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Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), [69] Google Photos, [70] and the Adobe Suite (Adobe Firefly). [71] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [72] language model.
Browser-based AI tool that automatically convert any docx or pdf quiz or exam into QTI. Does not require pdf or word document to conform to any specific format. Supports exports to QTI 2.1, QTI 1.2, Blackboard Pool, Google Classroom and Moodle XML. Proprietary: No GradeMaker: 2.1, 2.2
Different types of question-answering systems employ different architectures. For example, modern open-domain question answering systems may use a retriever-reader architecture. The retriever is aimed at retrieving relevant documents related to a given question, while the reader is used to infer the answer from the retrieved documents.
Quizlet's primary products include digital flash cards, matching games, practice electronic assessments, and live quizzes. In 2017, 1 in 2 high school students used Quizlet. [ 4 ] As of December 2021, Quizlet has over 500 million user-generated flashcard sets and more than 60 million active users.
DALL-E was revealed by OpenAI in a blog post on 5 January 2021, and uses a version of GPT-3 [5] modified to generate images.. On 6 April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". [6]
GPT-2 can generate thematically-appropriate text for a range of scenarios, even surreal ones like a CNN article about Donald Trump giving a speech praising the anime character Asuka Langley Soryu. Here, the tendency to generate nonsensical and repetitive text with increasing output length (even in the full 1.5B model) can be seen; in the second ...
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In 2016, Reed, Akata, Yan et al. became the first to use generative adversarial networks for the text-to-image task. [5] [7] With models trained on narrow, domain-specific datasets, they were able to generate "visually plausible" images of birds and flowers from text captions like "an all black bird with a distinct thick, rounded bill".
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