Maluuba Inc.
Industry Artificial Intelligence, Natural language processing
Founded Waterloo, Ontario
(2011 (2011))
Founder Sam Pasupalak
Kaheer Suleman
Zhiyuan Wu
Joshua Pantony[1][2][3][4][5]
Headquarters Montreal, Quebec, Canada
Parent Microsoft Corporation

Maluuba is a Canadian technology company conducting research in artificial intelligence and language understanding. Founded in 2011, the company was acquired by Microsoft in 2017.[6]

In late March 2016, the company demonstrated a machine reading system capable of answering arbitrary questions about J.K Rowling’s Harry Potter and the Philosopher’s Stone.[7] Maluuba's natural language understanding technology is used by several consumer electronic brands for over 50 million devices.[8]


Maluuba was founded by two undergraduate students from the University of Waterloo, Sam Pasupalak and Kaheer Suleman.[9] Their initial proof of concept was a program that allowed users to search for flights using their voice.

In February 2012, the company secured $2 million in seed funding from Samsung Ventures.[10]

Since 2013, Maluuba has partnered with several companies in the smart phone, smart TV, automotive and IoT space.[11]

In August 2015 Maluuba secured a $9 million of Series A investment from Nautilus Ventures and Emerllion Capital.[12][8] Then in December 2015, Maluuba opened an R&D lab in Montreal, Quebec.[13][14]

By 2016 the company employed more than fifty people, and had published fifteen peer-reviewed research papers focused on language understanding.[15]

On January 13, 2017, Maluuba announced they had been acquired by Microsoft for $140M.[16] In July 2017, according to the reports, Maluuba closed its Kitchener-Waterloo office and moved employees to its Montreal office.[17]


Maluuba's research centre opened in Montreal, Quebec in December 2015.[13] The lab is advised by Yoshua Bengio (University of Montreal) and Richard Sutton (University of Alberta). The lab has published fifteen peer-reviewed papers discussing some of its recent research.[18] The lab also partners with the University of Montreal MILA lab and McGill University.[19]

In late 2016 the company released two natural language datasets: NewsQA, focused on comprehension and Frames, focused on Dialogue.[20][21]

Maluuba has achieved 80% accuracy on the machine reading benchmark MCTest, outperforming other word-matching approaches by 8%, and surpassed the previous benchmark set for deep learning techniques, the DSTC2, by 3%, bringing it to 83%.[22]


In June 2016, the company demonstrated a program called EpiReader which outperformed Facebook and Google in machine comprehension tests. Several research teams were able to match Maluuba's results since the paper was released.[23] EpiReader made use of two large datasets, the CNN/Daily Mail dataset released by Google DeepMind, comprising over 300,000 news articles; and the Children's Book Test, posted by Facebook Research, made up of 98 children’s books open sourced under Project Gutenberg.[24][25]

Dialogue Systems

The company has published research findings into dialogue systems which comprises natural language understanding, state tracking, and natural language generation.[26] Maluuba published a research paper on policy manager (decision maker) where the system is rewarded for a correct decision.[27] In 2016, Maluuba also freely released the Frames dataset, which is a large human-generated corpus of conversations.[28][29]

Reinforcement Learning

The company conducts research into reinforcement learning in which intelligent agents are motivated to take actions within a set environment in order to maximize a reward.[30] The research team has also published several papers on scalability.[31][32][33]

In June 2017, the Maluuba team was the first to beat the game Ms. Pac-Man for the Atari 2600 system.[34][35]


Numerous applications for Maluuba's technology have been proposed in industry with several applications being commercialized.

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).[36] The company raised $9M in 2015 to bring their voice assistant technology to automotive and IOT sectors.[37]

Maluuba also offers a Google Chrome extension, NewsQA, that uses deep learning algorithms to read through news articles in order to answer questions posed by the user.[38]

