Today is an exciting day for the advancement of AI at Microsoft. We have agreed to acquire Maluuba, a Montreal-based company with one of the world’s most impressive deep learning research labs for natural language understanding. Maluuba’s expertise in deep learning and reinforcement learning for question-answering and decision-making systems will help us advance our strategy to democratize AI and to make it accessible and valuable to everyone — consumers, businesses and developers.
We’ve recently set new milestones for speech and image recognition using deep learning techniques, and with this acquisition we are, as Wayne Gretzky would say, skating to where the puck will be next — machine reading and writing.
Maluuba’s vision is to advance toward a more general artificial intelligence by creating literate machines that can think, reason and communicate like humans — a vision exactly in line with ours. Maluuba’s impressive team is addressing some of the fundamental problems in language understanding by modeling some of the innate capabilities of the human brain, from memory and common sense reasoning to curiosity and decision making. I’ve been in the AI research and development field for more than 20 years now, and I’m incredibly excited about the scenarios that this acquisition could make possible in conversational AI.
Imagine a future where, instead of frantically searching through your organization’s directory, documents or emails to find the top tax-law experts in your company, for example, you could communicate with an AI agent that would leverage Maluuba’s machine comprehension capabilities to immediately respond to your request. The agent would be able to answer your question in a company security-compliant manner by having a deeper understanding of the contents of your organization’s documents and emails, instead of simply retrieving a document by keyword matching, which happens today. This is just one of hundreds of scenarios we could imagine as Maluuba pushes the state-of-the-art technology of machine literacy.
Sam Pasupalak and his Maluuba co-founder, Kaheer Suleman, have created a very strong engineering and research team that will become part of our Artificial Intelligence and Research organization. We believe that together, we can achieve greater scale for Maluuba’s groundbreaking work and accelerate our ability to develop software so computers can read, write and converse naturally.
Additionally, Yoshua Bengio, one of the world’s foremost experts in deep learning, head of the Montreal Institute for Learning Algorithms and an advisor to Maluuba, will also be advising Microsoft and interacting directly with me. As an admirer of Yoshua’s work from a distance, I’m really looking forward to this opportunity to work more closely with him and to benefit from his deep learning systems expertise.
Much like its overall vision, Maluuba’s approach to research and engineering is also directly aligned with ours. Last fall, we formed the Artificial Intelligence and Research organization, bringing engineering and research closer together to accelerate the pipeline from cutting-edge research to product development. Maluuba, too, has closely aligned its research and engineering teams, and we’re looking forward to learning from their experiences as well. Part of that approach is contributing to the research community for the common advancement of AI systems. For example, last month we released the MS Marco dataset for machine reading; Maluuba also recently made its datasets for reading comprehension and conversational dialogue systems available as well.
We’ll have more to share about our plans for Maluuba in the coming months. In the meantime, I want to emphasize just how excited I am about the technology and talent Maluuba brings to Microsoft and the role they’ll play in helping us bring AI to every person and organization on the planet.
For Maluuba’s perspective on this acquisition, please read this blog post from Sam and Kaheer.
Update February 1, 2017: Microsoft has completed its acquisition of Maluuba.
Tags: AI, Deep Learning, image recognition, Machine Learning, speech recognition