WATERLOO, ON--(Marketwired - Mar 29, 2016) - Maluuba, a deep-learning company helping machines think, reason and communicate with human-like intelligence, today announced it has opened an R&D lab in Montreal. As part of the labs, Maluuba has partnered with machine learning and neural computation expert, Yoshua Bengio from the Montreal Institute for Learning Algorithms (MILA) and reinforcement learning expert Richard Sutton from the Alberta Innovates Centre for Machine Learning, to further Natural Language Understanding (NLU) and artificial intelligence (AI) advances. The research lab, staffed by 13 deep learning research scientists, is led by Maluuba's CTO, Kaheer Suleman, an information retrieval and artificial intelligence expert. With a focus on the development of proprietary algorithms to solve language problems, Maluuba's goal is to build the world's most advanced research facility in deep learning and AI. "While we're closer to the goal of getting machines to exercise reasoning and understand conversational language, we still have a long way to go," said Yoshua Bengio.
The CIFAR deep learning summer school in Toronto has been training the top AI researchers entering or finishing Ph.D. programs since 2005. Over 1,200 students from 60 different countries applied, of which 200 were selected to attend. Attendees represent some of the leading AI labs in the world, Montreal Institute of Learning Algorithms (MILA), University College London, University of Toronto, University of Alberta, Berkeley, NYU, Columbia, CMU, MIT, ETH Zurich, and Stanford. Every year, the school has trained the next generation of top AI researchers which now hold top posts at AI companies like Google, Facebook, Tesla, and Uber. During an intense 10-day period, students learn the tricks of the trade from top AI researchers like deep learning pioneers Yoshua Bengio (MILA), Geoff Hinton (UofT), and reinforcement learning pioneer, Richard Sutton (University of Alberta, Google Deepmind).
The next time you pull out your smartphone and ask Siri or Google for advice, or chat with a bot online, take pride in knowing that some of the theoretical foundation for that technology was brought to life here in Canada. Indeed, as far back as the early 1980s, key organizations such as the Canadian Institute for Advanced Research embarked on groundbreaking work in neural networks and machine learning. Academic pioneers such as Geoffrey Hinton (now a professor emeritus at the University of Toronto and an advisor to Google, among others), the University of Montreal's Yoshua Bengio and the University of Alberta's Rich Sutton produced critical research that helped fuel Canada's rise to prominence as a global leader in artificial intelligence (AI). Stephen Piron, co-CEO of Dessa, praises the federal government's efforts at cutting immigration processing timelines for highly skilled foreign workers. Canada now houses three major AI clusters – in Toronto, Montreal and Edmonton – that form the backbone of the country's machine-learning ecosystem and support homegrown AI startups.
Siri is about to get a lot smarter thank to Carnegie Mellon researcher Russ Salakhutdinov, who announced today that he is joining Apple to lead the company's artificial intelligence efforts. Excited about joining Apple as a director of AI research in addition to my work at CMU. Apply to work with my teamhttps://t.co/U2hQl2GdhA Although he's not a household name, Russ Salakhutdinov is one of the biggest deep learning figures in academia. His hiring by Apple comes after the company has been criticized for Siri's weak performance compared to rival digital assistants from Google, Amazon and Microsoft. Before working at CMU, Salakhutdinov worked at Toronto University and MIT.
Microsoft researchers have created an artificial intelligence-based system that learned how to get the maximum score on the addictive 1980s video game Ms. Pac-Man, using a divide-and-conquer method that could have broad implications for teaching AI agents to do complex tasks that augment human capabilities. The team from Maluuba, a Canadian deep learning startup acquired by Microsoft earlier this year, used a branch of AI called reinforcement learning to play the Atari 2600 version of Ms. Pac-Man perfectly. Using that method, the team achieved the maximum score possible of 999,990. Doina Precup, an associate professor of computer science at McGill University in Montreal said that's a significant achievement among AI researchers, who have been using various videogames to test their systems but have found Ms. Pac-Man among the most difficult to crack. But Precup said she was impressed not just with what the researchers achieved but with how they achieved it.