arghbleargh

Sounds intriguing, but after reading through the website for 5 minutes, I still have no idea how this is supposed to work.

From the pictures, it looks like they've set up some kind of system to translate certain physical movements into changes in the projected images. It seems like a non-technical user can only make superficial changes within a limited set of pre-defined behaviors. But from the description it sounds like there's a lot more to it than that...

I find a lot to agree with in this article, and it explains it pretty well, but the part about the Federal Reserve is a bit too simplistic. The value of money is determined by a complex interplay of factors, including government spending/taxation and international trade. The Federal Reserve has only limited influence on it via manipulation of interest rates. Nevertheless, the main point stands: the strength of fiat currency lies in the implied social contract that measures will be taken to ensure your savings today are not worthless tomorrow.

Crucial question whose answer I didn't catch while reading the articles: how do you determine who is considered a "scientist"?

Not to say that the thing about men vs. women isn't problematic, but that quote is taken out of context. The suffrage part is a completely separate bullet point and isn't saying that women shouldn't have the right to vote as your quote suggests.

Also, the main point preceding the quote that men and women may have different natural dispositions is a reasonable one. He only oversteps by implying men are better leaders, a claim that may be defensible under some very specific definition of "good leadership" but is mostly just inflammatory in this case.

This seems like a tricky case because if the users and drivers are being completely rational, it's not clear that Uber's deception should have affected their behavior. Sure, the drivers expected to make X% of fares, but realistically they probably estimated what that would come out to be not from looking at customer fare data (in which case the deception would have materially harmed them) but rather by tracking what they or other drivers were actually getting paid.

Still, if the allegations are true, a culture where it is fine to engage in such active deception is not a good sign.

50,000 miles/year * 20 years = 1,000,000 miles does not seem reasonable...

Isn't it simple enough to make three bounds checks to essentially do the same thing you did with the zero padding? You can even make it a function

    is_in_bounds(x, y, z)

It makes sense to control pedestrian traffic in some intersections, but it just seems like they're not even trying to optimize pedestrian throughput. What makes the lack of feedback for crossing buttons worse is the fact that many of them are so conservative about letting you cross that you question if they are working. I encounter a lot that will refuse to let you cross if the light is already green, even if there is still plenty of time left. Unfortunately, lowering pedestrian accidents looks great, while saving pedestrian time is hard to measure.

Wait, so will Sage still be open source?

Yes, I think this has to be a major reason why people distrust science, and frankly I am sympathetic to it. Too many studies amount to a 2% statistical difference (with an analysis rife with biases) with no causal mechanism (or a highly speculative one). Relying on this type of evidence, while not worse than pseudoscience, still drastically weakens your position when trying to argue against claims like "vaccines cause autism". (Remember when fat was bad?) Also, even for the examples given in the piece, I think the truly "scientific" view has to be more nuanced:

- vaccines cause autism: OK, we have no reason to believe that they do, but we don't know for sure that nothing in them can cause autism. But even if they did, we believe the effect must be quite small.

- people are safer owning a gun: The science can't possibly say whether a particular person is safer or not owning a gun. But it does suggest that you may be underestimating the dangers of owning a gun.

- genetically modified crops are harmful: So far they have not really been harmful, but we have only a vague idea of the likely consequences down the road.

- climate change is not happening: Well, it's obviously happening, but a lot of similar questions are not clear. There's really no way to prove for sure that it's caused by humans or predict its consequences. We should still worry about it because of the potential consequences, but at the end of the day, we are still mostly working with educated guesses.

At the end of the day, even when science is wrong, I think it tends to give answers that are much closer to the truth than e.g. random intuition. So that's why some people arguing against pseudoscience try to paint the actual science as absolute truth. They're using the ends to justify the means. But their arguments are sometimes over-reaching, and that can ultimately make them lose credibility.

I think this highly depends on what you mean by self-taught. Probably a majority of good mathematicians are "self-taught" in the sense that they learned most of the things they know from reading books or thinking on their own rather than taking classes. What you won't see commonly is someone who has a day job but becomes really good at mathematics on the side. This is probably because of the sheer amount of training it takes to become what is considered "professional-level" in math. It takes quite a bit of background before you can even hope to do something that hasn't been done before. The same is not true for programming.

As someone who has done a fair bit of both math and programming, I will agree that having some math knowledge has definitely helped at various times when programming, but I don't think these specific instances (e.g. the 10 examples given in the article) constitute the primary reason it's a good idea to learn math. For the purposes of handling those specific situations, I think you would do fine to just learn the math as you encounter the need for it (and 95+% of the time you don't need anything beyond high school).

To me, the primary value of math for programming is that it is a pure form of exercising many of the skills that help you write good code. For example:

- fluency in logical reasoning

- turning vague intuitions into precise statements (translating business requirements into code)

- formalizing proofs (covering all cases, establishing invariants)

- developing abstractions to succinctly describe relationships

- solving a problem systematically

You can also learn these skills by programming, of course, but probably not as quickly, because you will be distracted with other tasks such as debugging, setting up your dev environment, waiting for your program to run, etc. So in my opinion, what's important is not so much the immutability of the math itself, but rather the process of doing the math. I would be interested to know if others have had similar experiences.

While you are correct in pointing out that these contests only test a relatively narrow subset of the skills needed for good programming, I don't think it would be correct to say it's based on "rote learning". There can be some amount of pattern-matching, but it's not as if you can just read the solutions to 100 problems and expect to do well.

Also, I know for a fact that many (but not all) people who were good at these contests are also good all-around programmers. (In fact, you probably do better at these contests by writing more readable code, even accounting for the time cost. But some people can get by with messy code as well.)