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 explores two transformative frontiers where artificial intelligence is fundamentally reshaping our physical world: biotechnology and humanoid robotics. In the biotech domain, we examine how AI has evolved from a computational tool to an essential catalyst in biological discovery, enabling breakthroughs that were computationally intractable just years ago. We trace the remarkable journey from AlphaFold’s solution to the 50-year-old protein folding problem to today’s AI-driven drug discovery platforms, personalized medicine approaches, and synthetic biology applications. The convergence of AI with biological sciences—powered by exponential growth in genetic sequence data (surpassing Moore’s Law), advanced deep learning architectures, and multimodal data integration—is democratizing biotechnology innovation while raising critical questions about data quality, accessibility, and bias in training datasets.

Simultaneously, we witness the emergence of general-purpose humanoid robots as AI transitions from purely digital intelligence to embodied physical agents. Through detailed analysis of Tesla Optimus and Figure AI’s development trajectories, we examine how techniques from autonomous vehicles, reinforcement learning, and imitation learning are enabling robots to perform complex manipulation tasks and navigate human environments. These systems represent a fundamental shift from special-purpose industrial robots to adaptable platforms capable of learning through demonstration—a capability made possible by the same transformer architectures and neural network advances driving the LLM revolution. The technical specifications, development timelines, and commercial strategies of these platforms illuminate both the accelerating pace of progress and the substantial engineering challenges that remain.

The convergence of these domains carries profound implications extending far beyond technological capability. The humanoid robot market, projected to grow at 62.5% CAGR through 2030, signals massive industrial transformation and labor market restructuring. In biotech, AI’s ability to design novel proteins, predict drug-target interactions, and personalize treatments promises to revolutionize healthcare delivery—yet depends critically on equitable access to high-quality biological datasets and computational resources. Both frontiers raise urgent questions about human-AI collaboration, workforce displacement versus augmentation, data sovereignty, and the ethical frameworks needed to govern technologies that can directly modify biological systems and physical labor. This lecture provides technical grounding in these convergent revolutions while examining their societal, economic, and philosophical dimensions.