In the Spotlight: Smith School of Business Master's of Management in Artificial Intelligence (MMAI)
Smith School of Business of Queen’s University, one of Canada’s top educational institutions, has launched a new program, Master’s of Management in Artificial Intelligence (MMAI), a first of its kind in North America.
The 12-month program is designed for working professionals who are interested in studying “the application of artificial intelligence and machine learning in the context of modern business decision making.” The curriculum balances AI and machine learning with management skills. It is designed to produce professionals who can effectively lead and facilitate communication with and between Data Science teams and C-Suite executives, a blend valued in modern business. The program culminates with the student’s successful completion of a capstone project applying AI to a real business case provided either by the student’s sponsoring organization or the institution's program partners.
We had the privilege to discuss this inspiring new program with Qurat Anwer, Associate Director of the MMAI program at Smith School of Business. Her insights about the curriculum, admissions, the culture, and pre and post-MMAI opportunities are below.
You have launched the first Master’s of Management in Artificial Intelligence (MMAI) program in North America. How did this come about, and what market need is this program filling?
We launched a Master’s of Management in Analytics (MMA) program in 2013, whose graduates have been working in a variety of industries. In discussion with these companies as well as from our conversations with the members of our Advisory Board, we discovered that many companies were seeking cross-sector employees that can handle both AI technology and have the language necessary to communicate the benefits and adaptation of AI application and technology with c-suite executives. Because the industry had no program that offered these skillsets and our MMA graduates did not have enough of the right technical knowledge in AI, we saw a unique opportunity to launch our MMAI program.
Being a Data Scientist is excellent. But to be a leader in AI and manage the AI project as a manager and a leader is a rare and valuable skill set. We found that most do not have the education and training necessary; people were either scientists or were business managers without any knowledge of AI that became the head of a data science group. Therefore, we wanted to create a niche program, where we could work with people who want to have roles as managers in AI and emerge as knowing both AI technical skills as well as management and business skills. The MMAI graduates are expected to communicate equally well with Software Engineers and Software Developers who are very technical, as with c-suite executives, who do not know much about the details of deep learning and, instead, are more concentrated on how to solve business problems. That was the rationale behind the program, and it is one of a kind in North America. We launched the program based on feedback from organizations and envisioning current and future business needs.
You mention that you received feedback from companies. Who are the companies that are seeking these skills?
Mostly the companies from our Advisory Board leaders, but we also got feedback from companies where our current MMA students are presently working. These companies were diverse in terms of business as well as their scale. It is an excellent time for this program. Toronto and Montreal have evolved as AI hubs in the last year and a half. There are not only startups that want to use AI, but also other established companies are opening their AI labs here in Canada. Facebook has a lab in Montreal, Samsung has a lab here in Toronto, etc.. There are still lots of companies that are opening their AI labs here. Indeed, Eastern Canada is emerging as an AI leader in North America. We are convinced that launching this program now will not only bridge the gap between supply and demand in AI leadership, but also create new opportunities for graduates for all these AI labs and hubs which have recently opened or are opening very soon within Canada.
You describe the content of the 12-month study as being delivered through a variety of teaching approaches, including real-world problem solving and a capstone project. Tell me how this approach improves the learning experience? How do the real world problems manifest in the classroom? And what does the capstone look like and how is it structured?
Excellent questions! This program is structured for professionals, so we expect most of the candidates to be already working or have some work experience. The intent of this program is not merely to create Data Scientists who work in silos, nor do we want them to create their state of the art algorithms on site; instead, we want to train the graduates to have enough practical technical skills to code, implement and lead AI solution. The program is structured to have an almost 40/60 split between management and technical AI courses. We have a team-based approach where we form teams of people coming from different backgrounds, different technical competency levels, as well as different practical experiences for each project. We also have some individual assignments. For example, if a six (6) person team is enrolled in a Deep Learning course, we assign one group leader to the team. The team leader actually changes in each project, so every student gets an opportunity to develop their leadership skills. The teams are comprised of members coming from a variety of backgrounds yielding different perspectives and technical capabilities, ensuring that students can work and interact with other students who have different experiences. Having the ability to work and communicate with a variety of views will teach students/leaders how to get the most out of their team when working towards a common goal.
Then there is a capstone project where the students will implement an AI solution within the MMAI’s partner organization. This gives the student the opportunity to work on real company data and implement of solutions.
Some interested students have asked us to, instead, have a three-month course, or take several classes, instead of the capstone project. The problem with these proposed approaches is that when you don’t work with real-world data and don’t implement it, solving a business problem with AI is almost impossible. Theoretical knowledge of AI is quite different from the implementation of AI solutions. So we think that the capstone project is essential to creating a leader who knows technical AI algorithms that actually work in the production of any company and solve real business problems.
During their capstone project, students have the opportunity to work for a consulting company like Deloitte or LoyaltyOne, for example, where the student is exposed to real datasets to solve one of the company’s AI challenges. We will mentor and prepare the student on what works in production, or what works in a real organization with real-data to address a real business need.
