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Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [2] These models often generate output in response to specific prompts .
Generative AI models can generate unique works of art and design, or assist in graphic design. Applications include dynamic generation of environments, characters or avatars, and special effects for virtual simulations and video games.
Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.
Generative models have many short-term applications . But in the long run, they hold the potential to automatically learn the natural features of a dataset, whether categories or dimensions or something else entirely. Let’s make this more concrete with an example.
Generative AI is a type of AI (such as ChatGPT) that can generate new forms of creative content, such as audio, code, images, text, simulations and videos.
Generative AI is the branch of AI that enables machines to learn patterns from vast datasets and then to autonomously produce new content based on those patterns. Although generative AI is fairly new, there are already many examples of models that can produce text, images, videos, and audio.
Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.
A Google AI expert answers common questions about generative AI, large language models, machine learning and more.
Generative models capture the joint probability p (X, Y), or just p (X) if there are no labels. Discriminative models capture the conditional probability p (Y | X). A generative model...
Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.