Bio: Zhongqi Miao is an Applied Research Scientist at Microsoft’s AI for Good Research Lab, where he focuses on incorporating artificial intelligence into environmental science and ecology domains. He obtained his Ph.D. from UC Berkeley under the mentorship of Prof. Wayne Getz and Prof. Stella Yu, specializing in computer vision and deep learning applications, particularly in camera trap recognition. Currently, Zhongqi broadens his expertise in wildlife recognition from diverse sources such as ground-based imagery, bio-acoustics, and aerial imagery from planes, drones, and satellites. Motivated by real-world challenges, he is also enthusiastic about addressing complex issues like long-tail distribution, multi-domain applications, human-in-the-loop interactions, and enhancing the deployment efficiency of large foundation models.
Title of the talk: Challenges and solutions of deep learning in wildlife conservation
Abstract: Deep learning has garnered significant interest from the ecological community due to its ability to extract and generalize patterns from complex datasets, such as images, audio, and motion signals. However, despite its potential, deep learning may exhibit limitations when applied to real-world ecological datasets.
In this presentation, I will outline four characteristics of realistic datasets: 1) long-tailed; 2) open-ended; 3) multi-domain; and 4) imbalance between labeled vs. unlabeled data. I will then focus on one of our recent papers, “Iterative Human and Automated Identification of Wildlife Images,” which demonstrates how AI recognition systems can be made deployable with efficient human-in-the-loop and continuous domain adaptation, considering the advancements of state-of-the-art Large Foundation Models.
Bio: Victor serves as a Vice President, AI & Data, in Bosch’s Cross-Domain Computing Solutions area. He is an experienced leader with MIT and NASA research background in artificial intelligence, data science, software engineering, and parallel computing. Prior to that he was the director and Head of Global Software Engineering at Bosch Sensortec, where he led a department breaking new ground in sensor software and Edge-AI in mobile devices, wearables, automotive, AR/VR, IoT, gaming, industrial, and environmental applications. Prior to Bosch, he led a data science group at MIT advancing computer-aided discovery & AI and served as a principal investigator in NASA’s prestigious Advanced Information Systems Technology program. Victor earned a Habilitation degree in Computer Science from KIT and a doctorate with distinction from the University of Karlsruhe’s business school. For more details, please visit: http://www.victorpankratius.com
Title of the talk: Trends in Sensing Applications and AI at the Edge
Abstract: New generations of sensors are increasingly equipped with microcontrollers and computing capabilities that enable local machine learning in millimeter-sized packages. This keynote presents use cases where sensing applications have become a major driver for Tiny AI. Applications are shown for intelligent Micro-Electro-Mechanical Systems (MEMS) in motion learning, sports analytics, and gas and environmental sensing. Looking at the software stack, this keynote also addresses the importance of formalizing and including domain knowledge into AI for optimizations, such as shrinking memory footprints, making trade-offs in signal processing, and algorithmic choice. Learning from individual success stories, our insights help sketch a bigger picture for AI-IoT ecosystems and platforms.
Bio: Dr. Tung is the Rector/Vice Chairman of the Industrial University of Vinh City. Previously he was the Rector at Vabis International College. He is also the CEO/Co-Founder of Khai Minh Technology Group and Tuệ Đức Green School System (20+ campus). He is having more than 29 years of experience in Multinational Companies & Education organisations. He has a Strong personality and Initiative, leadership capability and proactive leadership/ attitude.
Title of the talk: Customize AI model in business: Case study in KMTG Vietnam (Khai Minh Technology Group): KMTG’s Brain
Abstract: Customizing AI models for business is becoming increasingly important as more organizations seek to leverage the power of AI to gain a competitive advantage. While pre-built AI models can be effective in some cases, they are not always suitable for every business’s specific needs. By customizing AI models, businesses can tailor them to their specific requirements, improving their accuracy, speed, and relevance. It involves several steps to customizing AI models, including selecting the appropriate AI algorithms, collecting and preprocessing data, and fine-tuning the model to optimize performance.
This can be a complex and challenging process, requiring specialized skills and expertise. However, with the right approach and tools, businesses can successfully customize AI models to achieve their goals.
KMTG (Khai Minh Technology Group) on processing build the KMTG’s Brain by customizing AI models to improve efficiency, increase accuracy, reduce error rates. It can also help KMTG to identify new opportunities, reduce costs, and improve customer satisfaction. Despite the potential benefits, customizing AI models is not without its challenges. These challenges include data quality issues, the need for specialized skills and resources, and the need to continuously monitor and update the model to ensure it remains relevant and effective.
Overall, customizing AI models is a critical component of a successful AI strategy for businesses. By leveraging the power of AI and customizing models to their specific needs, businesses can gain a competitive edge and achieve their goals more effectively and efficiently.
BIO: Rida Qadri is a Research Scientist in Google’s Responsible AI and Human-Centered Technology team. She leverages her interdisciplinary expertise and cross-cultural research experience to study the limitations of generative AI in non-western settings. Through this work, she seeks to build AI pipelines that are inclusive of global cultures and respect the situated expertise and knowledge of global communities. Her past research has examined mobility platform and gig work algorithms in Jakarta, looking at the failures and frictions of these technologies in a non-western context. She completed her PhD in Computational Urban Science.
|Full Paper Submission:||25th April 2023|
|Acceptance Notification:||11th May 2023|
|Final Paper Submission:||20th May 2023|
|Early Bird Registration:||20th May 2023|
|Presentation Submission:||29th May 2023|
|Conference:||7-10 June 2023|
IEEE AIIoT 2021
• Best Paper Award will be given for each track