AI for All Breaking Down Barriers to Adoption in Businesses
In this webinar, we will explore the newly emerged opportunities for building AI in various applications. The latest development of generative models for vision and language has expanded the arena for AI-enabled applications and inspired everyone to think about how to use a model like GPT for their everyday work and life. We will provide practical use cases and best practices for successful AI implementation, equipping you with valuable knowledge for leveraging AI in your business. In addition to exploring the potential benefits of AI, we'll also delve into its risks and societal impacts, and provide insights into how businesses can safeguard against producing inaccurate or harmful results.
Speaker Biography: Karen Yang leads AI products at Labelbox and helped enterprises iterate and produce AI models. Previously, she created intelligent warehouse-picking robots as first software engineer at Covariant.ai and built the perception and hand-tracking features of Oculus VR at Meta. During her master's study, she worked at Stanford IPRL lab on the intersection of language models and robotics research.
The New Branch of Fuzzy Inference Systems: A Brief Intro with its Impact on Intelligent Systems
Fractional fuzzy inference systems (FFISs), a new branch of fuzzy inference systems, draw inspiration from the informal ways in which humans relate imprecise information to make rational decisions based on value and volume. FFISs add new concepts to the branch of the calculi of fuzzy rules and fuzzy graphs, including fractional membership functions, fractional compositional rule of inference, fracture index, left and right orders, entanglement, touch points, degeneracy notion, etc. FFISs is based on a reasoning mechanism called fractionalism reasoning, whose operative principle rests on the fact that exploiting the maximum volume of information does not necessarily imply the maximum exploitation of the information. Fractionalism reasoning is a reasoning mechanism of fractional order, making FFISs more general than typical FISs, which rely on first-order reasoning. In this perspective, FFISs pave the way for designing machine learning algorithms that enable machines to learn how to think, rather than only how to perform tasks.
Speaker Biography: Dr. Mehran Mazandarani holds a Ph.D. in electrical engineering, specializing in control, from Ferdowsi University of Mashhad. From 2017 to 2018, he joined the Computational Mathematics and Engineering Division at Ton Duc Thang University in Ho Chi Minh City, Vietnam, as a researcher. Following this, he worked as a visiting researcher at the Harbin Institute of Technology in Shenzhen, China, from 2018 to 2019. As a postdoctoral researcher, he worked with the Department of Information Sciences and Technology at Tsinghua University in China from 2019 to 2022. Currently, he is associated with the Department of Mechatronics and Control Engineering at Shenzhen University in China. Dr. Mazandarani's research interests include several branches of fuzzy logic, such as using fuzzy mathematics in control theory. He made significant contributions to fuzzy mathematics, introducing type-2 fuzzy differential equations, and fractional fuzzy differential equations (type-2 fuzzy calculus). /p>
Resolving Cross Hospital Variation Effects in AI based Pathology Image Analysis
Pathology image reporting is a time-consuming process due to the extremely large image sizes. The advance of AI on its image analysis capability has triggered the installation of digital pathology in hospital to adopt AI as an assistant on pathology image analysis. However, due to the dyeing process and different scanners, the characteristics of the digitized pathology images could vary significantly across different hospitals. Therefore, an AI model trained by data from a certain hospital often does not work well on another hospital. In this talk, we will introduce issues caused by cross-hospital variations. Approaches to enhance AI models when facing cross-hospital variations will also be presented.
Pau-Choo (Julia) Chung (S’89-M’91-SM’02-F’08) received her Ph.D. in electrical engineering from Texas Tech University, USA, in 1991. She then joined the Department of Electrical Engineering, National Cheng Kung University (NCKU), Taiwan, in 1991 and became a full professor in 1996. She served as Head of Department of Electrical Engineering(2011-2014), Director of Institute of Computer and Communication Engineering (2008-2011), NCKU. She was elected Distinguished Professor of NCKU in 2005 and received the Distinguished Professor Award of the Chinese Institute of Electrical Engineering in 2012. She served as Program Director of Intelligent Computing Division, Ministry of Science and Technology (2012-2014), Director General of the Department of Information and Technology Education at Ministry of Education, Dean of Miin Wu School of Computing at NCKU. Currently, she is Dean of College of Electrical Engineering and Computer Science at NCKU, Taiwan. She served as ADCOM member (2009-2011, 2012-2014), Chair of CIS Distinguished Lecturer Program (2012-2013), Chair of Women in CI (2014), and the Vice President for Members Activities of IEEE CIS. She also served 2 terms of BoG member in IEEE Circuit and Systems Society. She is a Member of Phi Tau honor society, and an IEEE fellow since 2008.
