Welcome back to AI In Real Life, the podcast from the AI Institute for Societal Decision Making! In this episode, host Ariel talks with Professor Kun Zhang, professor in the CMU philosophy department and an affiliate faculty member in the machine learning department. His research interests lie in causality, and he develops methods for automated causal discovery, investigates learning problems, and studies philosophical foundations of machine learning.
They dive into the fascinating intersection between AI and higher education, using real-world cases of AI-assisted cheating and university responses as a springboard for a deeper conversation about learning, independence, and what the ultimate goal of education is.
They explore:
Current climate: Ariel explores how students are already using tools like ChatGPT, why traditional notions of “cheating” are being challenged, and how universities across America are responding
True learning vs. short-term results: Kun emphasizes education as a long-term process of self-improvement rather than assignment completion, arguing that AI can either undermine or strengthen learning depending on how it’s used.
Causality (what Kun is doing a lot of research on right now!): Kun introduces causality as a core concept missing from many AI systems today, explaining why understanding the why for occurrences is essential for reasoning, decision-making, and societal impact.
Going into the black box of LLMs: Kun breaks down tokens, next-token prediction, and the illusion of fluency.
Education reform and the future of learning: They discuss why education systems may need structural change, as well as why boredom and reflection are still essential for creativity and innovation.
Whether you’re a student, educator, or simply curious about how AI is reshaping learning and human thought, this episode offers valuable insight into how AI is changing the academic landscape!
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