In this Leading with Data session, we dive into the journey of Anand Ranganathan, a visionary of artificial intelligence and machine learning. From his early days at IBM to co-founding innovative startups like Unscramble and 1/0, Anand shares his insights on the challenges, transformations, and future of ai. Join us as we explore his entrepreneurial experiences, the impact of deep learning, and his vision for the future of ai and its applications.
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Key insights from our conversation with Anand Ranganathan
- Balancing symbolic ai and deep learning is key for accurate reasoning in specific domains.
- The rise of deep learning requires agility in product development and market strategies.
- ai services companies focus more on customer relationships and custom solutions than product companies.
- Agent workflows will transform ai integration, but the boundaries of human-ai collaboration need clarity.
- For ai/ML careers, domain expertise and staying up-to-date are essential to success.
- The future of ai will reshape software engineering, requiring continuous learning and adaptation.
- Domain knowledge is vital as ai disrupts generic software engineering functions.
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Let's check out the details of our conversation with Anand Ranganathanl!
<h2 class="wp-block-heading" id="h-how-did-your-journey-in-ai-and-ml-begin-and-what-were-the-early-days-like-for-you”>How did your journey in ai and ML start and what were your early days like?
My journey in ai began with my PhD at the University of Illinois, where I delved into the intersection of ai and distributed systems. Back then, ai was more about symbolic or logical reasoning, something very different from today's landscape. I worked on ai planning, which involves transitioning the world from one state to another through a set of actions. After my PhD, I joined IBM Research, where I tackled big data problems and was part of the team that built IBM's stream processing offering. It was an era dominated by classical ai, but as deep learning gained traction in the 2010s, the field transformed dramatically.
What motivated you to leave IBM and start your own company?
After a decade at IBM, I was eager to tackle interesting problems I identified in the industry. Meeting the right people who shared my vision and spotting a market opportunity were the catalysts that helped me co-found my first startup, Unscramble. Our goal was to be agile and innovative in solving challenges, which was a different experience than the IBM corporate environment.
Can you explain the two different problems that Unscramble focused on and how they were connected?
Initially, Unscramble addressed real-time data transmission problems, specifically in the telecommunications sector. Then we realized that historical data analysis also needed to be done. Although the domains were different, the underlying commonalities were in queries on structured data and triggers on streaming data. Our solutions ranged from natural language queries to databases to defining marketing campaigns in real time using a natural language interface.
How has the rise of deep learning affected your products at Unscramble?
The rise of deep learning was significant, especially for our natural language to SQL translation product. We had to evolve our techniques as deep learning models became more adept at handling such tasks. Finally, as refined SQL generation models emerged, it became clear that the space was being disrupted. We were already exploring an exit strategy and found it opportune to sell the product before the disruption became too great.
What are the differences between running a product company like Unscramble and a service company like 1by0?
Running a product business is about showing what you have and tailoring it to the customer's needs, while a service business is about understanding the customer's problem and designing the right solution. At 1by0, we focus more on account and project management, certifications, and maintaining close partnerships with providers like AWS and Databricks. It's a different trajectory, with a greater emphasis on customer relationships and delivering customized solutions.
Reflecting on your entrepreneurial journey, what are some key learnings and things you could do differently?
A key learning is the balance between tackling interesting problems and focusing on market demand. At Unscramble, we sometimes prioritized interesting challenges over market viability, which, while intellectually satisfying, was not always optimal for a startup's growth. In the services space, the challenge is deciding how much to invest in exploratory solutions versus more secure and well-understood ones.
<h2 class="wp-block-heading" id="h-how-do-you-envision-the-future-of-ai-particularly-in-the-context-of-symbolic-ai-and-deep-learning”>How do you envision the future of ai, particularly in the context of symbolic ai and deep learning?
I think there is a need to strike a balance between symbolic ai and deep learning, especially in domains that require precise reasoning, such as medicine. While LLMs are improving in their reasoning capabilities, there is still a need for demonstrable and accurate knowledge, which symbolic ai can provide. Advances in simplifying the construction of knowledge bases could be key to advancing symbolic ai.
<h2 class="wp-block-heading" id="h-what-trends-do-you-foresee-in-ai-in-the-near-future-and-how-do-you-see-agentic-workflows-evolving”>What trends do you foresee in ai in the near future and how do you think agent workflows will evolve?
Agent workflows are gaining traction and will continue to do so. They offer a way to integrate ai into daily work more seamlessly. However, the boundary between human collaboration and ai is still blurry. It will be critical to decide when ai can act automatically and when to involve a human. I also see ai becoming more integrated into software development, changing the skill set needed for software engineers.
<h2 class="wp-block-heading" id="h-what-advice-would-you-give-to-those-just-starting-their-careers-in-ai-and-ml”>What advice would you give to those just starting their careers in ai and ML?
Focus on gaining domain expertise in addition to technical skills. Domain knowledge is less likely to be compromised and can complement your technical skills. Stay on top of advances in ai and experiment with different tools and frameworks to improve their effectiveness. It is a rapidly changing field, so continuous learning is essential.
Final note
Anand Ranganathan's journey reflects the rapid evolution and potential of ai. From IBM to pioneering startups, their story underscores the importance of adaptability, domain expertise, and balancing innovation with market needs. As ai reshapes industries, his insights highlight the critical role of human-ai collaboration and continuous learning. The future of ai is exciting and leaders like Anand are paving the way for transformative advances.
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