Scientists have created the world's first nanophotonic electron accelerator, which speeds negatively charged particles with mini laser pulses and is small enough to fit on a coin.
How do you measure the amount of “true randomness”? CSPRNGs can use very little entropy to generate large amounts of random data. Mathematically speaking there isn’t any difference between that and what you call “true randomness” - if there was, they wouldn’t be CSPRNGs.
Truly random would be something that is impossible to reproduce. While you are correct that we can approximate randomness, the final calculation can always be replicated if the initial inputs are known. Just because something is exceedingly difficult to replicate, doesn’t mean it is truly random.
Think of it like cleaning your pool. You have a vacuum, chemicals, the system circulates, maybe a skimmer or a net. You can get the pool to the point that it is acceptable to swim in, but you’re never actually swimming in a clean pool. In a similar manner, current random number generators get you to a point that you are (usually) fine assuming the number is random, but it never really is.
I know what you’re trying to get at, but my point is this: Imagine you have two streams of data, one from a CSPRNG, and one from what you call “true randomness”. How can you tell which one is which (as long as you’re staying under the CSPRNGs limit from your initial entropy)?
If you can’t tell me a way, there is no functional difference between these two options. So what advantage would true randomness hold?
I said this in another comment, but while I agree that there is virtually no functional difference, and in the vast majority of cases truly random and functionally random are equivalent, that doesn’t mean that something which is functionally random is truly random.
But it is truly random for all intents and purposes, since the input is truly random. Just because the process contains deterministic steps doesn’t mean the input entropy isn’t true entropy anymore.
And a pool is clean for all intents and purposes. There is still a distinction though. The fact that it is deterministic inherently makes it less random than true randomness.
If you take the original values used to determine the final “random number” and run them through the same sequence of calculations, you will always reach the same value.
We rely on the fact that the inputs are so numerous and/or difficult to replicate to deem the final value “random”. But that doesn’t mean that the value cannot be reached by a second party given perfect knowledge of the original state of all inputs.
True randomness, on the other hand, is impossible to calculate even with that perfect knowledge, because we aren’t relying on the state of inputs running through a calculation.
But that’s my point: just because you apply deterministic steps to a truly random input doesn’t make the output not truly random. You use real entropy as your starting point, which is literally exactly what you call “true randomness”. This means the output has the same level of “true randomness” as your “truly random” input, because you mathematically don’t lose entropy along the way.
To put it more simply: you’re arguing from a philosophical perspective, not a mathematical one.
How do you measure the amount of “true randomness”? CSPRNGs can use very little entropy to generate large amounts of random data. Mathematically speaking there isn’t any difference between that and what you call “true randomness” - if there was, they wouldn’t be CSPRNGs.
Truly random would be something that is impossible to reproduce. While you are correct that we can approximate randomness, the final calculation can always be replicated if the initial inputs are known. Just because something is exceedingly difficult to replicate, doesn’t mean it is truly random.
Think of it like cleaning your pool. You have a vacuum, chemicals, the system circulates, maybe a skimmer or a net. You can get the pool to the point that it is acceptable to swim in, but you’re never actually swimming in a clean pool. In a similar manner, current random number generators get you to a point that you are (usually) fine assuming the number is random, but it never really is.
I know what you’re trying to get at, but my point is this: Imagine you have two streams of data, one from a CSPRNG, and one from what you call “true randomness”. How can you tell which one is which (as long as you’re staying under the CSPRNGs limit from your initial entropy)?
If you can’t tell me a way, there is no functional difference between these two options. So what advantage would true randomness hold?
I said this in another comment, but while I agree that there is virtually no functional difference, and in the vast majority of cases truly random and functionally random are equivalent, that doesn’t mean that something which is functionally random is truly random.
But it is truly random for all intents and purposes, since the input is truly random. Just because the process contains deterministic steps doesn’t mean the input entropy isn’t true entropy anymore.
And a pool is clean for all intents and purposes. There is still a distinction though. The fact that it is deterministic inherently makes it less random than true randomness.
The input is not deterministic.
If you take the original values used to determine the final “random number” and run them through the same sequence of calculations, you will always reach the same value.
We rely on the fact that the inputs are so numerous and/or difficult to replicate to deem the final value “random”. But that doesn’t mean that the value cannot be reached by a second party given perfect knowledge of the original state of all inputs.
True randomness, on the other hand, is impossible to calculate even with that perfect knowledge, because we aren’t relying on the state of inputs running through a calculation.
But that’s my point: just because you apply deterministic steps to a truly random input doesn’t make the output not truly random. You use real entropy as your starting point, which is literally exactly what you call “true randomness”. This means the output has the same level of “true randomness” as your “truly random” input, because you mathematically don’t lose entropy along the way.
To put it more simply: you’re arguing from a philosophical perspective, not a mathematical one.