The room is white—impossibly white. Racks glide past on silent rails: session notes, CSV exports, video frames, time-stamped events. A cable snakes from the ceiling into a black console labeled PIPELINE. A green prompt blinks: LOAD_DATA? You jack in. The conveyor whirs to life. By the end of the belt, a tidy “recommendation” card prints out.
The belt never stops... Should you trust the card?
Welcome to the Thirteenth Issue of Chiron
We’ve taken the red pill: neural networks aren’t brains, fluency isn’t validity, and “confidence” is a learned topography. This week, we open the machine room and walk the end-to-end AI pipeline—the actual sequence that turns raw behavioral data into model outputs. You’ll see where your behavior-analytic expertise must sit in the loop, and how to translate each step into familiar ABA workflows (measurement systems, IOA, treatment integrity, maintenance, and generalization).
The Six Stations (with ABA analogs)
Below is the canonical flow you’ll encounter in any serious AI build. Each station maps cleanly onto things you already do as an analyst.
Memorize the rhythm: Collect → Label → Engineer → Train → Evaluate → Deploy/Monitor. Miss any one, and you’re not in The Matrix, you’re in a mirage.
