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Why Do A Master Of Management In Artificial Intelligence?

Smith School of Business has just launched the first Master of Management in AI in North America, with classes kicking off in September this year


Thu Jun 7 2018

In a matter of years, the technology industry has embedded itself in virtually every aspect of modern life, and MBA programs are scrambling to catch up.

The demand for business leaders with technical knowhow is not lost on both MBA students and business schools. In short: there is now overwhelming demand for MBA programs that sell the hard stuff in addition to the traditional leadership, management, quantity analysis package.

Tech MBAs have increasingly become lucrative propositions for students who want to understand how to employ artificial intelligence (AI), for instance, within the context of a consulting gig. And master’s programs are getting involved too.

Smith School of Business at Queen’s University, Canada, has just launched a Master of Management in Artificial Intelligence (MMAI), the first program of its kind in North America.

MMAI candidates can expect to immerse themselves in a rigorous curriculum where they understand on a granular level how to apply AI to contemporary business problems. The 12-month MMAI program is hosted out of SmithToronto, a state-of-the-art facility in downtown Toronto, with classes slated to kick off in September 2018.

BusinessBecause caught up with MMAI director, Stephen Thomas, to find out more.

Stephen Thomas SmithToronto

What stands out about Smith’s new Master of Management in Artificial Intelligence?

It’s a technical program. So, students are going to start with a math course to learn linear algebra and some basic calculus. They're going to learn analytical decision-making where they do optimization and how to approach problems in a data-driven way.

Students will study machine learning techniques, deep learning techniques, reinforcement learning, and natural language processing. They’re going to write R code and work with large data sets on big machines. So, there’s a significant technical component but it doesn’t stop there.

What makes our program unique is the integration of all this back into business strategy. One thing we found among our industry partners is that a business can hire a lot of data scientists, but a lot of the time a project just dies there and nothing ever really happens next because it’s a significant challenge to actually deploy it within the business and have people change the way they work based on these new models and analytics.

That part, the implementation, is just as—if not more—important than the analytical piece in terms of bringing real change to the business; and that’s something we focus on quite a bit in our program.

What should candidates be asking themselves before applying?

Students who are a good fit for this program will have a technical background. Maybe they took an extra math, stats, or computer science class, or were a computer science double major. Or maybe they did a little programing on the side. They have a love for tech in addition to their business skillset.

So, what a lot of students tend to ask is, ‘How technical is the program?’ They don’t want to become programmers, and so they’re not looking for a computer science master’s degree. This program is for those interested in the application of the tech. They want to know how it works, and they’re also interested in how to apply it to a business function in order to increase profits or reduce costs.

Some specific questions someone might ask are, ‘What programming language do you use? How technical is this? Do I need to use any linear algebra or calculus?’ Or, ‘Do I get to use any cool tools like Tableau? Are we going to build prediction image recognition?’ These kinds of questions.

Why should you choose the MMAI over a Tech MBA?

It’s all about generalization versus specialization. A lot of tech MBAs are starting to get into analytics and some AI content, which is great. But, given the limited amount of time they can spend on those topics, the graduates of those programs will come away with a non-technical, high-level understanding. 

So, they may be able to understand the buzz words and move a project in the right direction, but they’re not going to understand the deep technical details of how the models are working or the trade-offs.

For example: Which model is best? Is it good enough? What parameters are best? When to use one technique versus another, and really be able to speak the language to the analysts themselves. The MMAI degree is a specialist degree for someone wants to manage projects that have a significant AI compulsion.