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Semantic Scholar is a research tool for scientific literature powered by artificial intelligence. It is developed at the Allen Institute for AI and was publicly released in November 2015. [ 2 ] Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated ...
The Research Brief is a short take about interesting academic work. The big ideaThe scientific community worldwide has mobilized with unprecedented speed to tackle the COVID-19 pandemic, and the ...
When used as an editing tool for scientific papers, Authorea will automatically format a paper in the preferred style and provide files suitable for online submission to peer-reviewed journals and conferences. Over 40 publisher- and journal-specific styles are currently supported. [3]
Full-text aggregator of open access journals and papers (>17,000 journals) from all academic disciplines. Free No [9] Paperity Sp. z o.o. Semantic Scholar: Multidisciplinary: 8,100,000 [10] (200,000,000 metadata [11]) Mostly computer science and biomedical publications. Powered by semantic analysis. Free Semi-free [10] Allen Institute for ...
Writing review articles, for example, is a task well suited to AI: it involves sifting through the existing research on a subject, analyzing the results, reaching a conclusion about the state of ...
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.
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. All elements of the papers are formed, including graphs, diagrams, and citations.
DeepMind is known to have trained the program on over 170,000 proteins from the Protein Data Bank, a public repository of protein sequences and structures.The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. [2]
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