Absolutely love your video. I was coding in Python, and i was using "random" library when I suddenly realised "How is a computer, the most pattern based machine in the world, able to generate randomness?".
Same. 0:31 Im watching the video, never looked into it, but Im pretty sure its impossible. 1:07 ok, thought so. U cant explain a machine "give me random".
Yeah so the only way to do that is getting it from somewhere else (time of day, temperature, increasing the seed by one each time, etc). Or you can just use the same seed and continue with the sequence of numbers
I'm trying to find the type that can create organic art very fast... hopefully each out of a billion numbers like perlin noise combined with mandelbrot
Which means slots are not unpredictable and random. I've been studying slots and patterns and I've noticed patterns that tend to win even amongst all the "randomness."
Hey,nice explanation and i really appreciate your work. Nice rendering of manim ,it would be really helpful if you can share the manim file for the animations in this video.
I’m pretty sure that the numbers for the algorithm itself are chosen specifically to work well, and it’s pretty easy to accidentally use a equation that just gives the same number over and over. Also just choosing the seed means that you will have an equal distribution of the numbers
Length of random data you feed in determines true randomness of the output. You can reduce true randomness by choosing inappropriate algorithms but can't increase it. If there is desire for more secure random numbers, I think focus should be on getting more randomness from some entropy source(s), while algorithms also important, but they can't do what they can't do, i.e. provide true randomness from nowhere.
I used www.manim.community, the same animation thing used by 3 blue 1 brown. I actually made a whole video about it: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-OOXmbB-Uqmc.html
@@TonyZhang01 ok. robustness is defined by asymptotic analysis on an algorithm and is not a synonym for "popular" or "good". it is a property that will be robust with respect to some variation that winds up not effecting the property. this is important since usually some imperfection, randomness, or noise in your initial conditions is considered likely, so you want a property of an algorithm to be "robust" against imperfections in your initial assumptions, as opposed to being "highly sensitive to initial conditions". however for a pseudo-random number generated you don't want the numbers produced to be "robust" you would prefer them to be highly sensitive to initial conditions because you want them to be hard to predict or to say anything else. the only property of a pseudo-random number generator you would want to be robust is its ability to pass statistical randomness tests over multiple trials. check out martin lof randomness
Thats determinism. Consequentialism is the idea that what makes an action wrong is its consequences. For example, a consequentialist would say that punching someone is bad because it causes them pain.
