Scaling Laws
Scaling laws are empirical relationships showing how model performance improves predictably with increases in model size, data, and compute. They guide decisions about resource allocation in training large AI models.
Understanding Scaling Laws
Scaling laws in AI describe the predictable mathematical relationships between model performance and key factors like parameter count, dataset size, and computational budget. Research by teams at OpenAI and DeepMind has shown that language model loss decreases as a power law function of these variables, enabling researchers to forecast the performance of larger models before investing in expensive training runs. These findings have profoundly influenced the development strategy of major AI labs, guiding decisions about resource allocation and architecture design. Scaling laws help explain emergent behavior, where capabilities appear suddenly at certain model sizes. They also inform the economics of AI chip development and the design of efficient training infrastructure. Understanding scaling laws is essential for benchmark interpretation, as models trained at different scales are not directly comparable, and for predicting when certain capabilities of artificial intelligence systems will become practically achievable.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.