Introducing Immersive Articles and How to Write Them
Speaker Biography: Alexander Dockhorn is Junior professor for Computer Science at the Institute for Information Processing of the Leibniz University Hannover. His research is focused on the topics of machine learning, decision-making, AI in games, and game development. He is an active member of the Institute of Electrical and Electronics Engineers (IEEE) and serves as the chair of the IEEE CIS Games Technical Committee and the Summer Schools Subcommittee. Previously, he has been the Chair of the IEEE CIS Competitions Subcommittee and organized the Hearthstone AI competition as well as several other competitions. Motivated by the idea to explain AI to a broader audience he has been proposing and driving forward the integration of interactive articles at the IEEE Computational Intelligence Society.
The Explainability Challenge in Descriptive Analytics: Do We Understand the Data?
Nowadays, ever more data is collected, for instance in the healthcare domain. The amount of patients’ data has doubled in the previous two years. This exponential growth creates a data flood that is hard to handle by decision makers. In many domains, humans are collaborating with machines for decision making purposes to cope with the resulting data complexity and size. This collaboration can be realized through machine learning, visual analytics, or online analytical processing, where a machine is just a tool – but often used to take important decisions. The question now is: do we really understand the data using the tool this way?
Explainability is a great challenge in data analytics, with the aim to explain to the user why certain decisions have been recommended or made. The challenge is how to make data more understandable for humans? Fuzzy techniques, or the application of the computing with words paradigm has the potential to close the gap by using natural language as the communication means. In this talk I focus on descriptive analytics and show with a set of examples how fuzzy techniques can provide better insight of data to the user. I pay special attention to the technique of linguistic summaries. I will discuss the status quo of this technique, as well as new opportunities lying ahead.
Speaker Biography: Anna Wilbik is currently Professor in Data Fusion and Intelligent Interaction in the Department of Advanced Computing Sciences of Maastricht University, in the Netherlands. Currently she is also a chair of The Fuzzy Systems Technical Committee (FSTC) within IEEE CIS. She received her PhD (with honors) in Computer Science from the Systems Research Institute, Polish Academy of Science, Warsaw, Poland, in 2010. In 2011, she was a Post-doctoral Fellow with the Department of Electrical and Computer Engineering, University of Missouri, Columbia, USA. Anna is an alumnus of the Stanford University TOP500 Innovators: Science - Management - Commercialization Program.
IEEE CIS Distingued Lecturer Hussein Abass' webinar on 𝗙𝗿𝗼𝗺 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻
IEEE Day is an annual event that celebrates the first time in history when engineers worldwide gathered to share their technical ideas in 1884. One of the IEEE Day's objectives is to show the ways IEEE members, in local communities, join together to collaborate on ideas that leverage technology for a better tomorrow. Celebrate IEEE Day with CIS witht the webinar: From Machine Learning to Machine Education with Professor Hussein Abbass, CIS Distingushed Lecturer. Machine learning focuses on algorithms and architectures to enable machines to improve performance from experience. Machine teaching focuses on the design of the experience required by a machine to learn. Machine education is concerned with pedagogical design of the processes to empower an AI-enabled system with the experiences and learning processes to design ethical, responsible, and safe smart autonomous systems. This talk will present on machine education and the pedagogical design of smart autonomous systems. Examples will be provided using neural-network-based machine education case studies. Hussein Abbass is a Professor at the School of Engineering and Information Technology at University of New South Wales, Canberra, Australia. He is the Founding Editor-in-Chief for the IEEE Transactions on Artificial Intelligence.
