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Document AI, also known as Document Intelligence, refers to a field of technology that employs machine learning (ML) techniques, such as natural language processing (NLP). [1] These techniques are used to develop computer models capable of analyzing documents in a manner akin to human review.
A 2016 research project called "One Hundred Year Study on Artificial Intelligence" named Wikipedia as a key early project for understanding the interplay between artificial intelligence applications and human engagement. [30] There is a concern about the lack of attribution to Wikipedia articles in large-language models like ChatGPT. [19]
An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic).
This may be directly involved with creation of text content, or in support roles related to evaluating article quality, adding metadata, or generating images. As with any machine-generated content, care must be used when employing AI at scale or in applying it where the community consensus is to exercise more caution.
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 ...
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