Majid Fekri
Founder at Edge AI Innovations Inc.
Toronto, Canada
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Dr. Majid Fekri is a dynamic and engaging speaker who is passionate about the transformative power of AI. With a PhD in Atmospheric Sciences from McGill University and a proven track record in both startups and large corporations, Dr. Fekri brings a unique blend of scientific expertise and real-world experience to the stage. He is the founder of Edge AI Innovations, a platform dedicated to education and consultation about Edge AI, empowering individuals and businesses to harness its potential. Through his engaging presentations and insightful newsletter, Dr. Fekri makes complex AI concepts accessible and inspires audiences to embrace the possibilities of this rapidly evolving technology.
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Context Engineering on Azure: Unlocking Persistent Agent Memory with MemAnto by Moorcheh.ai
Most AI agents built on Azure today are "knowledgeable"—they ground responses in enterprise data using standard RAG and rely on short-term, session-based memory. But once the session ends, the context disappears. The agent starts fresh, unable to recall prior interactions, user preferences, or previously established workflows.
Extending memory beyond a single session to create truly adaptive agents introduces a massive architectural challenge: the enterprise "RAM Wall."
This session introduces MemAnto, a newly launched open-source, deterministic agentic memory layer that solves the persistence problem on Azure. We will explore how MemAnto uses Information-Theoretic binarization (32x compression) to keep massive, user-scoped memory stores entirely within the CPU.
"Beyond Transformers: New AI Architectures Revolutionizing Edge Inference"
The edge AI landscape is evolving rapidly, with emerging AI architectures poised to redefine the limits of on-device intelligence. This session takes you beyond the familiar territory of Transformers, exploring novel approaches that promise to unlock greater efficiency, performance, and capabilities for edge inference.
We will present the groundbreaking potential of architectures like Mamba, which challenge the dominance of Transformers with their linear scaling and simplified structure. We'll examine how these innovations are enabling:
1. Faster and More Efficient Inference: architectures that deliver comparable or superior performance to Transformers while requiring significantly less computational resources.
2. Expanded Applications: new architectures are opening the door to deploying complex generative models, large language models, and other AI-intensive workloads on resource-constrained edge devices.
3. Real-World Impact: how these advancements are being applied in areas like computer vision, robotics, healthcare diagnostics, and personalized retail experiences.
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