Autonomous Bootstrapping of Collective Motion Behaviours for Swarming Robots
Collective behaviours such as swarm formations of autonomous agents offer the advantages of efficient movement, redundancy, and potential for human guidance of a single swarm organism. However, with the explosion in hardware platforms for autonomous vehicles, swarm robotic programming requires significant manual input for each new platform. This talk introduces two developmental approaches to evolving collective behaviours whereby the developmental process is guided by a task-non-specific value system. Two value systems will be considered: the first based on a survey of human perception of swarming and the second based on a computational model of curiosity. Unlike traditional approaches, these value systems do not need in advance the precise characteristics of the intended swarming behaviours. Rather they reward the emergence of structured collective motions, permitting the emergence of multiple collective behaviours, including aggregation and navigation behaviours. This talk will examine the performance of these value system in a series of controlled experiments on point-mass ‘boids’ and simulated robots. We will see how the value systems can recognise multiple “interesting” structured collective behaviours and distinguish them from random movement patterns. We will also see how the value systems can be used to tune random motions into structured collective motions.
Kathryn Kasmarik is a Professor of Computer Science at the University of New South Wales, Australian Defence Force Academy (UNSW Canberra). Kathryn completed a Bachelor of Computer Science and Technology at the University of Sydney, including a study exchange at the University of California, Los Angeles (UCLA). She graduated with First Class Honours and the University Medal in 2002. She completed a PhD in Computer Science through the National ICT Australia and the University of Sydney in 2007. She moved to UNSW Canberra in 2008. Kathryn’s research interests lie in the area of autonomous mental development for computers and robot.
Computational Intelligence for Disaster Planning and Mitigation
Infrastructure planning and restoration following a disaster is a complex problem requiring the integration of multiple data types and the evaluation of complex and potentially nonlinear relationships. This research uses publicly available data sets shared by the United States Geological Survey (The National Map, Digital Elevation Models, seismic data), the United States Army Corps of Engineers (River Discharge data, Lock & Dam and Levee inventory), and the U.S. National Weather Service (rainfall data, predicted weather patterns). This body of work examines evolutionary computation for infrastructure restoration planning in the aftermath of tornadoes and earthquakes. The use of neural networks is considered to predict changing water levels to capture the timing and impact of flooding and flash flooding events on transportation infrastructure. Algorithms have been used to develop plans for rerouting traffic to minimize the impact of the flooding event on the transportation system, as well as the risk to human lives. This allows emergency management and transportation engineering managers to make better decisions related to safety and the restoration of critical infrastructure elements.
Steven M. Corns an Associate Professor of Engineering Management and Systems Engineering at Missouri University of Science and Technology. He received his PhD degree in mechanical engineering from Iowa State University in 2008. Dr. Corns research interests include computational intelligence applications, the mechanics of information transfer in evolutionary algorithms, and model based approaches for complex systems design and analysis. Applications include computational biology/bioinformatics, transportation, and defense. He has applied computational intelligence techniques to disaster modeling and restoration planning for several years, including four projects involving flooding prediction and associated traffic rerouting.
Understanding the Complexity of Financial Systems of Systems
Why are the financial markets so problematical? One reason is that financial markets consist of many systems – having other complex systems as components – that interact in complicated ways. The focus of this webinar is to explain the complexity of several financial systems in terms of several properties that are characteristic of large, diverse, complex systems. We distill this framework for financial systems of systems to include properties describing interactions among other system components; properties relating to interactions within the system environment; and properties relating to interactions with time. In this webinar, we discuss how these properties impact financial systems involved with high frequency trading (connectivity vs. latency arbitrage); market fragmentation (autonomy vs. regulatory-induced feedback); and credit (model diversity vs. copula model risk).
Roy S. Freedman founded Inductive Solutions, Inc., to develop commercial AI software for genetic algorithms, neural networks, and case-based reasoning. His primary consulting interests involve solving problems associated with trading, operations, risk management, and compliance by using mathematics, statistics, and computer technology. As a consultant, he worked with stock exchanges, buy-side and sell-side firms, technology companies, startups, and government institutions. During the third term of the Bloomberg mayoral administration, he was the principal AI consultant to the Chief Information Officer of New York City Health & Human Services. As an expert witness, he testified on the federal level concerning financial and regulatory technology related to high-frequency trading. Dr. Freedman teaches various courses related to financial systems (FinTech) and regulatory systems (RegTech) as adjunct professor in the department of Finance and Risk Engineering at the New York University Tandon School of Engineering (formerly Polytechnic University).
