Generative Artificial Intelligence
Generative Artificial Intelligence (GenAI) focuses on creating new data samples—text, images, code, etc.—from existing datasets using deep learning models. Current research emphasizes diverse applications, including drug discovery, education, and industrial processes, with a focus on model architectures like transformers, diffusion models, and generative adversarial networks (GANs). The field's significance lies in its potential to automate complex tasks, accelerate scientific discovery, and reshape various industries, while also raising important ethical considerations regarding bias, data privacy, and the responsible use of AI.
Papers
Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines
Nora McDonald, Aditya Johri, Areej Ali, Aayushi Hingle
Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems
Rebekah Rousi, Hooman Samani, Niko Mäkitalo, Ville Vakkuri, Simo Linkola, Kai-Kristian Kemell, Paulius Daubaris, Ilenia Fronza, Tommi Mikkonen, Pekka Abrahamsson
HSC-GPT: A Large Language Model for Human Settlements Construction
Chen Ran, Yao Xueqi, Jiang Xuhui, Han Zhengqi, Guo Jingze, Zhang Xianyue, Lin Chunyu, Liu Chumin, Zhao Jing, Lian Zeke, Zhang Jingjing, Li Keke
Generation Z's Ability to Discriminate Between AI-generated and Human-Authored Text on Discord
Dhruv Ramu, Rishab Jain, Aditya Jain
Image Content Generation with Causal Reasoning
Xiaochuan Li, Baoyu Fan, Runze Zhang, Liang Jin, Di Wang, Zhenhua Guo, Yaqian Zhao, Rengang Li
The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment
Mike Perkins, Leon Furze, Jasper Roe, Jason MacVaugh