What are your thoughts on the evolution of agentic AI?
Te evolution of agentic AI - AI that can act autonomously - is accelerating rapidly. Tanks to the advances with reinforcement learning and systems integration, we're moving towards a non-static, dynamic era where AI can make decisions from experience. You just set goals, and the AI operates from that. However, we are not yet at the point of automation by any means. An AI can't manage complex tasks over time without any human oversight.
So that's something that will continue being a requirement for some time. Real autonomy still faces some hurdles, mostly due to lack of trust and generalisation across domains. Te most important point is safe decision making. You can't really let go of the wheel yet as a company. You still need to work with the AI, but absolutely it's moving towards becoming agentic.
Large Language Model AIs such as ChatGPT, Bing and Bard are becoming increasingly feature rich. What are your thoughts on the development of these platforms? Have they become more accurate, specific, and useful?
It really depends on where you go from. From our perspective, in a professional context it's becoming more accurate, more context aware, more versatile, and it's achieving this in a shorter amount of time. It's especially impressive how we're transitioning from general purpose text editors to deeply integrated multi-model capacity systems with tools and that more like a dynamic co-pilot to your daily activities as a human. We're moving towards specialisation, higher accuracy a little bit away from the general-purpose models within the professional context.
How are iGaming businesses typically utilising these Large Language Model platforms in their daily operations?
I'd say twofold, mostly from the perspectives of increasing player experience and operational efficiency. Tose are the two bigger ones. Specifically, smarter chatbots that can give you personalised gaming recommendations and doing that with natural language, which truly helps. In the back end, the LLMs are helping with content generation, scaling operations and customer support. Tat customer support piece is one of the biggest ones across industries. At a deeper level, if you really work with the model and build onto it, fraud detection and pattern analysis are important within the gaming industry. From a user's perspective, the objective is to make interactions more seamless and add
value to the player's experience by giving them specialised recommendations. Tat's where I see most of the efforts being made within the gaming industry now.
Are there security risks for businesses who are dependent on using Large Language Model AIs on public cloud platforms?
Absolutely. Tere's a big security and privacy risk and awareness piece around this for people in general, but especially for businesses. All these cloud platforms, no matter how deep the assurances that your data is going to be safe, your data is still going out there. Any company that would like to increase productivity needs to share their data to extrapolate inputs to build a better experience. In some cases, this could be customer data and proprietary data - and that is a huge risk.
Ultimately, the assurance that this data won't be used to train the AI or there won't be any leakages means nothing. It's just a disclaimer which isn't appropriate for some of the use cases companies want to use the LLMs for. Another consideration is GDPR and the AI EU Act with all the penalties that come with incorrect usage of AI. Whenever you're using a cloud provider, the assurance that your data isn't going anywhere should be questioned.
How does Private GPT compare to Large Language Model platforms in terms of performance? Are they better at delivering real value - practical application, data-driven decision making, knowledge optimisation?
Absolutely for the latter pieces, so for data-driven inputs, for decision- making on process work - yes. If you want to be more creative, if that's your biggest use, then you're probably better off with one of the LLM tools just because ingestion is constant, and it'll give you a bigger pool of resources to work from. If you want data-driven and accurate results, then our system is better adapted. Firstly, because it's completely controlled.
You know what data it was trained on, why it gave you the answer it did, everything on the back end. Cloud platforms typically have less capacity to influence their development and what happens with the future of the tool. Tis is something we bring to the table offering a boutique closeness with our customers to deliver a system that is adapted to their needs. Tis goes hand in hand with the agentic AI we discussed earlier in that we are moving towards tailored use cases. Tose are the areas where we offer a difference in comparison to cloud-based tools.
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