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Generative AI creates new content by learning patterns from training data and responding to prompts or other inputs.
Generative artificial intelligence (GenAI) is a subfield of AI that uses generative models to create new digital content, including text, images, video, audio, software code, translations, and 3D assets. These models learn patterns and structures from large training datasets and generate outputs in response to inputs, often natural-language prompts. Recent advances in deep learning, especially transformer-based large language models, have significantly expanded the availability and capabilities of generative AI tools. Generative AI can support content creation, software development, healthcare, finance, education, entertainment, marketing, robotics, scientific discovery, and product design. Its capabilities include natural-language generation, image and video synthesis, speech and music production, code generation, data creation, motion planning, and the proposal of mathematical or scientific solutions. However, outputs may be inaccurate or misleading, and the technology raises concerns about deepfakes, cybercrime, copyright, privacy, bias, misinformation, and environmental costs.
Generative AI creates new content by learning patterns from training data and responding to prompts or other inputs.
It supports multiple output types, including text, images, video, audio, music, translations, software code, and 3D models.
Transformer-based large language models and other deep-learning architectures have driven the rapid growth of generative AI since the early 2020s.
Applications span chatbots, content creation, programming, healthcare, finance, entertainment, robotics, mathematics, materials science, and business automation.
Generative AI can produce incorrect information or hallucinations and may be misused for deepfakes, manipulation, cybercrime, and copyright infringement.
Generative models include generative adversarial networks, variational autoencoders, and transformer-based models such as GPT systems.
A branch of AI that generates new digital content based on learned patterns in training data.
A machine-learning model that learns the distribution or structure of data and produces new examples resembling it.
An AI model trained on extensive text datasets to understand and generate natural language.
A neural-network architecture that uses self-attention to process relationships between tokens efficiently and generate coherent outputs.
An instruction or input, often written in natural language, that guides a generative AI system's output.
An inaccurate, fabricated, or unsupported output produced by a generative AI model.
A generative technique in which a generator creates synthetic data while a discriminator learns to distinguish it from authentic data.
A generative model that represents data as probability distributions in a latent space and samples from them to create or reconstruct data.
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