Recent advances in deep learning have all but solved speech recognition and image processing. The next frontier is natural language. Maluuba's vision is intelligent machines that can think, reason, and communicate, and we believe that language and intelligence are inextricable. Here we will describe steps we have taken toward next-generation dialogue systems. Such systems should include the capacity to retain memory across dialogue turns and from past conversations, to clarify user intents through dynamic, back-and-forth speech, and to acquire new knowledge through interaction with humans. By taking a deep-learning approach, we have achieved state-of-the-art performance in natural language understanding and dialogue state tracking, tasks vital for any goal-driven dialogue system. We also train a policy manager using deep reinforcement learning so that our system chooses optimal trajectories through goal-driven dialogues. Contemporary dialogue systems generally share a common flaw: the inability to inject external, unstructured knowledge into conversations. We are overcoming this flaw with a deep model for machine comprehension. This model can read texts and answer queries over them.
As CTO and co-founder, Kaheer led the creation of Maluuba’s Deep-Learning based Natural Language Understanding platform and is the technology visionary behind the algorithms that power voice search across millions of devices. Kaheer’s background is in information retrieval and artificial intelligence and previously worked in the A.I. Lab at the University of Waterloo under Information Retrieval experts - Professor Pascal Poupart and Olga Vectomova. Kaheer’s expertise in building language understanding and conversational systems has served as guide for Maluuba's R&D team through some of the toughest challenges in Machine Comprehension and Spoken Dialogue.