is a global leader in artificial intelligence research focused on teaching machines to think reason and communicate.our vision is a world where intelligent machines work hand in hand with humans to advance the collective intelligence of the human species and bring about positive social and economic impacts.we re an early leader in using deep reinforcement learning to solve language understanding problems and in training machines to model decision making capabilities of the human brain.our technologyour goal is to teach machines to understand human literacy.machine reading comprehensionread . understand . reason.our goal is to develop information seeking agents that find read and then reason over natural language texts . such an agent would have access to nearly all the human knowledge recorded in writing . we develop state of the art deep learning algorithms to teach literacy to machines.conversationalunderstandingnatural conversations.maluuba s work on conversational user interfaces focusses on building goal driven dialogue bots that learn to engage in natural conversations with humans.to achieve this maluuba is using novel techniques to optimize users satisfaction and agent s knowledge acquisition simultaneously.reinforcement learningact . observe . improve.reinforcement learning is about training a software agent how to behave in a desired way . in contrast to supervised learning with reinforcement learning the agent does not require examples of correct or incorrect behaviour . instead it can improve its behaviour by itself by interacting with the environment and observing the rewards it gets for its actions . at maluuba we build dialogue systems using cutting edge reinforcement learning techniques.research and publicationsour goal is to teach machines to understand human literacy . at maluuba we are fascinated by the potential of artificial intelligence . we operate one of the world s leading research facilities into natural language and deep learning . we take a multidisciplinary approach to our research and work closely with academia.we are making strong progress by focusing on challenging problems driving new techniques in reinforcement learning with a focus on machine comprehension and conversational interfaces.our research team publishes peer reviewed papers that provide insight into the work we re doing to advance knowledge in this field.publications natural language generation in dialogue using lexicalized and delexicalized data june 2016 . we present a new approach to natural language generation using recurrent neural networks in an encoder decoder framework . in contrast with previous work our model uses both lexicalized and delexicalized versions of the slot value pairs for each dialogue act iterative alternating neural attention for machine reading june 2016 . we propose an iterative neural attention model and apply it to machine comprehension tasks . our architecture deploys a novel alternating attention mechanism and tightly integrates successful ideas from past works in machine reading comprehension to obtain state of the art results on three datasets natural language comprehension with the epireader june 2016 . we present the epireader a novel model for machine comprehension of text . the epireader is an end to end neural model comprising two components the first proposes a small set of candidate answers and the second formulates hypotheses using the proposed candidates then reranks the hypotheses based on their concordance with the supporting text a sequence to sequence model for user simulation in spoken dialogue systems june 2016 . we introduce a data driven user simulator based on an encoder decoder recurrent neural network . the model takes as input a sequence of dialogue contexts and outputs a sequence of dialogue acts corresponding to user intentions . the dialogue contexts include information about the machine acts and the status of the user goal policy networks with two stage training for dialogue systems june 2016 . we use policy networks for dialogue systems and train them in a two stage fashion supervised training and batch reinforcement learning followed by online reinforcement learning . an important feature of policy networks is that they directly provide a probability distribution over the action space which enables supervised training . the combination of supervised and reinforcement learning is the main benefit of our method which paves the way for developing trainable end to end dialogue systems a parallel hierarchical model for machine comprehension on sparse data march 2016 . we investigate machine comprehension on the challenging mctest benchmark . partly because of its limited size prior work on mctest has focused mainly on engineering better features . we tackle the dataset with a neural approach harnessing simple neural networks arranged in a parallel hierarchy advisorsdave grannanmr . david l. grannan is a co founder of light and serves as its chief executive officer . prior to light dave was ceo of vlingo the first natural language speech recognition service for mobile phones . vlingo provided speech recognition for the first siri app and powered samsung s s voice product . nuance communications acquired vlingo in 2012 . yoshua bengioyoshua bengio is head of the machine learning laboratory mila cifar program co director of the cifar neural computation and adaptive perception program canada research chair in statistical learning algorithms and he also holds the nserc ubisoft industrial chair . his main research ambition is to understand principles of learning that yield intelligence.richard suttonrichard s. sutton is a fellow of the association for the advancement of artificial intelligence and co author of the textbook reinforcement learning an introduction from mit press . rich s research interests centre on the learning problems facing a decision maker interacting with its environment which he sees as central to artificial intelligence . he is also interested in animal learning psychology in connectionist networks and generally in systems that continually improve their representations and models of the world.careers work on cutting edge research and help create applications that will change the world . maluuba is looking for exceptional individuals from a range of backgrounds to join our growing teams in waterloo and montreal.see our openingspressnews highlightsventurebeat how to build smarter chatbotstechcrunch maluuba wants to make chatbots smarter by teaching them how to readthe verge maluuba is getting machines closer to reading like humans dodigital trends watch an ai bot instantly learn game of thrones plotlinesmit technology review software that reads harry potter might perform some wizardrypopular science the future of virtual assistants lies in them learning children s storieswallstreet journal cat zakrzewski maluuba grabs 9m for natural language technologybloomberg a startup called maluuba which makes technology that helps computers talk is opening a research office in montrealmedia inquiriesmiya shitama 415 684 9566 maluuba innercirclelabs.com press kitdownload our press kit here contact usmaluuba inccontact maluuba.comhttp www.maluuba.com waterloo office140 columbia st. w. waterloo ontario canada n2l3k8montreal officesuite 720 2000 peel st montreal qc h3a 2w5connect with maluuba 2011 2016 maluuba inc all rights reserved