Complexity Scaling

Complexity scaling investigates how the performance of artificial intelligence systems, particularly deep learning models, changes with increasing computational resources and model complexity. Current research focuses on understanding critical thresholds where increased complexity leads to performance plateaus or instability, exploring model architectures like expressive attention mechanisms and multi-path transformers to efficiently manage this scaling, and developing robust benchmarks for evaluating complexity in diverse applications such as autonomous vehicles and speech denoising. These studies aim to improve the design and deployment of AI systems by optimizing performance relative to computational cost and identifying potential limitations in scaling towards more general intelligence.