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Amazon and UIUC announce inaugural slate of funded research projects

Amazon-Illinois Center on Artificial Intelligence for Interactive Conversational Experiences also names first cohort of academic fellows.

Earlier this year, Amazon and the University of Illinois Urbana-Champaign (UIUC) announced the launch of the Amazon-Illinois Center on Artificial Intelligence for Interactive Conversational Experiences (AICE). The center, housed within the Grainger College of Engineering, supports UIUC researchers and students in their development of novel approaches to conversational-AI systems.

Amazon and UIUC are happy to announce the inaugural round of funded research projects along with the first cohort of annual fellowships. The research projects aim to further the development of intelligent conversational systems that demonstrate contextual understanding and emotional intelligence, allow for personalization, and are able to interpret nonverbal communication while being ethical and fair.

Fellowship recipients are conducting research in conversational AI, both to help advance the field and also to support the next generation of researchers. They will be paired with Amazon scientists who will mentor and provide them with a deeper understanding of problems in industry.

One of these mentors is Volodymyr Kindratenko, director for the Center for Artificial Intelligence Innovation and assistant director at the National Center for Supercomputing Applications. The project is “From personalized education to scientific discovery with AI: Rapid deployment of AI domain experts” and is co-mentored with Kastan Day, a research software engineer at NCSA.

“In this project, we aim to develop a knowledge-grounded conversational AI capable of rapidly and effectively acquiring new subject-knowledge on a narrowly defined topic of interest in order to become an “expert” on that topic. We propose a novel factual consistency model that will evaluate whether the answer is backed by a corpus of verified information sources. We will introduce a novel training penalty, beyond cross entropy, termed factuality loss, a method of retrieval-augmented RL with AI feedback. Our framework will also attempt to supervise the reasoning process in addition to outcomes.”

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