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Using Lasers To Generate Random Numbers Faster
Pranav writes "Using semiconductor lasers, scientists from Takushoku University, Saitama University, and NTT Corporation achieved random number rates of up to 1.7 gigabits per second, which is about 10 times higher than the second-best rate, produced using a physical phenomenon. Future work may center on devising laser schemes that can achieving rates as high as 10 Gbps."
I, for one, welcome our new levelheaded overlord.
"The generation of random numbers is too important to be left to chance." -- Robert R. Coveyou
I, for one, welcome our new levelheaded overlord.
Yeah, well. The Frankenstein Monster was levelheaded too.
The higher the technology, the sharper that two-edged sword.
"Fields and applications that could benefit from their work are numerous, including computational models to solve problems in nuclear medicine, computer graphic design, and finance."
This explains a great deal.
"I bless every day that I continue to live, for every day is pure profit."
So MT may be good enough for computational physicists, but not for strong cryptography.
Extreme Programming - Redundant Array of Inexpensive Developers
Has anyone out there actually had their system bottlenecked by lack of random numbers?
I know some guys doing quantum Monte Carlo simulations. And yes, fast RNGs are crucial for their algorithms.
OS Reviews: Free and Open Source Software
Austin Powers came out over 10 years ago. At some point (and that point was years ago), making references to it every time you see either the word shark or the word laser becomes old. It's really not funny.
Shhh!
Just know that I've got a whole bag of shhh! with your name on it.
Next, the article claims...
Generating random numbers using physical sources -- which can be as simple as coin-flipping and tossing dice -- are preferred over other methods, such as computer generation, because they yield nearly ideal random numbers: those that are unpredictable, unreproducible, and statistically unbiased.
This is garbage -- there are applications where people prefer physical sources, but those of us doing simulation work realized long ago that good algorithmic sources are far better for our needs: 1) It's mighty hard to debug a complex simulation model without reproducibility; 2) You can use the reproducibility to induce covariance between runs, greatly reducing the standard error of your estimates for a given sampling effort; 3) The distributions of algorithmically generated pseudo-random numbers are provably uniform, whereas for physical sources the best you know is that they haven't (yet) failed a hypothesis test for uniformity. Finally, the last statement about being "statistically unbiased" is utter nonsense -- unbiasedness is a property of an estimator, not a distribution.
Do not look into RNG with remaining eye!
(Hah! Bet you didn't see that one coming!)
My 0.02 cents
Nope, I had already looked at 2 lasers previously.
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