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Published in 2013.


So, was Paracetamol to blame?


Unlikely:

http://www.neurology.org/content/48/3/626.short


Who knows? I doubt reducing fevers is a good idea in the vast majority of cases though. Clearly they serve a purpose.


Depends if you believe the study or not.

Its conclusion is: Alzheimer disease is primarily a man-made condition with paracetamol as its principal risk factor.


It looks like it's less about "putting the fish to sleep" and more about simulating weeds with your fingers to avoid alarming the fish as you start to grab it.

http://drowningworms.com/tickle-trout/


Yes, you just need to work your hands into position and then grab it. I don't think that it's a good idea to do it if you aren't going to eat the fish, because I remember having to grab it very forcefully in the gills to be sure that it wouldn't slip away.


The "beserker equilibrium" hypothesis is a fun thought experiment, but it seems unlikely to me. Robin Hanson did a particularly good job writing about this here:

http://www.overcomingbias.com/2010/12/berserker-breakout.htm...


No.


Actually, the probability that all decks ever shuffled are unique is also very high. We can approximate the probability that any two of the n decks shuffled in human history were identical as p=1-n^2/52!

Using the same estimate as the OP for n (1.56x10^23) gives p=3.02x10^-22. Still fantastically low.


Nice, I came to the comments to see if someone knew how to calculate this.

Related question. If there are exactly 2N people who vote in a binary election (ie: for presidential candidates) and they have an even 50/50% chance of voting either way, how do I compute the odds that they will have a even split? This is a generous estimate for the probability my vote will matter.


The exact answer is (2n choose n) * (1/2)^(2n). This is approximately sqrt(1/Pi n) as n grows large, with error O(n^(-3/2))


That's the same as the probability that 2N flips of a fair coin result in exactly heads: (2N choose N)(.5^N)(.5^N).

Check out: http://en.wikipedia.org/wiki/Binomial_distribution


> We can approximate the probability that any two of the n decks shuffled in human history were identical as p=1-n^2/52!

I think that this is way too low. Shouldn't it be the quite large number

1 - \prod_{i = 1}^n (1 - (i - 1)/52!)

(a la the birthday paradox)?


Sorry, I was thinking of the complementary probability (that there has been a coincidence).

Mathematica overflowed when I tried to compute this by brute force. The next best thing I can think of is to use the exponential approximation

    1 - x ≈ e^{-x},
good for very small `x`, such as ours. Ignoring the cascading errors gives

    \prod_{i = 1}^n (1 - (i - 1)/52!)
    ≈ \prod_{i = 1}^n e^{-(i - 1)/52!}
    = e^{-n(n - 1)/52!}
    ≈ 1 - n(n - 1)/52!.
The error should be roughly of the size

    \frac1 2\sum_{i = 1}^n [(i - 1)/52!]^2
    ≈ n^3/(2(52!)^2),
which is relatively small. (That's stronger, here, than just saying that it is small.) That is to say: I guess I agree with jgershen after all!


Alright, fine, it is comparatively lower. Better? :)


Thanks for submitting this. After seeing the article that claimed the design of the iCloud icon was brilliant because it included Fibonacci ratios, I desperately wanted to nail this article to the (other) author's monitor.

Whenever I see that kind of unjustifiable Fibonacci-worship, I get the same feeling I do when I read creation myths or just-so stories. It honestly hurt the first time I read the truth, and realized I was taught many of the outlandish claims this author points out as fact in grade school math classes.


They were just fibbing.


I agree. Most books and articles on Fibonacci love don't provide enough data to verify the claims. I find the sequence fascinating, but I hold my wallet when someone claims they use it to predict the stock market.


What calculations did you do?

As cofounder of GazeHawk, I've written on different aspects of this topic previously [1, 2]. Is that information helpful / can you elaborate on your skepticism?

[1] http://www.gazehawk.com/blog/on-accuracy/

[2] http://www.quora.com/GazeHawk/What-broad-computer-vision-tec...


Thanks for the links, they were very interesting.

I did basic trigonometry, and came up with an estimate of about 50 pixels accuracy using a high-res, 3rd party webcam. That's why I doubt claims about built-in webcams, since they're typically pretty low resolution.

Your first link mentions an accuracy of around 70 pixels on a MacBook Pro, which is impressive but doesn't strike me as impossible (assuming FaceTime HD camera, which is 1280x720, I believe).


While the resolution of the webcam is (obviously) important when discerning accuracy, I feel like you may be conflating two terms here. Specifically, going from the resolution of the webcam to an estimate of so many pixels of accuracy using basic trigonometry will necessarily depend on the method you're using to convert the webcam input into eye-tracking data.

The <70 pixel figure for GazeHawk's accuracy is based on testing against real, labeled training data. That is the distance, on the screen, by which our calculated gazepoint differs from the true location at which the user's gaze was directed. It is only loosely correlated with webcam resolution, in that a higher webcam resolution corresponds to a larger pipeline - more input pixels being dumped into the eye tracking algorithm. I could be wrong, but it sounds like you're discussing the size of the eyes in the input image.

Also, at this point a discussion of accuracy vs. precision becomes germane. The use of higher resolution video as an input can often impact one but not the other.


I feel like you may be conflating two terms here.

Probably. :-)

I could be wrong, but it sounds like you're discussing the size of the eyes in the input image.

I believe I am. I assume that increased pixel count in the eye region corresponds directly with increased accuracy. This could be accomplished by either moving the camera closer to the eye, or by using a higher resolution camera.


I came across Scott Aaronson's review of NKS a few days ago and loved it: http://arxiv.org/abs/quant-ph/0206089.

I would recommend reading that to anyone who is also looking at Wolfram's work.


My theory is that that review, and Shalizi's, are as popular as they are because nothing is more convenient than learning one doesn't have to actually do the work of considering a (set of) new ideas and perspectives.


Well, I wrote my thesis about cellular automata, and I promise I've considered Wolfram's set of ideas and perspectives. So while I can't prove anything about the popularity of Aaronson's review in general, your hypothesis is demonstrably false in at least n=1 instance.

That being said, I took a look at your website and GitHub (not to mention your employer, Wolfram Research) and it's obvious you know what you're talking about - I don't think you should be being downvoted as heavily in this thread as you are. Plus you made a Futurama reference, so you're obviously a cool dude.

My point is, let's all avoid implying that people who don't agree with us aren't qualified to discuss this subject. I think both of us are, and Wolfram is a pretty polarizing subject - one that all sides need to keep civil about sometimes.


And you did Gazehawk! Pretty cool stuff, I was telling Stephen about that a few weeks ago.

But, let me say, you're right. But I do think its part of their popularity. Also the fact that they're both very entertaining and erudite writers. And sure, there are plenty of valid criticisms, and they make them.

Off to read your thesis..


I love self-sealing arguments that accuse others of making self-sealing arguments. They're simultaneously contradictory and consistent. The essence of paradox and self-parody.

If their arguments are invalid, then perhaps they should be rebutted, or perhaps someone could link to another resource where they have been rebutted.


My theory is they are popular just because rants are more entertaining than reason. People just want to high-five the person who expresses their hate most eloquently.


+1


Could you explain in more detail how these trigger a reboot? From writing a 32-bit kernel I understand how the triple fault method is supposed to work, for example, but would love to know a little more about these methods.


int 19 should just execute code in the BIOS that triggers a reboot. Setting CS and jumping is re-entering the BIOS at the same entry point as at power-on. The BIOS should then reinit all the hardware.


Donald Trump.


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