Artificial intelligence (AI) systems are algorithmic models designed to mimic or surpass certain aspects of human intelligence. AI can be built with a range of technological tools and techniques. Among these tools are deep learning (DL) methods. Deep learning is a method of processing data through artificial neural networks (ANNs). ANNs use a series of interconnected computational units, or neurons, similar to the neural network of the human brain. DO is realized by making ANNs multi-layered. This gives ANNs more complex and sophisticated data processing capabilities. DO is a technique used in many fields. In particular, it is used to achieve high accuracy rates on large datasets such as images, audio and natural language. DO involves many algorithms and methods, but the basic principle is always the same: Using ANNs to identify features of data and using them to perform data processing. The relationship between deep learning and artificial intelligence is that DO is a technique used in AI systems.

When DO is used in AI systems, these systems gain a more sophisticated and complex data processing and learning capability. The multi-layered nature of ANNs allows these systems to perform higher levels of abstraction and achieve better results. Therefore, DO influences many aspects of AI systems today and is critical for the future of AI. As a result, AI systems can be built using deep learning methods. Deep learning is realized through the multilayering of ANNs and enables AI systems to gain a more sophisticated and complex data processing and learning capability.