The goal of this research is to develop methods that improve interaction between AI systems and humans. AI systems are increasingly more prevalent in decision making processes, for instance, in navigation systems for avoiding traffic jams. However, while one could ask a peer for a recommendation and the reason behind it, today’s AI systems often cannot provide a justification for their output. Hence, users have to 1) trust the system recommendation blindly, 2) verify plausibility individually, or 3) ignore the recommendation. To address the limitation that none of those three options is desirable, the research develops models which can explain their output, the research develops algorithms which can be controlled, and the research develops methods which permit interaction with the model. Inspired by a human focusing on subsets of the data when making a recommendation, the research seeks to obtain explain-ability, control-ability and interact-ability by extracting which parts of the data provided most evidence. For this we use probability distributions inside AI systems. Furthermore, this research will support development of a cohort of PhD and undergraduate students at the University of Illinois at Urbana-Champaign, outreach activities in the local neighborhood and development of two classes: a novel undergrad class on entry-level machine learning and a novel grad class on distributions in AI systems.
Technically, distributions inside AI systems are often referred to as attention. Attention provides a compelling framework 1) to explain the decisions formed in discriminative networks; 2) to control the sampling process in generative models; and 3) to interact in reinforcement learning systems. The technical aims of this research are divided into three thrusts. The first thrust scales attention mechanisms to data that comprises multiple modalities and develops algorithms which better capture probability distributions in those high-dimensional settings. The second thrust generalizes those algorithms to more complex data structures and leverages those results for AI systems which generate high-dimensional output, e.g., a description of an image. The third thrust studies interaction between humans and AI systems that leverage distributions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
