Duality

Vincent Conitzer is the Kimberly J. Jenkins University Professor of New Technologies and Professor of Computer Science, Professor of Economics, and Professor of Philosophy at Duke University. He received Ph.D. (2006) and M.S. (2003) degrees in Computer Science from Carnegie Mellon University, and an A.B. (2001) degree in Applied Mathematics from Harvard University. Conitzer works on artificial intelligence (AI). Much of his work has focused on AI and game theory, for example designing algorithms for the optimal strategic placement of defensive resources. More recently, he has started to work on AI and ethics: how should we determine the objectives that AI systems pursue, when these objectives have complex effects on various stakeholders?

Odd Eric is an adjunct associate professor at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, where he teaches courses and supervises master students in AI. He received his PhD from the Norwegian University of Science and Technology. Gundersen has applied AI in the industry, mostly for startups, since 2006.  He has conducted several analysis of reproducibility in the artificial intelligence and machine learning literature, and has developed guidelines for reproducibility in data science.  Currently, he investigates how AI can be applied in the renewable energy sector and for driver training.

Emmanuel graduate research assistant at the University of Southern California’s Institute for Creative Technologies, advised by Dr. Jonathan Gratch. My interest is in building artificial intelligent negotiation training systems that personalized for each user. I hold a Masters in Robotics from the University of Birmingham, through a Fulbright Fellowship, and a Bachelor of Science Degree in Computer Engineering from North Carolina Agricultural and Technical State University. 

Prof. Barry Smyth holds the Digital Chair of Computer Science in University College Dublin and is a Director of the Insight Centre for Data Analytics. He is a Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and a Member of the Royal Irish Academy since 2011. In 2014 Barry was awarded an Honrary Doctor of Technology (Hons. D.Tech) from Robert Gordon University in the UK. Barry was the Director of the Clarity Centre for Sensor Web Technologies (2008 - 2013) and has previously held the position of Head of School for the School of Computer Science and Informatics in UCD.Barry's research interests fall within the field Artificial Intelligence and include case-based reasoning, machine learning, recommender systems, user modeling and personalization.

Kokil's research interests lie in examining the role of social media platforms in enabling self-presentation and social behaviour. Her particular interest is in developing computational models of language for the measurement and understanding of computer-mediated communication.  Before joining NUS in 2020, Kokil spent a year as a Presidential Postdoctoral fellow at Nanyang Technological University, and two years (2016-2018) as a CS postdoc at the University of Pennsylvania. She has also spent three years (2013-2016) in the industry, as a Data Scientist at Adobe developing research and analytical capabilities for digital marketing tools. Her research has been published in the Journal of Communication, Computers in Human Behaviour, and Telematics and Informatics.

John an Assistant Professor in the Department of Computer Science at the University of Maryland, with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). He holds a PhD in computer science from Carnegie Mellon. At Maryland, he is also formally affiliated with the Applied Mathematics & Statistics, and Scientific Computation (AMSC) program, as well as the Human-Computer Interaction Laboratory (HCIL) and Maryland Transportation Institute (MTI). 

Peter is the founder and director of the Learning Agents Research Group (LARG) within the Artificial Intelligence Laboratory in the Department of Computer Science at The University of Texas at Austin, as well as associate department chair and chair of the University's Robotics Consortium. He was a co-founder of Cogitai, Inc. and is now Executive Director of Sony AI America.

Dr. Stephen M. Russell is Chief of the Battlefield Information Processing Branch at the US Army Research Laboratory in Adelphi, MD. Previously, he was at the US Naval Research Laboratory. Dr. Russell has a long history of research in the areas of networking, information management, recommender systems, and network agents. Currently, his research is focused on Internet of Things and its applicability to the US Army and the US Military. He leads the ARL research program on Internet of Battlefield Things, which is focused on multiple challenges to incorporating IoT ideas and capabilities within the battlefield environment.

