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  2. Artificial intelligence content detection - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence...

    Artificial intelligence detection software aims to determine whether some content (text, image, video or audio) was generated using artificial intelligence (AI).. However, the reliability of such software is a topic of debate, [1] and there are concerns about the potential misapplication of AI detection software by educators.

  3. Content similarity detection - Wikipedia

    en.wikipedia.org/wiki/Content_similarity_detection

    How often and for which types of document fragments (paragraphs, sentences, fixed-length word sequences) does the system query external resources, such as search engines. Comparison algorithm type: The algorithms that define the way the system uses to compare documents against each other. [citation needed] Precision and recall

  4. GPTZero - Wikipedia

    en.wikipedia.org/wiki/GPTZero

    GPTZero uses qualities it terms perplexity and burstiness to attempt determining if a passage was written by a AI. [14] According to the company, perplexity is how random the text in the sentence is, and whether the way the sentence is constructed is unusual or "surprising" for the application.

  5. Undetectable.ai - Wikipedia

    en.wikipedia.org/wiki/Undetectable.ai

    Undetectable AI (or Undetectable.ai) is an artificial intelligence content detection and modification software designed to identify and alter artificially generated text, such as that produced by large language models.

  6. Turnitin - Wikipedia

    en.wikipedia.org/wiki/Turnitin

    Teachers can further set assignment-analysis options so that students can review the system's "originality reports" before they finalize their submission. A peer-review option is also available. Some virtual learning environments can be configured to support Turnitin, so that student assignments can be automatically submitted for analysis.

  7. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    Some unsupervised summarization approaches are based on finding a "centroid" sentence, which is the mean word vector of all the sentences in the document. Then the sentences can be ranked with regard to their similarity to this centroid sentence. A more principled way to estimate sentence importance is using random walks and eigenvector centrality.

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