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.

The human-AI hybrid model performed eight percent better than either the human or AI could on their own. Researchers at MIT have developed an artificial intelligence system that is able to understand when to defer a task to an expert, adapting to the collaborator's availability and level of expertise. A lot of AI systems use this collaborative approach, in which an automated service works in most cases, while a human is brought in for edge problems. Facebook's content moderation platform runs like this, using image and language recognition systems to automatically filter inappropriate content, while a large team of human moderators deal with more challenging content. According to the team, the human-AI hybrid model performed eight percent better than either the human or AI could on their own. It is also able to reduce the computational cost and train the AI platform with fewer data samples, saving businesses time and money.