MBZUAI · Notable
MBZUAI Visiting Assistant Professor Gus Xia studies music to understand how AI can act more human-like in high-context activities. Xia analyzes and creates music with computers to explore the differences between human and machine perception. He aims to leverage music's abstract nature to study creative intelligence in AI. Why it matters: This research could lead to AI systems that interact more naturally with humans, particularly in creative fields.
MBZUAI · Gus Xia · music · AI · human-computer interaction
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MBZUAI ·
Gus Xia, assistant professor of machine learning at MBZUAI, is exploring how teaching robots music can enhance their interaction with humans. Xia collaborates with robots on musical compositions as part of this research. He also holds affiliations at NYU Shanghai, Tandon, CILVR, and MARL, and has a Ph.D. from CMU. Why it matters: This interdisciplinary approach could lead to more intuitive and empathetic AI systems in the future.
MBZUAI ·
MBZUAI's Associate Professor of Machine Learning, Gus Xia, will co-teach an introductory AI course with Monojit Choudhury, emphasizing experiential learning and fundamental principles. Xia's background spans computer science, music, and metaphysics, aiming to inspire students to innovate in AI. More than 100 students will join MBZUAI's Bachelor of Science in Artificial Intelligence program. Why it matters: This interdisciplinary approach at MBZUAI could cultivate a new generation of AI researchers with diverse perspectives and innovative problem-solving skills.
KAUST ·
Nate Hagens from the University of Minnesota spoke at KAUST's Winter Enrichment Program (WEP) 2018 about the intersection of energy, human behavior, and economics. Hagens argued that society functions as an energy-dissipating "superorganism," with human preferences correlated with increasing energy needs. He emphasized that energy, not money, is the real capital, but global society is running out of it. Why it matters: The talk highlights the importance of viewing society through an ecological lens, particularly in the context of the GCC region's reliance on energy resources.
MBZUAI ·
MBZUAI researchers found that only 5.7% of music in existing datasets used to train generative music systems comes from non-Western genres. They discovered that 94% of the music represented Western music, while Africa, the Middle East, and South Asia accounted for only 0.3%, 0.4%, and 0.9% respectively. The team also tested whether parameter-efficient fine-tuning with adapters could improve generative music systems on underrepresented styles, presenting their findings at NAACL. Why it matters: This research highlights the critical need for more diverse datasets in AI music generation to better serve global musical traditions and audiences.