(in alphabetic order)
University of Oxford, UK
Talk Title: Long-Term Autonomy in Everyday Environments: A Challenge for AI
Abstract: The performance of autonomous robots, i.e. robots that can make their own decisions and choose their own actions, is becoming increasingly impressive, but most of them are still constrained to labs, or controlled environments. In addition to this, these robots are typically only able to do intelligent things for a short period of time, before either crashing (physically or digitally) or running out of things to do. In order to go beyond these limitations, and to deliver the kind of autonomous service robots required by society, we must conquer the challenge of combining artificial intelligence and robotics to develop systems capable of long-term autonomy in everyday environments. This talk will present recent progress in this direction, focussing on the mobile robots for security and care domains developed by the EU-funded STRANDS project (http://strands-project.eu) which completed over a year of autonomy in real service environments.
Bio: Nick Hawes is an Associate Professor of Engineering Science in the Oxford Robotics Institute at the University of Oxford, and a Tutorial Fellow at Pembroke College. His research is focussed on applying techniques from artificial intelligence to allow robots to perform useful tasks in everyday environments, with a particular interest in long-term autonomy, mobile service robots, and logistics. He researches how robots can understand the world around them (e.g. where objects usually appear, how people move through buildings etc.), and how they can exploit this knowledge to perform tasks more efficiently and intelligently.
Maritz Motivation, USA
Talk Title: Bridging Hype and Delivery: A Path to Delivering on the Promise of AI
Abstract: Artificial Intelligence has started to be integrated into every aspect of life, which has fueled a drive in large organizations to fully utilize AI. While this drive is great for initial momentum, deploying complex AI into legacy systems is not a quick and smooth process. Initial buy-in from leadership needs to be supported by short-term deliverables. Management of expectations and roles of IT, business, and external consultants needs to be considered. The balance between complex models needs to be balanced against the functional needs of the business users. All these factors cannot be decoupled from model selection, as each aspects feeds into the goals of the results. Marrying high expectations with the results of amazing models is not a straightforward process, this requires altering expectations to potential results and redefining models that can provide the greatest business value.
In this talk I will discuss some of the challenges in scoping, implementation and interpretation of AI systems that I have been involved in. I want to focus on two topics; first automated model selection for complex business problems that no one model can solve. Second, I will focus on the factors necessary for continual business engagement in deploying AI systems. This includes bringing leadership, IT, data scientists and end-users along the journey. I have seen a strong desire for this understanding both in profit, government, and non-profit organization.
Bio: Dr. Kyle T. Nakamoto is a Lead Data Scientist at Maritz Motivation, a behavioral insights company with 400 million program participants and counts ~50% of the Fortune 100 organizations as clients. His team supports the strategic development of rewards/retention programs and integrates advanced analytics into their design. Previously he was a senior data scientist at Ford Motor Company and his responsibilities included developing advanced analytics to support global order to delivery, enterprise risk, manufacturing and product development. Dr. Nakamoto is also part of the U.S. Japan Council, and was part of the 2019 Japanese American Leadership Delegation to Japan. Dr. Nakamoto co-founded Nichibei AI Services L3C, a social enterprise to support integration of AI into Japanese government, non-profit, and corporations in order to enhance US-Japan collaboration. Previously, Dr. Nakamoto founded a neuroscience lab at Northeast Ohio Medical University, where he was supported by grants from the National Institute of Health. He was also a fellow at the Medical Research Council Institute of Hearing Research in the United Kingdom. He holds a Ph.D. from the Department of Cognitive Sciences from the University of California at Irvine.
Ram D. Sriram
Chief of Software and Systems Division, National Institute of Standards and Technology
Talk Title: Explorations in Artificial Intelligence: A Personal Journey
Abstract: My first exposure to Artificial Intelligence (AI) was in the summer of 1981, when Carnegie Mellon University was tasked with the development of a knowledge-based expert system (KBES) to aid in the trouble shooting of the Atlanta People Mover. I was a student member of this team and went on to do my dissertation on AI in Design. Later, I joined MIT as an assistant professor (in 1986) and with my students built one of the most comprehensive computational frameworks for Internet-based collaborative design – called DICE. The DICE framework introduced several novel concepts in AI, including an active object-oriented blackboard, constraint satisfaction using asynchronous teams, merging qualitative geometry with traditional modeling, knowledge representation schemes for product and process models, and design rationale. In 1994, I moved to NIST and continued work on knowledge representation for entire product life cycle until 2010, when I took over as the chief of Software and Systems Division. Here, I have provided technical leadership for several AI projects, which include extending deep learning techniques in biomedical image processing, extracting protein-protein interaction sentences from documents, developing a novel natural language term extraction system based on Sanskrit, and applying Category Theory for AI knowledge representation. In this talk, I will describe my journey over nearly four decades with a particular focus on my recent work at NIST on knowledge representation, machine learning, and natural language processing.
Bio: Ram D. Sriram is currently the chief of the Software and Systems Division, Information Technology Laboratory, at the National Institute of Standards and Technology. Before joining the Software and Systems Division, Sriram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Laboratory, where he conducted research on standards for interoperability of computer-aided design systems. Prior to joining NIST, he was on the engineering faculty (1986-1994) at the Massachusetts Institute of Technology (MIT) and was instrumental in setting up the Intelligent Engineering Systems Laboratory. Sriram has co-authored or authored more than 250 publications, including several books. Sriram was a founding co-editor of the International Journal for AI in Engineering. Sriram received several awards including: an NSF’s Presidential Young Investigator Award (1989); ASME Design Automation Award (2011); ASME CIE Distinguished Service Award (2014), and the Washington Academy of Sciences’ Distinguished Career in Engineering Sciences Award (2015). Sriram is a Fellow of ASME, AAAS, IEEE and Washington Academy of Sciences, a member (life) of ACM, a and a member (life) of AAAI. Sriram has a B.Tech. from IIT, Madras, India, and an M.S. and a Ph.D. from Carnegie Mellon University, Pittsburgh, USA.