Generative AI, or Generative Artificial Intelligence, is a subfield of artificial intelligence (AI) that focuses on creating systems and algorithms capable of generating content that is similar to what a human might produce. This content can include text, images, audio, and even videos. Generative AI systems use machine learning techniques, particulasrly deep learning, to generate this content.

Types of Generative Models:

Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that are trained in a competitive manner. The generator tries to create content that is indistinguishable from real data, while the discriminator tries to distinguish between real and generated data. This adversarial process helps the generator improve over time.

Variational Autoencoders (VAEs): VAEs are probabilistic models that aim to learn the underlying distribution of data. They generate new data points by sampling from this learned distribution.

Recurrent Neural Networks (RNNs) and Transformers: These are popular architectures for generating sequences of data, such as text or music. RNNs use recurrent connections, while Transformers employ attention mechanisms to capture long-range dependencies in the data.

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