objectives
2 free lessons tagged objectives across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Ranking and Objectives: What Should the Model Optimise?
The ranker is a prediction machine, and the hard question is what it should predict. Clicks are plentiful and poisonous, watch time bends toward length, likes are rare and unrepresentative. This lesson covers implicit feedback, the position bias baked into every training log, multi-objective ranking, and calibration.
What the algorithm optimizes, and why it drifts
A feed does exactly what it was told to maximize, which is rarely what anyone wanted. Learn which signals rankers actually weight and why implicit ones beat likes, how proxy objectives produce clickbait and rage-bait as correct answers to badly posed questions, and the mechanisms platforms use to pull an optimizer back toward what people value.