Prof. Shahram Latifi, IEEE Fellow
University of Nevada,
USA

Biography: Shahram Latifi, received the Master of Science and the PhD degrees both in Electrical and Computer Engineering from Louisiana State University, Baton Rouge, in 1986 and 1989, respectively. He is currently a Professor of Electrical Engineering at the University of Nevada, Las Vegas. Dr. Latifi is the co-director of the Center for Information Technology and Algorithms (CITA) at UNLV. He has designed and taught undergraduate and graduate courses in the broad spectrum of Computer Science and Engineering in the past four decades. He has given keynotes and seminars on machine learning/AI and IT-related topics all over the world. He has authored over 300 technical articles in the areas of networking, AI/ML, cybersecurity, image processing, biometrics, fault tolerant computing, parallel processing, and data compression. His research has been funded by NSF, NASA, DOE, DoD, Boeing, and Lockheed. Dr. Latifi was an Associate Editor of the IEEE Transactions on Computers (1999-2006), an IEEE Distinguished Speaker (1997-2000), Co-founder and Chair of the IEEE Int'l Conf. on Information Technology (2000-2004) and founder and Chair of the International Conf. on Information Technology-New Generations (2005-Present) . Dr. Latifi is the recipient of several research awards, the most recent being the Barrick Distinguished Research Award (2021). Dr. Latifi was recognized to be among the top 2% researchers around the world in December 2020, according to Stanford top 2% list (publication data in Scopus, Mendeley). He is an IEEE Fellow (2002) and a Registered Professional Engineer in the State of Nevada.

 

Speech Title: AI Advancements and Challenges: Navigating the Future of Responsible AI
Over the past two decades, AI technology has advanced at an astonishing pace. Breakthroughs such as Deep Learning, Generative Adversarial Networks, Transfer Learning, and Large Language Models have accelerated this progress, enabling AI to revolutionize various aspects of society. AI has significantly enhanced the performance of systems in fields like education, healthcare, aerospace, manufacturing, security, e-commerce, and art. However, alongside these tremendous benefits come major concerns about the potential threats AI poses to humanity. How can we ensure our training data is unbiased and well-balanced? How can we guarantee that AI systems respect individual privacy? And most importantly, how can we ensure these systems remain controllable and act responsibly?
In this talk, I will provide a brief overview of AI, Machine Learning (ML), and Deep Learning (DL). While there are significant challenges in achieving general-purpose AI (as opposed to Narrow AI), there are even greater issues that must be addressed to ensure AI is safe, fair, and secure. I will also discuss recent efforts in the United States and around the world to build responsible AI.

 

 

Prof. Dr. Ho-Jin Choi
Korea Advanced Institute of Science & Technology (KAIST),
South Korea

Biography: Prof. Dr. Ho-Jin Choi is a professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. He received a BS in computer engineering from Seoul National University (SNU), Korea, an MSc in computing software and systems design from Newcastle University, UK, and a PhD in artificial intelligence from Imperial College London, UK. During 1980’s he worked for DACOM Corp., Korea, in later 1990’s he joined with Korea Aerospace University, before he moved to KAIST in 2009. In early 2000’s, he visited Carnegie Mellon University (CMU), USA, and served as an adjunct faculty for the Master of Software Engineering (MSE) program operated jointly by CMU and KAIST for 10 years. In 2010’s he participated research in Systems Biomedical Informatics Research Center at the College of Medicine, SNU, worked with Samsung Electronics on big data intelligence solutions, and with UAE’s Khalifa University on intelligent multi-sensor healthcare surveillance. He also participated in a Korean national project called Exobrain for natural language question/answering. Since 2018, he has been the director of Smart Energy Artificial Intelligence Research Center, and since 2020 the director of Center for Artificial Intelligence Research, both at KAIST. His current research interests include natural language processing, machine learning, explainable AI, and smart energy.

Speech Title: DialogCC for Creating High-Quality Multi-Modal Dialogue Datasets
For sharing images in instant messaging, active research has been going on learning image-text multi-modal dialogue models. Training a well-generalized multi-modal dialogue model remains challenging due to the low quality and limited diversity of images per dialogue in existing multi-modal dialogue datasets. In this research, we propose an automated pipeline to construct a multi-modal dialogue dataset, ensuring both dialogue quality and image diversity without requiring any human effort. In order to guarantee the coherence between images and dialogue, we prompt GPT-4 to infer potential image-sharing moments, e.g., utterance, speaker, rationale, and image description. Furthermore, we leverage CLIP similarity to maintain consistency between aligned multiple images to the utterance. Using this pipeline, we introduce DialogCC, a high-quality and diverse multi-modal dialogue dataset that surpasses existing approaches in terms of quality and diversity in human evaluation. Our experiments highlight multi-modal dialogue models trained using our dataset, and their generalization performance on unseen dialogue datasets.