‍Tell us a little about your journey and the moment when you decided that this is the career you want to pursue.

 My journey started when I was in Grade 12 and chose Mechanical Engineering as my major. All because my seniors said it was a very ‘interesting’ and ‘evergreen’ field. A notable moment during my engineering journey was during my second year of undergrad when I decided to join a group of students to participate in ROBOCON – an Asian-Oceanian college-level robotics competition. For us, it was like the Olympics of Robotics. We first had to compete at the national level, the winners of which got to represent India on an international platform.

 We started building our robot and soon realized that we lacked the skills to build the best one. We were successful in building the mechanical structure but it lacked the software part, which basically works as the nervous system of the robot. Yet, this laid the foundation for the very exciting journey that I had in store ahead. In the following years, my team and I worked hard on enhancing our skills to build something that could represent India on an international platform. And it was a matter of immense joy and pride when we secured the third position in the national-level competition.

 What’s more, I found my passion in Robotics, which led me to pursue a higher degree in Mechatronics from the Indian Institute of Technology (IIT), Patna. There I got an opportunity to work as a fully-funded intern with the IES Lab at TU-Darmstadt, Germany. The idea of this lab is to teach robots how to code – so, building a robot without us exclusively needing to code one. This required the use of Reinforcement Learning, which is also one of the courses I took at Univ.AI. After my time in Germany, I got an opportunity to work at Mercedes Benz where I now work as an Autonomous Driving Safety Engineer.

What is the one thing that excites you about the future of AI?

The one thing that I work on and one that truly excites me the most is Reinforcement Learning because you don’t need data to train a reinforcement learning model. You create your own data. The agent is creating the data and is also learning at the same time, which is unlike the other fields of AI. For example, the work that I did in Germany. My task was to teach the robot a policy to navigate in any given environment, be it a house or a restaurant. And for this, the robot also had to learn by itself, without anyone having to actually code it.

 Another thing that I learned after joining Univ.AI was generative adversarial networks (GANs). They help in creating images from noise, which is extremely fascinating. How can a model create something from nothing? That, to me, is supremely interesting!

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What, according to you, are the things that make Univ.AI different from any other online course?

 Something about Univ.AI that makes it different from any other online course is that you, as a student, are not going through any pre-recorded clips. Everything is LIVE, right from the labs to the lectures. And the second is the curriculum. The curriculum at Univ.AI is very similar to that which you have at top global universities. Univ.AI provides exclusive and direct access to faculty from Harvard & UCLA, who design and run the course. In addition, there are teaching assistants (Tas) who help the students with the labs. Everything you learn, you also apply.

These are some factors that make Univ.AI stand out from any other options currently available in the market.

 What is the one piece of advice that you would give to someone entering this field?

 Mathematics is everywhere. If you are able to correlate the theorems and equations that you learn with the applications in AI, then you’ve cracked the code!

 What are you most looking forward to in the field of AI?

 Artificial Intelligence is an ever-changing field, and something new is always coming up. But something that I want to see soon is an automated- or AI- agent driving a vehicle on Indian streets! If this happens, then we’ve achieved far more than we set out to.

[i] The GANs-Reinforcement Learning Program, on generative adversarial networks, is a six-week Fellowship, a prestigious and invite-only program for accomplished and advanced candidates. Our Fellows get a chance to learn from some of the best professors from Harvard and UCLA. The Fellowship enables them to work efficiently on Reinforcement Learning problems and build effective generative adversarial networks to advance their careers.