One of the last features that anyone would expect from an AI-driven algorithm is random output. After all, machine learning is based on rigid input and finite outcomes. As programmers write the software that drives our technology, one of their first missions is to create a clear path from Point A to Point B.
In other words, randomization in any form could compromise their programming. And the last thing anyone using an app, a radar, or any other piece of mobile technology wants is an unclear or seemingly accidental outcome. At least, in theory.
Randomness has been an important concept in a variety of fields (more on this below). Figuring out how to generate randomness on command has actually been a deceptively tricky process for millions of developers and programmers around the world. Despite our emphasis on certainty, it’s helpful (and sometimes necessary) to produce chances.
Let’s dig deeper and see how machine-learning algorithms and AI generate randomness. But first, why?
Why Randomness?
Randomness is helpful in a variety of ways. Certain fields of mathematics, for example, revolve around different types of randomness. The field of probability is all about predicting what might happen within a certain event. The stock market also deals with randomness as it relates to chaotic systems.
Clearly, there are quite a few brands of randomness that scientists, mathematicians, and traders will encounter regularly. But what about the average person who isn’t in an advanced professional field? For most of us, the most common way that we interact with randomness is through gaming.
Gaming is now one of the cornerstones of the entertainment industry. On one hand, there are larger-than-life eSports spectacles, from Dota 2’s The International to the Call of Duty League. Millions of viewers tune in for major matches, glued to their screens. But behind it all are randomization programs that ensure no match is predictable.
Randomization affects things like weapons, maps, loot, and more. They’re designed to hand players a brand new set of challenges each match. But randomization is important on a smaller scale, too.
Online gaming platforms, for example, have relied on randomization for decades. Once, players used things like manual shuffling and a throw of the dice. Today, crash gambling sites use software programs like RNGs to ensure the gaming experience runs smoothly. In other words, randomization is a critical feature that ensures every game is fair
But what do these programs actually look like?
Generating True Randomness
Randomization within software comes from something called a random number generator (RNG), which can be a true RNG (TRNG) or a pseudo RNG (PRNG). A TRNG is considered the ‘purest’ form of randomization because it comes from the natural world.
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Nature produces randomness all the time, from seismic activity to lightning strikes. Both phenomena can actually be used to create random outcomes. From lightning to seismic activity to other things like temperature spikes, frequency, and even entropy are recorded as random sequences.
Over the last few decades, scientists and programmers have worked together to create criteria to record and then use natural input as randomization. However, these programs are incredibly advanced and pricey, meaning they’re usually used for research purposes—not something like gaming.
Generating Pseudo-Randomness
The digital world usually relies on PRNGs. Though these aren’t based on natural input, they’re considered trustworthy and, most often, are verified by third-party groups. These PRNGs are used behind the scenes in gaming, including the examples mentioned above.
But how do they work? We have to go back to the example of mathematics. As mentioned above, mathematics fields rely on randomization for research. Most PRNGs work with multiple (if not dozens) mathematical formulas that generate random numbers.
Are these actually random? By definition, because they’re generated from logical formulas, they aren’t truly random. However, there are no cases in which a PRNG’s sequence has been predicted by any group. Though this is theoretically possible, it would involve a hefty amount of statistical analysis, and then a reproduction of the formulas in use.
In other words, it’s a pretty secure process. In fact, some researchers are starting to examine PRNGs that mimic TRNG outputs. As they become more advanced, they’ve started to see more attention from RNG-specific groups.
Who’s Behind RNGs?
RNGs are a fascinating subject. They’re used around the world for a variety of purposes and are created by different professionals. Software developers and hardware manufacturers deal with them often, especially when it comes to games. In fact, game development companies are some of the most active RNG developers.
As mentioned previously, researchers also rely on RNGs often. Not only are they helpful for mathematicians, but they can also be used in fields like simulations, statistical analysis, and even quantum physics.
But RNGs are also used by cryptographers. Cryptography revolves around coding information so that it’s secure, usually using a tailored form of encryption. RNGs can be used to generate these encryption codes so that they’re impossible (or nearly impossible) for others to decipher.
