Can AI help solve the music biz’s woes? One industry group is working with U of T to find out

The marriage of artificial intelligence, or AI, and music at the University of Toronto attracted worldwide attention last year when researchers trained a neural network to compose a holiday carol– complete with such lyrics as, “I’ve always been there for the rest of our lives.”

The Guardian newspaper dubbed the creation “vaguely unsettling,” but that hasn’t stopped Eric Baptiste from turning to U of T to help navigate the music industry’s digital future.

The CEO of the Society of Composers, Authors and Music Publishers of Canada (SOCAN), has formed a partnership with U of T’s Department of Computer Science Innovation Lab (DCSIL), one of several entrepreneurship hubs on campus.

Wired magazine: How a U of T researcher is trying to make the economics of self-driving cars work

As if building a self-driving car wasn’t difficult enough, the University of Toronto’s Raquel Urtasun is trying make it affordable, too.

Urtasun, who was tapped earlier this year to head Uber’s self-driving research lab in Toronto, has focused much of her research on figuring out ways to replace expensive lidar (light detection and ranging) sensors – basically pulsed lasers – with cheaper conventional cameras that use artificial intelligence, or AI, to learn how to process images in 3-D.

“If you want to build a reliable self-driving car right now we should be using all possible sensors,” Urtasun, who is also an associate professor in U of T’s computer science department, tells Wired magazine.

U of T researchers at Ontario Economic Summit to talk innovation, training students for ‘jobs of the future’

The University of Toronto is taking centre stage at this year’s Ontario Economic Summit as government officials, business leaders and key influencers meet to discuss how to take advantage of new technologies and disruptive business models to promote the province’s growth.

Organized by the Ontario Chamber of Commerce, the annual event is one of the biggest meetings on the economy in the province, featuring addresses from Premier Kathleen Wynne and several members of her cabinet, as well as appearances by other party leaders and representatives from Canada’s biggest companies.

U of T, one of the event’s presenting partners, kicked off the three-day summit in Niagara-on-the-Lake this week by underscoring the critical role the university and other post-secondary institutions will play in driving Ontario’s knowledge economy forward.

U of T researcher to talk about AI opportunities – and designing household robots

Sanja Fidler has a back-of-the-envelope way to track Toronto’s rapid emergence as a global centre for artificial intelligence, or AI: the number of machine learning graduate students who are jumping at offers to do their research at the University of Toronto.

“You typically have some ratio of ‘accepts’ because there is MIT, Stanford and Berkeley and we all compete for the same people,” says Fidler, an assistant professor at U of T Mississauga’s department of mathematical and computational sciences and a founding member of the Vector Institute for AI research.

Making medicine, not money: How one U of T researcher’s startup is rethinking Big Pharma’s business model

The latest medical innovation to spring from Aled Edwards’s University of Toronto lab isn’t a new protein structure or potential drug target – it’s a business model.

Thanks to advances in genomics research, it’s rapidly becoming apparent many illnesses can be splintered into ever smaller categories of disease, each affecting a relatively small number of people. Hence all the buzz about developing “personalized” medicines.

There’s just one problem. With the average cost of developing a new drug at US$2.6 billion, it’s not profitable for most pharmaceutical companies to pursue treatments that can only be administered to a few thousand patients – unless, of course, they charge outrageous sums.

How U of T’s ‘godfather’ of deep learning is reimagining AI

Geoffrey Hinton may be the “godfather” of deep learning, a suddenly hot field of artificial intelligence, or AI – but that doesn’t mean he’s resting on his algorithms.

Hinton, a University Professor Emeritus at the University of Toronto, recently released two new papers that promise to improve the way machines understand the world through images or video – a technology with applications ranging from self-driving cars to making medical diagnoses.

“This is a much more robust way to detect objects than what we have at present,” Hinton, who is also a fellow at Google’s AI research arm, said today at a tech conference in Toronto.

“If you’ve been in the field for a long time like I have, you know that the neural nets that we use now – there’s nothing special about them. We just sort of made them up.”