RNG is an acronym that stands for Random Number Generator. Random numbers have existed for centuries to date. Until now, the concept of random numbers has mostly been used in playing games, be it rolling of dice or coin flipping; the aim is to arrive at a random chance as a result when playing from the best online slot sites.


RNGs, therefore, are computer-generated programs with a similar purpose. They are specifically built to produce several random, unpredictable probabilities. In simpler terms, you cannot predict them except by a random guess. Comprendé?

Why Random Number Generators?


  • Why Random Number Generators?
  • Types of Random Numbers
  • Sources of Randomness for RNGs
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These RNGs have so many applications; however, they are generally employed wherever an unpredictable result is a paramount feature, such as:

· Gambling

· Computer simulation

· Statistical sampling

· Cryptography

· Randomized designs.

In certain algorithms that make use of letters and digits or a combination of both, and in cryptography, they require an advanced level of randomness. However, in some other applications, they only require an average level of unpredictability.

Types of Random Numbers

Now you may ask, how random can RNGs make these numbers? Is it just a block of codes? Can’t I reprogram these codes to make these numbers predictable? These questions bring us to classify these random numbers into two major categories to better understand the concept: True random numbers and pseudo-random numbers.

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True Random Numbers — They take form when the computer measures certain forms of physical changes taking place in the external environment of the computer. For example, the presses on a keyboard by a computer user can be used to generate truly random numbers. The calculation of time and space in between times the keys are pressed on a keyboard can be used to collect data for entropy further used to generate these numbers. The human being is not a programmed machine, so it is impossible to determine when and the regularity in which he presses the keyboard. Hence, these data are collected multiple times to generate a truly random number.

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Pseudo-random Numbers — These are the opposite of true random numbers because they are not generated by natural phenomena. The computer rather uses a seed value and a string of procedures to formulate random numbers, which in truth, are predictable. There is no foreign data from the computer’s external environment to add complexity to the randomness; therefore, it is purely programmed and hence can be reprogrammed. There goes the answer to your questions from earlier.

Sources of Randomness for RNGs

Below we will briefly elaborate on some sources of entropy RNGs make use of:

DICE — As old-fashioned as it is, it remains one of the most basic means of obtaining a random chance of numbers ranging from 1 to 6.

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RADIOACTIVE SUBSTANCES — It is highly unpredictable to observe when a beta-decay process of a radioactive element will take place.

ATMOSPHERIC NOISE — This type is often deduced from radio static noises. Those annoying sounds you hear when tuning to a radio station.

CAPPED WEBCAM — Those thermal noises the webcam emits when recording is digitized and further enhanced for more randomness.

LASERS — The spontaneous emissions and rapid fluctuation in the photons of the laser make them one of the best sources of entropy for RNGs.

As a result, we can conclude that the randomness of RNGs is determined by the source from which they obtain their random values.

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