{"id":2271,"date":"2018-05-11T14:32:19","date_gmt":"2018-05-11T18:32:19","guid":{"rendered":"https:\/\/www.porticomagazine.ca\/?p=2271"},"modified":"2020-10-28T14:40:20","modified_gmt":"2020-10-28T18:40:20","slug":"of-minds-and-machines","status":"publish","type":"post","link":"https:\/\/porticomagazine.ca\/2018\/05\/of-minds-and-machines\/","title":{"rendered":"Of Minds And Machines"},"content":{"rendered":"
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COMPUTERS THAT THINK AND ACT LIKE HUMANS ARE THE GOAL FOR AI RESEARCHERS<\/h2>\n<\/div>\n<\/div>\n<\/div>\n

Outside the Richards Building on campus one winter morning, it\u2019s a few degrees above freezing. Inside, Prof. Graham Taylor reaches down to the heating control below the window to boost the temperature in his School of Engineering office. The device isn\u2019t very smart, he says \u2013 and certainly not in comparison with the electronic climate controls at home. Installed in his century home in Guelph\u2019s St. George\u2019s Park neighbourhood, that Nest thermostat has learned his family\u2019s daily and weekly routines and adjusts the temperature round-the-clock to suit. Maybe the house\u2019s double-brick construction isn\u2019t the best option for a frigid Canadian winter. But Taylor figures the computing smarts nestled in that 21st century thermostat help offset some of the built-in inconveniences of a 19th century dwelling. \u201cWe can have old-world charm, and we can make it more efficient by integrating machine learning,\u201d he says.<\/p>\n

That example brings home \u2013 in literal and figurative ways \u2013 Taylor\u2019s studies since he arrived at U of G six years ago. Whether it\u2019s your thermostat or other household fixtures and appliances, your phone or your (future) self-driving car, tomorrow\u2019s smarter devices will increasingly rely on machine learning, a promising avenue being pursued by more artificial intelligence (AI) researchers in Canada and abroad all intent on making computers think and act more like humans. The U of G engineer hopes future breakthroughs \u2013 particularly in computer vision applications — will come from his own machine learning research group of some 20 students, post-docs and other investigators. Along the way, Taylor also aims to use so-called deep learning to help Canada further elevate its already-prominent international standing in the AI field.<\/p>\n

From his office overlooking the Bullring and Reynolds Walk, he is tapped into regional and national networks of academics, companies and governments aiming to make this country a hub for artificial intelligence research and technology adoption. That goal is shared by Ottawa, which last year provided $125 million for a pan-Canadian AI strategy that encompasses research clusters in Toronto, Montreal and Edmonton. \u201cCanada is really a leader worldwide when it comes to research in AI,\u201d says Taylor.<\/p>\n

Maintaining that lead by attracting and retaining AI talent and expertise is a key target of those networks, including the Vector Institute for Artificial Intelligence. Launched last year in Toronto, the institute aims to train the world\u2019s largest cohort of graduate students in AI and to help commercialize research and technology. As its sole U of G member, Taylor brings his interests in machine learning and computer vision to that group. He\u2019s also academic director of NextAI, a Toronto incubator for AI-enabled ventures in fields from finance and human resources to health care. There, he teaches machine learning and advises ventures in a program to launch businesses enabled by artificial intelligence. In 2016, the U of G professor was named as an Azrieli Global Scholar in the Learning in Machines and Brains program run by the Canadian Institute for Advanced Research (CIFAR) in Toronto. That program links researchers worldwide in efforts to create computers that can \u201cthink\u201d as humans do \u2013 the basis of Taylor\u2019s work in deep learning.<\/p>\n

Taylor and other researchers hope to enable computers to teach themselves<\/strong><\/h3>\n

