Maluuba
| 300px | |
| Privately held | |
| Industry | Artificial Intelligence, Natural language processing |
| Founded | Waterloo, Ontario (2011) |
| Founder | Sam Pasupalak Kaheer Suleman Zhiyuan Wu |
| Headquarters | Waterloo, Ontario, Canada |
| Website | www |
Maluuba is a Canadian startup conducting research in deep and reinforcement learning to solve the problem of machine literacy through common sense reasoning, memory and communication. This technology will allow machines to understand and answer questions about written documents, and have natural conversations with users. In late March 2016, the company made headlines by demonstrating a machine reading system capable of answering arbitrary questions about J.K Rowling’s Harry Potter and the Philosopher’s Stone.[1]
Their natural language understanding technology has been adopted by major consumer electronic brands like LG and can be found on over 50 million devices shipping globally[2] in the smart phone, smart TV and IoT space.
History
Maluuba was founded in 2011 by undergraduate students at the University of Waterloo as a spin out of research they conducted.[3] Their initial proof of concept was a voice activated travel tool that allowed users to search for flights using their voice.
In February 2012, the company secured $2 million in seed funding from Samsung Ventures.[4] Within the span of 6 months following the investment, the company built out an engineering team, technology platform and Android personal assistant application that rivalled Siri and Google Now in terms of functionality. In September of that year, Maluuba officially launched their Android application as a finalist on stage at the Techcrunch Disrupt.[4]
Having noticed the launch and early market success of personal assistants like Siri and Google Now, consumer electronics companies and other device makers became interested in incorporating the technology into their products. Since 2013, Maluuba has partnered with several companies in the smart phone, smart TV, automotive and IoT space. For instance, Maluuba's personal assistant technology powers LG's VoiceMate application found on the company's flagship G series smartphones.[5]
Maluuba's vision from the beginning has been to bring human level literacy to machines. However, it was realized when the company was founded that it would be some time before technology and basic research in deep reinforcement learning became mature enough to allow this vision to be achieved. By 2014, it became apparent that the technology had finally caught up to support research into machine literacy. DeepMind developed general AI techniques using deep learning that allowed it to learn to play a variety of videos games without supervision. In August 2015 Maluuba secured a $9 million of Series A investment from Nautilus Ventures and Emerllion Capital.[2] Then in December 2015, Maluuba opened an R&D lab in Montreal, Quebec, widely considered one of the epicentres for deep learning research.[6]
Applications
Numerous applications for Maluuba's technology have been proposed in industry with several applications being commercialized.
The automotive industry is a key market where conversational dialogue technologies being developed by Maluuba can be applied. Motorists want the able to stay in touch with their friends, family and business colleagues behind the wheel. Yet it is well known that distractions from users taking their eye off the wheel are a leading cause of motor accidents.[7] Consequently, many jurisdictions have adopted laws that make distracted driving illegal. Maluuba's technology allows the driver to hold a natural conversation with their car to accomplish tasks that would otherwise be distracting (i.e. sending a text message), just like they may have with a passenger. Maluuba is currently in discussions with leading automakers to integrate their technology into future car models.
One of the first applications of Maluuba's natural language technology has been the smartphone assistant. These systems allow users to speak to their phone and get direct results to their question (instead of merely seeing a sea of blue web links that point to possible answers to their question). For instance a user could say, "When is Batman playing in San Francisco" and be shown the movie showtimes for the movie Batman v. Superman: Dawn of Justice for San Francisco, California. These personal assistants could also help keep their users organized by monitoring and managing the user's communication (i.e. the personal assistant could schedule meetings in a user's calendar by recognizing meeting requests within a conversation the user is having with someone else via email).
Research
Maluuba's deep reinforcement learning research centre opened in Montreal, Quebec in December 2015.[6] The lab is staffed by a team of 12 research scientists and is advised by Yoshua Bengio (University of Montreal) and Richard Sutton (University of Alberta), widely considered as one of the fathers of deep reinforcement learning. Recognizing the importance of its research to the broader AI community, the lab has recently published papers discussing some of its recent research.[8] The lab also partners with the University of Montreal MILA lab and McGill University and actively contributes to the AI research community.
The lab's research focus is to develop models to solve problems in the field of learning, reasoning, memory and attention which form the foundation of basic human literacy. It is hoped that these models will yield new discoveries in the field of open domain question answering and conversational dialogue. The lab has made significant progress towards that goal including beating well known machine reading benchmarks like MCTest[9] and being able to answer questions about short stories[10] and well known books like J.K. Rowling’s Harry Potter and the Philosopher’s Stone.[1]
Several characteristics of Maluuba's deep learning research centre separate it from existing AI research labs. Firstly, the lab focus is on developing AI systems the can solve general purpose problems in machine literacy. These systems would be able to learn new concepts and solve problems without human intervention or supervision. Secondly, the lab is focused on solving a specific area of artificial general intelligence, rather than solving general problems in artificial general intelligence. This could result in systems that outperform existing general purpose artificial general intelligence systems. Finally, the lab's focus is solving real-world problems in machine literacy (i.e. answering questions about a complex legal document) rather than toy problems designed to elicit media attention (i.e. playing board game like Chess or Go).
References
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