Alaa Youssef
Abstract Title
Where are we heading with Generative AI?
The generative AI landscape is undergoing rapid transformation across multiple dimensions, promising unprecedented advances in capability. Will it offer equal advances in compute and energy efficiency? This talk examines multiple critical evolution paths that we believe will define the next generation of AI systems.
We begin by exploring the ambitious trajectory toward massive improvements in combined hardware and software efficiency through co-design optimization. Hardware accelerators evolution reveal dramatic scaling in compute density, memory bandwidth, while simultaneously reducing form factor and energy consumption—critical for sustainable AI deployment at scale.
On the algorithmic front, we observe the shift from Transformer architectures with quadratic attention complexity to emerging linear and sub-linear approaches, including Mamba and next-generation state space models. These innovations promise to unlock longer context windows and improved computational efficiency without sacrificing model quality.
The inference platform landscape is transitioning toward open source software frameworks that support multi-user, multi-model, multi-architecture, and multi-cluster deployments, enabling flexible, resource sharing, with cost and energy effective serving strategies. Parallel to this, agentic platforms are evolving toward open source based frameworks featuring no to low code AI agent composition, automated lifecycle management, and streamlined simple deployment workflows.
Finally, we examine the fundamental shift in how application developers interact with AI systems—moving from prompt engineering to structured programming paradigms that offer greater control, more precise and reproducible generated outcome, besides integration with traditional software development practices.
This talk provides a roadmap of where generative AI is heading in the near future, and the architectural decisions that will shape the next wave of AI systems and applications, in an attempt to understand what impact they will have on future energy and compute demands.
Biography
Alaa Youssef is a Senior Manager and Master Inventor at IBM T.J. Watson Research Center. He leads the cloud native AI platform research team, contributing to IBM’s Watsonx and OpenShift AI platforms for training, tuning and inference of large generative AI and foundational models. His research interests are in hybrid cloud, cloud native AI and HPC platforms, resource management and optimization, sustainable and trusted distributed cloud computing. He has co-authored technical publications in top conferences, received two best paper awards, and has over 40 patent inventions.
Dr. Youssef is currently co-leader of the Hybrid Cloud & AI thrust of IBM-Illinois Discovery Accelerator Institute, where he is co-leading a number of research collaborative initiatives between IBM Research and UIUC, in the areas of hybrid cloud platform and infrastructure for AI, model optimization and runtimes, and agentic systems. He also serves as a member of the Scientific Advisory Committee for the SUNY-IBM AI Research Alliance. SAC members are responsible for reviewing and awarding joint research projects in the areas of AI software, hardware, and models innovation.
Dr. Youssef has held multiple technical and management positions in IBM Research, and in IBM Software Services in multiple geographies, including USA, Egypt, and KSA. He received his PhD in Computer Science from Old Dominion University, Virginia, USA, and his BSc and MSc in Computer Engineering from Alexandria University, Egypt.
Contact
asyousse@us.ibm.com