Episode Details

A correction to episode 1 becomes the perfect teaching moment: the one untagged number sailed through quality checks while all the tagged numbers held up. The hosts explore why data provenance is everything in ML systems, then dive into ground truth problems lurking in labeling pipelines. The system worked. The system also had a gap. 00:00 - Episode 1 correction and the gap that caught them 03:30 - Why data provenance tags actually matter 08:00 - Ground truth and the circular labeling problem 12:00 - What makes something truly an ML problem --- Sources & further reading: • game-senser-fable repo — docs/podcast/LEARNING-CONTEXT.md, including the • guist-bot repo — data/series/ml-volleyball/ERRATA.md (the three-years correction • 2026-07-27 corrections block (source document for the series) • and the ground-truth clarification, from Joey, 2026-07-27) • game-senser-fable repo — JOURNAL.md (89-variant sweep; skill classifiers parked twice • rally-grammar decoder 44%→64.8%; serve identity 100%) • game-senser-fable repo — GAPS.md (the prefilled-labeler circularity; digs 1/22) • game-senser-services repo — docs/CV_PIPELINE.md (the 18-experiment history and the • hand-written fusion constants) • The history of email spam filtering, rules-to-statistical-learning — [GENERAL-PRACTICE] • not checked against a primary source for this episode This podcast episode was fully generated by AI — research, script, voices, and production. Built with Claude, Piper TTS, and automated pipeline tooling.