriage.
But we have to respect the science here. Your causative assertion about causes of structural changes to American families isn't justified, even by the people most eager to prove such a link. And as a result, we don't have any reason to believe that reforming welfare will have anything other than a "modest" effect on the structure of American families.
So, what variables do effect family structure? Even according to [2], the answer isn't welfare, but "stable, decent-paying work and good relationships are far more important predictors of marriage and childbearing decisions for such couples than are calculations related to social-welfare benefits."
Which, incidentally, dovetails nicely with the article's conclusions: lack of access to stable, decent-paying work and a breakdown in social relationships.
So the article may be "rife with misconceptions", as you say, but its root cause analysis appears closer to the mark than your assertions. And that's even true when we only consider evidence from people who are most aligned with your world-view!
[1] https://www.washingtontimes.com/news/2016/aug/3/welfare-disc...
[2] https://ifstudies.org/ifs-admin/resources/welfare-and-marria...