Latest Articles
Teaching Machine Learning Everyday Reasoning
Learn to teach machine learning through everyday reasoning: turn familiar decisions into data, features and labels, uncertainty, bias, and feedback loops.
Madison Evans
AI Explains Language Processing
AI explains language processing in modern LLMs—tokenization, embeddings, next-token prediction, attention, and why bias and hallucinations happen.
Georgia Vincent
Understanding Black-Box AI Models
Understand black-box AI models: why opacity is risky, how global vs local explanations work, where interpretability misleads, and how to operationalize trust.
Georgia Vincent
Large-Scale Databases Enable Faster AI Search
Learn how large-scale databases speed up AI search by pushing filters into indexing, using hybrid keyword+vector retrieval, improving caching/locality, and managing freshness at scale.
Isabella Moss
Biologically Inspired AI Models Mimic Natural Learning
Learn how biologically inspired AI models enable natural learning—self-supervision, continual adaptation, robustness under drift, and efficient neuromorphic options.
Alison Perry
Rule-Based Tests Reveal AI Judgment Gaps
Learn how rule-based tests expose AI judgment gaps: enforce pass/fail contracts for policy compliance, edge cases, paraphrase consistency, and tool truth.
Noa Ensign
KAN Demystified: Exploring the Math Behind Kolmogorov-Arnold Networks
Explore the math behind Kolmogorov-Arnold Networks and their function approximation power.
Tessa Rodriguez