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Measuring ability of artificial intelligence to learn is difficult

In a study published in Nature Machine Intelligence, Waterloo researchers found that contrary to conventional wisdom, there can be no exact method for deciding whether a given problem may be successfully solved by machine learning tools.

For example, tasks like identifying the best place to locate a set of distribution facilities to optimize their accessibility for future expected consumers.

'This finding comes as a surprise to the research community since it has long been believed that once a precise description of a task is provided, it can then be determined whether machine learning algorithms will be able to learn and carry out that task,' said Ben-David.

Waterloo EDC Blog

Among the reasons for its selection, the University was lauded for the recent launch of the Waterloo AI Institute, which supports research and development in healthcare, urban planning, autonomous systems and human-machine interaction.

In fact, the University is home to the world’s largest collection of math and computer science talent, was just named “Canada’s Most Innovative University” for the 27th consecutive year, and has close ties to the Vector Institute for Artificial Intelligence in Toronto.

Toronto’s SickKids announces first-of-its-kind artificial intelligence position

Inside the pediatric intensive care unit at Toronto’s Hospital for Sick Children, an infant recovering from open-heart surgery is barely visible through the forest of whizzing and beeping machines that monitor his every vital sign.

In the old days, those vital signs – a baby’s heart rate, blood pressure, oxygen levels and other signals – would have flashed across a screen and then been lost to posterity.

On Tuesday, SickKids will announce the appointment of Dr. Goldenberg as the hospital’s first chair in biomedical informatics and artificial intelligence, a post funded in part by a $1.75-million donation from Amar Varma, a Toronto engineer and entrepreneur whose newborn son underwent surgery at SickKids six years ago.

“It will take time, but I think we are getting closer and closer to seeing it happen.” Among the projects that Dr. Goldenberg is keen to accelerate is one that would use AI to help predict when and in what part of the body a malignancy will develop in patients with Li-Fraumeni syndrome, a rare hereditary disease that predisposes people to cancer.

Working with Peter Laussen, the hospital’s chief of critical care, Dr. Goldenberg and her team used the beat-to-beat data from past patients to develop a computer model that can predict up to 70 per cent of cardiac arrests five minutes before the heart stops beating.

'When I was smaller [other children] would dump my bag into the toilet just because of my last name.” After completing an undergraduate degree at the University of Louisville, she moved on to Carnegie Mellon University in Pittsburgh, where she completed her master’s degree and PhD in data mining and machine learning.

Mathematicians Have Developed a Computing Problem That AI Can Never Solve

At least, that's the case according to a new international study by a team of mathematicians and AI researchers, who discovered that despite the seemingly boundless potential of machine learning, even the cleverest algorithms are nonetheless bound by the constraints of mathematics.

In their research, the team investigate a machine learning problem they call 'estimating the maximum' (EMX), in which a website seeks to display targeted advertising to the visitors that browse the site most frequently – although it isn't known in advance which visitors will visit the site.

According to the researchers, in this kind of case, the mathematical problem to be solved bears similarities to a machine learning framework known as probably approximately correct learning (aka PAC learning), but it's also similar to a mathematical paradox called the continuum hypothesis, another field of investigation for Gödel.

'[The researchers] identify a machine-learning problem whose fate depends on the continuum hypothesis, leaving its resolution forever beyond reach,' mathematician and computer scientist Lev Reyzin from the University of Illinois at Chicago, who wasn't involved with the work, writes in a commentary on the research for Nature.