Generative AI
Generative AI focuses on creating new content, ranging from text and images to code and even simulations of complex systems like fluid flows, primarily using large language models (LLMs) and generative adversarial networks (GANs). Current research emphasizes improving model accuracy, addressing biases and ethical concerns, and exploring effective human-AI collaboration in diverse applications like education, healthcare, and software development. This rapidly evolving field holds significant potential to accelerate scientific discovery and transform various industries by automating tasks, generating insights from large datasets, and personalizing services.
Papers
Rethinking Image Compression on the Web with Generative AI
Shayan Ali Hassan, Danish Humair, Ihsan Ayyub Qazi, Zafar Ayyub Qazi
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Petr Anokhin, Nikita Semenov, Artyom Sorokin, Dmitry Evseev, Mikhail Burtsev, Evgeny Burnaev
How Stable is Stable Diffusion under Recursive InPainting (RIP)?
Javier Conde, Miguel González, Gonzalo Martínez, Fernando Moral, Elena Merino-Gómez, Pedro Reviriego
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
Mahmoud Ibrahim, Yasmina Al Khalil, Sina Amirrajab, Chang Sun, Marcel Breeuwer, Josien Pluim, Bart Elen, Gokhan Ertaylan, Michel Dumontier
The Great AI Witch Hunt: Reviewers Perception and (Mis)Conception of Generative AI in Research Writing
Hilda Hadan, Derrick Wang, Reza Hadi Mogavi, Joseph Tu, Leah Zhang-Kennedy, Lennart E. Nacke
Encouraging Responsible Use of Generative AI in Education: A Reward-Based Learning Approach
Aditi Singh, Abul Ehtesham, Saket Kumar, Gaurav Kumar Gupta, Tala Talaei Khoei
ConvoCache: Smart Re-Use of Chatbot Responses
Conor Atkins, Ian Wood, Mohamed Ali Kaafar, Hassan Asghar, Nardine Basta, Michal Kepkowski