IEEE Computational Intelligence Society

News

IEEE Day 2022

 Upcoming IEEE CIS Webinar with Distingushed Lecturer Hussein Abbass 

CIS IEEE Day L2L Webinar Live 2

From Machine Learning to Machine Education

Monday, 10 October 2022  7:00 AM - 8:00 AM EDT

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 and register for our live 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.

 

 IEEE Day CIS Chapter Banner Contest

How are you celebrating IEEE Day on Tuesday, October 4? This year, CIS
will be marking it with a number of activities, including holding an IEEE Day
Banner Contest.

Here are some things to keep in mind:

  • To ENTER: Share a picture of your group with the banner by October 15 on Facebook, LinkedIn or Twitter using #IEEEDay22CIS as one of the hashtags.
  • All entries should contain the CIS logo AND the latest IEEE Day logo in it. Find IEEE Day logos here: https://ieeeday.org/ toolkit/
  • All entries should show IEEE members at their IEEE Day celebrations ideally. Examples: https:// ieeeday.org/#images-6 | https: //ieeeday.org/#images-4 | http s://ieeeday.org/#images-1
  • The Contest Entry submission period is from 1 October to 15 October 2022
  • Winner(s) will be decided through voting. Results will be shared 25 October 2022.
  • Winning entry will receive $500 for their IEEE group.
  • Everyone will receive a participation prize of $100
  • All OUs (Student branch, Student branch chapter, Society Chapters, Sections, Sub-sections, Regions) can participate in these contests.

We will be sharing your banner entries across our social media profiles as well as highlighting entries in upcoming CIS newsletters. Best of luck and happy IEEE Day!

IEEE CIS On-Demand Resources: 

Creative AI through Evolutionary Computation 

Professor Riisto Mikkulainen (EC Pioneer) 

https://ieeetv.ieee.org/ creative-ai-through- evolutionary-computation 

Fuzzy Management of Data and Information Quality 

Bernadette Bouchon-Meunier (Fuzzy Pioneer)

https://ieeetv.ieee.org/ bernadette-buchon-meunier- fuzzy-management-of-data-and- information-quality

Deep Learning and the Representation of Natural Data

Yann LeCunn (Neural Networks Pioneer) 

https://ieeetv.ieee.org/deep_ learning_and_the_ representation_of_natural_data 

Fuzzy Logic

The Sorites Paradox: Introduction to Fuzzy Logic

https://ieeetv.ieee.org/ ieeetv-specials/the-sorites- paradox-introduction-to-fuzzy- logic

Artificial Neural Networks

introduction to Artificial Neural Networks

https://ieeetv.ieee.org/ artificial_neural_networks_ intro

Evolutionary Computation

Evolutionary Computation - A Technology Inspired by Nature

https://ieeetv.ieee.org/ technology/evolutionary_ computation_a_technology_ inspired_by_nature

 

Past IEEE Day Events

Celebrate IEEE DAY with CIS Distinguished Lecturer Dipankar Dasgupta with Live Talk: AI vs AI: Viewpoints

Tuesday, October 4, 2022 10:00 AM - 11:00 AM EDT

 

This talk will discuss some important use of AI in search, optimization, prediction, and discovery; how algorithmic bias can impact decisions; how AI can play dual-role and can be applied in many ways with varying intent. I will also exhibit use cases on “Defensive AI and Offensive AI”, and in designing “Digital Twin”. Finally, I will argue that with the significant business benefits of using AI/ML techniques, there exist possibilities of misuse/abuse or inappropriate use of such techniques. So, regulations such as Algorithmic Accountability Act. become essential for AI-based developers to take responsibilities of their products and services.

Biography

Dipankar Dasgupta obtained his bachelors degree in Electrical Engineering in 1981 and his masters degree in Computer Engineering in 1986 in India. He received his Ph. D. in Computer Science from the University of Strathclyde, Glasgow, in 1993. Before his Ph. D., he was teaching Computer Science in India as an assistant professor.He was a post doctorate researcher at the University of New Mexico from January of 1994 until August of 1995 and worked on an immune system based model for Novelty Detection in sensory data. He was a visiting faculty at the University of Missouri - St. Louis until December of 1996.Dr. Dipankar Dasgupta joined the University of Memphis as an assistant professor in 1997 and became a full professor in 2004. He is the recipient of the 2011-2012 Willard R. Sparks Eminent Faculty Award, the highest distinction and most prestigious honor given to a faculty member by the University of Memphis.Prof. Dasgupta is one of the founding fathers of the field of artificial immune systems, making major contributions in applying bio-inspired approaches to intrusion detection, spam detection, and building survivable systems . His latest book, Immunological Computation, is a graduate-level textbook published by CRC Press in 2008. He has also edited a Springer-Verlag book on artificial immune systems and another book on genetic algorithms.Prof. Dasgupta has more than 292 publications. A search with his name in Google Scholar indicates more than 16,490 citations, and an academic search at Microsoft shows that he has collaborated with 106 co-authors -- extraordinary testimony to the broad influence of his contributions within the research community. With an h-index of over 57, he is featured on UCLA's list of prominent computer scientists.In addition to Prof. Dasgupta's research and creative activities, he spearheads the University of Memphis' education, training, and outreach activities on Information Assurance. He is the founding Director of the Center of Information Assurance , which is a nationally designated Center for Academic Excellence in Information Assurance Education and Research. He developed the University of Memphis' Graduate Certificate Program in Information Assurance and has established research collaborations with Oak Ridge National Laboratory. He regularly serves as panelist, keynote speaker and offer tutorials in leading conferences, and has given more than 50 invited talks in different universities and industries. His research activities and achievements appeared in Computer World Magazine, NASA site, and in local newspapers.Click Here to read more about his researches.
To view his coverage by media Click Here.

CALL FOR PARTICIPATION IEEE P3187™, Guide for Framework for Trustworthy Federated Machine Learning

IEEESA Call for participation

IEEE Standards Association (IEEE SA) invites you to participate in the Working Group for IEEE P3187™, Guide for Framework for Trustworthy Federated Machine Learning.

 

 

WHY GET INVOLVED

This guide provides a reference framework for trustworthy Federated Machine Learning. The document provides guidance with respect to provable security for data and models, optimized model utility, controllable communication and computational complexity, explainable decision making and supervised processes. It describes three main aspects:

  1. Principles for trustworthy Federated Machine Learning
  2. Requirements for different roles in trustworthy Federated Machine Learning
  3. Techniques to realize trustworthy Federated Machine Learning

The purpose of this guide is to provide credible, practical and controllable solution guidance for Federated Machine Learning and other privacy computing applications.

For additional information, contact the IEEE P3187™ Working Group Chair, Zuping Wu , at wuzp@chinatelecom.cn or the IEEE SA Program Manager, Christy Bahn , at c.bahn@ieee.org .

CIS Resource Center Launched

 

2022 CIS RC graphic 1

CIS members: Access conference materials, webinars, interviews and more resources through the CIS Resource Center, for low or no-cost! Topics range across industries and address the theory, design, application, and development of biologically and linguistically motivated computational paradigms, as well as hybrid intelligent systems. https://resourcecenter.cis.ieee.org/