Active Learning by Learning
Active learning is an important technique that helps reduce labeling efforts in machine learning applications. Previously, most active learning strategies are constructed based on some human-designed philosophy; that is, they reflect what human beings assume to be "good labeling questions." However, given that a single human-designed philosophy is unlikely to work on all scenarios, choosing and blending those strategies under different scenarios is an important but challenging practical task. This paper tackles this task by letting the machines adaptively "learn" from the performance of a set of given strategies on a particular data set. More specifically, we design a learning algorithm that connects active learning with the well-known multi-armed bandit problem. Further, we postulate that, given an appropriate choice for the multi-armed bandit learner, it is possible to estimate the performance of different strategies on the fly. Extensive empirical studies of the resulting algorithm confirm that it performs better than strategies that are based on human-designed philosophy. We release an open-source package libact that includes the algorithm to make active learning more realistic for general users.
Prof. Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008, and was promoted to an associate professor in 2012, and has been a professor since August 2017. Between 2016 and 2019, he worked as the Chief Data Scientist of Appier, a startup company that specializes in making AI easier in various domains, such as digital marketing and business intelligence. Currently, he keeps growing with Appier as its Chief Data Science Consultant.
Intelligent Biomedical Image Analysis
Abstract: Medical imaging inherently entails imperfection, and is therefore an appropriate domain for involving computational intelligence. We introduce the concepts of quantitative imaging and radiomics, followed by a discussion on segmentation by clustering. Next we describe an automated and fast detection algorithm for brain tumor in MRI, and its efficient segmentation. Visual saliency is utilized for a fast localization and detection of the tumor. Use of just a single user-provided seed for an efficient delineation of the GBM tumor is also elaborated. The role of neuro-fuzzy feature selection in brain tumor detection and its classification is also detailed. Finally a discussion on Radiogenomics, and Personalized Medicine concludes the talk. Biography: Sushmita Mitra is a full professor at the Machine Intelligence Unit (MIU), Indian Statistical Institute, Kolkata. From 1992 to 1994 she was in the RWTH, Aachen, Germany as a DAAD Fellow. She was a Visiting Professor in the Computer Science Departments of the University of Alberta, Edmonton, Canada; Meiji University, Japan; and Aalborg University Esbjerg, Denmark. Dr. Mitra received the National Talent Search Scholarship (1978-1983) from NCERT, India, the University Gold Medal in 1988, the IEEE TNN Outstanding Paper Award in 1994 for her pioneering work in neuro-fuzzy computing, the CIMPA-INRIA-UNESCO Fellowship in 1996, and Fulbright-Nehru Senior Research Fellowship in 2018-2020. She was the INAE Chair Professor during 2018-2020.
Sushmita Mitra is a full professor at the Machine Intelligence Unit (MIU), Indian Statistical Institute, Kolkata. From 1992 to 1994 she was in the RWTH, Aachen, Germany as a DAAD Fellow. She was a Visiting Professor in the Computer Science Departments of the University of Alberta, Edmonton, Canada; Meiji University, Japan; and Aalborg University Esbjerg, Denmark. Dr. Mitra received the National Talent Search Scholarship (1978-1983) from NCERT, India, the University Gold Medal in 1988, the IEEE TNN Outstanding Paper Award in 1994 for her pioneering work in neuro-fuzzy computing, the CIMPA-INRIA-UNESCO Fellowship in 1996, and Fulbright-Nehru Senior Research Fellowship in 2018-2020. She was the INAE Chair Professor during 2018-2020.
Webinar Title: Convolutional Networks for Medical Image Analysis: Its Past, Future, and Issues
Webinar Speaker: Prof. Pau-Choo Chung (Julia)
Webinar Chair: Dr. Sansanee Auephanwiriyakul
Webinar Recording: https://attendee.gotowebinar.com/recording/8557093691478808080
PowerPoint Slides: https://attendee.gotowebinar.
