So if we see deep learning it is an approach to artificial intelligence. it involves an artificial intelligence which acts as in input to other artificial intelligence. there is a large number of machine learners which gives an approach to other solutions in this architecture which is difficult. The following are illustrative examples.
Artificial intelligence learns to explain the difference between multiple languages. It decides a person is speaking English and invokes an AI that is learning to tell the difference between different regional accents of English. In this way, each conversation can be interpreted by a highly specialized AI that has learned their dialect.
The street in front of a moving vehicle is interpreted by a large number of specialized AI. For example, one learner is only training to recognize pedestrians, another is learning to recognize street signs. you might see multiple numbers of visual recognition AI which is specialized that all feed their opinions into an AI that interprets driving events. In theory, a single car could use the opinions of thousands or even millions of individual AI as it navigates a street.
For completing the everyday task a housekeeping robot might use the opinions of a large number of AI. For example, the robot might have a few AI devoted to dog psychology that helps it deal with the household pet over the course of its day.
Overview of deep learning:
Artificial intelligence that contains many specialized artificial bits of intelligence that act together in a coordinated way.
The examples above are somewhat simplified. For example, a self-driving car might have several levels of learning just to recognize street signs. At each level, you could potentially have a large number of AI with results decided by a committee machine.