Generative AI (Generative Artificial Intelligence) is a type of Artificial Intelligence that is capable of creating new data based on patterns or instructions given to it. This type of AI is used for a variety of tasks such as image processing or creating new music compositions.
Generative AI generally use deep learning algorithms and can be divided into two main types: generative adversarial networks (GANs) and autoencoders (AEs). GANs are two-stage deep networks that contain two deep learning models, a generator and a discriminator. The generator model takes inputs, such as noise signals or random numbers, and creates new data that is not in the training data set. The discriminator model then helps to recognize between real data and the new data that was created. Autoencoders use a single layer of neural network that encodes inputs into a small number of output values and then reconstructs them from the compressed values.
Generative AI can be used to generate original artwork, create new music or stories, and develop medical treatments. As AI technology continues to improve, it is expected that generative AI will become increasingly popular in a range of industries. Generative AI can also be combined with reinforcement learning techniques, allowing AI agents to develop notions of reward and punishment when interacting with external environments.
In short, Generative AI is a type of Artificial Intelligence that produces new materials from existing data. This technology is useful in a variety of industries and has a wide range of applications. As generative AI technology continues to develop, it will have a significant impact on both business and society.
