Issue link: https://maltatoday.uberflip.com/i/1514967
12 25.01.2024 Alexiei Dingli Prof Alexiei Dingli is a Professor of AI at the University of Malta and has been conducting research and working in the field of AI for more than two decades, assisting different companies to implement AI solutions. He forms part of the Malta.AI task-force, set up by the Maltese government, aimed at making Malta one of the top AI countries in the world NEWS Unveiling the next AI superstar after ChatGPT A s we stand on the verge of what many perceive as the apex of Artificial Intelligence (AI), we must recognise that we are merely at the tip of the iceberg. The advancements in AI, particu- larly in the last two decades, are not the culmination but the beginning of a revolution that will redefine our in- teraction with technology for genera- tions to come. Tim Berners-Lee, the visionary be- hind the World Wide Web, along with James Hendler and Ora Lassila, fore- saw this evolution in 2001 when they endeavoured to create a second ver- sion of the Internet, which was then referred to as the Semantic Web. This concept was not just an exten- sion of the existing web but a fuller realisation of the current web, where information was understandable by both humans and machines. The basic idea was to create "agents" – sophisticated software entities dis- tinct from conventional software in their ability to autonomously under- stand, process, and interact with web content. In this context, an agent is akin to a personal assistant who un- derstands your preferences, antici- pates your needs, and acts proactively on your behalf. Unlike traditional software pro- grammed for specific tasks, these agents operate by learning the mean- ing of information. This distinction enables them to perform complex tasks such as scheduling appoint- ments, finding information, and mak- ing decisions based on user preferenc- es and constraints. Imagine the use of agents in man- aging healthcare. In this scenario, an intelligent agent is programmed to handle a user's hospital appointments. This agent, equipped with advanced AI, first understands the user's med- ical needs, preferences for healthcare providers, and schedule availability. It then autonomously searches through various healthcare providers, analys- ing factors such as the types of servic- es they offer, their geographical loca- tion, and user reviews. Crucially, the agent also checks for compatibility with the user's health insurance, ensuring that any appoint- ments are covered under their plan. This involves understanding the in- tricate details of the user's insurance policy and cross-referencing it with the providers' billing and insurance acceptance policies. Once a suitable provider is found, the agent will book an appointment, aligning it with the user's schedule. It considers other commitments and preferences, such as avoiding appoint- ments during work hours or aligning visits with other activities in the area to save time. This process, while seemingly straightforward, involves complex interactions and data processing that were beyond the capabilities of tech- nology in 2001. The level of contextual understand- ing, natural language processing, and decision-making required for such a task was not feasible with the AI tech- nology of that era. Fast forward to the present, 20 years later, the landscape has dramatically changed. The evolution of AI, espe- cially with the advent of large lan- guage models like ChatGPT, lays the groundwork for the realisation of Berners-Lee's vision. These AI models are not just sophis- ticated in processing language but also exhibit an understanding of con- text, consider user preferences, and can generate human-like responses, forming the foundation of the intelli- gent agents envisaged in the Semantic Web. We are now witnessing the early stages of this transformation. AI sys- tems are becoming increasingly capa- ble of complex tasks such as language translation, content generation, and even coding, which were once thought to be exclusively within the human domain. The next step is the integration of these capabilities into agents that interact seamlessly with the vast ex- panse of the Internet, turning the ocean of online data into actionable intelligence. The future of AI, particularly with the advent of autonomous agents, her- alds a transformative era in both tech- nology and economy. These agents, capable of executing complex tasks autonomously, promise to revolu- tionise business operations, enabling large-scale automation and efficient decision-making. As per McKinsey's analysis, the eco- nomic impact is substantial, with AI potentially contributing $13 trillion to the global economy by 2030. How- ever, this advancement brings chal- lenges, notably the widening disparity between countries, companies, and workers in their ability to adopt and benefit from AI. Developed countries and leading companies are poised to reap significant benefits, while others risk falling behind. The unfolding AI landscape thus presents a dual challenge of harness- ing its immense potential while man- aging its socio-economic impacts, ne- cessitating a balanced approach from governments, industries, and individ- uals alike. AI systems are becoming increasingly capable of complex tasks such as language translation, content generation, and even coding, which were once thought to be exclusively within the human domain