Artificial Intelligence System
Artificial intelligence (AI) systems are computational models designed to mimic human cognitive functions, with current research focusing on improving their reliability, safety, and explainability. Key areas of investigation include developing more robust and trustworthy AI models, often employing large language models (LLMs) and other deep learning architectures, as well as enhancing human-AI interaction and understanding AI decision-making processes through explainable AI (XAI) techniques. The field's impact spans diverse applications, from improving medical diagnoses and assisting in game design to enhancing cybersecurity and supporting policy analysis, while also raising crucial ethical and societal considerations.
Papers
AI system for fetal ultrasound in low-resource settings
Ryan G. Gomes, Bellington Vwalika, Chace Lee, Angelica Willis, Marcin Sieniek, Joan T. Price, Christina Chen, Margaret P. Kasaro, James A. Taylor, Elizabeth M. Stringer, Scott Mayer McKinney, Ntazana Sindano, George E. Dahl, William Goodnight, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T Saensuksopa, Kris Liu, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty
Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems
Sarah Fabi, Thilo Hagendorff