Tag Archives: microsoft

Microsoft is Opening Toronto Headoffice, 60,000 New Jobs Projected

Big news for Toronto today, Microsoft has announced that it will be opening a huge office downtown Toronto.

Microsoft is committed to Toronto, so much so, it will be investing $570 million dollars into the city and the province of Ontario. At first they want to hire 500 employees as well as 500 students, but they have also announced that with this new office they can see an estimated 60,000 new jobs to be created around the “Microsoft ecosystem. by the time they open at the end of 2020.  Right now Microsoft employs 2,300 employees across Canada.

Microsoft’s new office will be at 81 Bay Street taking over  CIBC Tower 132,000 sq. ft. with opening date in 2020.

Now that Microsoft has made this big decision, Amazon needs to make up their mind as well, and choose Toronto as well for their HQ2.

Some were dissatisfied with the announcement:

Microsoft also has offices in Vancouver where they they have development centre and Montreal where they have AI research lab. In a recent announcement they said in Vancouver they will be adding 50 employees, and 75 more employees in Montreal.

Vancouver to Seattle Highspeed Train Almost a Reality

Update: While High speed train is being studied by both provincial and state governments, a private company, Harbour Air Seaplanes, has launched a seaplane that gets you in Seattle in around 1 hour from Vancouver.

The tickets are around $400 but if speed is what you are after – this will be the best option for you right now.  Please note Nexus, Enhanced Drivers Licenses and passport cards are not accepted at this border crossing. Passport is Required.


Who needs Amazon HQ2 in Canada, when you can soon be able to  travel by high speed train reaching speeds of about 400 km/h from Vancouver to Seattle. This means you can live and enjoy your free health care in Vancouver Canada while working and making double Canadian salary at Amazon HQ in Seattle in under 1 hour. Right now same that same trip takes around 4.5 hours.

British Columbia Premier John Horgan spoke approvingly of the possible high speed train after it was announced that BC provincial government will spend additional $300k to study the plan while Washington state approved funding of up to US$1.2 million toward the new in-depth study.

John Horgan, BC premier said:

“It’s our view that this is an opportunity that we shouldn’t let pass by. It’s a physical link between our two jurisdictions and one that will get cars off of the road and will move people and goods in a fast and effective way.”

“The convenience of a one-hour trip between Vancouver and Seattle would create countless opportunities for people in both B.C. and Washington, from sports or concert getaways for families, to untold economic growth potential for businesses. Exploring the possibility of creating a clean, efficient high-speed corridor is particularly important as the Pacific Northwest grows in economic importance, and we look to reduce barriers to expansion across our borders.”

This train corridor service would cut travel times between Vancouver and Seattle to about 60 minutes, from three to five hours.

If train link is successful it can help create up to 200,000 jobs for B.C. and U.S. workers, and generate billions of dollars in economic benefits.

 

Montreal / Waterloo Maluuba, AI deep learning startup, has been acquired by Microsoft

Microsoft has agreed to acquire Maluuba, a Montreal Waterloo based company with one of the world’s most impressive deep learning research labs for natural language understanding. Maluuba’s expertise in deep learning and reinforcement learning for question-answering and decision-making systems will help Microsoft advance their strategy to democratize AI and to make it accessible and valuable to everyone — consumers, businesses and developers.

No purchasing costs were disclosed.

Two Maluuba co-founders could not be happier – they wrote:


“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.”

Harry Shum, EVP of Microsoft’s AI and research group, said :


“Maluuba’s impressive team is addressing some of the fundamental problems in language understanding by modeling some of the innate capabilities of the human brain, from memory and common sense reasoning to curiosity and decision making,” said Shum. “I’ve been in the AI research and development field for more than 20 years now, and I’m incredibly excited about the scenarios that this acquisition could make possible in conversational AI.”