Generative AI

Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, computer code or other forms of digital data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.

The prevalence of generative AI tools has increased significantly since an AI boom beginning in the early 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include chatbots such as ChatGPT, Claude, Microsoft Copilot, DeepSeek, Doubao, Google Gemini, Grok, Kimi and Qwen; text-to-image models such as DALL-E, Firefly, Stable Diffusion, and Midjourney; and text-to-video models such as Veo, LTX and Sora.

Companies in a variety of sectors have used generative AI, including those in software development, healthcare, finance, entertainment, sales and marketing, writing, and product design.

Generative AI has been used for cybercrime, and to deceive and manipulate people through fake news and deepfakes. Generative AI models have been trained on copyrighted works without the rightholders' permission. Many generative AI systems are reliant on large-scale data centers, whose environmental impacts include electronic waste, consumption of fresh water for cooling, and high energy consumption that is estimated to be growing steadily.

History

Early history

The origins of algorithmically generated media can be traced to the development of the Markov chain, which has been used to model natural language since the early 20th century.