posted by user: guoao || 341 views || tracked by 1 users: [display]

GAI-HyperI 2024 : 1st IEEE International Workshop on Generative AI and Hyper Intelligence

FacebookTwitterLinkedInGoogle

Link: https://gai-hyperi.github.io/
 
When Nov 5, 2024 - Nov 8, 2024
Where Boracay Island, Malay, Philippines
Submission Deadline Aug 10, 2024
Notification Due Sep 1, 2024
Final Version Due Sep 27, 2024
 

Call For Papers

The 1st IEEE International Workshop on Generative AI and Hyper Intelligence
(GAI-HyperI 2024)
https://gai-hyperi.github.io/
held in conjunction with IEEE CyberSciTech 2024
5-8 November, 2024 - Boracay Island, Malay, Philippines


============ IMPORTANT DATES ===========
Submission Deadline: 10 Aug. 2024 (Extended)
Acceptance Notifications: 01 Sep. 2024
Camera-ready Submission: 27 Sep. 2024


============= AIM AND SCOPE ============
Generative AI (GAI) has seen rapid advancements and gained immense popularity in recent years. With its extensive model parameters and learning from vast datasets, generative AI is capable of creating new content, such as text, images, music, and videos, which were once considered exclusive to human creativity. Popular generative AI including Large Language Models (LLMs) and Generative Adversarial Networks (GANs) have demonstrated remarkable capabilities in conversation systems and image generation. The creativity and adaptivity of generative AI are essential for the development of Hyper Intelligence.

Hyper Intelligence (HyperI) is an emerging interdisciplinary field focused on achieving super-intelligent abilities to tackle complex tasks. It involves the study of hyper-connections, hyper-compositions, hyper-collaborations, and hyper-cognition among intelligent entities. Hyper-intelligent systems are increasingly prevalent in various applications, such as smart transportation, intelligent healthcare, and personalized education. However, the rise of these systems presents numerous challenges, including theoretical and framework development, security and safety concerns, and issues related to human interaction and personalization. We believe that generative AI can enable hyper-intelligent systems to devise innovative and adaptive solutions to handle the above challenges.

This workshop aims to investigate both theoretical foundations and practical applications to address emerging challenges and opportunities in Hyper Intelligence. We welcome researchers to discuss and examine ongoing research on Hyper Intelligence by leveraging Generative AI.


================ TOPICS ================
Topics of interest include, but are not limited to:
- Techniques for efficient application of generative AI
- Emerging capabilities and trends of generative AI
- Interpretable and explainable generative AI models
- Emotional AI and personalized generative intelligence
- Generative video, image, text, music, and animation
- Applications of generative AI for education, research,
work, design, medicine, entertainment, art, etc.
- Frameworks and methods for hyper-intelligence
- Hybrid collective systems for hyper-intelligence
- Hyper-connections and hyper-collaborations
- Hyper-cognition and adaptive intelligence systems
- Security and safety in hyper-intelligent systems
- Ethical issues, including misuses and abuses,
provenance, copyright, bias, and diversity


====== SUBMISSION AND PUBLICATION ======
Please follow the guideline in CyberSciTech 2024 Submission Site (https://cyber-science.org/2024/cyberscitech/papersubmissions/) to submit your work via EDAS (https://edas.info/N32365).
The submitted papers should be 4-6 pages long including figures and references, and prepared in IEEE CS Proceedings format. IEEE formatting information:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html

We also welcome Position Statement Papers (2-4 pages), which present novel ideas, hypotheses, and emerging research directions in Generative AI and Hyper Intelligence. These papers should be prepared in IEEE CS Proceedings format and will be peer-reviewed for novelty and impact.

At least one of the authors of the accepted paper is requested to register and present the paper at the conference in hybrid mode (in person or virtually). All accepted papers will be published in an IEEE Computer Society proceedings (IEEE-DL and EI indexed).


======== BRAINSTORMING SESSION =========
We are pleased to host a special brainstorming session to promote creative discussions and collaborations among participants. This session will provide a platform for sharing ideas and exploring new research directions in Generative AI and Hyper Intelligence. Participants are encouraged to bring forward innovative concepts and challenging questions to stimulate lively and productive exchanges.


============= ORGANIZERS ===============
-General Chair-
- Jianhua Ma, Hosei University, Japan

-Program Chair-
- Ao Guo, Nagoya University, Japan

-Contact E-mail-
- gai.hyperi@gmail.com

Related Resources

GenAI and LVMs for Biometrics 2025   IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM) Special Issue on Generative AI and Large Vision-Language Models for Biometrics
IEEE-Ei/Scopus-ITCC 2025   2025 5th International Conference on Information Technology and Cloud Computing (ITCC 2025)-EI Compendex
IEEE-Ei/Scopus-CNIOT 2025   2025 IEEE 6th International Conference on Computing, Networks and Internet of Things (CNIOT 2025) -EI Compendex
IEEE BDAI 2025   IEEE--2025 the 8th International Conference on Big Data and Artificial Intelligence (BDAI 2025)
IEEE AMCAI 2025   IEEE Afro-Mediterranean Conference on Artificial Intelligence
IEEE ACIRS 2025   IEEE--2025 10th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS 2025)
IEEE IRCE 2025   IEEE--2025 The 8th International Conference on Intelligent Robotics and Control Engineering (IRCE 2025)
AI4Energy&Environment 2025   Special Issue on AI-Driven Innovations for Renewable Energy and Environmental Sustainability
BDAI 2025   IEEE--2025 the 8th International Conference on Big Data and Artificial Intelligence (BDAI 2025)
ACDL 2025   8th Advanced Course on Data Science & Machine Learning