A team of researchers recently presented their paper on KiloGram, a new algorithm for managing large n-grams in files, to improve machine-learning detection of malware. The new algorithm is 60x faster than previous methods and can handle n-grams for n=1024 or higher. The large values of n have additional application for interpretable malware analysis and signature generation.
Source: infoq.com
Computerized question-answering systems usually take one of two approaches. Either they do a text search and try to infer the semantic relationships between entities named in the text, or they explore a hand-curated knowledge graph, a data structure that directly encodes relationships among entities. With complex questions, however — such as “Which Nolan films won an Oscar but missed a Golden Globe?” — both of these approaches run into difficulties.
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We’ve seen profound advances in technology, especially with the development of artificial intelligence and deep learning which are increasingly for voice assistants. This, in turn, promises to bring about huge changes in consumer behavior — what’s being called “voice commerce”. This is a new channel, governed by a new set of rules.
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Natural language understanding (NLU) and language translation are key to a range of important applications, including identifying and removing harmful content at scale and connecting people across different languages worldwide. Although deep learning–based methods have accelerated progress in language processing in recent years, current systems are still limited when it comes to tasks for which large volumes of labeled training data are not readily available. Recently, Facebook AI has achieved impressive breakthroughs in NLP using semi-supervised and self-supervised learning techniques, which leverage unlabeled data to improve performance beyond purely supervised systems.
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