Sunghee Yun (He/Him)

Co-founder & CTO @ Erudio Bio, Inc., Chief Business Development Officer @ WeStory.ai, Advisor & Evangelist @ CryptoLab, Inc., KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, Adjunct 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

Abstract

This presentation explores the intersection of Industrial AI and biotechnology, examining how artificial intelligence is transforming both manufacturing and biological sciences. The first section delves into Industrial AI (inAI), which focuses on practical applications for customer value creation, productivity improvement, and cost reduction across industries like semiconductors, steel, and oil & gas. Key challenges in manufacturing AI include handling challenging data characteristics such as huge volumes, multi-modality, high velocity requirements, severe data drift, and quality issues. The presentation details computer vision applications for defect inspection and automatic feature measurement, as well as time-series machine learning for virtual metrology, predictive maintenance, and root cause analysis, highlighting Gauss Labs’ successful virtual metrology solution that outperformed competitors at major companies like Samsung and Intel.

The biotechnology section examines how AI is revolutionizing biological sciences by processing vast amounts of unstructured genetic data, enabling researchers to “read and write” DNA more effectively. As a multidisciplinary field leveraging biology, genetics, quantum computing, and robotics, biotechnology benefits significantly from AI’s ability to analyze exponentially growing genetic sequence databases and predict biological processes. The presentation covers emerging trends including personalized medicine based on individual genetic profiles, AI-driven drug discovery that streamlines traditional development processes, synthetic biology for designing custom microorganisms, and regenerative medicine applications. Key challenges include data quality, bias in genetic datasets, and the need for robust data integration platforms to combine genomic, proteomic, and clinical information.

The final section addresses current AI industry dynamics, particularly the financial challenges facing companies like OpenAI with their projected $8.5B expenses versus $3.5-4.5B revenue, reflecting the resource-intensive nature of cutting-edge AI research. The presentation discusses how open-source models like Meta’s Llama 3.1 are democratizing AI capabilities while creating business model challenges for proprietary AI companies. The evolving relationship between tech giants and AI startups shows increasing consolidation through partnerships, mergers, and ecosystem development, as companies seek to balance innovation with sustainable business models. These trends highlight the shift from purely model-focused approaches to specialized services and applications built on top of AI foundations.