Title: Computational Intelligence for Health

Ophir Frieder

Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the Association for Computing Machinery (ACM), Fellow of the Institute of Electrical and Electronics Engineering (IEEE)
Georgetown University, USA

Abstract: We are just now slowly, physically recovering from the recent pandemic; mentally we have a long journey ahead of us, and many are touting a looming mental health crisis.  Thus, initially, we describe a web-intelligent, social-media monitoring approach for depression detection and continue with a presentation of a patented, licensed, and proprietary intelligent agent that identifies behavioral deviancy, an early warning for potential mental health concerns.  We then turn our attention towards web-intelligent monitoring of social media to detect physical disease outbreaks and describe the implications of such surveillance schemes to healthcare planning for a major children-focused hospital.  We conclude by, once again, focusing on patented, licensed, and proprietary intelligent agent technology this time to screen for covid via the use of surrogates.  Other medically oriented mining and search applications are briefly mentioned.

Short Bio: Ophir Frieder focuses on scalable information processing systems with particular emphasis on health informatics. He is a Fellow of the AAAS, ACM, AIMBE, IEEE, and NAI, and a Member of the Academia Europaea, the European Academy of Sciences and Arts, and the ACM SIGIR Academy.  Heavily involved with industrial efforts, he is Chief Scientific Officer of Invaryant, Inc and Lead Science and Technology Advisor for Aurora Forge. He is a member of the computer science faculty at Georgetown University and the biostatistics, bioinformatics and biomathematics faculty in the Georgetown University Medical Center.

Title: Better Peer Review via AI

Kevin Leyton-Brown

Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Fellow of the Association of Computing Machinery (ACM)
University of British Columbia, Canada

Abstract: Peer review is widespread across academia, ranging from publication review at conferences to peer grading in undergrad classes. Some key challenges that span such settings include operating on tight timelines using limited resources, directing scarce reviewing resources in a way that maximizes the value they provide, and providing incentives that encourage high-quality reviews and discourage harmful behavior. This talk will discuss two large-scale, fielded peer review systems, each of which was enabled by a variety of AI techniques.

First, I will describe and evaluate a novel reviewer-paper matching approach that was first deployed at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021); it continues to be used by AAAI and has since been adopted (wholly or partially) by ICML, IJCAI, and ACM EC. This approach has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer-paper scores; (2) formulating and solving an optimization problem to find good reviewer-paper matchings; and (3) employing a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. Our evaluation of these innovations is based on an extensive post-hoc analysis on real data.

Second, I will discuss a peer grading system designed for repeated settings such as weekly assignments in large undergraduate courses. Our approach uses probabilistic modeling to simultaneously estimate each submission’s true quality and all students’ (and TAs’) grading accuracy. We go beyond existing methods by detecting strategic behavior by students (reporting grades close to the class average without doing the work); correctly handling censored observations arising from discrete-valued grading rubrics; and improving the interpretability of the grades we ultimately assign to students. We evaluated our approach on real-world data obtained from four large classes, showing that our techniques accurately estimate true grades, students' likelihood of submitting uninformative grades, and the variation in their inherent grading error. We furthermore used synthetic data to characterize our models' robustness.

Short Bio: Kevin Leyton-Brown is a professor of Computer Science and a Distinguished University Scholar at the University of British Columbia. He also holds a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute and is an associate member of the Vancouver School of Economics. He is an Fellow of the Association for Computing Machinery (ACM; awarded in 2020) and the Association for the Advancement of Artificial Intelligence (AAAI; awarded in 2018). He was a member of a team that won the 2018 INFORMS Franz Edelman Award for Achievement in Advanced Analytics, Operations Research and Management Science, described as "the leading O.R. and analytics award in the industry." Leyton-Brown also received UBC's 2015 Charles A. McDowell Award for Excellence in Research, a 2014 NSERC E.W.R. Steacie Memorial Fellowship and a 2013 Outstanding Young Computer Science Researcher Prize from the Canadian Association of Computer Science.

Title: A Model for Human K-shot Learning

Ming Li

Fellow of the Royal Society of Canada, Fellow of the Association for Computing Machinery (ACM), Fellow of the Institute of Electrical and Electronics Engineering (IEEE)
University of Waterloo, Canada

Abstract: From a basic principle of thermodynamics, we derive a model for human k-shot learning. We further justify our model with experiments, showing it has advantages over other deep learning models in various k-shot learning scenarios. From k-shot learning, we then explore consciousness.

Short Bio: Ming Li is a Canada Research Chair in Bioinformatics and a University Professor at the University of Waterloo. He is a fellow of Royal Society of Canada, ACM, and IEEE. He is a recipient of Canada's E.W.R. Steacie Fellowship Award in 1996, the 2001 Killam Fellowship and the 2010's Killam Prize. Together with Paul Vitanyi they have pioneered the applications of Kolmogorov complexity and co-authored the book ""An introduction to Kolmogorov complexity and its applications"". His recent research interests recently include bioinformatics, natural language processing, deep learning, and information distance.

