Batch Normalization
A technique that normalizes the inputs to each layer in a neural network, stabilizing and accelerating the training process by reducing internal covariate shift.
Introduced by Ioffe and Szegedy in 2015, batch normalization normalizes layer inputs to have zero mean and unit variance across a mini-batch. This allows higher learning rates and reduces sensitivity to weight initialization, often cutting training time in half. The technique has become standard in convolutional neural networks and many deep learning architectures. It also provides a mild regularization effect, reducing the need for dropout in some cases.
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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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