Why did the Division of Engineering Science launch a major in Machine Intelligence?
Engineering Science has a long history of developing educational opportunities for students in emerging and rapidly developing disciplines. Our program has been on the forefront of undergraduate education in areas such as robotics, engineering physics, and biomedical engineering.
The demand for specialists in machine intelligence is outpacing supply. Given the strengths of the Engineering Science program, in particular the multidisciplinary foundation curriculum and the rigorous approach to mathematics, this program is the ideal home for an undergraduate engineering major in machine intelligence. The University of Toronto has unique strengths in the field, given its connections to the newly launched Vector Institute, and has been the launching pad for several start-ups in the area.
What are the opportunities for graduates of the Machine Intelligence major?
The rapid growth of research in this field provides many opportunities for graduates in academia, research labs and industry. The number of graduate programs in this emerging discipline is growing, and we expect that our graduates will be attractive to top graduate schools and research labs. A large number of companies are launching machine intelligence initiatives/divisions, and are seeking new people to help shape the evolution of this technology. Examples of such companies include consulting, finance, healthcare and publishing firms. Finally, there is a rich AI-ecosystem in Toronto and Canada with many new start-ups and opportunities for new graduates to pave their own way.
What is the difference between the machine intelligence major and the existing majors in Engineering Science, such as Robotics Engineering, Electrical & Computer Engineering, and Engineering Mathematics, Statistics and Finance?
The machine intelligence major will allow students to focus on the development and application of machine intelligence in a wide variety of industries and contexts.
- The Electrical and Computer Engineering major provides students with a broad background in both disciplines and their integration, with some opportunity to learn about machine intelligence, rather than a specific focus on machine intelligence across various applications.
- The Robotics major emphasizes a whole-system perspective for robotics, requiring knowledge of sensors, control, electro-mechanical systems and computer programming. The roboticist has a unique role in system integration and needs an understanding of how machine intelligence is used by robotic systems, but will not require the same knowledge base as the machine intelligence graduate – and conversely, the machine intelligence graduate does not require or have the same understanding of robotic systems.
- Finally, Engineering Mathematics, Statistics and Finance graduates apply mathematics, optimization and modelling principles to financial engineering instruments. Machine intelligence is a potential tool for those working in the field, and students in this major have the opportunity for some exposure to machine intelligence, but not to the same rigour or depth as those in the machine intelligence major.
What is the difference between the Machine Intelligence major in Engineering Science, and an undergraduate degree in Computer Science?
While there are some commonalities between the Machine Intelligence major and what is offered through Computer Science, engineering offers a unique perspective.
First, graduates will have a systems perspective on machine intelligence, which integrates computer hardware and software with mathematics and reasoning. This enables a focus on algorithm development and the relationship between machine intelligence with computer architecture and digital signal processing.
Secondly, graduates will benefit from an approach that encourages problem framing and design thinking. Design thinking is a method for the practical and creative resolution of problems, which encourages divergent thinking to ideate many solutions, and convergent thinking to realize the best one. Students will be able to frame and solve problems in the MI field, and apply MI tools to problems in many application areas. These include finance, education, advanced manufacturing, healthcare and transportation. This field is in a phase of rapid development, and engineers are well equipped to contribute as a shaping force.