The Master of Management of AI at Smith
This post aims to answer a question on RedFlagDeals, GMATClub, and Reddit: is the MMAI worth it? MMAI at Smith School of Business, Queen’s University, is a professional programme that prepares its students to lead A.I. organizations, from minor technical teams to enterprises.This post aims to answer a question on RedFlagDeals, GMATClub, and Reddit: is the MMAI worth it?
MMAI at Smith School of Business, Queen’s University, is a professional programme that prepares its students to lead A.I. organizations, from minor technical teams to enterprises. Its scope is not to compete directly against computer science departments. It prepares neither Ph.D-level neural network designers nor data analysts with Einsteinian clarity of mind. Its scope is to train motivated A.I. translators: leaders who can lead A.I. developers, theoreticians, and business folks to concrete goals. Its candidates for my cadre (2021-2022) are a diverse sample of engineers, government wonks, business people, data scientists, financial analysts, an Olympian, and at least two I.T. guys (my team-member H and myself). Ages range from the early twenties to mid-fifties. All have at least four years of office work experience. The cultural diversity is outstanding.
MMAI is not easy. There is business stuff that engineers need to learn. There is software stuff that business people need to know. There is leadership stuff for intermediate-level nerds to learn. Product ideas to generate. Essays to write. Ethics to contemplate. Teams to nurture. Friends to support. Social faux pas to fix. Presentations to write, practice and execute. Fixed costs and variable costs to calculate. Math to master with horrible symbols and concepts like Σ and μ and σ. Odd ideas like q-tables, gradient descents and LSTMs and on and on and on. The due dates hang like the sword of Damocles over the head of anyone who wants to take a break.
It is survivable, but one question I had is – and this question is neither unique to me nor to the programme – was it worth it? It is, after all, an expensive programme in all senses of money, time, relations, and health.
Was it worth it?
Yes, but the win is not automatic. The opportunities are there, but just as fresh MBAs need to fight for a place in the sun, I suspect that so do fresh MMAIs.
That being said, the facts are that the MMAI students from previous years that I chatted with personally have leadership roles in A.I. businesses, including their own start-ups. Some of the MMAI students I met this year changed careers to data science mid-term. The salary that I know about is competitive.
Retaining the knowledge
Now that two weeks have passed since I handed in the last assignment, I have to review the math, the vocabulary, and the techniques. If I interviewed for an A.I. job tomorrow, I would need a crib sheet. I might understand the concepts, but tripping over the official terms while explaining them under pressure would hardly be inspiring. I remember the Agile stuff only because my current employer uses it as its main framework. Perhaps I could always redirect questions to easy things like Python classes and list comprehensions…
I note, though, that many successes of MMAI are not apparent during the year. For example, take the name Kaggle. Considering the number of times I bandied that name about, I did not appreciate at the time that I had never heard of it before taking the programme. Likewise, words like simulation, cost optimization, activation function, or even the dreaded hyperparameter. The amount of vocabulary I have, in fact, retained is as valuable as it is invisible. Once again: the scope of MMAI is not to write formal reinforcement learning equations. One just has to understand the reasoning of the even more talented engineer who develops extraordinary (and possibly profitable) applications with, say, OpenAI Gym.
That which is forgotten can be relearned … with momentum! I am reminded of the time I applied for a senior I.T. position that required an intimate knowledge of Puppet, the configuration management software. I had not used it in years. One weekend of study made me ready to ace the interview and then ace the job. So would it be with Pandas dataframes and investment break-even timelines.
Soft skills for leadership
Hard skills are not the only point of the programme. Soft skills are also the points of the programme, primarily persistence. Persistence in a programme that took in maybe 10% of its applicants and finished with around 90% of the remainder during its twelve months is a big thing. For those looking for senior employment, that is bankable. For those who will remain where they are, it is character-building.
Likewise, the team essays and presentations have merits beyond the obvious. Obviously, they reinforce concepts in the students’ minds and prove to the professors that their students will work hard for their marks. Lord knows, I fretted, sweated, pored, and lost sleep over my fair share of the assignments. Still, I cannot remember what I wrote other than a pretty damn cool recommender engine.
However, not too worry! It is the practice of negotiating and working within an A.I. business team under pressure that defines MMAI. One learns to defer when it is time to defer, acknowledge the gifts others bring to the table, and deliver quality presentations in front of an audience of business consultants. The work over the year is a marketable benefit. The team is tangible: every member of my team I would recommend to H.R. in an instant and I like to think they would reciprocate.
Put it this way: I will not impress any bosses or customers with my knowledge of gradient descent. But combining knowledge of gradient descent (and backpropagation, whee) with the experience of dreaming up ideas, then setting up business proposals and presentations with a bunch of diverse people, some from different cultures, with their own agendas and biases? Nice.
And so
More than one of my academically-inclined friends confessed that MMAI was the toughest and most frustrating graduate programme they had experienced. I myself had a rough go, although my personal and work situation dictated some of that. This is a good thing! Just as anyone would want a tough friend around if a fight breaks out on the street, so would a management team want an MMAI grad in their midst when a product needs to ship.
Advice for future MMAI Students
- I believe that MMAI is worth it. You might not think so in mid-February. You might not think so during the more boring slides on standard deviations on a Tuesday at 8:30pm. You might not think so when debating the point of an essay. You might not think so when the last assignment just refuses to write itself. But, now that I’ve had time to consider it, it is worth it.
- The fact that you are being fire-hosed is marketable.
- Your team is your first and second priority. Take care of it, bear it goodwill, and do your part. Unnurtured teams are unbearable. Nurtured teams are a jewel in your existence and support in bad times.
- Learn to read and interpret summation equations before the first technical course.
- If you feel your family will not support you, either fix the issue or do not take MMAI. It is a strain.
- If your work won’t support you during the year, lose the programme or lose the job.
- Book time for family, body, and mind explicitly, and do not give it up.
- Fatigue will set in. Mid-winter is bad. The time between the last course and Capstone assignment turn-in is worse. Have a plan to deal with it ahead of time.
- Recognize let-downs and crises of confidence for what they are: fatigue. Do not forget to plan a post-course review.
- MMAI is worth it.
MMAI2022 Syllabus
| Title | Orientation | Practical Exercises | Depends on |
|---|---|---|---|
| High-Performance Teams* | Leadership | Y | |
| Introduction to Management | Business | N | |
| Analytical Decision-Making | Business | Y | |
| Mathematics and Development Techniques for AI | AI | N | |
| Machine Learning and AI Technology | AI | Y | Math & Dev Tech |
| Agile Project Management for AI | Leadership | N | |
| Deep Learning | AI | Y | Mathematics and Dev Tech, ML and AI |
| AI Ethics and Policy | AI, Leadership | N | |
| Natural Language Processing | AI | Y | ML and AI, Deep Learning |
| Reinforcement Learning and Application | AI | Y | Math & Dev Tech |
| Capstone | AI, Business | Y | ML and AI |
| AI in Marketing | AI, Business | Y | ML and AI |
| AI in Finance | AI, Business | N | ML and AI |
| AI Innovation and Leadership | Leadership | Y | Intro to Mgmt |
| Leading Change | Leadership | N | Intro to Mgmt |
* All courses depend on high-performance teams.