by Angela Guess
Alex Brokaw reports in The Verge, “Today, Canadian artificial intelligence company Maluuba released state-of-the-art results for a type of machine learning focused on teaching computers to read like humans do. Companies like Google, Facebook, and IBM have actively been working on this area of research — known as machine learning comprehension — but experts agree it’s not nearly as advanced as image and voice recognition technology. Maluuba’s results show that, in the near future, machines could be able to understand text like we do.”
Brokaw goes on, “Maluuba has built a program called EpiReader. It’s designed to solve a specific kind of machine comprehension task: a word is removed from a block of text and EpiReader determines the missing word based on context. EpiReader does this using two neural networks, a type of AI inspired by how neurons work in the human brain. The first neural net picks a set of likely answers based on its understanding of the paragraph. The second evaluates the reasoning used by the first to come up with the right answer.”
He continues, “Maluuba tested EpiReader on two very large collections of texts. The CNN / Daily Mail collection, which Google DeepMind released last summer, is comprised of over 300,000 articles from those news websites. (Maluuba only used the CNN portion of the dataset in their research.) The Children’s Book Test, which Facebook posted online in February, is made up of 98 classic children’s books sourced from Project Gutenberg. EpiReader was trained on these collections, reading them each roughly a dozen times and using machine learning to build a semantic meaning of the words.”
Photo credit: Maluuba