Artificial intelligence may be rapidly changing the way people interact with technology in real time. Today, advances in AI mean that games can change and adapt on the fly, and that each user may get a unique experience based on what works best for them. No longer is AI about simply automating mundane tasks and leaving people to stare at the same unchanging screens while they wait for something to happen — today it’s about making on-the-fly decisions, many of them, that matter.
AI and the challenge of real-time decision-making
Poker is fast becoming a proving ground for developing AI. Poker is quite difficult for both humans and computers to play, but a number of AI programs have been developed to try and gain an edge in this popular card game. MIT Technology Review discusses how the AI technologies behind the programs can learn to adapt and play poker in increasingly dynamic environments. This same approach is increasingly relevant in digital environments that require real-time interaction, with live poker platforms like Lottoland often cited as examples of how AI-driven systems can support responsive gameplay, manage data instantly, and enhance the overall user experience. Evolution of Digital Systems – From Rules to Adaptation The new era of digital systems is characterized by a fundamental shift from rule-based systems to adaptive systems. The ability to make real-time decisions based on incomplete information is becoming a norm and a key competitive feature in a growing number of digital products across various industries.
Broader trends shaping AI in digital environments
The Pew Research Center earlier this year released a report on how humans interact with and rely on AI, which found that the expectations of users are increasing in lockstep with the capabilities of the technology. Humans would like more personalisation and faster response times, rather than sitting through long loading screens or interacting with stale, non-interactive interfaces. The OECD recently released the Artificial Intelligence policy observatory, which points out that “real-time” AI is being implemented the fastest in the “entertainment and consumer technology” sectors – sectors where timing is everything.
At the same time, the UK National AI Strategy reinforces this direction at a policy level and calls for an increase in the country’s capability in high-performance real-time processing and learning and adaptable systems.
Standards and the path to reliable real-time AI
As a system is deployed at scale, it will likely need to be reliable and high availability. Having AI technical standards such as those defined by organizations such as IEEE can provide a structure and set of best practices for designing more reliable systems. Otherwise, the unintended variability of real-time deep learning systems could very well result in erroneous or unpredictable system behaviors during their most critical moments of use.
What real-time AI means for digital platforms
The inexorable march of technology suggests that, in the world of the web for AI to be anything other than a backstage or enabling technology is unavoidable. Technologies that integrate the advent of AI in recognised policy and technology frameworks may be set to deliver end-users with the continuity and quality of experience they expect online today.
