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2. Generative artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Generative_artificial...

   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 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...

3. Generative adversarial network - Wikipedia

   en.wikipedia.org/wiki/Generative_adversarial_network

   A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [ 1 ]

4. Category:Generative artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Category:Generative...

   Main page; Contents; Current events; Random article; About Wikipedia; Contact us

5. Microsoft Copilot - Wikipedia

   en.wikipedia.org/wiki/Microsoft_Copilot

   Microsoft Copilot is a generative artificial intelligence chatbot developed by Microsoft. Based on the GPT-4 series of large language models , it was launched in 2023 as Microsoft's primary replacement for the discontinued Cortana .

6. Artificial general intelligence - Wikipedia

   en.wikipedia.org/wiki/Artificial_general...

   Weak AI hypothesis: An artificial intelligence system can (only) act like it thinks and has a mind and consciousness. The first one he called "strong" because it makes a stronger statement: it assumes something special has happened to the machine that goes beyond those abilities that we can test.

7. Generative pre-trained transformer - Wikipedia

   en.wikipedia.org/wiki/Generative_pre-trained...

   Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.