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Place {{AI-generated|date=December 2024}} at the top of the disputed article, then explain your reasons on the page's talk page. If the date is not included it will likely be added later by a bot. Optional parameters include: |1= (or an empty parameter): changes the default descriptor (i.e., "article").
To change this template's initial visibility, the |state= parameter may be used: {{Generative AI | state = collapsed}} will show the template collapsed, i.e. hidden apart from its title bar. {{Generative AI | state = expanded}} will show the template expanded, i.e. fully visible.
See a monthly parameter usage report for Template:AI-generated in articles based on its TemplateData. TemplateData for AI-generated This tag is intended to identify articles that need extensive examination because they appear to have been generated using a large language model without rigorous scrutiny.
{}, {}, {}, and {} are easy to read "plain text" date and time templates that emit microformat dates for events. These templates are an alternative to the {{ Start date }} / {{ Birth date }} and {{ End date }} / {{ Death date }} templates that also emit microformats but require dates to be expressed in standard, unambiguous and international ...
Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), [85] Google Photos, [86] and the Adobe Suite (Adobe Firefly). [87] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [88] language model.
It is a text-based template language and thus can be used to generate any markup as well as source code. The Jinja template engine allows customization of tags , [ 3 ] filters (for formatting or transforming values [ 4 ] ), tests (for evaluating conditions [ 4 ] ), and globals . [ 5 ]
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.
To change this template's initial visibility, the |state= parameter may be used: {{ Google AI | state = collapsed }} will show the template collapsed, i.e. hidden apart from its title bar. {{ Google AI | state = expanded }} will show the template expanded, i.e. fully visible.
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