Artificial intelligence (AI) is making inroads across many industries. But what impact could AI have on random number generators (RNGs)? Let’s explore what RNGs are, how they’re used, and the ways AI is changing the game.
What Are RNGs?
As the name suggests, RNGs generate non-sequential random numbers. There are two primary types of RNGs: pseudo-RNGs (PRNGs) and true RNGs (TRNGs).
PRNGs utilize complex algorithms to produce random numbers that are not predetermined. These RNGs widely used in online applications, with online casinos being perhaps the most prominent example. In fact, PRNGs are the backbone of online slot games, as they mimic the same element of luck that players experience from pulling a lever to spin the reels. For example, when slot enthusiasts play Fishin Frenzy online slot, RNGs algorithms determine which angling-themed symbols land on the reels and in what order. This process also triggers bonus features, providing an authentic sense of luck and ensuring that gameplay remains fair and immersive.
On the other hand, TRNGs typically use a piece of hardware, which may also be known as hardware-based RNGs (HRNGs). This equipment uses a physical source of entropy to produce random numbers. For example, one offshoot of TRNGs are quantum RNGs, which rely on the unpredictability and multiplicity of quantum physics to generate random numbers. Toshiba’s QRNG chip can securely generate true random numbers at a speed of 2 Gbit s-1. Due to their reliance on physical entropy, TRNGs are often applied in high-stakes industries such as cryptography and security.
So, now you have the basics on what RNGs do, let’s look at how AI could be used in the development of RNGs.
AI and RNGs: A New Frontier
Since PRNGs are based on algorithms, AI-based PRNG’s are already emerging. These AI PRNGs are web-based or downloadable applications that use machine learning processes to generate unique sets of numbers. Depending on the specific product at hand, the AI system can use various techniques to produce these random numbers.
In some cases, AI can use noise from various sources stored as an entropy input – something typically found only in Gaussian RNG hardware models and certain simulation systems. However, AI can use this as a random seed, feeding it into a mathematical model to produce numbers with enhanced randomness.
When it comes to TRNGs, the challenge lies in their need to generate truly random numbers. AI technology, as it stands, has not yet evolved to the point where it can generate random numbers without relying on a coded algorithm – since algorithms are foundational to AI.
That said, it is thought that GPT architecture does support a theoretical possibility of AI-based TRNGs. While this idea remains speculative, it highlights the immense potential of AI within the RNG industry and the impact it could have on future applications. We’ll have to wait and see how these developments unfold, but one thing is certain – the future of AI in RNG technology is looking bright!
