Artificial Intelligence Model
Artificial intelligence (AI) models are rapidly evolving, with current research focusing on improving their reliability, security, and fairness. Key areas of investigation include mitigating model errors (including adversarial attacks), ensuring robustness across diverse datasets and contexts, and addressing biases that may lead to unfair or culturally insensitive outputs. These advancements are crucial for building trust in AI systems and enabling their safe and effective deployment across various sectors, from healthcare and finance to manufacturing and autonomous systems.
Papers
Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines
Ruochen Zhao, Xingxuan Li, Yew Ken Chia, Bosheng Ding, Lidong Bing
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
Sunil Gupta, Alistair Shilton, Arun Kumar A, Shannon Ryan, Majid Abdolshah, Hung Le, Santu Rana, Julian Berk, Mahad Rashid, Svetha Venkatesh