Task Force on American Innovation Webinar
Future Forward: Frontiers in Artificial Intelligence and Machine Learning Research and Innovation

Date: Thursday, July 22, 2021
Time: 11 am – 12 pm EDT

Recent years have seen rapid advancements in artificial intelligence (AI), and machine learning (ML) in particular (ML is a subfield of artificial intelligence where computers “learn” from data and act without specific programming). These breakthroughs are fueled by innovation in both ML algorithms and specialized ML hardware, and U.S. leadership in AI and ML is grounded in long-standing U.S. government investments in R&D and STEM education. However, there is growing concern that China is poised to overtake the United States in AI/ML.

This briefing will examine the state of ML research in the United States and where it is headed. We’ll provide an overview of past ML algorithmic and hardware breakthroughs and current cutting-edge research; show how those breakthroughs are being used in innovative, real world applications and products; and forecast future discoveries including computer hardware advances, multimodal ML models, human/machine teaming, and distributed AI; and investments that are needed to achieve them in the United States.

With Guest Participants:

  • Rep. Jerry McNerney (D-CA09)
    • Co-Chair of the Congressional Artificial Intelligence (AI) Caucus
  • Rep. Jay Obernolte (R-CA08)
  • Rep. Bill Foster (D-IL11)


AI’s Past, Present, and Future
Dr. Chad Jenkins, University of Michigan Robotics Institute



Thursday, July 22, 2021



Introductory Remarks:

Dr. Chad Jenkins
Associate Director, Michigan Robotics Institute
Professor of Electrical Engineering and Computer Science, University of Michigan

Odest Chadwicke Jenkins, Ph.D., is a Professor of Computer Science and Engineering and Associate Director of the Robotics Institute at the University of Michigan. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). He previously served on the faculty of Brown University in Computer Science (2004-15). His research addresses problems in interactive robotics and human-robot interaction, primarily focused on mobile manipulation, robot perception, and robot learning from demonstration. His research often intersects topics in computer vision, machine learning, and computer animation. Prof. Jenkins has been recognized as a Sloan Research Fellow and is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE). His work has also been supported by Young Investigator awards from the Office of Naval Research (ONR), the Air Force Office of Scientific Research (AFOSR) and the National Science Foundation (NSF). Prof. Jenkins is currently serving as Editor-in-Chief for the ACM Transactions on Human-Robot Interaction. He is a Fellow of the American Association for the Advancement of Science and Association for the Advancement of Artificial Intelligence, and Senior Member of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers. He is an alumnus of the Defense Science Study Group (2018-19).


Dr. Blaise Aguera y Arcas
Vice President
Google Research

Dr. Blaise Aguera y Arcas leads an organization at Google AI working on both basic research and new products. Among the team’s public contributions are MobileNets, Federated Learning, Coral, and many Android and Pixel AI features. They also founded the Artists and Machine Intelligence program, and collaborate extensively with academic researchers in a variety of fields. Until 2014 Blaise was a Distinguished Engineer at Microsoft, where he worked in a variety of roles, from inventor to strategist, and led teams with strengths in inter­ac­tion design, pro­to­typ­ing, machine vision, augmented reality, wearable com­put­ing and graphics. Blaise has given TED talks on Sead­ragon and Pho­to­synth (2007, 2012), Bing Maps (2010), and machine creativity (2016). In 2008, he was awarded MIT’s TR35 prize.

Dr. Talitha Washington
Inaugural Director, Atlanta University Center Data Science Initiative
Professor of Mathematics, Clark Atlanta University

Dr. Talitha Washington is a Professor of mathematics at Clark Atlanta University and the inaugural Director of the Atlanta University Center Data Science Initiative. In her role as Director, she oversees and provides strategic direction and coordination of data science education, research, and outreach as well as development and fundraising activities across 4 Historically Black Colleges and Universities – Clark Atlanta University, Morehouse College, Morehouse School of Medicine, and Spelman College. Washington is a former Program Director at the National Science Foundation (NSF) in the Convergence Accelerator that brings together multiple disciplines to solve national-scale societal challenges. Previously, as a Program Director in the Division of Undergraduate Education, she was instrumental in building and establishing NSF’s first Hispanic-Serving Institutions (HSI) Program, which funded $85M over two years to 56 HSIs. She previously held positions at Howard University, the University of Evansville, The College of New Rochelle, and Duke University. She has a Bachelor’s degree in mathematics from Spelman College, and Master’s and doctorate degrees in mathematics from the University of Connecticut. In 2020, she received the NSF Director’s Award for Superior Accomplishment. In 2021, Dr. Washington became a Fellow of the American Mathematical Society and the Association for Women in Mathematics, becoming the first person named a Fellow of both of these organizations in the same year.

Dr. John R. Smith
IBM Fellow, AI Tech, IBM Research AI
IBM T. J. Watson Research Center

Dr. John R. Smith is IBM Fellow at IBM Research working at the intersection of Artificial Intelligence, Future of Computing and Accelerated Discovery. Previously, he led Computer Vision and Multimedia research at IBM. In October, 2020, John was awarded the ACM SIGMM Technical Achievement Award for his outstanding, pioneering and continued research contributions in the areas of multimedia content analysis and retrieval.  Dr. Smith is a Fellow of IEEE.

Dr. John R. Smith received his B.S., M.S., M. Phil, and Ph.D. in Electrical Engineering from Columbia University.  He has authored several hundreds of articles at top conferences and journal (>30,000 citations, h-index = 77, i-10 index = 387) and is an inventor of over 100 United States patents.

Dr. Court Corley
Chief Data Scientist and Group Leader, Data Sciences and Analytics Group
Pacific Northwest National Laboratory

Dr. Corley is a Chief Data Scientist and group leader for Data Sciences and Analytics in the Computing & Analytics Division at Pacific Northwest National Laboratory. He is leader in the field of data science and biosurveillance and his current work focuses on deep learning and narrow AI methods across DOE and USG missions. Dr. Corley co-led the Deep Learning for Scientific Discovery internal PNNL research investment that has applied deep learning across the breadth of PNNL’s science and security missions through dozens of efforts resulting in an order of magnitude increase in AI publications and in the number AI researchers at the Laboratory. His specific areas of expertise are few-shot learning, natural language processing, AI for science, and assured AI (including safety, security, and adversarial machine learning).

Moderated by:

Anita Nikolich
Director of Research and Technology Innovation and Research Scientist
University of Illinois

Anita Nikolich works at the intersection of cybersecurity, privacy and networked technologies. She is the recent co-recipient of an NSF MidScale Award addressing design of a secure future Internet. She helps organize hands-on cybersecurity events for K-8 students, the AI Village at DEFCON, a security conference that attracts over 25,000 attendees, and the Taste of Science program in Chicago. She works to engage people in understanding the privacy implications of mass data collection, algorithmic models for decision-making, and the need for better information authenticity indicators in an era of “deep fakes.” Nikolich has an M.S. in engineering from the University of Pennsylvania.