Associate Professor, Virginia Tech- NetSciWiS lab (USA)

Title of talk: The Road to Artificial General Intelligence (AGI) Runs Through Next-Generation Wireless Networks

Next-generation wireless systems, such as 6G and beyond, are expected to tightly embed artificial intelligence (AI) into their design, giving rise to what is termed AI-native wireless systems. Remarkably, despite significant academic, industrial, and standardization efforts dedicated to AI-native wireless systems in the past few years, even the very definition of such systems remains ambiguous. Presently, most endeavors in this domain represent incremental extensions of conventional "AI for wireless" paradigms, employing classical tools like autoencoders or large-language models to replicate established wireless functionalities. However, such approaches suffer from inherent limitations, including the opaque nature of the adopted AI models, their tendency toward curve-fitting, reliance on extensive training data, and limited generalizability to new, unseen scenarios and out-of-domain/out-of-distribution data points. To address these challenges, in this talk, we unveil a bold, pioneering framework for the development of artificial general intelligence (AGI)-native wireless systems. We particularly show how the fusion of wireless systems, digital twins, and AI can catalyze a transformative paradigm shift in both wireless and AI technologies by conceptualizing a next-generation AGI architecture imbued with "common sense" capabilities, akin to human cognition and founded on three components: a) perception, b) world model, and c) action-planning. This architecture will empower networks with reasoning, planning, and other human-like cognitive faculties such as imagination and deep thinking. We first define the technical tenets of common sense and, subsequently, we demonstrate how the proposed AGI architecture can instill a new level of generalizability, explainability, and reasoning into tomorrow’s wireless networks. We then present our recent key results, rooted in AI, theory of mind, digital twins, and game theory, laying the groundwork for the realization of AGI-native wireless systems, and illustrating how our designed framework reduces data volume in networks while enhancing reliability, crucial for next-generation wireless services like connected intelligence, extended reality, and holography. We conclude with a discussion on the exciting opportunities in this field that can help redefine the intersection of wireless communications and AI.