AI is getting bigger, but does bigger always mean better? As Large Language Models (LLMs) dominate the scene, their staggering resource consumption raises urgent questions about sustainability and

AI is getting bigger, but does bigger always mean better? As Large Language Models (LLMs) dominate the scene, their staggering resource consumption raises urgent questions about sustainability and efficiency. In this webinar, we challenge the notion that AI must be massive to be powerful. We introduce the Expressive Neural Network (ENN), a novel architecture that rethinks activation functions through the lens of classical signal processing, specifically, the Discrete Cosine Transform (DCT). This innovative approach not only enhances a network's flexibility and expressiveness but also leads to faster convergence and significantly smaller models, reducing both energy consumption and computational costs. ​

Our discussion bridged the gap between traditional signal processing techniques and modern AI, demonstrating how established mathematical tools can inspire next-generation machine learning. We explored how ENNs can revolutionize edge computing, enabling efficient AI in resource-constrained environments, and why expressiveness – not just size – is the key to the future of neural networks. If LLMs are the brute force of AI, could ENNs be its precision tool?

Speaker:
Ana Pérez-Neira, Centre Tecnològic de Telecomunicacions de Catalunya, Spain​​

Moderators:
Ian F. Akyildiz, Editor-in-Chief, ITU Journal on Future and Evolving Technologies (ITU-J FET)​​

Alessia Magliarditi, ITU Journal and ITU-T Academia Coordinator, International Telecommunication Union (ITU)

This webinar is organized by the ITU Journal on Future and Evolving Technologies (ITU J-FET), an international journal providing complete coverage of all communications and networking paradigms, free of charge for both readers and authors. The ITU Journal considers yet-to-be-published papers addressing fundamental and applied research. Open topics for future research will be discussed. See more information on the ITU Journal webinar series and the open Calls for Papers for the upcoming ITU Journal’s issues here.