Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
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Facebook AIWissenschaft, Technik und Ingenieurwesen in Menlo Park
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AI needs boundaries. The amount of info that FB collects on EACH user is NUTS! They have files on ev...ery user and are recording every little Love we make within the site! I still don’t understand why FB has screen shots of my banking info, contents of my wallet, intimate conversations that were private ..... I get you want to make the user experience better but stop creating/influencing a society by dumbing us down!
IF I WOULD I WOULD RATE 0 STARS Mehr anzeigen
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Facebook AI researchers are sharing an all-attention layer to simplify the Transformer model and an adaptive attention span method to make it more efficient. Even with a much simpler architecture, these methods match or improve state-of-the-art results. https://ai.facebook.com/blog/making-transformer-networks-simpler-and-more-efficient/
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We’ve developed a tool that applies natural language processing and information retrieval techniques directly to source code text, in order to produce a machine learning-based code search system.
The system, Neural Code Search (NCS), accepts natural language queries and returns relevant code fragments retrieved directly from the code corpus.
https://ai.facebook.com/blog/neural-code-search-ml-based-code-search-using-natural-language-queries/
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Facebook AI researchers have released PHYRE, a new open benchmark for assessing an AI system’s capacity for reasoning about the physical laws that govern real-world environments. The release consists of a collection of physics puzzles that AI players try to solve in as few attempts as possible.
https://ai.facebook.com/blog/phyre-a-new-ai-benchmark-for-physical-reasoning/
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Beiträge
Facebook AI researchers are releasing a new feature for the fastText library which provides hyper-parameter autotuning for more efficient text classifiers. https://ai.facebook.com/…/fasttext-blog-post-open-source-i…/
Facebook AI researchers are sharing an all-attention layer to simplify the Transformer model and an adaptive attention span method to make it more efficient. Even with a much simpler architecture, these methods match or improve state-of-the-art results. https://ai.facebook.com/…/making-transformer-networks-simp…/
