25 Mar 2026
46m

AI at the Edge is a different operating environment

Podcast cover

Practical AI

Edge AI's current state and future possibilities are explored with Brandon Shibley, Edge AI Solutions Engineering Lead at Edge Impulse, a Qualcomm company. The definition of "edge" is established as anything outside the cloud, emphasizing AI's increasing presence closer to real-world data capture. The discussion highlights the shift towards smaller, specialized language models (SLMs) that can be embedded into edge devices, balancing knowledge capacity with task-specific fine-tuning. Overcoming constraints such as cost, size, power, and connectivity is crucial for edge environments, while privacy becomes an opportunity by keeping data local. The conversation also covers the shift in workflow and objective as one moves from cloud to edge.

Outlines

Part 1: Introduction, Defining Edge AI

Part 2: Models, Efficiency, Physical AI

Part 3: Technical Optimization, Latency

Part 4: Tooling, MLOps, Governance

Part 5: Hardware, Silicon, Specialized Techniques

Part 6: Practical Applications, Future Outlook

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