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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.
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.
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
Document capacity / Batch processing: Number of documents the system can process per unit of time. [citation needed] Check intensity: 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
Data is typically distinguished in spatial data and time-series data, the former can be things like images, maps, graphs, etc. the latter can be e.g. stock-price or a voice recording. Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial.
The AI technology is designed to identify hidden connections and links between research topics. [14] Like the previously cited search engines, Semantic Scholar also exploits graph structures, which include the Microsoft Academic Knowledge Graph , Springer Nature's SciGraph , and the Semantic Scholar Corpus (originally a 45 million papers corpus ...
Another study [37] showed that Turnitin failed to detect text produced by popular free Internet-based paraphrasing tools. Besides, more sophisticated machine learning techniques, such as automated paraphrasing , can produce natural and expressive text, which is virtually impossible for Turnitin to detect.
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