Sidd is an Assistant Professor of Computer Science in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. His research objective is to develop intelligent robots and software agents that assist humans in their daily lives. Towards this objective, his research focuses on developing formal frameworks, algorithms and implementations that allow autonomous agents to reason and act efficiently under uncertainty. The results of his work range from theoretical analyses to empirical demonstrations in the real world.

Ariel's main research area is heuristic search . He is also interested in mobile agents in unknown physical environments.

Following his Ph.D., Stan was an Assistant Professor in the Department of Mathematics and Computer Science, Warsaw University. He joined University of Guelph in 1977, and Acadia University in 1980. Since 1981 at the University of Ottawa, as of 2011 a Distinguished University Professor (on leave). For many years in charge of graduate studies in Computer Science at the University of Ottawa, and a founding father of the Graduate Certificate in Electronic Commerce at University of Ottawa in 1999. Also affiliated with the Institute for Computer Science of the Polish Academy of Sciences as a Professor, Stan has worked at universities in the U.S, Europe, and Latin America. Recognized internationally for his work in text mining, applications of Machine Learning, and data privacy, author and co-author of more than 250 research paper. Former president of the Canadian Artificial Intelligence Association (CAIAC) and of the IFIP Working Group 12.2 (Machine Learning). Stan has significant experience and interest in innovation and technology transfer. One of the founders of Distil Interactive Inc. and Devera Logic Inc.

Bill is Professor Of Mathematics, Sciences And Technology / Professor Of Social Sciences in the School Of Arts & Sciences.

From the artificial intelligence storied institution Carnegie Mellon, Professor Steve Smith joins us and discusses his work, but first outlines where we are with AI, "there are people there claiming that super intelligence will be a point of singularity, where machines will sort of take over. I could believe that we might get to that point, but we're so far from that point right now. Artificial intelligence really still is pretty narrowly focused. The systems that are built still don't have this sort of broad general intelligence. One thing that humans have a lot of is common sense- humans are very flexible in reacting and adapting to any situation. Typically, an intelligent system is focused in one direction."

Universidad Simón Bolívar Professor Blai Bonet joins us and discusses the issue at hand as he sees it, "In computer science and AI in particular, all the problems that we try to solve, almost all of them, they are intractable. Intractability means that you need an exponential amount of resources. It could be time, it could be space. And by space I mean memory, computer memory. Time is easier to trade off. It's easy to handle time than memory. Memory is expansive and maybe you don't have enough memory. Time otherwise, you can put your problem to run and then you go on vacation. Hopefully, by the end of the vacation, the problem will have ended."

Tulane University Associate Professor Dr. K. Brent Venable joins us and discusses her work focusing on how to bring the rational and irrational together. Actually, part of my research is that. My research in artificial intelligence is in knowledge representation, and in these ways of modeling preferences. Something that appears to be trivial or common, different degrees of rejections or acceptance. For humans, we use preferences, or we make choices based on our preferences every day. Yet to do this in an efficient way in artificial agents is not as trivial. A lot of my research is how to model preferences."

University of Maryland Asst. Professor John Dickerson joins us and discusses his work at the intersection of computer science, AI, and economics, with a focus on solving practical problems using stochastic optimization and machine learning. He studies, "anything having to do with markets which sounds traditionally economic, but think about all the markets that you interact with every day for advertising. Facebook exists because of advertising. Most tech money these days exists because of advertising. This is a market-design problem, and it's one that I don't think traditional economics entirely would be able to capture. Computer science allows us to bring techniques that CS people have been developing for decades into this world."

Cornell University Assistant Professor Ross Knepper joins us and shares his thoughts on robotics being additive to the human experience, "robots have penetrated most of the domains that have no people in them. So we have robots in factories, we have robots exploring Mars. We have robots exploring Antarctica and under the sea. It's easy to solve those problems because you can make strong assumptions. You don't have to worry about harming people or cooperating with people. What we've seen is very limited penetration into human domain."

University of Pennsylvania Postdoctoral Research Fellow Kokil Jaidka joins us and discusses her very different work, "with the explosion of social media, there's just so much more that we can get to know about people by looking at what they're saying and what they're not saying. The limitation there is that while traditional methods or traditional computer science problems don't care so much about people getting better. Rather, the psychological correlates of, for example, language and online activity can be used to make people's lives better and that's where the well-being comes in."

