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Learn about artificial intelligence (AI), the field of computer science that develops and studies methods and software that enable machines to perceive and act in their environment. Explore the major goals, techniques, applications, and challenges of AI, as well as its origins and development over time.
Explainable artificial intelligence (XAI) is the ability to understand and explain the reasoning behind AI decisions or predictions. XAI aims to improve trust, transparency, and accountability of AI systems, and to overcome the "black box" problem of machine learning.
Learn about the history, applications, and challenges of generative AI, which is AI capable of creating text, images, videos, or other data using generative models. Explore examples of generative AI systems, such as chatbots, text-to-image models, and transformer networks.
Learn how AI is used in various fields such as internet and e-commerce, language translation, facial recognition, games, agriculture, and cyber security. Explore the examples, challenges, and benefits of AI applications with Wikipedia.
DALL-E, DALL-E 2, and DALL-E 3 are deep learning models that generate images from natural language descriptions. They are based on GPT-3 and CLIP, and can create various styles, manipulate objects, and generate watermarks.
OpenAI Codex is an artificial intelligence model that parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool, and is based on OpenAI's GPT-3 model, fine-tuned for use in programming applications.
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