AI for Humanity 20230927

INTRO SECTION

2:00-2:05 EDT

Welcome from AFOSR
TBD

2:05-2:08 EDT

Outlining Goals
Doug Riecken, AFOSR

THINKER/SPEAKER SECTION

X-X ET

Remarks and Panel Discussion

Each speaker will present in X min their question(s) and a couple of comments to communicate the key ideas – then at least two or more of the other four speakers will comment on the question(s) for X min in order to explore more details. Speaking order:

List of speakers:

Carla Gomes
Eric Horvitz
Kimberly Sablon
Steven "Cap" Rogers
Tom Mitchell
Yann LeCun

OPEN DISCUSSION BY SPEAKERS WITH ALL ATTENDING

X-X ET

Interactive Discussion

We invite all attendees to pose questions/topics for the panel speakers

Carla Gomes is the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, the director of the Institute for Computational Sustainability at Cornell University, and co-director of the Cornell University AI for Science Institute. Gomes received a Ph.D. in computer science in artificial intelligence from the University of Edinburgh. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. Recently, Gomes has become deeply immersed in research on scientific discovery for a sustainable future and, more generally, in research in the new field of Computational Sustainability. Computational Sustainability aims to develop computational methods to help solve some of the key environmental, economic, and societal challenges to help put us on a path toward a sustainable future. Gomes was the lead PI of two NSF Expeditions in Computing awards. Gomes has (co-)authored over 200 publications, which have appeared in venues spanning Nature, Science, and a variety of conferences and journals in AI and Computer Science, including five best paper awards. Gomes was named the “most influential Cornell professor” by a Merrill Presidential Scholar (2020). Gomes was also the recipient of the Association for the Advancement of Artificial Intelligence (AAAI) Feigenbaum Prize (2021) for “high-impact contributions to the field of artificial intelligence, through innovations in constraint reasoning, optimization, the integration of reasoning and learning, and through founding the field of Computational Sustainability, with impactful applications in ecology, species conservation, environmental sustainability, and materials discovery for energy” and of the 2022 ACM/AAAI Allen Newell Award, for contributions bridging computer science and other disciplines. Gomes is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association for Computing Machinery (ACM), and a Fellow of the American Association for the Advancement of Science (AAAS).

Artificial Intelligence (AI) is a rapidly advancing field inspired by human intelligence. AI systems are now performing at human and even superhuman levels on various tasks, such as image identification and face and speech recognition. Can AI also dramatically accelerate scientific discovery and perhaps even win a Nobel prize in Science? Hiroaki Kitano first posed this question in 2016. We further discussed this question at a Turing Institute workshop in 2020, chaired by Gil, Kitano, and King, and formulated the AI Scientist Grand Challenge. In my own research, I am interested in accelerating scientific discovery for a sustainable future, particularly materials discovery for clean energy.

The tremendous AI progress that we have witnessed in the last decade has been largely driven by deep learning advances and heavily hinges on the availability of large annotated datasets to supervise model training. However, scientists generally only have access to small datasets and incomplete data: Scientists amplify a few data examples with human intuitions and detailed reasoning from first principles for discovery.

Our AI systems need to encapsulate the scientific process for scientific discovery: We need AI systems that combine learning with reasoning about scientific knowledge and find suitable problem representations for scalable solutions. Our AI systems need to be able to predict far outside the training distributions for scientific discovery, while current machine learning systems primarily perform data interpolation. Furthermore, our AI systems need to interpret results and understand causation beyond correlation to discover new scientific concepts and knowledge. Can we automate such a hybrid scientific discovery strategy? Full Bio

Eric Horvitz, Microsoft Chief Scientific Officer

Eric Horvitz serves as Microsoft’s Chief Scientific Officer. He spearheads company-wide initiatives, navigating opportunities and challenges at the confluence of scientific frontiers, technology, and society, including strategic efforts in AI, medicine, and the biosciences.

Dr. Horvitz is known for his contributions to AI theory and practice, with a focus on principles and applications of AI amidst the complexities of the open world. His research endeavors have been direction-setting, including harnessing probability and utility in machine learning and reasoning, developing models of bounded rationality, constructing systems that perceive and act via interpreting multisensory streams of information, and pioneering principles and mechanisms for supporting human-AI collaboration and complementarity. His efforts and collaborations have led to fielded systems in healthcare, transportation, ecommerce, operating systems, and aerospace.

