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In an unassuming building on the University of Torontos downtown campus, Geoff Hinton laboured for years on the lunatic fringe of academia and artificial intelligence, pursuing research in an area of AI called neural networks.

Also known as deep learning, neural networks are computer programs that learn in a similar way to human brains. The field showed early promise in the 1980s, but the tech sector turned its attention to other AI methods after that promise seemed slow to develop.

The approaches that I thought were silly were in the ascendancy and the approach that I thought was the right approach was regarded as silly, says the British-born professor, who splits his time between the university and Google, where he is a vice-president of engineering fellow.

Now, neural networks – which allow computers to do things like teach themselves to play games likeTexas hold em- are considered techs next big thing, and Hinton is recognised globally for his work.

Neural networks perform complex and intuitive tasks through exposure to huge amounts of data. Todays more powerful computers and massive sets of data allowed for breakthroughs in neural network technology, improving accuracy in speech recognition and computer vision, which is helping make self-driving cars a reality.

Neural networks are used by the likes of Netflix to recommend what you should binge watch and smartphones with voice assistance tools. Google DeepMinds AlphaGo AI used them to winagainst a humanin the ancient game of Go in 2016.


Anything thats powering Googles engines right now is powered by deep learning, she says.

Developments in the field helped jumpstart innovation and paved the way for the technologys commercialisation. They also captured the attention of Google, IBM and Microsoft, and kicked off a hiring race in the field.

The renewed focus on neural networks has boosted the careers of early Canadian AI machine learning pioneers like Hinton, the University of Montreals Yoshua Bengio, and University of Albertas Richard Sutton.


Toronto will soon get the Vector Institute for Artificial Intelligence, geared to fuelling Canadas amazing AI momentum.

The new research facility, which will be officially launched on Thursday, will be dedicated to expanding the applications of AI through explorations in deep learning and other forms of machine learning. It has received about C$170m (US$127m/102m) in funding from the Canadian and Ontario governments and a group of 30 businesses, including Google and RBC.

Hinton will be the institutes chief scientific adviser.



Clark says they want to wake up Canadian industry to the possibilities of AI, which is expected to have a massive impact on fields like healthcare, banking, manufacturing and transportation.

The Vector Institute is just the latest infusion of cash into Canadas AI sector.

Google invested C$4.5m (US$3.4m/2.7m) last November in the University of Montreals Montreal Institute for Learning Algorithms.



Earlier this month, the federal government announced C$125m ($94m/75m) for a pan-Canadian AI strategy.


Those trying to build Canadas AI scene admit places like Silicon Valley will always be attractive to tech talent. But they hope strategic investments like these will allow Canada to fuel the growth of domestic startups.

Canadian tech also sees the travel uncertainty created by the Trump administration in the USas making Canada more attractive to foreign talent. (One of Clarks selling points is that Toronto as an open and diverse city).

I would hate to see one more professor moving south, Agrafioti says. Really, I hope that five years from now we look back and say we almost lost it but we caught it in time and reversed it.

The robots will learn from their environment, each other – and us

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