AAAI-22/IAAI-22/EAAI-22 Invited Speaker Program

AAAI 2022 Presidential Address

Title: The State of AI

AAAI 2022 Invited Talk

Recipient of the 2022 AAAI Squirrel Award for for Artificial Intelligence for the Benefit of Humanity

Title: Interpretable Machine Learning: Bringing Data Science Out of the “Dark Age”

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.

Cynthia Rudin is a professor of computer science and engineering at Duke University, and directs the Interpretable Machine Learning Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds degrees from the University at Buffalo and Princeton. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from AAAI. She is also a three-time winner of the INFORMS Innovative Applications in Analytics Award, and a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Her goal is to design predictive models that people can understand.

Title: The Data-Centric AI

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.

Andrew Ng is Founder of DeepLearning.AI, Founder and CEO of Landing AI, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University.

As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 200 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world’s largest MOOC platform. Dr. Ng now focuses his time primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy. Follow Dr.Ng on Twitter (@AndrewYNg) and LinkedIn.

AAAI-22 Invited Talk

Title: Toward an AI Network for Trustworthy AI

Trustworthy AI is being enabled by new federal strategies, policies, and collaborative possibilities – bringing to bear a new paradigm. AI also brings the promise of better and more efficient care, especially for our nation’s Veterans. This session will share several use cases from the work of the National Artificial Intelligence Institute (NAII) at the VA and collaborations, including pilots that empower Veterans to take control of medication adherence, physicians to evaluate COVID-19-associated prognosis and needs, and VHA staff to triage text-based input to quickly identify and assist Veterans in crisis. Establishing an AI network to scale up such research and development will also be covered.

Bio: Dr. Gil Alterovitz is the inaugural Director of the National Artificial Intelligence Institute at the U.S. Department of Veterans Affairs. He is focusing on leveraging health information for AI as well as building AI research and development capacity to help our nations Veterans through the Office of Research and Development. He is also a faculty member with Harvard Medical School.
Dr. Alterovitz was one of the core authors of White House Office of Science Technology and Policy’s The National AI R&D Strategic Plan from 2019. He has also spearheaded the “AI-able Data Ecosystem” pilot, creating a new approach for public-private collaborations with personnel/resources across a dozen agencies and working with companies internationally.

AAAI-22 Invited Talk

Title: Safety and Robustness for Deep Learning with Provable Guarantees

Computing systems are becoming ever more complex, with automated decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning components. This lecture will describe progress with developing automated certification techniques for learnt software components to ensure safety and adversarial robustness of their decisions, including discussion of the role played by Bayesian learning and causality.

Marta Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College, University of Oxford. She is known for fundamental contributions to the theory and practice of model checking for probabilistic systems, and is currently focusing on safety, robustness and fairness of automated decision making in Artificial Intelligence. She led the development of the PRISM model checker (www.prismmodelchecker.org), which has been adopted in diverse fields, including wireless networks, security, robotics, healthcare and DNA computing, with genuine flaws found and corrected in real-world protocols. Her research has been supported by two ERC Advanced Grants, VERIWARE and FUN2MODEL, EPSRC Programme Grant on Mobile Autonomy and EPSRC Prosperity Partnership FAIR. Kwiatkowska won the Royal Society Milner Award, the BCS Lovelace Medal and the Van Wijngaarden Award, and received an honorary doctorate from KTH Royal Institute of Technology in Stockholm. She is a Fellow of the Royal Society, Fellow of ACM and Member of Academia Europea.

AAAI 2022 Invited Talk 

Title: Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report

The One Hundred Year Study on Artificial Intelligence (AI100) is a long-term investigation of the field of AI and its influences on people, their communities, and society. With oversight from a standing committee administered out of Stanford University, the AI100 convenes a study panel every 5 years with the goal of issuing a report accessible to AI researchers, policy makers, industry leaders, and the public at large. The reports describe the technical and societal challenges and opportunities that have arisen since the previous report and envision potential future advances. This talk will summarize the 2nd report, issued September 2021.

AAAI 2022 Invited Talk 

Title: Thinking Fast and Slow in AI

AAAI-22 Invited Talk

Title: Advancing Agricultural Genome to Phenome Research

Our goal is to develop statistical models that will predict crop performance in diverse environments. Crop phenotypes such as yield and drought tolerance are controlled by genotype, environment and their interactions. The necessary volumes of phenotypic data, however, remain limiting and our understanding of the interaction between genotypes and environments is limited. To address this limitation, we are building new sensors and robots to automatically collect large volumes of phenotypic data.

Pat Schnable is a distinguished professor at Iowa State University where he holds an endowed chair and directs the Plant Sciences Institute that is fostering collaborations among plant scientists, engineers, and data scientists. Schnable received his BS in Agronomy from Cornell University and his PhD in Plant Breeding and Cytogenetics from Iowa State University; he conducted post-doctoral research in Molecular Genetics at the Max Planck Institute in Köln, Germany.

Schnable’s wide-ranging investigations of the maize genome have resulted in over 200 peer-reviewed publications, an h-index of 77, and over 24,000 citations. He is a fellow of the American Association for the Advancement of Science, co-lead of the Genomes to Fields Initiative, the PI of the Agricultural Genomes to Phenomes Initiative (AG2PI), a past chair of the American Society of Plant Biology’s Science Policy Committee, and a past chair of the Maize Genetics Executive Committee.

Schnable is also a serial entrepreneur and serves on the scientific advisory boards of several ag-tech companies.