This graduate seminar investigates cutting-edge research shaping the future of artificial intelligence. Focusing on recent advances in areas such as large language models, multimodal learning, reinforcement learning, trustworthy and explainable AI, and human–AI collaboration, the course emphasizes critical reading, discussion, and replication of contemporary research papers. Students will engage in seminar-style presentations, code walkthroughs, and small-scale experimental reproductions to deepen their understanding of current methodologies and evaluation practices. Through these activities, participants will develop the skills to analyze emerging AI paradigms, critique their theoretical and practical implications, and identify promising directions for original research.
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