Video: Innovation – Trinty: Trusted, Resilient and Interpretable AI framework (9:04 mins)

The increasing adoption of artificial intelligence and machine learning in systems, including safety-critical systems, has created a pressing need for developing scalable techniques that can be used to establish trust over their safe behavior, resilience to adversarial attacks, and interpretability to enable human audits. This tutorial is comprised of three components: review of techniques for verification of neural networks, methods for using geometric invariants to defend against adversarial attacks, and techniques for extracting logical symbolic rules by reverse engineering machine learning models. These techniques form the core of TRINITY: Trusted, Resilient and Interpretable AI framework