From his beginnings as a mechanical engineer after graduating from the Massachusetts Institute of Technology in the US to transitioning into sales and marketing, and eventually moving into academia, Dr Simon Mak, Vice-Chancellor of Maharashtra’s Universal AI University (UAIU), has continuously embraced new challenges and opportunities. In this interview with Education Post’s Pragya Kumari, he shares his unique career journey and perspectives on AI’s impact on engineering, emphasizing the importance of “Augmented Intelligence.” saying, “AI helps people perform their work faster and with higher quality.”
Q. Please share a bit about your journey.
I’m now in my third career – from engineer to entrepreneur to academics. After completing my BTech from the Massachusetts Institute of Technology (MIT), I started as a mechanical engineer in manufacturing and product design. Wanting to try something new, I transitioned into sales and marketing, beginning as a sales engineer and eventually becoming vice president of sales and marketing while earning an MBA in finance.
My career then took a turn towards academia, where I now work as a professor and administrator, holding a PhD in Applied Science Systems Engineering. During my sales tenure, I moved from Fortune 500 companies to a Silicon Valley startup that went Initial public offering (IPO), and then to other startups, including my own dot com venture.
Although my initial plan was to rise to vice president of engineering in a large tech company, I have always embraced new challenges and opportunities, leading to an unconventional yet enriching career path. This non-traditional journey has provided me with diverse career and life experiences beyond my imagination. For instance, I never envisioned becoming the vice-chancellor for a startup university in India, but here I am!
Q. How did you transition from engineering to the fascinating world of AI research and leadership?
My journey into AI started at MIT, where I took a computer science course using the LISP programming language, an early tool for AI. Although I didn’t fully grasp the technology then, it sparked my interest in recursive thinking and logic, which I applied in engineering as “Natural Intelligence.”
My significant shift towards AI came at Mercury Interactive, a Silicon Valley startup specializing in client/server software testing. Here, I relearned programming to automate regression testing, simulating the actions of a software quality engineer. This experience solidified my understanding of AI, or what I prefer to call “Augmented Intelligence,” emphasizing its role in improving work speed and quality.
At Universal AI University, I leverage my background in distributed computing and blockchain to research Small Language Models (SLMs) and enhance AI quality. In my leadership role, I support faculty, staff, and students in tackling both current and emerging challenges using AI, aiming to drive impactful research that benefits society.
Q. Universal AI is at the forefront of cutting-edge technologies. Could you elaborate on how AI is revolutionizing traditional engineering disciplines? Are there any specific breakthroughs or applications that excite you?
My answer applies to both hardware engineering and software engineering. In general, a large portion of an engineer’s time is spent on design. For new engineers, there is a significant learning curve of all previous designs that worked and didn’t work, inside the company and in the marketplace. This is where I believe AI can make a substantial contribution. The ability to prompt an AI bot for design recommendations based on all previous designs in engineering textbooks, in the company design database, and in the public domain will provide a tremendous time-saving value to engineers. In software engineering this is already beginning to happen due to the easily programmable nature of software. In hardware engineering this is much more difficult and as a mechanical engineer I see these AI solutions as game changers for the design of physical objects and products we see and experience in the world, from mobile phone designs to large-scale commercial buildings.
Q. As we bridge the gap between academia and industry, what skills do you believe engineering graduates should prioritize to thrive in an AI-driven landscape?
Again, if an engineer views AI as Augmented Intelligence, then this sets the mindset of using AI to help people do their jobs better and quicker. To do this, engineers need to better understand the voice of the customer. This is where design thinking education comes into play. However, you must balance the voice of the customer with the “innovators dilemma” which is that customers often only have incremental needed not disruptive needs. To prepare students for this dilemma, we also need education in creativity and play. At UAIU we plan to educate our students in design thinking, creativity, and yes play.
Q. Talking about ethics, AI systems can have biases, and engineering decisions impact society. What role do engineers play in ensuring fairness and transparency?
Questions of ethics in engineering are easy and difficult to discuss. The easy discussion is to make sure your work product does no harm. Then we dive into a discussion of intentional harm or unintentional harm, which now becomes more difficult to discuss. You then layer in the discussion of legal versus ethical versus moral and now the discussion becomes much more difficult. So engineers need to have an understanding of the broader context and implications of their work, and to not just pursue cool technology without regard to the societal impact of the technology. This is why I’m a big advocate of multi-disciplinary education, especially for engineers since the products of engineering (and AI) can be used to help or hurt society. I’m also a big advocate of engineers pursuing policy careers to help policymakers better understand the societal implications of their decisions. At UAIU, we want all our students to have a multi-disciplinary education so that our students can become better global citizens contributing to society.
Q. The buzzword these days is “explainable AI.” How can engineers strike a balance between creating powerful, complex models and ensuring they remain interpretable?
I like the idea behind “explainable AI”, to develop tools to better validate the quality of AI results. I previously mentioned the quality of AI results as a major challenge in AI. However, if you discover low quality in your AI results, what’s next? Can you “fix” the code easily? This is another topic I learned when I worked at Mercury Interactive, the existence of “spaghetti code.” What this means is that there is software that is nearly impossible to update and understand due to poor programming standards, comments, and documentation. In the haste to beat competition to the market, shortcuts in software documentation are quite common. Then you add software updates by several developers and poor version control you have spaghetti codes. Yes the software works but it hasn’t been updated in years due to the complex nature of debugging the software. You see this example in airline reservation and seating systems that still use old mainframe code and essentially haven’t changed in decades. This is one of my concerns of AI, trying to debug the source of error (or bias) but the code (or algorithm) has become so complex that making changes is very difficult.
Q. What’s your take on the age-old debate — should engineers specialize deeply in one area or maintain a broader skill set?
As an entrepreneurship professor, I’m biased. I believe everyone should develop deep expertise in a core domain, complemented by entrepreneurship courses and mindset. For engineers, this is especially important because many good ideas and inventions cannot help society if they remain inside the engineering notebook or lab. We are creating a culture of innovation and entrepreneurship to encourage and assist our students, faculty, and staff to pursue commercializing their inventions.
Q. What’s your favorite engineering-related book or resource that you’d recommend to aspiring engineers and AI enthusiasts?
My favorite engineering-related books are from my PhD in Systems Engineering program. The first is a book on Design of Experiments. Many engineers think engineering is deterministic but in the real-world it is probabilistic and dynamic. So engineers have to understand creating experiments on your engineering designs to determine the optimal design. The second book is on linear programming — to better understand how to optimize a specific design parameter given several diverse factors. Finally, I also believe in lifelong learning and the best way to learn a subject is to try to teach it — so here I recommend a book called Teaching to Change Lives.
Q. If you could give one piece of advice to our young readers who dream of shaping the future through engineering and AI, what would it be?
I have two pieces of advice for your young readers:
Pursue your calling, not your passion — find a problem domain that really resonates with you and then work to provide engineering solutions to problems in that area. For example, maybe you really resonate with physically handicapped children and sense a calling to help them. Then pursue engineering work that helps you follow this calling, even if you’re not passionate about it at first.
If you follow my first advice, then you will quickly discover that working as an employee for someone else may take you away from your calling. Then I suggest you then pursue the entrepreneurship startup path. If you pursue this path, then THINK BIG, BUT START SMALL, and get some QUICK WINS.
