What does AI mean for IoT development?
summary
Claims about where compute lives and what actually ships: real-time heartbeat and breathing detection runs on the edge, longer training and aggregation happens in the cloud; models are treated as IP and encrypted in transit and at rest; teams report migrating prototypes from cloud to edge to cut cost and latency; one deployment example is 10,000 sensors deployed for telemetry, another is telecom-site inventory and rust detection; investable gaps are ultra low power semiconductors and hands-on deployment services.
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chapters (9)
- 0:00AI in IoT development
- 2:27Edge vs cloud in AI IoT
- 4:12Edge AI impact on design
- 6:20Economic models for edge AI
- 8:26AI processing in IoT systems
- 11:36AI IoT security challenges
- 14:23Starting AI IoT projects
- 18:40AI IoT market outlook
- 20:47Q&A
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