Random number generators are actually pseudo-random number generators because they depend on designed algorithms:

Robert J. Marks:There are deterministic aspects of randomness. And this is a difficult concept to explain. But examples are obvious. If you flip a coin a million times, about 50% of the time, it will come up heads if it’s a fair coin. And that is a deterministic output of randomness.So imagine setting up an evolutionary computing program where you have a specific outcome in mind and you performed this operation a million times. Well, it’s going to converge to that output, just like the coin flip converges to a 50% success rate. And putting together the stochastic framework in order for this to happen is what the people in evolutionary computing do.

News, “How even random numbers show evidence of design” atMind Matters News

*Robert J. Marks:* Well, it’s a highly designed thing. And I would also argue that all random numbers generated by computers are themselves deterministic, believe it or not. In fact, they refer to them as *pseudo*random number generators. There’s a little algorithm that spits out numbers that look random but underneath, they’re not random.

In fact, I have a student right now who is looking at training a neural network to *forecast* random numbers. If these random numbers are being generated by a deterministic algorithm, then we should be able to discover what the deterministic algorithm is.

Is there a way we can game that system and literally figure out the next random number? In fact, the only place in the world that randomness exists is in quantum collapse. That’s the only true randomness.

*Takehome:* Claims for randomness in, say, evolution don’t withstand information theory scrutiny.

Here are all the episodes in the series. Browse and enjoy:

- How information becomes everything, including life. Without the information that holds us together, we would just be dust floating around the room. As computer engineer Robert J. Marks explains, our DNA is fundamentally digital, not analog, in how it keeps us being what we are.
- Does creativity just mean Bigger Data? Or something else? Michael Egnor and Robert J. Marks look at claims that artificial intelligence can somehow be taught to be creative. The problem with getting AI to understand causation, as opposed to correlation, has led to many spurious correlations in data driven papers.
- Does Mt Rushmore contain no more information than Mt Fuji? That is, does intelligent intervention increase information? Is that intervention detectable by science methods? With 2 DVDs of the same storage capacity — one random noise and the other a film (BraveHeart, for example), how do we detect a difference?
- How do we know Lincoln contained more information than his bust? Life forms strive to be more of what they are. Grains of sand don’t. You need more information to strive than to just exist. Even bacteria, not intelligent in the sense we usually think of, strive. Grains of sand, the same size as bacteria, don’t. Life entails much more information.
- Why AI can’t really filter out “hate news.” As Robert J. Marks explains, the No Free Lunch theorem establishes that computer programs without bias are like ice cubes without cold. Marks and Egnor review worrying developments from large data harvesting algorithms — unexplainable, unknowable, and unaccountable — with underestimated risks.
- Can wholly random processes produce information? Can information result, without intention, from a series of accidents? Some have tried it with computers…

Dr. Marks: We could measure in bits the amount of information that the programmer put into a computer program to get a (random) search process to succeed. - How even random numbers show evidence of design Random number generators are actually pseudo-random number generators because they depend on designed algorithms. The only true randomness, Robert J. Marks explains, is quantum collapse. Claims for randomness in, say, evolution don’t withstand information theory scrutiny.

No need to argue that it’s determined. This is well known, and most software uses the same algorithm. The ‘Mersenne Twister’ is most common. It performs a bunch of transformations on the digit sequence of a huge prime number.

It would be more interesting to see if you can predict the output of a natural random generator that samples a Zener diode.

This is a fascinating subject, Polistra!

To your point . . .

http://reallyreallyrandom.com/.....ts-random/

While our reality seems to be fundamentally digital, there are some intriguing aspects to it. The granularity is extremely fine. The output of the algorithms generating pseudorandom numbers is either closer or further from random. And all this reminds us of encryption, which converts specified information into a pseudorandom string.

I’ve sometimes speculated on how the results of physical measurements tend toward pseudorandomness as the precision increases. I wonder whether pseudorandom numbers derived pseudorandomly from other pseudorandom numbers will yield better ones.

In reading more of the article, I ran across this great observation by Robert J. Marks that reminds me of the unhelpfully pugilistic trolls posting here:

-Q