Explores provably beneficial AI, aligning AI goals with human preferences and behaviors, illustrating complexities through examples like image classification and fetching coffee.
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
Explores the impact of machine learning in understanding human diseases, focusing on historical significance, natural products discovery, and challenges in designer drugs.
Covers wildfire susceptibility mapping using ML-Al robotics and various related topics, including experimental protocols, DFT feature engineering, SimpedCLIP, and Covid-19 detection.
Explores the ethical implications of brain-computer interfaces and the societal challenges at the human-machine interface, emphasizing the importance of cognitive liberty and mental privacy.