How would you train a robot to tend your garden with all the unknowns from weather to soil conditions? Or how would you teach a self-driving car to navigate safely and efficiently through rush-hour traffic in Vancouver or Montreal, let alone Guelph? Following the conventional route of programming systems to account for every scenario and surprise, you\u2019d quickly run up against roadblocks. \u201cHumans can\u2019t think through all the possibilities,\u201d says Taylor. \u201cYou\u2019d have to represent all these scenarios by programming, and include all sorts of rules and exceptions. To imagine all the possibilities is beyond our abilities.\u201d<\/p>\n

Instead, he and other researchers hope to enable computers to teach themselves. That challenge involves feeding in piles of data about a given scenario or application, and then allowing the machine\u2019s interconnected neural networks to seek out patterns \u2013 a so-called \u201cdeep learning\u201d process originally conceived to mimic the operations of neuronal networks in the human brain. That non-learning thermostat in his office understands \u201con\u201d and \u201coff\u201d but not much more. By contrast, deep learning (what Taylor calls today\u2019s \u201csexier\u201d term for neural networks) involves more inputs, outputs and computation, as in a smart thermostat that uses those data to learn and adjust to the habits and patterns of building occupants. \u201cYou can build a system more efficiently by learning than by programming,\u201d says Taylor, who credits his former PhD supervisors, Geoff Hinton and the late Sam Roweis, for much of this thinking.<\/p>\n

Originally from London, Ont., Taylor worked with Hinton — considered a pioneer in the neural network approach to machine learning and now an adviser for both the Vector Institute and the CIFAR program \u2013 during grad studies in machine learning at the University of Toronto. After finishing his doctorate in 2009, he pursued a post-doc at New York University with Yann LeCun, co-director of the CIFAR program and now Facebook\u2019s director of AI research; Taylor arrived in Guelph in 2012.<\/p>\n

Along the way, he became fascinated by computer vision, or teaching computers to \u201csee.\u201d Working with his lab members at U of G and with various collaborators, including researchers in France, he hopes to train machines to recognize body pose and activity. Interpreting what others are doing is second nature for humans, but making sense of human poses and activities is a hugely complicated task for a computer.<\/p>\n

Such research may find its way into various fields, including security and health care. In Taylor\u2019s machine learning lab, master\u2019s student Brendan Duke says his grandmother lost part of her vision after a recent fall. \u201cShe needs help to read the buttons on the microwave,\u201d he says. \u201cA lot of things would be more convenient if she had a better interface to the world.\u201d His project partner, Alaa Ali, also a master\u2019s student, says machine vision might be useful for airport surveillance. He says AI systems might also be used in human resources to help shortlist interview candidates and even to help conduct impartial interviews.<\/p>\n

Post-doc Danesh Ramachandram works with Swift Medical, a Toronto startup company tracking wound healing for diabetic ulcers, surgical wounds or cancer lesions in hospitals or long-term care facilities. Treating and tending wounds costs Canada\u2019s health-care system almost $4 billion a year; a machine-learning algorithm might better monitor healing and provide more accurate prognoses.<\/p>\n

In an undergraduate project, Angus Galloway looked at using machines to help predict pending brain seizures. Now pursuing a master\u2019s degree, he\u2019s testing vulnerabilities in AI systems to protect them from hacking. Master\u2019s student Terrance De Vries works on training algorithms to \u201cknow what they know\u201d to improve machine learning, work that might apply in anything from medical diagnosis to driving navigation.<\/p>\n

Reflecting Taylor\u2019s proximity to plant and animal scientists on campus, the professor\u2019s own research has ranged from use of aerial drones to assess soil health on farms, to sifting through video information to identify individual animals, to monitoring for a moth pest in orchards. Further afield, he\u2019s lent his expertise in other ways. For an assignment with Google in California, he recently tested \u201ccontinuous passive authentication\u201d for keeping mobile phones secure. Instead of having to swipe your smartphone or enter a security code, wouldn\u2019t it be more convenient if the device ID\u2019d you by tracked your body language, movements, gait and other idiosyncrasies?<\/p>\n

