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There are few technologies in the world hotter than artificial intelligence, or AI – as evidenced by the recent NIPS (Neural Information Processing Systems) academic conference in Montreal that sold out in less than 12 minutes.  That’s faster than last year’s Burning Man festival.  At the same time, the list of big, multinational companies that are setting up AI research labs in Canada – and Toronto in particular – continues to grow. But where is all this research headed, and what do we non-AI experts need to know about it?  The University of Toronto’s Sanja Fidler, a leading computer vision researcher and the head of NVIDIA’s research lab in Toronto, says progress in self-driving cars and certain health-care applications is moving “pretty fast.” However, she also notes the problems AI researchers seek to solve are growing ever more complex, necessitating a more co-operative approach.   “People used to just work in computer vision or image processing, but now there’s a lot interdisciplinary work to connect these things together,” says Fidler, who is an assistant professor at U of T Mississauga’s department of mathematical and computational sciences and a faculty member at the Vector Institute for Artificial Intelligence.  As for Toronto’s growth as a global hub for AI research and development, Fidler doesn’t see the trend slowing down any time soon.

People are also going to start looking more into the idea of fairness – meaning the fairness of machine learning models and the issue of training on biased datasets.  In my domain, people have started looking into embodied agents – so not just an algorithm that only sees images, but one that exploits the fact that it’s an agent that performs actions in the world.

There’s actually been a lot of work in designing simulations where you can train these embodied agents – so I think this will be a new big thing, where people make more sophisticated simulators and train more sophisticated algorithms.

Typically, the way computer vision works is you have an image and you want to segment different objects because someone has decided this is an important task – for example, this is a car, this is something else and so on.

It links all these fields and adds control – how to move in this environment. A lot of computer vision people have started working in simulated environments where you use vision as a sort of auxiliary task needed to solve a more complex problem.

I think a lot of people are exploring this.  When it comes to your own work, what are you most excited about?  I’m actually looking into these embodied agents – including in a simulated environment for household activities.

We designed this simulator for crowdsourcing information about tasks that people do in the home, and then converted that into robot language – language that a robot could understand.

On your phone, Google recently released a tool that blurs the background of a photo – that’s all based around deep learning algorithms.

But if you look at more complicated applications like self-driving cars, drones or general robotics – that’s still further down the road.

U of T is basically a pioneer in deep learning – we have Geoff Hinton and there’s so many renowned faculty working on various AI fields, particularly machine learning and deep learning.

Artificial Intelligence and Access to Justice

It is difficult, here in London, to concentrate on anything that is not the Brexit slow car crash.

And, back here in the territory of legal services and access to justice, we need to talk about the potential impact of artificial intelligence.

Lawyers in large commercial firms are happily cavorting with shoals of tech start ups in  a frenzy of feeding activity.

Paul Philip, its chief executive, said, ‘“Our report highlights the potential for technology to add further value in the workplace and we are looking further at how AI can enable the provision of high-quality legal services through the government Pioneer Fund award.

Many firms are already exploring the possibilities and I would urge all law firms to consider how technology can help you and your business.” Let us begin with some definition of what we are talking about.

that facilitate the interaction between humans and computers, allowing users to get responses to sometimes complex questions put in ordinary and simple language.

‘on the information you have given me, you have 83 per chance of lung cancer and 17 of a broken toe’.

It should be noted that there are a number of points inherent to AI’s statistical methodology.  Answers can only relate to the data that has been given to the machine: AI will not predict a black swan.

And, used in any legal situation, AI decisions should be explicable by a human whether they are made to (or by) a court or tribunal or to a person seeking advice and information.

came to an abrupt stop when the government pulled the plug as costs escalated and it became clear that IBM’s much hyped Watson programme was not powerful enough at that time.

One or two sites like MyLawBC.com are using guided pathways or interactive self completion document assembly tools like those provided by A2J author in the US or the various disability application/review/appeal letters or CourtNav in England and Wales.

It developed the idea of guided pathways and it intercut these with nuggets of information which assisted in decision-making as users progressed up the decision-tree as well as allowing online intervention by third parties such as mediators.

It could advise you on the predicted outcome of any decision you were about to make.  So, you can begin to see how AI driven provision might work in outward facing provision.

Unfortunately, most existing chatbots, despite these assertions, are actually pretty crude and amount to little more than the guided pathways with more or less facility to handle requests in ordinary language.

This type of loss is hardly welcome to the venture capitalists who backed it but probably not fatal to them.  But, no legal aid authority or legal services business could afford this kind of failure.

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