Informatika | Mesterséges intelligencia » Artificial Intelligence Cluster

Source: http://www.doksinet ARTIFICIAL INTELLIGENCE CLUSTER U of T’s CONTRIBUTION Bench Strength in Related Research & Innovation Research funding attracted in last 3 years: $30M Artificial intelligence (AI)the ability of computers and other machines to exhibit intelligent behaviouris becoming a transformative business platform and area of significant investment in the world economy. The global market for smart machines (neurocomputers, autonomous robots and vehicles, smart embedded systems, intelligent assistance systems) is anticipated to grow to $15.3 billion annually by 2019 By 2025 the global economic impact of AI is expected to be between $7.1 trillion and $13.1 trillion AI, especially in the areas of machine and deep learning, is expected to become embedded in every industry, from finance to farming, aerospace to manufacturing. Facebook, Alphabet (Google), Microsoft and other technology companies are already using pioneering predictive machine and deep learning

technologies developed in Canadian labs and startups. The Toronto ecosystem is a hotbed for AI, powered by world-class research, the greatest presence of tech jobs in Canada across all sectors of the economy, and a growing number of tech startups. Faculty members: 67 Canada Research Chairs: 5 Graduate enrolment: 300 Research-based startups created in last 3 years: 12 HOW U of T ENHANCES THE CLUSTER For more than 30 years, U of T researchers have been at the forefront of advancing artificial intelligence in such areas as computer vision, computational linguistics and natural language processing, knowledge representation and reasoning, cognitive robotics, and machine learning. Geoffrey Hinton, a father of artificial neural networks and deep learning, founded the U of T Machine Learning Group in the Department of Computer Science in 1985 and is home to many international leaders in the field, including Brendan Frey and Radford Neal. Raquel Urtasun, Canada Research Chair in Machine

Learning and Computer Vision, is a pioneer in the area of machine perception and is receiving recognition for her work on self-driving cars. And Hector Levesque’s distinguished contributions to knowledge representation and reasoning, multi-agent systems, and cognitive robotics are prompting researchers to reconsider how they view intelligence. U of T is also a leader in AI entrepreneurship. U of T’s Rotman School of Management runs the Creative Destruction Lab (CDL), an accelerator that has admitted nearly 50 AI ventures, and hosts the annual must-attend Machine Learning and the Market for Intelligence conference. Canada’s biggest bank, RBC, has created RBC Research in Machine Learning, a novel research practice with academics from U of T and other institutions that aims to advance the frontiers of AI and machine learning research. KEY EDUCATIONAL & RESEARCH PROGRAMS • • • • • • • • • • • • Applied Computing Applied Genomics Computer Science Data

Sciences Electrical & Computer Engineering Linguistics Management Analytics Mathematics Philosophy Physics Psychology Statistical Sciences & Applied Statistics KEY FACILITIES, INITIATIVES & PARTNERSHIPS • Centre for Aerial Robotics Research and Education • Centre for Applied Genomics • Compute Ontario • Computer Science Innovation Lab • Creative Destruction Lab (CDL) • Donnelly Centre for Cellular and Biomolecular Research • Engineering Entrepreneurship Hatchery • Fields Institute for Research in Mathematical Sciences • • • • • • • • • Health Innovation Hub Institute for Clinical Evaluative Sciences (ICES Institute for Robotics and Mechatronics Intelligent Transportation Systems (ITS) Centre and Testbed NanoMechanics and Materials Laboratory RBC Research in Machine Learning SciNet Southern Ontario Smart Computing Innovation Platform (SOSCIP) Toronto Institute of Advanced Manufacturing Source: http://www.doksinet U OF T & HOSPTIAL

INNOVATION IMPACT BLUE J LEGAL Founded by University of Toronto Professors Benjamin Alarie, Anthony Niblett, Albert Yoon, and collaborator Brett Janssen, Blue J Legal uses the power of IBM’s Watson cognitive computing technology to transform legal research. Watson can synthesize and understand vast amounts of data, including legislation, academic publications and administrative documents such as Canada Revenue Agency bulletins, to analyze fact situations and discover in seconds distinctions that humans miss. This technology has the potential to provide a much deeper legal analysis than has ever been possible, incorporating multiple factors such as the role of strong and weak precedents, court hierarchies and judges’ motivations. DEEP GENOMICS Founded by University of Toronto Professor Brendan Frey and his students Babak Alipanahi and Andrew Delong, Deep Genomics combines artificial intelligence and genomic medicine in the firstever deep learning application for determining the

specificities of DNA- and RNA-binding proteins. Deep Genomic’s technology is able to handle millions of sequences per experiment to create a “mutation map” that reveals how genetic variations cause disease such as cancer and illnesses linked to aging. Genomics medicine holds the promise to significantly reduce health-care costs and improve the lives of millions. SYSOMOS Founded by Professor Nick Koudas and Nilesh Bansal, Sysomos is a spinoff of the University of Toronto research project BlogScope. Sysomos equips the world’s best digital marketers with the technology they need to demonstrate and optimize the value of their work to their business, clients or partners. Through the use of contextual text analytics and sophisticated data-mining technology, the Sysomos social intelligence engine collects data from blogs, Twitter, social networks, message boards, wikis and major news sources, and integrates all of that data into one, intuitive user interface. WHETLAB A machine

learning startup whose founders include several U of T alumni and a U of T PhD student, Whetlab was purchased by Twitter in 2015. Whetlab created a technology to make machine learning for companies better and faster automatically, a time-saving alternative to hiring experts to architect and tune a machine learning system in-house