Speaker

Seong Joon Oh (오성준)

Title

Deploying General AI in the Private World

Abstract

General-purpose AI has achieved remarkable capabilities but struggles in real-world private settings. This talk examines three barriers to deployment. 1. Human-to-machine communication. Encoding human intent into AI remains hard. Modularity and agentic architectures offer promising solutions. 2. Machine-to-human communication. Users must understand AI decisions. This drives research in explainable AI and training data attribution. 3. Privacy and security. Deployment in sensitive domains is blocked by PII leakage, adversarial attacks, and membership inference risks. Regulatory compliance adds further constraints. I present our recent work on these challenges. Future directions. AI research is shifting from model scaling to adaptation, personalisation, and agent-based interfaces. I conclude with a new direction - separating knowledge from intelligence in AI systems. Software engineering decoupled code from data 50 years ago. A similar decoupling in AI could unlock transparent, editable, and trustworthy systems.

Bio

Seong Joon Oh is an associate professor at KAIST AI. He believes trustworthiness is the last barrier to the AI productivity revolution. He researches Scalable Trustworthy AI: making AI systems reliable, explainable, and aligned with human intent at scale. He has led the Scalable Trustworthy AI (STAI) group since 2022. He moved the group from the University of Tübingen to KAIST in February 2026. He was a research scientist at NAVER AI Lab for 3.5 years. He received his PhD in computer vision and machine learning at the Max-Planck Institute for Informatics in 2018, under the supervision of Bernt Schiele and Mario Fritz, with a focus on the privacy and security implications of CV and ML (Thesis). He received the Master of Mathematics with Distinction in 2014 and Bachelor of Arts in Mathematics as a Wrangler in 2013, both at the University of Cambridge.

Language

English (Offline)