Overfitting
A common machine learning failure where a model learns the training data too well — including its noise and outliers — and performs poorly on new, unseen data.
Overfitting occurs when a model is too complex relative to the amount of training data, effectively memorizing rather than learning generalizable patterns. Techniques to combat overfitting include dropout, regularization, data augmentation, early stopping, and cross-validation. Large language models can memorize training data verbatim, raising both overfitting and copyright concerns. The bias-variance trade-off — balancing underfitting against overfitting — remains a fundamental challenge in all machine learning applications.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Machine Learning
A subset of AI where systems learn patterns from data rather than being explicitly programmed, improving their performance on tasks through experience without human-written rules.
Model Training
The computationally intensive process of teaching an AI model by feeding it data and adjusting its parameters to minimize errors, often requiring thousands of GPUs running for weeks or months.
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