Definition
Unlike static systems with fixed architectures or predetermined execution paths, dynamic AI systems can modify their operations during runtime. In deep learning, dynamic computation graphs allow the network structure to change depending on the input, enabling variable-length sequence processing. In broader contexts, dynamic systems might adjust hyperparameters on the fly or alter their decision boundaries based on new data streams. This flexibility enhances robustness and efficiency in non-stationary environments where conditions evolve continuously.
Summary
Dynamic refers to AI systems or computational graphs that adapt their structure, behavior, or execution flow in real-time based on input data or environmental changes.
Key Concepts
- Runtime Adaptation
- Variable Computation
- Non-Stationary Environments
- Flexible Architecture
Use Cases
- Processing variable-length sequences in RNNs or Transformers
- Real-time anomaly detection systems
- Adaptive control systems in robotics