Beyond his scientific work, he has pursued programs, organizations, and studies on ethics, values, and safety with applications and influences of AI. He founded and chairs Microsoft’s Aether committee on AI, effects, and ethics in engineering and research. He established the One Hundred Year Study on AI at Stanford University and co-founded and serves as board chair of the Partnership on AI (PAI).  He served as a Congressionally appointed commissioner on the National Security Commission on AI, where he chaired the line of effort on ethical and trustworthy AI.

Dr. Horvitz received the Feigenbaum Prize and the Allen Newell Prize for his fundamental contributions to the science and practice of AI. He received the CHI Academy honor for his work at the intersection of AI and human-computer interaction. He has been elected fellow of the National Academy of Engineering (NAE), the Association of Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence (AAAI), the American Association for the Advancement of Science (AAAS), the American Academy of Arts and Sciences, the American College of Medical Informatics, and the American Philosophical Society.

He currently serves on the President’s Council of Advisors on Science and Technology (PCAST) and advisory boards of the Allen Institute for AI and Stanford’s Institute for Human-Centered AI (HAI). He served as president of the AAAI, as a board member on the Computer Science and Telecommunications Board (CSTB), and on advisory committees for the National Science Foundation (NSF), National Institutes of Health (NIH), Defense Advanced Research Projects Agency (DARPA), and the Computing Community Consortium (CCC).

He received Ph.D. and M.D. degrees at Stanford University. Before moving into the role of Chief Scientific Officer, he served as director of Microsoft Research overseeing research labs in Redmond, Washington; Cambridge, Massachusetts; New York City, New York; Montreal, Canada; Cambridge, United Kingdom; and Bangalore, India. More information can be found on his home page. A selected list of publications can be found here.

Tom Mitchell, School of Computer Science at Carnegie Mellon University

Mitchell's research lies in machine learning, artificial intelligence, and cognitive neuroscience. His current research includes developing machine learning approaches to natural language understanding by computers, as well as brain imaging studies of natural language understanding by humans. A pioneer in artificial intelligence and machine learning, Mitchell’s research focuses on statistical learning algorithms for discovering how the human brain represents information and for enabling computers to understand the meaning of what humans say and write. His work with colleagues in the Psychology Department produced the first computational model to predict brain activation patterns associated with virtually any concrete noun, work that has since been extended to other word types, word sequences and emotions. His Never Ending Language Learner is a computer program that searches through web pages 24/7 as it teaches itself to read. Full Bio

Doug Riecken, AFOSR program officer for the Science of Information, Computation, Learning, and Fusion

Riecken is a trained concert pianist with a B.A. from the Manhattan School of Music and studies at the Julliard School of Music. He spent many years performing classical, jazz, and rock styles on international concert tours with world-renowned artists before he switched to a career in cognitive and computing science. He received his PhD from Rutgers University under thesis advisor Dr. Marvin Minsky from MIT; a founding father of artificial intelligence. Riecken and Minsky spent 30+ years in friendship researching learning and the mind. Riecken is a thought leader in the areas of big data analytics and machine learning, human-computer interaction and design, knowledge discovery and data mining, global cloud enterprise architectures, and privacy management. He joined the Air Force Office of Scientific Research as a program officer in 2014 and is a senior member of the AFRL ACT3 team. Full Bio

Stephen "Cap" Rogers, AFRL Automatic Target Recognition and Sensor Fusion 

Cap serves as the principal scientific authority and independent researcher in the field of multi-sensor automatic target recognition and sensor fusion. He initiates, technically plans, coordinates, evaluates, and conducts research and development to advance the knowledge of interdisciplinary ATR and sensor fusion systems for all Air Force aircraft, missile and space systems. Rogers leads collaboration across AFRL in object detection, tracking, geo-location, identification and supporting technologies. He also conducts research and development activities in the broad area of ATR and sensor-fusion technology including phenomenology modeling, model-based and learning algorithms, evaluation and tracking. He also conducts research and development in image and signal processing, synthetic target and scene modeling, resource allocation and evidence accrual aimed at decreasing the cost and improving the performance of Air Force and Department of Defense systems. Full Bio