Zvi Topol
Data professional with 15+ years of experience in software, data engineering, analytics, data science, and AI/ML. Graduate degrees in Computer Science and Statistics. Domain knowledge and background in multiple verticals, including media and entertainment, marketing analytics, and finance.
Session
AI red teaming is crucial for identifying security and safety vulnerabilities (e.g., jailbreaks, prompt injection, harmful content generation) of Large Language Models. However, manual and brute-force adversarial testing is resource-intensive and often inefficiently consumes time and compute resources exploring low-risk regions of the input space.
This talk introduces a practical, Python-based methodology for accelerating red teaming using model uncertainty quantification (UQ).