Sunghee Yun (He/Him)

Co-Founder & CTO @ Erudio Bio, Inc., Leader of Silicon Valley Privacy Preserving AI Forum (K-PAI), Advisor @ Korean American Semiconductor Professional Alliance (KASPA), CGO / Global Managing Partner @ LULUMEDIC, KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, AI-Korean Medicine Integration Initiative Task Force Member @ The Association of Korean Medicine, Director of AI Semiconductor @ K-BioX, 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, and Chief Business Development Officer @ WeStory.ai

Abstract

The past three years have witnessed an unprecedented transformation in artificial intelligence that fundamentally challenges our understanding of intelligence, reasoning, and knowledge itself. From the vantage point of Silicon Valley—where these technologies are being developed and deployed at breathtaking speed—this lecture offers legal professionals an insider’s perspective on what has actually changed, why it matters, and what recent breakthroughs in Large Language Models (LLMs) reveal about the nature of human cognition. Rather than focusing narrowly on legal tech applications, we examine the deeper paradigm shift: how AI systems that excel at pattern recognition are forcing us to reconsider the relationship between statistical inference and logical reasoning, between precedent-based thinking and rule-based codification, and ultimately between different modes of intelligence.

At the heart of this transformation lies a fascinating tension that legal minds are uniquely positioned to appreciate. Lawyers, prosecutors, and judges undergo rigorous training in logical reasoning, formal argumentation, and systematic analysis—cognitive skills honed over decades of practice. Yet modern LLMs achieve remarkable capabilities through fundamentally different mechanisms: massive-scale pattern matching across billions of textual examples rather than explicit logical rules. This raises profound questions that bridge cognitive science, philosophy of mind, and legal theory: What is the relationship between these two modes of “reasoning”? How does the distinction between continental law’s codified systems and common law’s precedent-based traditions illuminate—or complicate—our understanding of how AI systems process legal concepts? When we say an AI “understands” a legal principle, what cognitive and philosophical claims are we actually making?

Drawing on Sunghee’s experience spanning semiconductor optimization, e-commerce recommendation systems, and now AI-powered biotech platforms, this lecture traces how AI’s capabilities have evolved and what that evolution reveals about both artificial and human intelligence. We explore the cognitive science foundations that explain why LLMs can perform sophisticated legal analysis without “understanding” in the human sense, why they excel at certain legal tasks while failing at others, and how their pattern-recognition architecture differs from—and potentially complements—the logical frameworks central to legal reasoning. This cross-disciplinary lens offers fresh perspectives on long-standing questions about the nature of expertise, the structure of legal knowledge, and the future of professional judgment.

This seminar is designed as intellectual dialogue rather than one-way instruction. Your experience in rigorous logical analysis, complex argumentation, and systematic reasoning provide invaluable frameworks for interrogating these technologies at a deeper level than typical “AI for lawyers” discussions permit. Together, we will examine not just what AI can do for legal practice, but what the legal profession’s intellectual traditions can teach us about the nature, limits, and implications of artificial intelligence. The goal is mutual learning - to help you navigate the AI landscape with genuine understanding while exploring how legal reasoning itself might inform our comprehension of machine intelligence.