Invited Speakers

Field Notes: Organizational Models for Responsible AI

Monday, May 17, 2021

Susan Etlinger

Senior Analyst, Altimeter, a Prophet CompanySenior Fellow, Centre for International Governance Innovation
Abstract: During the past year, we’ve seen a dramatic increase in public conversation about the ethics of Artificial Intelligence (AI). Concerns about the impact of biased algorithms, lack of transparency, and how AI technologies are being and should be used have consistently garnered headlines in major news outlets around the world. But while news coverage has raised awareness of ethical issues of AI, it generally hasn’t addressed the implications for the organizations that buy, build, and implement these technologies.
This talk, based on interviews with leaders from business, academia, and non-governmental organizations, defines issues resulting from the development and deployment of AI, lays out the methodologies being tested and used, and proposes an approach to address them. While this is a highly complex field and no single strategy is appropriate for all industries or companies, this material may be used to better understand the unique implications of AI, begin to socialize them within your business, and build organizational capability to foster both trust and innovation among customers, employees, shareholders, partners, and the general public.
TAKEAWAYSWhy and how AI changes the relationship between people and organizationsThe biggest risks—and opportunities—for business in the age of AIOperating principles, recommendations and best practices for designing innovative, engaging and ethical products, services and brand experiences.
SOURCESThe content is based on a research report entitled “Innovation + Trust: The Foundation of Responsible Artificial Intelligence,” which is based on interviews with leaders in academia, industry and the non-profit community.

Bio: Susan Etlinger is a globally recognized expert in digital strategy, with a focus on artificial intelligence, AI ethics and big data. In addition to her work at Altimeter, Susan is a senior fellow at the Centre for International Governance Innovation, an independent, non-partisan think tank based in Canada.
Susan’s TED talk, “What Do We Do With All This Big Data?" has been translated into 25 languages and has been viewed more than 1.3 million times. Her research is used in university curricula around the world, and she has been quoted in numerous media outlets including The Wall Street Journal, Fast Company, The New York Times and the BBC. Susan holds a Bachelor of Arts in Rhetoric from the University of California at Berkeley.

Deep Learned Models from Medical Images: Successes and Big Challenges

Tuesday, May 18, 2021


Co-Director Institute for Artificial Intelligence

Department of Computer Science and Engineering

University of South Florida

Abstract: Medical images may consist of camera-like images (e.g. Dermoscopy), Computed Tomography images (e.g. lung cancer screening), X-ray images (e.g. chest imaging), magnetic resonance images (e.g. brain imaging) and more. This talk will cover how deep learning approaches applied to medical images can be used as an aid to diagnosis and treatment planning. There are medical problems where models learned from images have performance nearly equivalent to the very best medical experts (e.g. dermatology) and others where they can provide useful information that is not typically provided by physicians (e.g. future lung nodule progression). There remain challenges in acquiring good data for training and data that is diverse enough to build a robust classifier. An analysis of pitfalls and potential solutions focused on diagnosing COVID-19 from images will be presented.
Bio: Dr. LAWRENCE O. HALL is a Distinguished University Professor in the Department of Computer Science and Engineering at University of South Florida and the co-Director of the Institute for Artificial Intelligence + X. He is the 2021 IEEE Vice President for Publications, Products and Services. He received his Ph.D. in Computer Science from the Florida State University in 1986 and a B.S. in Applied Mathematics from the Florida Institute of Technology in 1980. He is a fellow of the IEEE. He is a fellow of the AAAS, AIMBE and IAPR. He received the Norbert Wiener award in 2012 and the Joseph Wohl award in 2017 from the IEEE SMC Society. He is a past President of the IEEE Systems, Man and Cybernetics Society, former EIC of what is now the IEEE Transactions on Cybernetics. He is on the editorial boards of the Proceedings of the IEEE and IEEE Spectrum. His research interests lie in learning from big data, distributed machine learning, medical image understanding, bioinformatics, pattern recognition, modeling imprecision in decision making, and integrating AI into image processing. He continues to explore un and semi-supervised learning using scalable fuzzy approaches. He has authored or co-authored over 100 publications in journals, as well as many conference papers and book chapters. He has received over $6M in research funding from agencies such as the National Science Foundation, National Institutes of Health, Department of Energy, DARPA, and NASA.

Applications and Challenges in Artificial Intelligence for National Defense

Wednesday, May 19, 2021

Dr. Michael Van Lent

CEO SoarTech Inc.
Abstract: While there’s been recent explosion of commercial interest in artificial intelligence (AI), the Department of Defense (DoD) has been steadily exploring AI and its application to national defense for decades. However, recent changes in the international investment in AI, recent technological advances, and the opportunity to leverage the explosion of commercial research efforts has made AI a high priority for both science & technology and acquisition organizations across the DoD. The problems and constraints posed by national defense applications present challenges for AI that demand technological innovations beyond the current state of the art and beyond what will be achieved by commercial research alone. In many cases, the current challenges encountered in applying AI to national defense will be future challenges for commercial applications. Drawing on almost 30 years of experience working on AI for national defense, Dr. Michael van Lent will discuss the motivations behind the increased emphasis on AI in the DoD and present examples of the challenges encountered in applying AI to national defense and the research innovations that are solving these challenges.
Bio: Dr. Michael van Lent leads research and development of artificial intelligence for training and serious games, transitioning research into engineered solutions. Dr. Van Lent received a PhD at the University of Michigan in 2000. Prior to receiving his PhD, he worked at the Navy Center for Applied Research in Artificial Intelligence (NCARAI) at the U.S. Naval Research Laboratory. He joined the Institute for Creative Technologies in 2001, where he became the Associate Director for Games Research and a Research Associate Professor in the Computer Science department at USC. Dr. Van Lent has participated in the design and development of many immersive training applications including Full Spectrum Warrior, Full Spectrum Command, the Joint Fires and Effects Trainer System (JFETS), and ELECT BiLAT. He serves on various editorial boards and edits IEEE Computer’s Entertainment Computing Column.