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  2. SCIgen - Wikipedia

    en.wikipedia.org/wiki/SCIgen

    SCIgen is a paper generator that uses context-free grammar to randomly generate nonsense in the form of computer science research papers.Its original data source was a collection of computer science papers downloaded from CiteSeer.

  3. Semantic Scholar - Wikipedia

    en.wikipedia.org/wiki/Semantic_Scholar

    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 ...

  4. Paper generator - Wikipedia

    en.wikipedia.org/wiki/Paper_generator

    A paper generator is computer software that composes scholarly papers in the style of those that appear in academic journals or conference proceedings. Typically, the generator uses technical jargon from the field to compose sentences that are grammatically correct and seem erudite but are actually nonsensical. [ 1 ]

  5. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    A paper published by researchers at Amazon Web Services AI Labs found that over 57% of sentences from a sample of over 6 billion sentences from Common Crawl, a snapshot of web pages, were machine translated. Many of these automated translations were seen as lower quality, especially for sentences were translated across at least three languages.

  6. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]

  7. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    The original Transformer paper reported using a learned positional encoding, [69] but finding it not superior to the sinusoidal one. [1] Later, [ 70 ] found that causal masking itself provides enough signal to a Transformer decoder that it can learn to implicitly perform absolute positional encoding without the positional encoding module.

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