Referring to the range of projects in the machine learning research group, lab manager Brittany Reiche says, \u201cThere are endless possibilities of applications to help with everyday life.\u201d Those everyday applications will multiply in our \u201cInternet of Things\u201d future, says Taylor. As machines mediate more of our lives at home, at work and in public places, they will converse among themselves in ways that will be largely invisible and inaudible to us. Tomorrow\u2019s ever-smarter home thermostat will think for itself \u2013 but it will also talk to the other electronic brains around it. The smart fridge will keep tabs on its own contents and recommend suitable recipes, and maybe even maintain the shopping list and order the week\u2019s groceries. Not that Taylor would mind: \u201cI\u2019d love to see deep learning reduce waste, enable us to eat healthier meals and expose us to new foods.\u201d<\/p>\n

He\u2019s also eager to see how machines might help kids like his robot-crazy four-year-old learn STEM (science, technology, engineering and mathematics) topics. Recently he placed a deposit on a Tesla Model 3, an electric car with certain self-driving features. \u201cI\u2019m looking forward to a future where we don\u2019t have to drive ourselves around. I think it will be safer.\u201d He figures it will be at least another decade before self-driving cars become common. Whenever it happens, he says, he might be able to claim at least some credit by association. \u201cIt will be a deep learning car.\u201d<\/p>\n

Ensuring Data Security And Privacy In An AI World<\/h3>\n
\"Rozita<\/a>
Rozita Dara<\/figcaption><\/figure>\n

Artificial intelligence \u2013 especially the automation of massive amounts of personal data needed for learning by AI systems — raises various security, privacy and ethical concerns, says computer science professor Rozita Dara.<\/p>\n

Dara, who heads the data management and data governance research program at U of G, says AI may threaten information security. How to secure data against outright hacking as well as various uses \u2013 authorized or not \u2013 by governments, businesses and other organizations? She says artificial intelligence also raises privacy concerns. Might sensitive personal data be integrated or analyzed without users\u2019 knowledge or consent? Might systems be smart enough to predict passwords or slip through security holes to gain access to data?<\/p>\n

Paradoxically, the very systems that pose a threat may also prove the best tools for countering it, says Dara, whose interests range from big data analytics and data management, to information privacy, to ethical implications of technology. \u201cWe can use AI to process and analyze millions of data points to detect anomalies and suspicious behaviour online.\u201d Her research might help ensure privacy through systems that understand users\u2019 consent and preferences, and that share or protect data accordingly. She also studies the use of AI in data management and security technologies such as \u201csmart contracts,\u201d or intelligent software protocols that validate digital contracts.<\/p>\n

AI can help ensure transparency and accountability in building and controlling these systems, says Dara, who was a privacy and information technology officer with the Office of the Information and Privacy Commissioner of Ontario before joining U of G. She calls for education in AI for researchers, practitioners and the public, as well as development of appropriate policies, procedures and government oversight.<\/p>\n

As well as raising information security and privacy concerns, AI poses ethical issues, she says. How will AI affect human behaviour, values and social norms? How to account for potential biases in AI algorithms and the databases that systems learn from? Can we predict how intelligent systems will perform and prevent unintended consequences, including loss of human control?<\/p>\n

\u201cWhat concerns me are the massive amounts of data that are being collected in real time from billions of people around the world. No effective mechanism for governance, management and protections of data exist. We need to think about this as a society.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

Computers that think and act like humans are the goal for AI researchers<\/p>\n","protected":false},"author":5,"featured_media":2291,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_FSMCFIC_featured_image_caption":"","_FSMCFIC_featured_image_nocaption":"","_FSMCFIC_featured_image_hide":""},"categories":[2],"tags":[184,296,295],"yoast_head":"\nOf Minds And Machines -<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/porticomagazine.ca\/2018\/05\/of-minds-and-machines\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Of Minds And Machines -\" \/>\n<meta property=\"og:description\" content=\"Computers that think and act like humans are the goal for AI researchers\" \/>\n<meta property=\"og:url\" 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