Adi Tyagi's career journey has been shaped by his passion for continuous learning, the diverse range of projects he has worked on, and his exploration of the human aspect of AI.

Can you share the most interesting part of your career journey?

The most interesting part of my career journey is threefold: being a student at heart, what I call the “quarterback” advantage, and exploring the human + AI interplay.

During my graduation ceremony from Northwestern University’s MS in Analytics program, our program director stressed that our educational journey was just beginning. Two years later, I can emphatically agree. In fact, this constant emphasis on learning is what makes me excited to wake up each morning. The field of artificial intelligence, data science, and machine learning is evolving so rapidly that it is incumbent on a successful practitioner to continually adopt a learner’s mindset. As someone who continues to be a student at heart, this dynamic aligns well with my personality.

Secondly, as a consultant working in artificial intelligence and data science, I have been afforded the unique opportunity to solve data and AI problems in a wide variety of domains.

I call this the “quarterback advantage,” as it has given me a privileged view of data science and AI across disparate economic sectors. Here are a few examples to illustrate my point: I helped a large US steel manufacturer infuse simulation and inference analysis techniques to identify levers of improvement for increasing plant throughput. Soon after, I worked on an internal initiative for which we developed a targeting platform to help a nonprofit agency optimize donations by predicting which donors are likely to move into higher donation tiers. In the past, as pro bono work, I also assisted a local city council in evaluating factors affecting food insecurity among residents in the wake of the COVID-19 pandemic.

Lastly, I have gained a unique appreciation for the human angle of AI—this incorporates not just the challenges but also the opportunities that present themselves as mankind continues its adoption of AI. For example, this includes answering the fundamentally human questions of trust (can we really trust AI?), explainability (how do we understand decisions made by AI?), and transformation (how do we best position AI to complement human strengths?).

What excites you most about working with data and AI?

I think the defining partnership of the 21st century will be between mankind and artificial intelligence. Throughout our history, we, as a species, have cemented our place in nature through our intelligence and creativity. I think we are currently at an inflection point where we are attempting to reproduce our own intelligence. What does that future hold? It is anyone’s bet, but I think it is a frontier we are only now scarcely beginning to imagine.

AI is akin to the workers who built San Francisco’s Golden Gate Bridge, the scientists at NASA who worked on the Apollo missions to take mankind to the moon, and the creatives and tinkerers who launched us into the Industrial Revolution.

As a result, the excitement I feel working in AI is akin to the workers who built San Francisco’s Golden Gate Bridge, the scientists at NASA who worked on the Apollo missions to take mankind to the moon, and the creatives and tinkerers who launched us into the Industrial Revolution. It is a task both monumental and humbling—one in which each one of us, both technical and non-technical, has a unique part to play.

Describe an interesting project that you have worked on.

I am currently working on an incredibly exciting internal project spanning some very fascinating technologies. We are building a proof of concept for an autonomous mobile robot called the MIR100 to navigate a virtual world using reinforcement learning. Our eventual target deployment environment is Deloitte’s Smart Factory in Wichita, Kansas. To accomplish our goal, we are using NVIDIA Omniverse’s Isaac Sim, which is a scalable robotics simulation tool that powers photorealistic, physically accurate virtual environments.

This project is helping me and my team grasp the conceptual interplay and technical know-how underpinning spatial computing, virtual worlds, robotics, and artificial intelligence.

This technology gives us a better, faster way to develop, test, and manage AI-based robots. On the artificial intelligence front, we are using a selection of reinforcement learning algorithms to train the robot and help it detect obstacles, avoid collisions, and intelligently reroute to continue toward its goal. This project is helping me and my team grasp the conceptual interplay and technical know-how underpinning spatial computing, virtual worlds, robotics, and artificial intelligence. In the future, this knowledge will be critical to delivering a successful client project in this area.