The reason that you can hardly use CPU in the field of artificial intelligence

Apologies for any confusion caused by my previous response. While GPUs are widely used for AI tasks due to their parallel processing capabilities, CPUs still play a crucial role in the field of artificial intelligence. Here are some reasons why CPUs are still relevant in AI:

1. Versatility and General Purpose Computing: CPUs are designed for general-purpose computing and can efficiently handle a wide range of tasks beyond AI. They are well-suited for tasks that require complex decision-making, managing system resources, running operating systems, handling I/O operations, and executing single-threaded or lightly parallelized workloads.
2. Control and Management: CPUs play a critical role in managing overall system operations and orchestrating the execution of various tasks, including AI computations. They handle task scheduling, memory management, I/O operations, and coordinate the overall functioning of the system.
3. Preprocessing and Data Manipulation: Before feeding data into AI models, preprocessing and data manipulation tasks are often performed. These tasks may involve data cleaning, feature extraction, data integration, or other operations that can be efficiently executed on CPUs.
4. Small-Scale Models and Inference: For small-scale AI models or when performing inference on limited datasets, CPUs can provide sufficient processing power. CPUs are capable of running AI models and making predictions, albeit at a slower pace compared to GPUs. In scenarios where real-time constraints are not critical, CPUs can still deliver satisfactory performance.
5. Deployment on Edge Devices: In edge computing scenarios, where AI models are deployed on resource-constrained devices such as smartphones, IoT devices, or embedded systems, CPUs are often the primary processing units. These devices may not have dedicated GPUs or may have power and thermal limitations that make CPUs a more practical choice.
6. Hybrid CPU-GPU Systems: Many AI applications leverage a combination of CPUs and GPUs in a hybrid configuration. CPUs handle tasks such as data preprocessing, model loading, and managing system-level operations, while GPUs are utilized for computationally intensive tasks like neural network training and inference. This hybrid approach optimizes resource utilization and takes advantage of the strengths of both CPU and GPU architectures.

In summary, while GPUs are extensively used for AI tasks due to their parallel processing capabilities, CPUs still have a vital role to play in AI applications. CPUs offer versatility, control, and are well-suited for various tasks, including data preprocessing, system management, and deployment on resource-constrained devices. The choice between CPUs and GPUs depends on the specific requirements, scale, and constraints of the AI task at hand.