Model Training
Self-Supervised Learning
Training an AI on unlabeled data by having it predict missing parts of the data, like a fill-in-the-blank quiz at scale.
Definition
A training paradigm where the model creates its own labels from the structure of the data. Examples include masked language modeling (BERT) and next-token prediction (GPT). Eliminates the need for manual labeling.
Why it matters
The reason foundation models are possible, no human could label the trillions of examples needed to train GPT-4.
Related terms in Model Training
Adversarial Training
Teaching an AI to defend itself by constantly attacking it with tricky or malicious inputs during training.
Autoencoders
A neural network that learns to compress data into a small code and then unzip it back to the original.
Distillation (Model Distillation)
Teaching a small, fast AI model to mimic a large, expensive one, so you get similar results at a fraction of the cost.
Dropout
Randomly turning off some neurons during training so the AI doesn't over-memorize and can generalize better.
From vocabulary to outcomes
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