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Today, we're joined by Lucas García, principal product manager for deep learning at MathWorks to discuss incorporating ML models into safety-critical systems. We begin by exploring the critical role of verification and validation (V&V) in these applications. We review the popular V-model for engineering critical systems and then dig into the “W” adaptation that’s been proposed for incorporating ML models. Next, we discuss the complexities of applying deep learning neural networks in safety-critical applications using the aviation industry as an example, and talk through the importance of factors such as data quality, model stability, robustness, interpretability, and accuracy. We also explore formal verification methods, abstract transformer layers, transformer-based architectures, and the application of various software testing techniques. Lucas also introduces the field of constrained deep learning and convex neural networks and its benefits and trade-offs.
MathWorks is the leading developer of mathematical computing software. Engineers and scientists worldwide rely on MATLAB® and Simulink® to accelerate the pace of discovery, innovation, development, and learning.
With MATLAB, engineers and scientists can create better AI datasets, develop and operationalize AI solutions, and continuously test AI models in a system-wide context. In just a few lines of MATLAB code, AI can be incorporated into your applications whether you’re designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems.
To learn more about how MATLAB is the enterprise engineering platform for AI, visit mathworks.com/ai.