See also


  1. "Startup tech companies flourishing in Waterloo Region". Retrieved 2 February 2017.
  2. "Startup raises millions to get computers to understand dialogue". Retrieved 2 February 2017.
  3. "". Retrieved 16 January 2016.
  4. "Maluuba Angel List". Retrieved 16 January 2016.
  5. "Globe and Mail". Retrieved 16 January 2016.
  6. "Microsoft Acquires Artificial-Intelligence Startup Maluuba". Retrieved 2017-01-16.
  7. Knight, Will (28 March 2016). "Software that Reads Harry Potter Might Perform Some Wizardry". MIT Technology Review. Retrieved 2 April 2016.
  8. 1 2 "Maluuba Closes $9 Million in Series A Financing to Further Achievements in Deep Learning" (Press release). Maluuba Inc. 20 January 2016. Retrieved 2 April 2016.
  9. "Company". Maluuba. Retrieved 2016-12-25.
  10. Lardinois, Frederic (11 September 2016). "Maluuba Wants to Challenge Apple's Siri with Its Do Engine". Retrieved 2 April 2016.
  11. Bader, Daniel (24 September 2013). "LG G2 Review". Retrieved 2 April 2016.
  12. "Machine learning startup Maluuba raises $9 million Series A". BetaKit. Retrieved 2017-11-14.
  13. 1 2 "Maluuba Opens Deep Learning R&D Research Lab" (Press release). Maluuba Inc. 29 March 2016. Retrieved 2 April 2016.
  14. Lowrie, Morgan (21 November 2016). "Why tech giants like Google are investing in Montreal's artificial intelligence research lab". Retrieved 16 January 2017.
  15. Heller, Lauren (6 January 2017). "Maluuba team explains why language is the key to making machines intelligent". Retrieved 16 January 2017.
  16. "Maluuba + Microsoft: Towards Artificial General Intelligence". Maluuba. Retrieved 2017-01-13.
  17. "Maluuba closes Kitchener-Waterloo office, consolidating employees in Montreal". BetaKit. Retrieved 2017-10-25.
  18. Trischler, Adam; Ye, Zheng; Yuan, Xingdi; He, Jing; Bachman, Phillip; Suleman, Kaheer (29 March 2016). "A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data". arXiv:1603.08884 [cs.CL].
  19. "Maluuba and McGill U to teach common sense to machines". Montreal in Technology. 13 December 2016. Retrieved 16 January 2017.
  20. "Maluuba Datasets for Natural Language Research". Retrieved 16 January 2017.
  21. "Deep Learning Startup Maluuba's AI Wants to Talk to You". IEEE Spectrum. 1 December 2016. Retrieved 16 January 2017.
  22. Dawes, Terry. "Maluuba uses Harry Potter to improve artificial language comprehension". Cantech Letter. Retrieved 2 April 2016.
  23. Brokaw, Alex. "Maluuba is getting machines closer to reading like humans do". The Verge. Vox Media. Retrieved 9 June 2016.
  24. Hermann, Karl (2015). "Teaching Machines to Read and Comprehend". arXiv:1506.03340 [cs.CL].
  25. Hill, Felix; Bordes, Antoine; Chopra, Sumit; Weston, Jason (2015). "The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations". arXiv:1511.02301 [cs.CL].
  26. "Publications". Maluuba. Retrieved 31 January 2017.
  27. Fatemi, Mehdi (2016). "Policy Networks with Two-Stage Training for Dialogue Systems". Proceedings of the SIGDIAL 2016 Conference. Association for Computational Linguistics. pp. 101–110. arXiv:1606.03152. Bibcode:2016arXiv160603152F.
  28. Hsu, Jeremy. "Deep Learning Startup Maluuba's AI Wants to Talk to You". IEEE Spectrum. IEEE. Retrieved 31 January 2017.
  29. Suleman, Kaheer; El Asri, Layla. "How to build smarter chatbots". Venture Beat. Retrieved 31 January 2017.
  30. Bachman, Philip; Sordoni, Alessandro; Trischler, Adam (2016). "Towards Information-Seeking Agents". arXiv:1612.02605 [cs.LG].
  31. "Decomposing Tasks like Humans: Scaling Reinforcement Learning By Separation of Concerns". Maluuba. Retrieved 31 January 2017.
  32. Laroche, Romain; Fatemi, Mehdi; Romoff, Joshua; Harm van Seijen (2017). "Multi-Advisor Reinforcement Learning". arXiv:1704.00756 [cs.LG].
  33. Harm van Seijen; Fatemi, Mehdi; Romoff, Joshua; Laroche, Romain; Barnes, Tavian; Tsang, Jeffrey (2017). "Hybrid Reward Architecture for Reinforcement Learning". arXiv:1706.04208 [cs.LG].
  34. "Microsoft AI plays a perfect game of Ms Pac-Man (BBC website)".
  35. "Robots to Humans: You Lose. We Just Finally Conquered Ms. Pac-Man (Time website)".
  36. Lardinois, Frederic. "Maluuba Wants To Challenge Apple's Siri With Its "Do Engine"". TechCrunch. TechCrunch. Retrieved 24 January 2017.
  37. Maluuba. "Maluuba Closes $9 Million in Series A Financing to Further Advancements in Deep Learning". Market Wired. Retrieved 31 January 2017.
  38. Lardinois, Frederic. "Maluuba wants to make chatbots smarter by teaching them how to read". TechCrunch. TechCrunch. Retrieved 25 January 2017.
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