学术前沿讲座——Explainable AI: From Data to Symbols and Information Granules

报告题目

Explainable AI: From Data to Symbols and Information Granules

报告人（单位）

Witold Pedrycz (University of Alberta，Canada)

点评人（单位）

刘新旺（东南大学）

时间地点

2019年12月13日星期五 14:30-15:30

东南大学九龙湖校区经管学院B201

报告内容摘要

With the progress and omnipresence of Artificial Intelligence, two aspects of the discipline of AI become more and more apparent.  When tackling with some important societal underpinnings, especially those encountered in strategic areas, AI constructs call for higher explainability capabilities. Some of the recent advancements in Artificial Intelligence (AI) fall under the umbrella of industrial developments (which are predominantly driven by numeric data). With the vast amounts of data, one needs to resort herself to engaging abstract entities in order to cope with complexity of the real-world problems and transparency of solutions. All of those factors give rise to a recently pursued discipline of explainable AI. From the dawn of AI, symbols and ensuing symbolic process have assumed a central position and ways of symbol grounding become of interest. We advocate that in the realization of these two timely pursuits, information granules and Granular Computing play a significant role. First, it is shown that information granularity is of paramount relevance in building linkages between real-world data and symbols encountered in AI processing. Second, we stress that a suitable level of abstraction (specificity of information granularity) becomes essential to support user-oriented framework of design and functioning AI artifacts. In both cases, central to all pursuits is a process of formation of information granules and their prudent characterization. We discuss a comprehensive approach to the development of information granules by means of the principle of justifiable granularity. Here various construction scenarios are discussed including those engaging conditioning and collaborative mechanisms incorporated in the design of information granules. The mechanisms of assessing the quality of granules are presented. In the sequel, we look at the generative and discriminative aspects of information granules supporting their further usage in the AI constructs. A symbolic manifestation of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data and relationships among data. With this regard, selected aspects of stability and summarization of symbol- oriented information are discussed.

报告人简介

 Witold Pedrycz (IEEE Fellow, 1998) 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. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.

His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He has published numerous papers in these areas; the current h-index is 109 (Google Scholar) and 82 on the list top-h scientists for computer science and electronicshttp://www.guide2research.com/scientists/. He is also an author of 18 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering.

Dr. Pedrycz is vigorously 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).  He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals.