Scatter Plot
Machine Learning
Empirical principles describing how AI model performance improves with increases in model size, data, and compute.
Detailed Explanation
Scaling Laws in machine learning describe empirical relationships showing that as models grow larger, are trained on more data, and use increased computational resources, their performance typically improves predictably. These principles help researchers optimize model architectures, determine necessary resources, and forecast future performance gains, guiding efficient development of more powerful AI systems.
Use Cases
- •Use case: Planning model upgrades by estimating how larger models will improve accuracy with more data and compute resources.
Related Terms
Other terms in the Machine Learning category