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
Generative AI in the Construction Industry: Opportunities & Challenges
Prashnna Ghimire, Kyungki Kim, Manoj Acharya
Learning from Teaching Assistants to Program with Subgoals: Exploring the Potential for AI Teaching Assistants
Changyoon Lee, Junho Myung, Jieun Han, Jiho Jin, Alice Oh
Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs
Ben Goertzel
CppFlow: Generative Inverse Kinematics for Efficient and Robust Cartesian Path Planning
Jeremy Morgan, David Millard, Gaurav S. Sukhatme
Generative AI-Driven Storytelling: A New Era for Marketing
Marko Vidrih, Shiva Mayahi
BGGAN: Generative AI Enables Representing Brain Structure-Function Connections for Alzheimer's Disease
Chen Ding, Shuqiang Wang