Sunghee Yun (He/Him)

Co-Founder & CTO @ Erudio Bio, Inc., Co-Founder & CEO @ Erudio Bio Korea, Inc., Leader of Silicon Valley Privacy Preserving AI Forum (K-PAI), Director of AI Semiconductor @ K-BioX, AI-Korean Medicine Integration Initiative Task Force Member @ The Association of Korean Medicine, KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, Visiting Professor @ Sogang University, Advisory Professor @ DGIST, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, Network Expert @ Gerson Lehrman Group, Chief Business Development Officer @ WeStory.ai, and Advisor @ CryptoLab, Inc.

Abstract

This lecture examines the philosophical foundations underlying artificial intelligence, asking fundamental questions that bridge technical implementation and humanistic inquiry: Does AI possess knowledge or belief? Can machines truly reason? What distinguishes human consciousness from computational processes? Drawing from cognitive science, philosophy of mind, and rigorous mathematical analysis, we’ll explore why LLMs—despite their remarkable capabilities—function as conditional probability estimators rather than entities that genuinely “know” or “believe.” Through examining the sudden leap in LLM performance (explained via token correlation mathematics), cognitive biases inherited from training data, and the critical distinction between bare-bones models and the systems embedding them, we’ll develop precise language for discussing what AI can and cannot do.

Beyond abstract philosophy, we’ll confront urgent ethical and legal questions facing our field: Who bears responsibility when self-driving algorithms cause fatal accidents? How should we balance utilitarian outcomes against humanitarian values? What are the copyright implications of training on vast public corpora? The lecture argues that the real dangers lie not in science fiction scenarios of conscious AI, but in more immediate threats—the misuse of AI by those wielding social and economic power, social welfare impaired by either exploitation or ignorance of AI’s inner workings, and the anthropomorphization that obscures both AI’s limitations and its genuine capabilities.

For AI researchers, engineers, and practitioners, developing rigorous understanding of these philosophical dimensions is not academic indulgence—it’s professional imperative. When advising policymakers, collaborating with humanities scholars, or making critical business decisions, we must resist the seductive anthropomorphism encouraged by AI’s seemingly intelligent outputs. The lecture concludes with a call for informed stewardship: we must make trustworthy decisions about AI safety, avoid ascribing capacities AI lacks, take full advantage of AI’s remarkable capabilities, and above all, ensure that meaning, values, and human welfare guide technological progress rather than being displaced by it.