Scale By The Bay 2019: Rob Monro, Discovering Your Model's Known Unknowns and Unknown Unknowns

ai.bythebay.io Nov 2025, Oakland, full-stack AI conference Selecting the right training data for human review is known as Active Learning. Almost every company invents (or reinvents) the same Active Learning strategies and too often they repeat the same avoidable errors. This talk will share some common Active Learning strategies, with PyTorch examples, covering: Least Confidence Sampling, Entropy-based Sampling, Cluster-based Sampling, Model-based Outliers, Monte Carlo Dropouts (Deep Bayesian Active Learning), Representative Sampling, and Sampling for Real-World Diversity. Rob Munro Silicon Valley Humanitarian and Technology experience includes: working in post-conflict development in Liberia and Sierra Leone for UNHCR; researching health communications in Malawi; software development supporting endangered languages; running crowdsourced translation following disasters in Haiti, Pakistan and MENA; hosting aerial image analysis for FEMA following Hurricane Sandy; completing a Stanford PhD focused on AI for low resource languages; tracking epidemics globally; founding Silicon Valley companies focused on technology for all languages for F100s and UNICEF; running product for NLP and Machine Translation at AWS.