Maluuba

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]

Maluuba Inc.
TypeSubsidiary
IndustryArtificial Intelligence, Natural language processing
Founded2011 (2011) in Waterloo, Ontario
FounderSam Pasupalak
Kaheer Suleman
Zhiyuan Wu
Joshua Pantony[1][2][3][4][5]
Headquarters
Montreal, Quebec
,
Canada
ParentMicrosoft Corporation
Websitewww.maluuba.com

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]

History

Maluuba was founded by four undergraduate students from the University of Waterloo, Zhiyuan Wu, Joshua Pantony, 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 (~$2.37 million in 2021) 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 (~$10.2 million in 2021) 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 (~$154 million in 2021).[16] In July 2017, according to the reports, Maluuba closed its Kitchener-Waterloo office and moved employees to its Montreal office.[17]

Research

Maluuba's research centre opened in Montreal, Quebec in December 2015.[13] The lab was advised by Yoshua Bengio (University of Montreal) and Richard Sutton (University of Alberta). Prior to its acquisition by Microsoft , the lab published fifteen peer-reviewed papers.[18] The lab also partnered with local universities: University of Montreal MILA lab and McGill University.[19]

Machine reading comprehension (MRC)

In March 2016, Maluuba demonstrated their machine reading comprehension technology on the MCTest[20] outperforming other word-matching approaches by 8%

Maluuba continued their work on MRC throughout 2016. In June, 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.[21] 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.[22][23]

Following this achievement, the company released two natural language datasets: NewsQA, focused on comprehension and Frames, focused on Dialogue.[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 learning dialogue policies with deep reinforcement learning.[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]

Applications

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 (~$10.2 million in 2021) in 2015 to bring their voice assistant technology to automotive and IOT sectors.[37]

See also

References
  1. "Startup tech companies flourishing in Waterloo Region". 2 October 2012. Retrieved 2 February 2017.
  2. "Startup raises millions to get computers to understand dialogue". 20 January 2016. Retrieved 2 February 2017.
  3. "Maluuba.com". Retrieved 16 January 2016.
  4. "Maluuba Angel List". Retrieved 16 January 2016.
  5. "Globe and Mail". The Globe and Mail. Retrieved 16 January 2016.
  6. Greene, Jay (13 January 2017). "Microsoft Acquires Artificial-Intelligence Startup Maluuba". Wall Street Journal. 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. "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. "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. 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].
  21. Brokaw, Alex (8 June 2016). "Maluuba is getting machines closer to reading like humans do". The Verge. Vox Media. Retrieved 9 June 2016.
  22. Hermann, Karl (2015). "Teaching Machines to Read and Comprehend". arXiv:1506.03340 [cs.CL].
  23. 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].
  24. "Maluuba Datasets for Natural Language Research". Retrieved 16 January 2017.
  25. "Deep Learning Startup Maluuba's AI Wants to Talk to You". IEEE Spectrum. 1 December 2016. Retrieved 16 January 2017.
  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 (December 2016). "Deep Learning Startup Maluuba's AI Wants to Talk to You". IEEE Spectrum. IEEE. Retrieved 31 January 2017.
  29. Suleman, Kaheer; El Asri, Layla (11 October 2016). "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)". BBC News. 15 June 2017.
  35. "Robots to Humans: You Lose. We Just Finally Conquered Ms. Pac-Man (Time website)". 14 June 2017.
  36. Lardinois, Frederic (12 September 2012). "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.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.