Pete Warden, founder of Useful Sensors and co-author of TinyML, discusses use cases for artificial intelligence that we rarely think about: how can you run AI on very small systems? How can you put AI on consumer devices in ways that are actually useful and not just buzzword-compliant? AI doesn’t have to rely on massive GPU farms. Pete talks about what happens when you exchange one set of requirements (extreme power, heat, and expense) for another (minimal size, cost, and heat).
Points of Interest
- 00:00: Introductions, including Pete’s introduction to his company.
- 2:22: What are some of the challenges and use cases for sensor-driven AI?
- 4:11: Is sensor-driven AI relevant to industries other than hardware?
- 6:22: Now we’re in the age of foundation models and large language models. Is “large” incompatible with “tiny”? Can you run language models on smaller devices?
- 8:00: Will there be developments in tinyML that will benefit the broader LLM community?
- 9:30: What’s deployable today in computer vision, speech, and language? What can be done with hardware that’s constrained by cost, size, and power consumption?
- 11:15: How will product designers work with sensor-driven AI? Will they simply select from a palette of optional modules?
- 12:37: Pete walks us through the development of AI-in-a-Box, from its conception to its reception.
- 15:31: Your devices don’t have network connections. Without a network connection, how do you update models? Is it necessary?
- 19:00: Do you do Retrieval Augmented Generation (RAG) on your devices?
- 20:35: Our devices have user interfaces that combine voice and presence. A voice interface is central, but visual (and other channels) help to create an awareness of the speaker.
- 21:35: What are some of your specific challenges, like power consumption and latency? How do you make tradeoffs?
- 22:45: What is the future of large language models for sensor-driven AI?
- 26:50: What are some of the security concerns for sensor-driven AI and what are you doing about them?
- 28:22: What is Dark Compute and why is it important?
- 30:48: What are the biggest opportunities for pushing AI into consumer devices? We need to start with problems that users actually care about.
- 32:30: How can listeners connect to the broader movement around TinyML?
