Computer System for Learning Process of Artificial Intelligence
Abstract
Artificial Intelligence (AI) is part of computer science to develop algorithms that mimic and display human cognitive skills. Many AI deep learning (DL) models can find acceptable to excellent approximation solution to the given problem by learning from an extensive dataset. Typically, the learning process requires a dataset that experts made labels on and may take time to perform. Deep learning can effectively solve problems even from an unknown dataset after finishing learning. The learning process requires updating many parameters simultaneously many times before the completion. Therefore, the massive parallel-updated requirement needs to be processed in the graphics processing unit (GPU), which has many parallel units, or cores, to perform specialized computation, compared to just a few in the computer processing unit (CPU) designed for general computation A computer system for the DL learning process can be leased on a computing cloud system or implemented on site. For medical imaging processing, we found that having the computer system on-site for the learning is more cost-effective than using a cloud system and more complies with patient privacy and confidentiality regulation.
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