The capstone is a comprehensive project that the student has three (3) months to complete. Where and what type of project varies from person to person. Several companies, some vast corporations, and other startups, have expressed an interest in MMAI graduates. There are also other people who are interested in this type of program. A person working for a bank, for example, might be interested in this program because s/he foresees a bank issue being resolved with AI. This type of candidate would take this AI capstone project within their own organization, which might enable him/her to move to an AI position and incorporate their capstone project within their organizations business model.
There is also that candidate who wants to switch jobs while he/she is in this program. For example, one might be working at a bank and have an interest in going into healthcare in the near future. In that case, that student would take the capstone in a healthcare organization that could ease his/her transition into the new career path.
Classes are taught by faculty at Smith School of Business as well as the Vector Institute for Artificial Intelligence. Using two distinct faculties may provide different perspectives and opposing viewpoints to students. How do you see students benefiting from this approach?
The two are complimentary. One one hand, there are instructors from Smith and Queen’s who will teach the very theoretical knowledge of AI and management. On the other hand, there are lecturers from Vector institute and research industries who will equip the students with technical and very practical knowledge in the field. For example, I declare that Deep Learning is a perfect approach for facial recognition because it has very high prediction rate. However, a lecturer from Vector institute or Element AI with the practical application of such method in the real world, would argue that this technique doesn’t work in production because of the SLA restriction or because of the data restriction, etc. This approach allows students to benefit from the fact that the professors have the theoretical knowledge and the guest lecturers bring the external expertise that can teach the students what works in the real world.
Like many graduate programs, Queen’s Master of Management in Artificial Intelligence has its own list of requirements. Can you describe the traits you see in your most successful applicants?
We have candidates from across the board, and it’s hard to pinpoint. We wanted to have as diverse a background as we could with different experience levels. We have candidates who came from mechanical engineering, commerce, software engineers, healthcare and medical backgrounds as well as those with Physics and Mathematics background. The ideal candidate would be someone who demonstrates a motivation to apply an AI program to solve a business problem. The candidate should have mathematical knowledge and skills, and at least some programming experience, because the technicality of some of the courses related to AI are in-depth, adapting math and calculus. As far as AI, there is no specific background regarding what environment and where the ideal candidate can come from. Technology is being adopted everywhere from healthcare to construction, to the automotive industry. The candidate can come from any sector as long as he/she has the right motivation and a bit of background in mathematics and computer science.
How are candidates assessed for admissions beyond quantitative metrics?
This program is not intended to train software developers who specialize in AI, but we desire to make experts in both AI and management. We appreciate the use of AI across different industries, so candidates can come from a variety of backgrounds both professionally and academically. For example, consider a seasoned financial service professional who may have a great idea about how AI can solve some of the problems his/her company and sector face. He/she may not have a computer background but knows that AI could solve a specific problem. The prospective student's clarity of objective, their ability to identify and explain this AI opportunity, can represent as much as 80% of the decision process and is largely determined by the cover letter and interview.
What advice would you give a candidate preparing their application?
If you are interested in this program or management of AI, I would say that it doesn’t matter what type of organization you are working in. You should do some research to show where and how AI has potential application in your field. To best prepare, I recommend you show your motivation as a change agent in AI and read blogs, learn Python, work on some exciting problem with AI at home, etc..
While the program offering is new, where do you foresee alumni making a significant impact on society?
There are a lot of opportunities for an AI graduate. Many of the candidates may work in consulting, for companies like Deloitte, PwC, etc.. AI is an open market! We foresee our graduates as heads of venture initiatives, machine learning, or involved in machine learning strategy. We also see them working in bigger companies across a variety of different sectors leading data science departments as well as AI departments. There are a lot of opportunities out there...
What opportunities are offered to students to work or network with organizations looking to use or are using AI?
We have a Career Advancement Center, a very strong alumni network from both the MMA program and Smith School of Business, as well as a corporate relations team who can help our graduates expand their network and opportunities. We also organize networking events where different companies present their cases or issues and discuss areas where AI can be applied. There will be a lot of opportunities allowing our students to be exposed to different companies and organizations and different people while in the program. Not to forget that there could be employers in the same class as well.
In speaking with alumni, what are some fond experiences and themes they share about their time at Queen’s?
Most of our alumni loved their time and experiences during their residential sessions. Although the residential sessions are usually very time-intensive, our alumni still remember that time fondly; that is where they bonded with their team members and formed their lifelong friendships. We have a tight network for our MMA program, and MMAI graduates would, of course, be a part of that alumni group.
Thank you very much for your time, Qurat. We have reached the end of our interview. Do you have any final remarks you would like to add for prospective students considering this program?
Sure. I would like to end by saying that, while AI has become more of a buzzword right now, this program is much more than that and integrates an extensive and robust AI solution for businesses, as it relates to AI knowledge, AI skills, and management skills. In achieving AI success, you need to have all of these skills, AI depth as well as management and communication skills. This program gives you the opportunity to develop both skills, technical and business acumen, to be an AI change agent and leader.
Spotlight is a periodical profiling top master's programs. The series’ aim offers universities an opportunity to expand their reach and prospective students an opportunity for a deeper understanding of the institutions. Universities differentiate themselves by focusing on the program’s culture, its distinguishing features, and its enthralling characteristics; prospective students gain insider knowledge on the program and the culture, facilitating their university selection and ensuring programs fit their personal and intellectual needs.
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