Recent advancement of image understanding with deep learning neural networks has brought great attraction to those in image analysis into the focus of deep learning networks. While researchers on video/image analysis have jumped on the bandwagon of deep learning networks, medical image analyzers would be the coming followers. The characteristics of medical images are extremely different from those of photos and video images. The application of medical image analysis is also much more critical. For achieving the best effectiveness and feasibility of medical image analysis with deep learning approaches, several issues have to be considered. In this talk we will give a brief overview of the development of neural networks for medical image analysis in the past and the future trends with deep learning. Several issues in regard of the data preparation, techniques, and clinic applications will also be discussed.
Pau-Choo (Julia) Chung (S’89-M’91-SM’02-F’08) received the Ph.D. degree in electrical engineering from Texas Tech University, USA, in 1991. She then joined the Department of Electrical Engineering, National Cheng Kung University (NCKU), Taiwan, in 1991 and has become a full professor in 1996. She served as the Head of Department of Electrical Engineering (2011-2014), the Director of Institute of Computer and Communication Engineering (2008-2011), the Vice Dean of College of Electrical Engineering and Computer Science (2011), the Director of the Center for Research of E-life Digital Technology (2005- 2008), and the Director of Electrical Laboratory (2005-2008), NCKU. She was elected Distinguished Professor of NCKU in 2005 and received the Distinguished Professor Award of Chinese Institute of Electrical Engineering in 2012. She also served as Program Director of Intelligent Computing Division, Ministry of Science and Technology (2012-2014), Taiwan. She served asthe Director of the Department of Information and Technology Education, Ministry of Education.
Dr. Chung’s research interests include computational intelligence, medical image analysis, video analysis, and pattern recognition. She applies most of her research results to healthcare and medical applications. Dr. Chung participated in many international conferences and society activities. She served as the program committee member in many international conferences. She served as the Publicity Co-Chair of WCCI 2014, SSCI 2013, SSCI 2011, and WCCI 2010. She served as an Associate Editor of IEEE Transactions on Neural Network and Learning Systems(2013-2015) and IEEE Transactions on Biomedical Circuits and Systems.
Dr. Chung was the Chair of IEEE Computational Intelligence Society (CIS) (2004-2005) in Tainan Chapter, the Chair of the IEEE Life Science Systems and Applications Technical Committee (2008-2009). She was a member in BoG of CAS Society (2007-2009, 2010-2012). She served as an IEEE CAS Society Distinguished Lecturer (2005-2007) and the Chair of CIS Distinguished Lecturer Program (2012-2013). She served on two terms of ADCOM member of IEEE CIS (2009-2011, 2012-2014), the Chair of IEEE CIS Women in Engineering (2014). She is a Member of Phi Tau Phi honor society and is an IEEE Fellow since 2008. She also served as the Vice President for Members Activities of IEEE CIS (2015-2018).
Past Webinars: 2020
Women in Computational Intelligence: Membership Promotion and Insight on Competitions in the IEEE Computational Intelligence Society
Let us measure STEAM
STEAM is an extension of the well-known teaching approach called STEM (Science, Technology Engineering, and Mathematics). Adding the A for Arts, it becomes STEAM. Many people are working in something that became a new trend: STEM teaching. But there are very few metrics defined to evaluate the goodness of this type of activity. This webinar presents some concepts, the work of a team led by the Digital Communications Institute of the Argentine Scientific Society, and the first findings.
Introduction of STEAM, the cognitive problem in the learning process the life cycle of a STEAM project data, metrics, and findings conclusions .
Daniela López De Luise is a doctor in Computer Sciences. Se founded and lead CETI (Center for Intelligent Technologies) at National Academy of Sciences in Buenos Aires, is the main project leader of STEM/STEAM’s activities at the Scientific Society of Argentina, and the leader of Museum-Lab LINCIEVIS for STEAM activities. Director of CI2S Lab (Computational Intelligence & Information Systems Lab), since June 2013. Director of IDTI Lab (Institute of Information Technology) since March 2017. Director of project LEARNITRON at CAETI research center. Undergraduate and graduate teacher of Universidad Autónoma de Entre Ríos (UADER), Universidad Tecnológica Nacional (UTN), and Universidad Abierta Interamericana (UAI). Consultant in Intelligent Systems