How does parallel computing affect AI performance?

Can you explain how parallel computing improves AI performance?

Parallel computing allows multiple processes to run simultaneously, which speeds up data processing and model training in AI.

What are some examples of parallel computing in AI applications?

Examples include training deep learning models on GPUs, distributed computing for large datasets, and real-time data processing.

Are there any limitations to using parallel computing in AI?

Yes, limitations include increased complexity in programming, potential for bottlenecks, and challenges in data synchronization.

How does parallel computing compare to traditional computing for AI tasks?

Parallel computing significantly outperforms traditional computing for AI tasks, especially in handling large datasets and complex algorithms.