Testing the blind spots in artificial intelligence

Deep-trained artificial intelligence models can make mistakes if they encounter scenes that they do not recognize, such as an object in an orientation, color, lighting or weather (like in the example above) that conflicts with the datasets used to train the model. By investigating the robustness of deep learning models using a context-based approach, KAUST researchers have developed a means to predict situations in which artificial intelligence might fail. Artificial intelligence (AI) is becoming increasingly common as a technology that helps automated systems make better and more adaptive decisions. AI is an algorithm that allows a system to learn from its environment and available inputs. In advanced applications, such as self-driving cars, AI is trained using an approach called deep learning, which relies solely on large volumes of sensor data without human involvement.