rtificial intelligence (AI) is a broad subject field that generally when you ask the public, the answer that is front of mind is that of a robot that has consciousness. The reality is that it isn’t the robot, but the computer program that has the intelligence. For example, the software behind Apple’s virtual companion application Siri is AI, the woman’s voice you hear is a projected personification of that AI, with no physical robot component involved.
AI and its applications for us mere mortals can be summarized in three usage cases or categories’. The first is referred to ANI or Artificial Narrow Intelligence and sometimes referred to as weak AI. This means the AI specializes in one particular area of application and doesn’t have genuine intelligence capabilities. A good example of ANI is our previous example of Siri or when you contact your credit card provider or bank, and you talk to the automated voice recognition service that can never understand your accent.
The second category is Artificial General Intelligence (AGI). This is often referred to as strong AI or human-level AI, in other words, the machine can perform human-like intellectual orientated tasks, such as problem solving and to think abstractly. An example of AGI often cited is IBM’s Deep Blue computer. It was a chess-playing computer that gained notoriety by beating reigning world chess champion Garry Kasparov in game one of a six-game match.
The third category and perhaps at this moment in time, would be regarded the futuristic of the previous two AI applications, is Artificial Superintelligence (ASI). This is the computer that is much smarter than the best human minds in every academic field; it’s the super-computer that can evolve into a higher state of consciousness’ and of transcendence.
The Deep Impact for iGaming
We know we are sometime away from fully self-aware AI application’s such as the fictional Skynet or so big brother government or Google tells us. But what are the day-2-day applications for the iGaming industry?
Well, most of the applications involve machine-based learning programs. This is where, yes a human, writes a series of algorithms that looks for patterns in data. The objective of machine-based learning programs is to use data to improve the program's understanding and adjust the program outputs in real-time. A good example of this is Facebook's News Feed, which changes according to the user's personal interactions with other users. If a user frequently tags a friend in photos, writes on his wall or "likes" his links, the News Feed will show more of that friend's activity in the user's News Feed related to the algorithms criteria.
Indeed just last year the well-known gaming brand, Betsson announced that their Big Data team in Malta had developed various AI algorithms to solve different business requirements. Some specifically were improving the customer experience and ability to detect fraud, automate payment processes and customer segmentation.
Other areas being impacted include in-play or live betting that has seen enormous growth both from the operators and players alike, has seen numerous developments in the usage of AI and machine-based algorithms. This makes absolute sense, when you think about the thousands’ of in-play betting markets offered, never mind cash-out and partial cash-out offers now available. These businesses need to adopt a lean approach and reduce operational costs regarding headcount within the trading and risk teams, and improve bottom line margins. If they didn’t use machine based AI, they would have to hire teams in their hundreds to monitor the betting transaction flow and likely it would be an unviable product as the current depth of in-play markets available to bet wouldn’t be as numerous.
In user journeys and user experience or the actual digital sales funnel, programmatic advertising and real-time buying based on hard data or data science is using more machine-based algorithms to increase new player acquisition programs. In fact there is now an application called the Grid.io, a US software digital company in San Francisco which uses AI to design and optimize websites based in real-time and by device. Wave goodbye to front-end developers for desktop, tablet and mobile!
Certainly, everything and everyone shall be distilled down to a machine-based algorithm, just in the same way that childless couples can determine the sex and genetic disposition of the child, so shall businesses determine the selection and hiring processes of people within the operational iGaming teams.
Personally, I believe this to be the elephant in-the-room. Why, well machine-based learning is only as good as the human programming the initial algorithms and that code needs continually checked and for a human to actually make reasoned judgement on its validity. Or is it the case that, another AI code shall check the other code and so forth infinitum.
The future of AI application is within your hands as the creator, choose it wisely for the success of both your business and the people it employs and the customers it serves depends on it.