"Generative AI is a kind of artificial intelligence that creates new content, including text, images, audio, and video, based on patterns it has learned from existing data. Today's generative models are typically built on foundation-model architectures such as large-language models (LLMs) and multimodal systems, enabling them to carry on conversations, answer questions, write stories, generate code, and produce images or videos from brief prompts."
"Generative AI is different from discriminative AI, which draws distinctions between different kinds of input. Where discriminative AI answers questions like "Is this image of a rabbit or a lion?", generative AI instead responds to prompts such as "Describe to me how a rabbit and lion look different from one another" or "Draw me a picture of a lion and a rabbit sitting next to each other""
Generative AI produces novel content—text, images, audio, and video—by learning statistical patterns from large datasets. Foundation models such as LLMs and multimodal systems enable conversational behavior, question answering, storytelling, code generation, and image or video synthesis from short prompts. Generative systems contrast with discriminative models by generating outputs rather than classifying inputs. Adoption has expanded from chatbots and image art to enterprise applications including content creation, software development, customer support, and analytics. Key limitations include alignment failures, hallucinations, governance challenges, and data-integration hurdles that reduce reliability and require careful implementation and oversight.
Read at InfoWorld
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