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

Authors - Kyungpook National University, School of Computer Science

Feedback on AI Lecture Series

Overall Impression

The five AI lectures delivered over the past four days covered an exceptionally broad and deep range of topics—from the history of AI, core algorithms, and the nature and limitations of LLMs, to ethical and philosophical discussions, and Silicon Valley’s technological culture. The lectures were received by students not merely as knowledge transfer, but as a transformative experience that expanded their entire framework of thinking.

The following aspects were particularly impressive to students:

  • Viewing AI not as an algorithm or tool, but as a “social system”
  • Understanding the essence of LLMs (conditional probability models), their limitations, and recognizing hallucination as a structural characteristic rather than a defect
  • The interactive, discussion-based lecture format centered on Q&A rather than theory-focused instruction
  • Insights into Silicon Valley culture, trust in engineers, and the significance of physical proximity and clustering
  • Real-world case studies from bio AI, multimodal systems, and robotics

Many students expressed sentiments such as:

Deep insights that are difficult to find on the web

My entire perspective on AI has completely changed

They reported gaining tremendous inspiration both academically and in terms of career direction.

Key Impressive Topics

Paradigm Shift in Understanding LLMs and Hallucination

  • Understanding hallucination not simply as an error or bug, but as the structural engine that enables LLM creativity and performance
  • The discussion that “a model with hallucination completely suppressed might lose its appeal”
  • Through comparisons with human cognitive biases and confirmation bias, gaining a multidimensional understanding of commonalities and differences between AI and human intelligence

Historical and Structural Understanding of AI Technology Evolution

  • The progression from Symbolism → Connectionism → Transformer → LLM
  • Clear understanding that performance breakthroughs result not from simple model size increases, but from the combination of architectural changes + data + computing resources
  • Explanation of nonlinear performance improvements that occur when crossing critical thresholds

Ethical, Philosophical, and Social Discussions Beyond Technology

  • The risks of AI anthropomorphization
  • Legal responsibility, fairness, and issues in social decision-making applications
  • Awareness of AI not as “well-functioning technology” but as a system with social influence

Requests for Advanced Learning

Common Requests for the Remaining Three Lectures

Student requests can be organized into four main axes:

Real-World, Production-Level AI Use Cases from Silicon Valley

This was the most commonly requested topic among students.

  • How Big Tech and startups are actually integrating LLMs into real services
  • How RAG, AI Agents, and multimodal AI are implemented not as conceptual ideas, but as operational workflows and technology stacks
  • Production environment challenges (bottlenecks, data pipelines, operational issues) beyond simple demos

We’re curious about how it’s actually being used in the field, more than theory.

Business Impact and Market Changes Driven by AI Technology

  • How generative AI and LLMs are reshaping:
    • Corporate revenue structures
    • Existing business models
    • Sources of competitive advantage
  • How technological possibilities are transformed into “monetizable value” through strategy and decision-making
  • How technology–market–organization align in Silicon Valley

Realistic Stories About Career Paths, Careers, and Entrepreneurship

Many students showed significant interest in your personal choices and experiences.

  • The decisive moment that led you to decide on entrepreneurship
  • The most challenging moments in the startup process and how you overcame them
  • Criteria for whom you would recommend entrepreneurship
  • Realistic routes and examples for Korean undergraduate students to enter Silicon Valley startups/Big Tech
  • Competencies currently required in the Silicon Valley job market

Questions About “Human Capabilities That Remain in the AI Era”

  • To what extent can developers delegate to AI, and what must they verify?
  • Beyond simple coding ability, what are the enduring core competencies (thinking skills, questioning ability, verification skills)?
  • Human roles and tasks that will become more important in the AI era

Conclusion and Words of Gratitude

This lecture series became a turning point that helped students recognize AI technology as “an entity to think with” rather than “a subject to learn.”

Many students reported that they began to seriously reconsider:

  • Their major choices
  • Whether to pursue graduate studies
  • Entrepreneurship and career directions

On behalf of the students in the School of Computer Science at Kyungpook National University, we sincerely thank you for sharing deep insights and candid perspectives despite your busy schedule.

We look forward to the remaining lectures with great anticipation.

Appendix

Detailed Student Feedback and Advanced Learning Requests

Student Key Takeaways Topics for Future Lectures / Questions
Jin - Understood the big picture from AI history to industrial application Real Silicon Valley LLM use cases, product workflows and tech stacks for RAG·AI Agent
Lee - Impressive flow of AI development (Transformer~LLM), Silicon Valley innovation ecosystem, and ethical/philosophical discussions Case studies on how AI creates business impact and revenue structures
Lee - Deep explanation of LLM essence and structural characteristics of hallucination How Silicon Valley’s tech culture and trust-based environment is formed
Park - Appreciated ethical/social issues and LLM principles understanding, discussion-based class format History and current trends in RL and robotics
Yang - Understanding KNN limitations and data importance, concept clarification of hallucination through discussion Motivation for entrepreneurship, startup process story, career choice process
Lee - Great inspiration from free Q&A and real case-based insights Startup triggers·challenges·overcoming, qualities suited for entrepreneurship, promising human roles in AI era
Nam - Particularly beneficial explanation connecting introductory knowledge and bio AI cases Reality of bio AI career path (demand·salary), outlook for Korean bio industry
Park - Technology + humanistic insights, discussions on LLM essence and limitations AI regulation·copyright debates, engineer qualities, competencies required in Silicon Valley job market
Hong - Essence of language model learning, value of hallucination, impressive Silicon Valley clustering culture Domains to delegate to AI vs. verify in practice, learning direction in AI era
Park - Systematic understanding from AI history to multimodal·robotics, impressive robot demo Real product applications of multimodal·agentic AI, tech stack·operational know-how
Park - Insight into viewing hallucination as engine of intelligence and importance of suppression·control Realistic routes for Korean undergrads to Silicon Valley internships·employment
Lee - Connectionism-centered AI development, social meaning of bio AI, emphasis on human verification capabilities AI research flow from theory→application, enduring core competencies, Dr. Yun’s driving force

This document represents a comprehensive summary of student feedback and learning requests following Dr. Sunghee Yun’s AI lecture series at Kyungpook National University.