230 karma · joined April 18, 2013
Other than that it's a neat demo.
https://en.wikipedia.org/wiki/Figure_drawing
Interesting post though. I liked seeing the Cowboy Bebop characters again, and the point about the need for the body to be identifiable without clothes makes a lot of sense.
And anyway, it's not necessarily the spending itself, but the way that he announces it that makes me feel uneasy.
I find this rather awkward. Here we have someone with a ton of money giving a tiny fraction of it away in a very public manner to a cause that seeks to remedy one of his own personal afflictions. He comes off looking like a good guy, and in the process generates excellent PR for his company and social network. What is the downside? It's as if there isn't one. I don't know why, but it gives me the squeamy jibbly icks. Same vibe as when he gave away flu shots at Target.
I guess I have the following questions. Why do the rich need to be our saviors? Do we really need to justify extreme wealth disparity with extreme condescension? Can't we just shuffle some defense money to healthcare and make democratic decisions about how to allocate healthcare funds? Or, god forbid, raise taxes on the wealthy?
Is the answer really, sorry, no, that's how it has to work in "free market" capitalism?
Another way to look at this is that it's Canada's way of increasing the publication rate of its scientists.
Talking about what didn't work is good because it stops other people from repeating failed experiments.
Finally, in this case, it does contradict the NY study.
You'd probably be interested in Godel's incompleteness theorem.
Also, exact reproduction of other people's experimental work is highly unusual in CS, despite the theoretical possibility. Usually the materials and methods sections of papers in hard sciences like physics, chemistry, and biology are much more detailed, to the point of being recipes.
Finally, CS is not really a science. It lies somewhere between math and engineering, which are also not sciences.
I've seen quite a bit of film, but honestly, if it's really that generic, what are some other examples?
http://www.physics.ohio-state.edu/~kilcup/262/feynman.html
But otherwise, I think we basically agree. Mostly I thought you were talking about the SF vs. NY thing and this article, not about inane investigations into arbitrary correlations (e.g., Is there a relationship between the number of steps someone takes per day and the number of spoons in their apartment? - well, actually, there probably is, especially if you start taking away knives and forks too). The most important thing I guess is to have a question that the researcher is interested in answering.
What do the transit vs. income graphs for SF look like?
How do the SF graphs compare to the NY ones?
Is there a clustering of rich and poor stops in SF like there is in NY?
And then finally, what are the possible explanations?
Sure, they didn't answer the last question, and you have to inspect the data to answer the second and third ones, but it's okay to provide data for other people to look at.
Surely if the first question was worth answering for NY, it's worth answering for SF. You don't answer questions simply because you expect to find something, you answer them because you're curious.
Do you know the story about Richard Feynman and the wobbling plate in the cafeteria? It's another question that "nobody wanted to answer".
"A man who hates his life to the point of suicide is shown by a guardian angel how much the other people around him depend on him. As a result of seeing what the world would be like without him, he changes his mind about the suicide and ends up embracing the life he has, recognizing the simple yet wonderful things about it for what they are."
is not a conventional Hollywood story. Do you have any other examples? I'm mostly interested not because I want to prove you wrong, but because I actually want to be wrong and I want to watch these movies if they exist. Groundhog Day is the only other one that comes to mind.
The primary questions of good science are what, where, when, and who. These are the questions you answer when you collect data. Once you've answered them you can address secondary questions of why and how. Asking why and how without giving priority to what, where, when, and who is putting the cart before the horse.
When you are unable to answer why and how for a given set of data, it is not meaningless. Rather, the lack of correlation or explanations just says that perhaps we need to look into this more deeply. "I've looked at the data and I don't know" is a profound statement, and it can be inspiring.
Science also has to be falsifiable, and effectively that's what these graphs do, at least as far as extrapolating from the NY study goes.
I agree it would have been more helpful if the author had presented conclusions about what the data mean or don't mean, but they aren't a priori meaningless simply because there isn't a visible correlation. No correlation, which is the rather obvious conclusion, is just as meaningful. I hope this is more clear.
Cognitive distortion #8: should statements
Cognitive distortion #10: labeling (sissies)
It doesn't have anything to do with paying the people who work on Linux, it's that the manufacturers of these devices don't have a commercial interest in making their hardware work in 30 different Linux distributions. They do have an interest in their products working flawlessly on all versions of OS X and Windows, and so they do what it takes to make it happen.
Linux-based systems are great, but not when it comes to desktops where productivity is the point. They are good for desktops where tinkering and copyleft are the point. They are obviously a superior choice for anything that isn't a desktop.
http://www.cptryon.org/prayer/special/serenity.html
if you can get over the pronouns.