Our vision is to solve 'Artificial General Intelligence' by creating literate machines that can think, reason and communicate like humans

 
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Our Story

Back in 2010, as classmates in our AI class (CS 486) at the University of Waterloo, we started to think about the way humans interacted with machines. Graphical User Interfaces (GUI) had been in use for 30 years and yet, they hadn’t changed much. For simple tasks they were easier to use than the command line interfaces, but for complex tasks we still resorted back to programming. We wondered why was this the case? Why couldn’t we just interact with computers the same way we interacted with each other everyday? We had to go to first principles and came to the realization that in order to achieve this level of natural interaction, we had to first develop algorithms that understand the way human beings communicate. Therefore, we had to have a very deep understanding about the fundamentals of human language; our memory and reasoning capabilities; as well the decision making process in our brain.

A couple of years later, we started to develop technology that could solve some of the basic problems of language understanding. At the time, the language understanding community (both academia and industry) was very intrigued by the early success of statistical machine learning algorithms in Personal Assistant systems like Siri. Users could make voice commands and do simple tasks like finding the weather, making a restaurant reservation or even playing some music from the phone. Besides the fact that these systems were extremely unscalable (built by engineers in a domain-by-domain fashion), brittle (keyword style queries worked) and gave users a very poor experience, these systems had a more fundamental flaw - they lacked the intelligence that humans have. In fact, this fallacy didn’t just hold for Personal Assistants, this was true for every machine out there. Machines just don’t think, reason or learn from their mistakes like we humans do. Machines neither have any common sense reasoning, nor they do have short-term, long-term or working memory like us. 

In early 2014, we observed that great leaps had been achieved in the fields of computer vision and speech recognition through the application of Deep Learning algorithms. We were excited - if deep learning techniques could enable machines to see   and hear like humans, then why not communicate like humans? As we all know, understanding human language is extremely complex and is ultimately the holy grail in the field of Artificial Intelligence. We finally saw a great opportunity to apply Deep Learning and Reinforcement Learning techniques to solve fundamental problems in language understanding, with the vision of creating a truly literate machine - one that could actually read, comprehend, synthesize, infer and make logical decisions like humans. This meant we had to heavily invest in research, therefore we started our Research lab in Montréal in late 2015 (in addition to our awesome engineering team in Waterloo). Our research lab, located at the epicentre of Deep Learning, is focused on advancing the state-of-the-art in deep learning for human language understanding. We have built a team of top Deep Learning Research Scientists and Engineers from around the world and built partnerships with leading academics in the field. We are extremely proud of the breakthroughs we have accomplished over the course of the year. 

So where are we in our quest for achieving ‘Machine Literacy’? Well, we are just getting started and are excited about the long road ahead.

Sam Pasupalak

CEO and Co-Founder

Kaheer Suleman

CTO and Co-Founder

 
Maluuba founders Sam Pasupalak and Kaheer Suleman. Photo: Phil Froklage, Communitech

Maluuba founders Sam Pasupalak and Kaheer Suleman. Photo: Phil Froklage, Communitech

 
 

Advised by one of the forefathers of Deep Learning

 

Deep learning pioneer Yoshua Bengio  has advised Maluuba since 2015, providing guidance and insight to our continued research into language understanding.

He is Professor at the Université de Montréal, head of the Montreal Institute for Learning Algorithms, co-director of the CIFAR Neural Computation and Adaptive Perception program, Canada Research Chair in Statistical Learning Algorithms, and he also holds the NSERC-Ubisoft industrial chair.

His main research ambition is to understand principles of learning that yield intelligence.

 

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Events

Maluuba speaks, presents and runs workshops at the world's leading conferences and events focused on artificial intelligence and deep learning. 

View our schedule of recent and upcoming events below. We'll update this list as new events are confirmed. If you'd like to know more or would like to invite Maluuba team members to keynote or participate in an event please contact us.

 

 

Vancouver

1 - 4 Aug, 2017

 

 

Sydney

6 - 11 Aug, 2017

 

 

Saarbrücken

13 - 14 Aug, 2017

 

 

Saarbrücken

15 - 17 Aug, 2017

 

 

Copenhagen

7 - 11 Sep, 2017

 

Toronto

13 Sep, 2017

 

 

Waterloo

14 Sep, 2017

 

 

San Francisco

19 Sep, 2017

 

Montreal

10 - 11 Oct, 2017

 

 

Montreal

10 - 11 Oct, 2017

 

Montreal

11 Oct, 2017

 

 

Online

14-15 Oct, 2017

 

 

Long Beach

4 - 9 Dec, 2017