ML Models for Safety-Critical Systems with Lucas García - #705

This article from Practical AI discusses the integration of Machine Learning (ML) models into safety-critical systems, focusing on verification and validation (V&V) processes. It highlights the challenges of using deep learning in such applications, using the aviation industry as an example. The discussion covers data quality, model stability, interpretability, and accuracy. The article also touches upon formal verification, transformer architectures, and software testing techniques, including constrained deep learning and convex neural networks. The episode provides a comprehensive overview of the considerations necessary for deploying ML in high-stakes environments.

• •Verification and Validation (V&V) are crucial for integrating ML models into safety-critical systems.
• •Data quality, model stability, interpretability, and accuracy are key considerations.
• •Formal verification methods and specialized architectures like transformers are being explored.