Definition
Energy has two primary meanings in AI. First, it denotes the electrical power required to run hardware, a growing concern for sustainability as models scale. Second, in statistical mechanics-inspired models like Boltzmann Machines or Energy-Based Models (EBMs), energy is a scalar value representing the compatibility between inputs and outputs, where lower energy states correspond to higher probability configurations. Understanding both aspects is vital for sustainable and theoretically sound AI development.
Summary
In AI, energy often refers to the computational power consumed during model training and inference, or a mathematical potential function in probabilistic models.
Key Concepts
- Carbon Footprint
- Energy-Based Models
- Hardware Consumption
- Probabilistic Inference
Use Cases
- Measuring the environmental impact of LLM training
- Training Boltzmann Machines for unsupervised learning
- Optimizing data center power usage for AI workloads