The talk will focus on data-driven challenges in AI. First, the talk will focus on scaling algorithms to massive data sets. The multi-class and multi-label classification problems will be addressed, where the number of classes (k) is extremely large, with the goal of obtaining train and test time complexity logarithmic in the number of classes. A reduction of these problems to a set of binary classification problems organized in a tree structure will be discussed. A extensions to deep learning will be provided. Second, the talk will consider a problem of information selection for efficient inference in the context of autonomous platforms equipped with multiple perception sensors. Third, the talk will address safety issues in modern AI systems and develop GAN-based on-line monitoring framework for continuous real-time safety/security in learning-based control systems dedicated to autonomous vehicles.