Summary

In this conversation, Jacob and Daniel Situnayake discuss the future of AI and machine learning in embedded software development. They explore the challenges and opportunities of implementing AI and machine learning at the edge, and how tools like TensorFlow Lite for Microcontrollers and Edge Impulse are making it easier for developers to deploy models on resource-constrained devices. They also discuss the importance of balancing model accuracy with resource constraints and the potential for AI-generated models in the future. Overall, the conversation highlights the growing interest and potential of AI and machine learning in the embedded space.

Keywords

AI, machine learning, embedded software development, TensorFlow Lite, Edge Impulse, resource constraints, model accuracy, AI-generated models

Takeaways

• AI and machine learning are being increasingly applied to embedded software development, opening up new possibilities for edge devices.
• Tools like TensorFlow Lite for Microcontrollers and Edge Impulse are making it easier for developers to implement AI and machine learning on resource-constrained devices.
• Balancing model accuracy with resource constraints is a key consideration in embedded AI development.
• The future of embedded AI and machine learning holds the potential for AI-generated models and more sophisticated applications at the edge.