Title: Green Machine Learning and Granular Modeling: Fostering New Development Avenues

Witold Pedrycz

Fellow of the Royal Society of Canada, Fellow of the Institute of Electrical and Electronics Engineering (IEEE)
University of Alberta, Canada

Abstract: The visible trends of Machine Learning (ML) are inherently associated with the diversity of data and innovative ways they are used in order to carry out learning pursuits. The ongoing objectives of the research agenda are also investigated in the context of green ML (usually referred to as green AI). One can identify three ongoing challenges with far-reaching methodological implications, namely (i)completing designs in the presence of strict constraints of privacy and security, (ii) efficient model building completed with limited data of varying quality, and (iii) a reduction of computing effort knowledge transfer and distillation.

We advocate that to conveniently address these quests, it becomes beneficial to engage the fundamental framework of Granular Computing to enhance the existing approaches (such as e.g., federated learning in case of (i) and transfer knowledge in (iii)) or establish new directions to the problem formulation. Likewise, it is also essential to establish sound mechanisms of evaluation of the performance of the ML architectures. It will be demonstrated that various ways of conceptualization of information granules in terms of fuzzy sets, sets, rough sets, and others may lead to efficient solutions.

To establish a suitable conceptual ML framework, we include a brief discussion of concepts of information granules and Granular Computing. We show how granular models endow numeric models with their quantification mechanisms.

To proceed with a detailed discussion, a concise information granules-oriented design of rule-based architectures is outlined. A way of forming the rules through unsupervised federated learning is investigated along with algorithmic developments. A granular characterization of the model formed by the server vis-a-vis data located at individual clients is presented. It is demonstrated that the quality of the rules at the client’s end is described in terms of granular parameters and subsequently the global model becomes represented as a granular construct. The roles of granular augmentations of models in the setting of granular knowledge distillation are outlined. It is shown how the agenda of green ML is effectively realized by exploring information granules and stressing an importance of the holistic perspective at critical trade-offs among interpretability, enormous computational overhead, and transparency of predictors and classifiers.

Short Bio: Dr. Witold Pedrycz (IEEE Life Fellow) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.,His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery, pattern recognition, data science, knowledge-based neural networks among others.,Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).

Title: Do Simpler Machine Learning Models Exist and How Can We Find Them?

Cynthia Rudin

Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Fellow of the American Statistical Association (ASA), Fellow of the Institute of Mathematical Statistics (IMS)
Duke University, USA

Abstract: While the trend in machine learning has tended towards building more complicated (black box) models, such models are not as useful for high stakes decisions - black box models have led to mistakes in bail and parole decisions in criminal justice, flawed models in healthcare, and inexplicable loan decisions in finance. Simpler, interpretable models would be better. Thus, we consider questions that diametrically oppose the trend in the field: for which types of datasets would we expect to get simpler models at the same level of accuracy as black box models? If such simpler-yet-accurate models exist, how can we use optimization to find these simpler models? In this talk, I present an easy calculation to check for the possibility of a simpler (yet accurate) model before computing one. This calculation indicates that simpler-but-accurate models do exist in practice more often than you might think. Also, some types of these simple models are (surprisingly) small enough that they can be memorized or printed on an index card.

Short Bio: Cynthia Rudin is the Earl D. McLean, Jr. Professor of Computer Science and Engineering at Duke University. She directs the Interpretable Machine Learning Lab, and her goal is to design predictive models that people can understand. Her lab applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability. She holds degrees from the University at Buffalo and Princeton. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (the “Nobel Prize of AI”). She received a 2022 Guggenheim fellowship, and is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Association for the Advancement of Artificial Intelligence. Her work has been featured in many news outlets including the NY Times, Washington Post, Wall Street Journal, and Boston Globe.

Title: Symbols-Meaning-Value (SMV) Space Perspectives on Web Intelligence

Yiyu Yao

Fellow of the International Rough Set Society (IRSS)
University of Regina, Canada

Abstract: Twenty-two years ago, Professors Ning Zhong, Jiming Liu, Setsuo Ohsuga, and I wrote a two-page position paper that envisions a new field of Web Intelligence (WI). On the one hand, although profound advances in Artificial Intelligence (AI), Information Technology (IT), and the Web have been made over the last two decades, our vision that “WI exploits AI and advanced information technology on the Web and Internet” still remains meaningful. On the other hand, it might be a good time to take another look at Web Intelligence based on the experience of the twenty plus years. Since 2009, I have been working on a theory of three-way decision that encompasses thinking, problem-solving, and computing in threes (i.e., triads). My talk will focus on three-way decision perspectives on Web Intelligence. First, I will briefly comment on the past, present, and future of Web Intelligence, under the main theme of the Conference, “WI = Artificial Intelligence in the Connected World.” Then, I will introduce the concept of a Symbols-Meaning-Value (SMV) space and its interpretations based on the triads of Data-Knowledge-Wisdom, Perception-Cognition-Action, and Seeing-Knowing-Doing. Finally, I will elaborate on an important implication of SMV spaces to research in Web Intelligence, based on the triad of the (Data) Web, the Semantic Web, and the Wisdom Web.

Short Bio: Yiyu Yao is a professor of computer science with the Department of Computer Science, University of Regina, Canada. His research interests include three-way decision, granular computing, Web intelligence, rough sets, formal concept analysis, information retrieval, and data mining. He proposed a theory of three-way decision, a decision-theoretic rough set model, and a triarchic theory of granular computing. He has published over 400 papers. He was selected as a highly cited researcher by Clarivate from 2015 to 2019.