Introduction
In our recent Leading with Data session with Dr. Gautam Shroff, Senior Vice President & Head of Research at Tata Consultancy Services, we delve into the dynamic journey from academia to industry research. Dr. Shroff sheds light on TCS’s AI research evolution, emphasizing the transformative shift from big data to the frontiers of deep learning and generative AI. Exploring neuro-deductive learning and the groundbreaking advancements in generative AI paints a vivid picture of the evolving field.
Let’s look at the details of the session!
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Key Insights from our Conversation with Dr. Gautam Shroff
- The move from academia to industry research often arises from a desire to apply theoretical knowledge to real-world problems.
- TCS’s AI research evolution showcases a shift from big data technologies to the realms of deep learning and generative AI.
- Neuro-deductive learning combines traditional search techniques with neural networks, offering a hybrid AI approach for more efficient problem-solving.
- Generative AI’s groundbreaking ability to produce code and solve mathematical problems signifies a new era in neuro-symbolic learning.
- Significant research areas in the coming years include trustworthiness and effective utilization of AI tools in mission-critical applications.
- Future AI researchers are encouraged to focus on solving meaningful problems using AI as a tool, rather than chasing the latest trends in the field.
Now, let’s look at Dr. Gautam Shroff’s responses to the questions asked in the Leading with Data.
How did your journey lead you from academia to heading TCS research?
Well, the decision to return to India was more about not wanting to stay abroad indefinitely. I was offered a position at IIT by the then director, which I accepted. After several years in academia, I was regularly invited by Mr. F.C. Kohli of TCS to join the company. One day, I decided to take up the offer, and that’s how my journey in industry research began.
Reflecting on your time at TCS, how has the focus on AI and ML research evolved?
When I joined TCS, AI wasn’t prominent in the industry. However, with the advent of big data around the mid-2000s, we realized the importance of data and started working with technologies like Hadoop even before it was released. The real excitement about AI began around 2015 with the advancements in deep learning.
Can you share significant milestones in AI research at TCS?
Initially, our work revolved around big data technologies and building data lakes. The AI component became more evident when we started exploring data mining and pattern recognition. A decade later, the breakthroughs in deep learning led us to explore a variety of new applications.
How has the strategy for building the research team at TCS changed over the years?
Our mantra has always been to hire good researchers and let them work on cutting-edge problems. We focus on creating an environment where they can publish in top venues and make a significant impact.
What are your thoughts on the recent advancements in generative AI?
The ability of generative models to produce good code and solve new mathematical problems has been one of the most surprising developments. It’s a new form of neuro-symbolic learning where neural networks generate programs that can be used for symbolic search.
Could you explain the concept of neuro-deductive learning?
Neuro-deductive learning involves guiding the search for solutions with very little data. By combining traditional AI techniques with neural networks, we can perform symbolic reasoning in a more efficient way. It’s about learning better ways of searching based on past experiences.
How do you envision the future of AI research and its applications?
I believe the next few years will focus on effectively utilizing AI tools for various applications and addressing trustworthiness concerns, especially in mission-critical scenarios. AI has become a commodity, and it’s fascinating to think about how these tools can accelerate human thinking and learning.
What advice would you give to aspiring AI researchers?
Stay away from the bandwagon and focus on impactful contributions addressing real-world problems. It’s essential to have a passionate problem that you want to solve, and AI should be a tool to solve it, not the end goal.
Summing Up
Dr. Gautam Shroff’s extensive experience, from academia to leading TCS’s research endeavors, provides a unique perspective on the evolution of AI. His journey reflects the industry’s dynamic shift towards cutting-edge technologies. As AI becomes more ubiquitous, the focus on trustworthiness and practical application in mission-critical scenarios emerges as a critical research area.
Dr. Shroff’s advice to aspiring researchers resonates—emphasizing the importance of solving impactful, real-world problems with AI as a tool, not just following trends. This insightful conversation opens a window into the fascinating world of AI research and its past, present, and promising future.
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