Plenary
Attendees in both buildings of the AAAI-22 virtual venue will be able to view and participate in all plenary sessions described below. Session Chairs and speakers have been informed in advance whether each activity is held via livestream or Zoom and can provide guidance to the audience accordingly. We encourage all participants to take advantage of the live chat options available to them in the virtual venue to propose questions or show their enthusiasm for a particular topic.
Opening Ceremony and Conference Awards
Katia Sycara, Vasant Honavar, Matthijs Spaan, Mark Boddy, Meinolf Sellman, Michael Guerzhoy, Marion Neuman
Thu, February 24 2:30 PM - 3:30 PM (+00:00)
Red Plenary Room, Blue Plenary Room
Presidential Address: The State of AI
Bart Selman
We are witnessing a highly accelerated phase of progress in AI, largely due to the deep learning revolution. This revolution is also reunifying our field, with researchers building bridges across different research areas, such as computer vision, natural language understanding, and decision making. In this work, we see significant progress on the big questions that have challenged our field since its inception. I will review the current state of AI and outline challenges that need to be addressed to develop genuinely robust and reliable AI systems. I postulate that the next level of AI will require an integration of the highly successful data-driven paradigm with a knowledge-driven approach coupled with human feedback for human-aligned intelligent systems.
Thu, February 24 3:30 PM - 4:45 PM (+00:00) Red Plenary Room, Blue Plenary Room
The Data-Centric AI (IAAI-22 Robert S. Engelmore Memorial Lecture Award)
Andrew Ng (DeepLearning AI, Landing AI, and Coursera)
Data-Centric AI (DCAI) represents the recent transition from focusing on modeling to the underlying data used to train and evaluate models. Increasingly, common model architectures have begun to dominate a wide range of tasks, and predictable scaling rules have emerged. While building and using datasets has been critical to these successes, the endeavor is often artisanal — painstaking and expensive. The community lacks high productivity and efficient open data engineering tools to make building, maintaining, and evaluating datasets easier, cheaper, and more repeatable. In the presentation, Dr. Ng will talk about what DCAI is, the challenges, and tips for using the DCAI approach.
Thu, February 24 8:00 PM - 9:00 PM (+00:00) Red Plenary Room, Blue Plenary Room
AAAI Organizational Awards Ceremony
Bart Selman, Yolanda Gil
Thu, February 24 10:00 PM - 11:00 PM (+00:00) Red Plenary Room, Blue Plenary Room
Interpretable Machine Learning: Bringing Data Science Out of the “Dark Age” (Recipient of the 2022 AAAI Squirrel Award for for Artificial Intelligence for the Benefit of Humanity)
Cynthia Rudin (Duke University)
With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions in criminal justice, healthcare, financial lending, and beyond. Interpretability of machine learning models is critical when the cost of a wrong decision is high. Throughout my career, I have had the opportunity to work with power engineers, doctors, and police detectives. Using interpretable models has been the key to allowing me to help them with important high-stakes societal problems. Interpretability can bring us out of the “dark” age of the black box into the age of insight and enlightenment.
Thu, February 24 11:30 PM - Fri, February 25 12:30 AM (+00:00) Red Plenary Room, Blue Plenary Room
