The AIForge cast
Each friend shows you one idea by how they act. Meet them all and see how the ideas fit together.
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Sort Classifier — the simplest ML; putting things in categories -
Feed Training data — the examples a model learns from; garbage-in-garbage-out -
Skew Bias — where AI systems go wrong when training examples lean -
Edge Model limitations — what a model can't do; modeling 'I don't know' as a good answer -
Stake Ethics — what's at stake in deploying AI; people choosing, not rules-from-the-sky -
Split Train/test split — keep some examples hidden to tell learning from memorizing -
Cue Features — a model decides from the clues you give it; choose good clues -
Sure Confidence — a model reports how sure it is; low confidence means check, not trust -
Mirage Hallucination — when a model confidently makes something up that sounds true but isn't; check, don't just trust -
Rote Overfitting — when a model memorizes the exact examples instead of learning the general idea