Generative AI 101

Generative AI is many things: it is simultaneously scary, useful, flexible, opaque, exciting, and dangerous. Researchers, policymakers, creatives, and executives around the world are turning to these models to answer complex questions, rethink practices and procedures, and save time. Large language models are performing scientific research in many fields while democratizing access and understanding of some types of AI. Yet researchers struggle to explain what these systems are actually doing and how they utilize personal, public, and proprietary data to answer our prompts. The data giants with huge computing power, skilled workers, and large troves of data are the main suppliers of generative AI and reaping many of the potential income and investments.

Moreover, these systems are not perfect: they make mistakes, do not have real-time information, and can perpetuate inaccuracies and disinformation. Finally, there is growing evidence that these chatbots can both upskill (help less educated workers be productive) and deskill. In this webinar, we ask three researchers of varied backgrounds (computer science, communications, and systems engineering) to discuss generative AI–its potential and pitfalls. We’ll engage in a moderated discussion for about 30 minutes, and then we will open up the floor to audience questions.

Speakers

• Hal Daumé III, Ph.D. - Volpi-Cupal Family Endowed Professor in Computer Science - University of Maryland
• David Broniatowski, Ph.D. - Associate Professor, Engineering Management and Systems Engineering, Director of the Decision Making and Systems Architecture Laboratory - George Washington University
• Sarah Myers West, Ph.D. - Managing Director - AI Now Institute

Moderator

• Susan Aaronson, Ph.D. - Director of the Digital Trade and Data Governance Hub, George Washington University Elliott School of International Affairs