Yet another mathematician who uses a silly premise to get Halloween headlines. This one’s kind of a flop, though. Costas Efthimiou claims that the population growth rate of vampires makes them fundamentally impossible. His reasoning is that, starting in 1600 from a population of 538 million, if each vampire converts one person into a vampire once a month, it will be only 2.6 years until all humans have been converted.
Now, this is a bit like saying that if a grasshopper eats a pound of grass a minute, given 1 billion grasshoppers and 500 million pounds of grass, then the Earth will be barren in about 30 seconds. Sure, the math works just fine, but the data is obviously wrong. Dracula was hundreds of years old (potentially thousands; the book doesn’t actually say,) and had created half a dozen childer. Lestat is 300 years old, and has only created two. Unfortunately I don’t wear enough white makeup and black hair dye to name any other vampires, but their birth rate is on the order of either decades or centuries, not single months.
So, fear not, Vlad: no matter what biology, history, zoology, archaeology or common sense may say about you, math hasn’t disproven a thing.
And no, these dots aren’t being agitated. This is a simple visual/auditory demo with delusions of grandeur, but on the other hand, it is surprisingly pretty; very reminiscent of 80s demoscene stuff, back when (insert obviously false nostalgia here.) Granted, this is something of an old news moment, but hey, it’s the first time I saw it, and almost everyone I’ve showed it to thinks it’s new to, so good enough.
It’s been a good year for holy-crap technologies. This one - an MIT tool called ASSIST - boggles my mind, and it’s given me some seriously woot ideas. I’m filing it under Nintendo DS because, even though it’s not a game, that’s just the ideal platform for a better-developed such tool.
The YouTube video is short, but there’s a longer one at the main page.
That’s right. Netflix will give you a million bucks if you can write an algorithm which one-ups their existing algorithm by 10% or better on grounds of predicting what their customers will like, based on their prior history.