The future of AI isn't just about bigger models, it's about smarter deployment. Discover how agentic AI systems and federated small models are revolutionising privacy, performance and user experience, by operating directly on devices rather than relying solely on centralised infrastructure.

At IBM, responsible AI implies transparency in training data: Introducing GneissWeb (pronounced “niceWeb”), a state-of-the-art LLM pre-training dataset with ~10 Trillion tokens derived from FineWeb, with open recipes, results, and tools for reproduction! In this session we will go over how we created GneissWeb and discuss tools and techniques used. We will provide code examples that you can try at your leisure.

Overview of how Small Language Models (SLMs) can solve business problems and boost device intelligence, with a comparison to Large Language Models (LLMs). Focus on fine-tuning and customization, edge deployments for real-time processing, and a cloud-to-edge deployment roadmap using Azure to maintain IP ownership and integration. Includes perspectives on leveraging SLMs to revolutionize devices and mentions Microsoft’s Student Ambassador Program energizing students to advance intelligent tech.

A presentation on the role of Small Language Models (SLM) in solving business problems and improving device capabilities. It covers comparing SLM to Large Language Models (LLM), the importance of customization in fine-tuning, deploying models on edge devices for real-time processing, and a roadmap from cloud development to deployment across platforms with Azure. The session concludes with a discussion of Microsoft's Student Ambassador Program and its role in fostering innovation and skill-building.

Webinar exploring Emerging Technologies in Networking, including how 5G, Wi‑Fi 6, and edge computing are shaping networking capabilities. Date: 2024-07-16; Time: 01:00 PM–01:40 PM EST.