MIT Researchers Develop AI System To Cope With Imperfect Inputs

Researchers from MIT have developed a new AI approach that could soon find its way into self-driving cars and industrial robots in smart factories. Designed to handle unpredictable interactions safely, the deep-learning algorithm promises to enhance the robustness of AI systems in safety-critical scenarios. From avoiding a pedestrian dashing across the road in unusually bad weather to overcoming the malicious obstruction of sensors in a manufacturing plant, the new system can enable AI systems to react in a robust manner even when critical inputs deviate due to either unreliable inputs or noise. The details of this new approach are outlined in a study by Michael Everett, Björn Lütjens, and Jonathan How from MIT. Titled "Certifiable robustness to adversarial state uncertainty in deep reinforcement learning", the study was published last month in IEEE's Transactions on Neural Networks and Learning Systems. The algorithm works by building a healthy "skepticism" of the measurements and inputs AI systems receive to help machines to navigate our real, imperfect world.