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AI & MLSpecializedAIML-LM-029

Synthetic Data and AI Feedback

Models generating training data and critiques for other models, and the risks of feedback loops.

Difficulty
5 / 5
Importance
3 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can describe a synthetic-data or AI-feedback pipeline and name two failure modes such as model collapse or bias amplification.

The route to this topic

Route AIML-LM-029
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Classification Framing Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Scores and Logits Loss and Fitting: Squared Error Sigmoid and Probabilities Multiclass Scores and Softmax The Artificial Neuron Autoregression Objective Functions and Empirical Risk Logistic Regression Activation Functions Next-Token Prediction Gradient Descent in Machine Learning Likelihood The Multilayer Perceptron Binary Cross-Entropy Cross-Entropy Loss Stochastic and Mini-batch Optimization Transfer Learning The Language-Modeling Objective Fine-Tuning Pretraining Supervised Fine-Tuning Preference Optimization Reward Models from Human Preferences Synthetic Data and AI Feedback