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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 ...
Based on the results, AI models seem to be a very powerful brainstorming tool, and via prompt engineering, these AI do allow an impressive amount of refinement to the plan. AI can also be a great tool as a pointer to potential sources and can remind editors of Wikipedia's content policy (NPOV, RS, etc.)
Black Forest Labs was founded in 2024 by Robin Rombach, Andreas Blattmann, and Patrick Esser, former employees of Stability AI. [2] [3] All three founders had previously researched the artificial intelligence image generation at Ludwig Maximillian University of Munich as research assistants under Björn Ommer.
Automated medical scribes (also called artificial intelligence scribes, AI scribes, digital scribes, virtual scribes, ambient AI scribes, AI documentation assistants, and digital/virtual/smart clinical assistants [1]) are tools for transcribing medical speech, such as patient consultations and dictated medical notes. Many also produce summaries ...
The terms "free", "subscription", and "free & subscription" will refer to the availability of the website as well as the journal articles used. Furthermore, some programs are only partly free (for example, accessing abstracts or a small number of items), whereas complete access is prohibited (login or institutional subscription required).
Presentation software that supports free form placement and zooming on a single sheet; Offers Android, iPhone and iPad applications work both offline and in sync with the cloud; Qiqqa: Quantisle Ltd. Windows: Minds maps for academics oriented around their research papers, notes and annotations: Semantica: Semantic Research OS X, Windows
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Format name Design goal Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration DNN model interconnect Common platform TensorFlow, Keras, Caffe, Torch: Algorithm training No No / Separate files in most formats No No No Yes ONNX: Algorithm training Yes
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