Deep learning is a type of machine learning that involves the use of artificial neural networks to learn and make decisions. It is a subfield of machine learning that has gained significant attention in recent years due to its ability to solve complex problems and achieve state-of-the-art results in a wide range of applications, including image and speech recognition, natural language processing, and machine translation.
Deep learning algorithms are inspired by the structure and function of the human brain, and are composed of multiple layers of interconnected nodes, or “neurons,” that process and transmit information. These layers of neurons are often organized into “hidden” layers, which are responsible for extracting features and patterns from the data, and an output layer, which produces the final prediction or decision.
Deep learning algorithms are trained using large datasets, and are able to learn complex patterns and relationships in the data by adjusting the weights and biases of the connections between the neurons in the network. This process is known as “backpropagation,” and involves the use of an optimization algorithm to minimize the error between the predicted output and the true output.
Deep learning has been applied to a wide range of applications, including image and speech recognition, natural language processing, and machine translation. It has the potential to revolutionize many industries and has already been adopted in a variety of sectors, including healthcare, finance, and retail. However, deep learning algorithms can be resource-intensive to train and may require significant amounts of data and computing power. They also raise ethical and social concerns related to privacy, bias, and the potential for automation to displace human workers. The following are examples.
An AI learns to tell the difference between 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. The AI decides the person is speaking Cardiff English and invokes an AI that is learning to speak Cardiff 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. There might be hundreds of such specialized visual recognition AI 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.
A housekeeping robot might use the opinions of a large number of AI in order to complete everyday tasks. For example, the robot might have a few AI devoted to dog psychology that help it deal with the household pet over the course of its day.