The desire to source sets of unique, randomly-generated number sequences is prevalent across various sectors and industries.

For example, within the cryptography discipline, whereby coders attempt to create secure channels of communication which cannot be compromised by unauthorized parties, independent number combinations are used both in the key generation process and in the deployment of initialization vectors for the purposes of mitigating against the threat of cyberattacks. There is also a demand for this style of number generation within the ‘completely randomized design’ sector. Indeed, experiment coordinators utilise random number sequences in order to ensure testing components can be equally impacted by what is being tested. This enables those studying the effects of a particular treatment to have more control over experimental conditions and eliminate nuisance variables which no longer add value.


In order to obtain such randomly-orchestrated number sets, the aforementioned stakeholders take advantage of RNGs (Random Number Generators), which are able to consistently produce bespoke number combinations without the requirement of user intervention. Although we’ve already touched upon the presence of RNGs in several other contexts, they are perhaps most commonly found in the gaming industry. RNG technology is an essential mechanic in the facilitation of online roulette and slot games, as it ensures outcomes are completely random and not subject to any bias elements. This helps to establish a fair and consistent environment for players and enables games to retain their unpredictability and intrigue.

Artificial Intelligence in RNGs

There are some fleeting examples of AI software being introduced into RNGs, but it’s debatable whether such technology is required in the use of these platforms. Clearly, AI systems can deliver complex, repetitive tasks at the touch of a button, with ‘smart’ versions able to learn new information, and therefore adapt to changing conditions and inputs as they operate. Furthermore, many have the ability to exploit advanced algorithms and various mathematical models and conduct statistical analysis with astonishing speed and accuracy.
As a consequence, AI software could competently produce random number combinations and therefore could be utilized by the likes of casino operators, cryptographers, and scientific testers. However, given the basic function of RNGs, and the fact that these platforms are suitably equipped to perform their required task, harnessing AI technology in these contexts seems a rather superfluous endeavour – indeed, in practical terms, this would arguably be an exercise in ‘double-handling’.

Current considerations

In recent months, AI usage has increased exponentially, with the general public now able to easily access a range of artificial intelligence text production tools such as ChatGPT, Bard, YouChat, and Chatsonic. Given their widespread popularity, it’s likely the AI market will continue to expand at a rapid rate of knots. However, given the reasons articulated above, it’s unlikely we’ll see AI applications permeate all scientific, commercial, and technology industries in the years to come.

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