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Explores the impact of machine learning in understanding human diseases, focusing on historical significance, natural products discovery, and challenges in designer drugs.
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 narratives and interpretations of the end of the world, from religious eschatologies to modern scenarios, including nuclear threats and ecological disasters.
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
