{"id":20767,"date":"2018-07-18T08:30:46","date_gmt":"2018-07-18T15:30:46","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=20767"},"modified":"2018-07-19T10:44:56","modified_gmt":"2018-07-19T17:44:56","slug":"movies-neural-networks-boost-ai-language-skills","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2018\/07\/18\/movies-neural-networks-boost-ai-language-skills\/","title":{"rendered":"Movies, Neural Networks Boost AI Language Skills"},"content":{"rendered":"

\"\"When we discuss about artificial intelligence (AI), how are machines learning? What kinds of projects feed into greater understanding? For our friends over at IBM, one surprising answer is movies<\/em>. To build smarter AI systems, IBM researchers are using movie plots and neural networks to explore new ways of enhancing the language understanding capabilities of AI models.<\/p>\n

IBM will present key findings from two papers on these topics at the Association for Computational Linguistics<\/a> (ACL) annual meeting this week in Melbourne, Australia.<\/p>\n

In the first paper<\/a>, scientists found a surprising side effect of the movie business \u2013 movies can teach machines what humans view as common sense. This research focuses on IBM\u2019s DuoRC dataset (see below for a sample of the movies this project looked at), which contains 186,089 unique question-and-answer pairs based on 7,680 pairs of movie plots. Each movie plot pair reflects two different summaries of the same movie \u2013 one from Wikipedia and the other from IMDb \u2013 written by two different authors. This approach minimizes overlapping vocabulary and ensures different levels of plot detail in the pairs. This forces the AI models to perform complex reasoning to answer questions about each movie while demonstrating an effective new technique for improving the reading comprehension of AI systems.<\/p>\n

The second paper<\/a> addresses how neural networks can improve the speed and accuracy of the AI algorithms behind search engines and question-answering systems. When teaching a machine to read text, AI models typically use two basic techniques \u2013 \u201cco-reference resolution<\/a>,\u201d which finds all expressions that refer to the same entity in a document, and \u201centity linking<\/a>,\u201d which resolves any ambiguous names of entities in a text using a knowledge base like Wikipedia (e.g., to help the AI system understand if it needs to talk about \u201cBush\u201d the rock band or \u201cGeorge W. Bush\u201d the former U.S. president to answer a question).\u00a0 The IBM\u2019s team innovation is that the model is trained in one language and can be applied to another without the need for new training data making it possible for cross-lingual applications.<\/p>\n

\"\"<\/p>\n

**<\/strong>IBM\u2019s DuoRC dataset has about 8,000 movies.\u00a0Here are some sample titles:<\/p>\n

The Shawshank Redemption\u00a0(1994)
\nThe Godfather\u00a0(1972)
\nThe Dark Knight\u00a0(2008)
\n12 Angry Men\u00a0(1957)
\nSchindler’s List (1993)
\nThe Lord of the Rings: The Return of the King (2003)
\nPulp Fiction (1994)
\nThe Good, the Bad and the Ugly (1966)
\nFight Club (1999)
\nForrest Gump (1994)
\nStar Wars: Episode V – The Empire Strikes Back (1980)
\nInception (2010)
\nOne Flew Over the Cuckoo’s Nest (1975)
\nThe Matrix (1999)
\nCity of God (2002)
\nSe7en (1995)
\nThe Silence of the Lambs (1991)
\nThe Usual Suspects (1995)
\nSaving Private Ryan (1998)
\nL\u00e9on: The Professional (1994)
\nThe Green Mile (1999)
\nInterstellar (2014)
\nAmerican History X (1998)
\nPsycho (1960)
\nThe Intouchables (2011)
\nThe Pianist (2002)
\nThe Departed (2006)
\nTerminator 2 (1991)
\nBack to the Future (1985)
\nRaiders of the Lost Ark (1981)
\nRear Window (1954)
\nGladiator (2000)
\nThe Lion King (1994)
\nThe Prestige (2006)
\nMemento (2000)<\/p>\n

 <\/p>\n

Sign up for the free insideBIGDATA\u00a0newsletter<\/a>.<\/em><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

When we discuss about artificial intelligence (AI), how are machines learning? What kinds of projects feed into greater understanding? For our friends over at IBM, one surprising answer is movies. To build smarter AI systems, IBM researchers are using movie plots and neural networks to explore new ways of enhancing the language understanding capabilities of AI models.<\/p>\n","protected":false},"author":10513,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,87,180,173,56,84,1],"tags":[324,264,95],"acf":[],"yoast_head":"\nMovies, Neural Networks Boost AI Language Skills - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2018\/07\/18\/movies-neural-networks-boost-ai-language-skills\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Movies, Neural Networks Boost AI Language Skills - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"When we discuss about artificial intelligence (AI), how are machines learning? 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