Cornell University Professor Joe Halpern joins us and takes us through his decades of work as well as his 'of the moment' work. He also shares his thoughts on the current state of AI, "AI folks and theory folks were always comfortable with randomization or approximate correctness in various guises. I think the real world is less comfortable. People like to feel ... I'm guessing psychologically, people don't like algorithms that only give approximate guarantees. People don't like algorithms that randomize. People like sort of deterministic stuff."

From Technion - Israel Institute of Technology, Erez Karpas discusses the differences between how a robot models the world and a human's inherent understanding of the model of the world, if the robot knows that traversing carpet is much more expensive than traversing hardwood floors, because it's less accurate, and it shakes. But maybe the human doesn't know that, right? It's not about the communication, it's the root cause analysis. You're looking at the model that the human has of the world, and you want to find the mismatch. And there might be lots of mismatches. You want to find the relevant ones. That is an intensive model based reasoning problem."

Washington State University Assistant Professor Matthew E. Taylor joins us and discusses the resourcing of carbon-based and digital assets, "right now there's lots of well-paid, well trained technicians who can go and set up your data center for you, but they're humans. They're only human. They're going to be suboptimal. Instead, you can have a reinforcement learning algorithm go and tweak things so that you're more efficient and you can save lots of money. You can also save lots of energy, which is better for the environment."

Ben-Gurion University of the Negev Professor Kobi Gal joins us and shares that to fully realize the potential of artificial intelligence, we first have to truly understand human intelligence and emotion, "people exhibit varying degrees of motivation. And a big part of my work is understanding what makes each student tick, and personalize. So the sequencing problem, what's important is to be able to personalize the question to choose for the student. And of course, what's the right question for me might not be the right question for you."

University of Massachussetts Professor Shlomo Zilberstein joins us and takes us through a history of artificial intelligence. "The quest for AI is started very early with the first computer was being built. And actually with Touring, where you were just thinking about the possibility of electronic computing– people immediately felt this machine could be a thinking machine. Asking, 'What could it do that is better than people? What can you do that is as good as people?' That foundational work actually benefited computer science as a whole."

Georgia Tech University Professor Charles Isbell, Brown University Professor Michael Littman and University of Texas Professor Peter Stone discuss and debate the value of human/AI interaction. "A lot of people have been working for many, many, many, many, many years on the problem of leveraging humans and leveraging human behavior to make machine learning better. We talk about it all the time- humans in the loop. But it's always fun to take a moment, step back and ask whether all of your assumptions are actually valid and real- and here the assumption is that humans actually can help us but it's not clear that that's true."

Professor at UC Berkeley, Peiter Abbeel joins us. Pieter grew up in Belgium, came to the US and got his PhD in Robotics and Machine Learning from Stanford. He notes that he and Andrew Ng pushed the envelop at the time on how robots learn from humans demonstrations as well as their own trial and error. Peter graduated and came to Berkeley to continue to work on the junction of robotics and learning, machine learning. He’s been focused on end-to-end reinforcement learning, end-to-end imitation learning. Training the neural net end-to-end without specific structure. Singularity is the notion that a system you build is smart enough to self improve...and things accelerate out of control. How far are we away from this? Pieter notes that 10 years ago computer vision it was difficult to conceive of a solution. Enabling factors and breakthroughs are the keys. Data is an enabling factor. Neural nets are now data driven as opposed to algorithm designed. Will we continue to have more data and can you do things with unlabeled and unstructured data. Being better at unsupervised learning is a frontier that once reached will open up all sorts of possibilities. One question to answer in understanding where we might be in relation to singularity is how many compute cycles were effectively used to go from where we were 5 Billion years ago to where we are now. Do we think we can short cut this? Or will we need the same amount of compute to get to singularity? Pieter doesn’t have the answers. Yet. Both he and the artificial intelligence research community and industry need the best possible global talent to answer those very questions.