A degree in data science is not important – Debdoot Mukherjee, Head of AI, Meesho

Debdoot Mukherjee is the Chief Data Scientist and Head of AI at Meesho, the Indian origin social commerce platform at the forefront of the boundaryless workplace model that became a norm in the aftermath of the Covid-19 pandemic. Upon completing his postgraduate degree from IIT-Delhi, Mukherjee began his career in the research division at IBM, where he attained expertise in Information Retrieval and Machine Learning techniques. He then journeyed on to work in impactful roles at companies like Hike, Myntra and ShareChat before leading the AI and data science division at Meesho. 

In an exclusive interview with Analytics India Magazine, Debdoot Mukherjee opened up about his journey into data science, machine learning and everything AI. 

AIM: What attracted you to this field?

Debdoot: My first brush with machine learning was during my masters where I took a few courses related to the subject. As I progressed, my interest in the field kept growing. Post graduation, I joined IBM research where I got a chance to go deep into new technologies. It became a routine where machine learning was turning out to be a great tool to apply in every project. In the last decade, progress in the field of AI/ML has trumped all of us. Later when I moved to Myntra, I got the opportunity to apply all the techniques that I’d learnt to achieve significant results. That’s what keeps me going in this field.

AIM: Would you say holding a degree in data science/AI is enough?

Debdoot: Machine learning is a field where theoretical knowledge is very important. Awareness of the right state-of-the-art ML techniques and knowing how to implement them on the problem statement requires a great deal of clarity on the theoretical foundations of the subject. So, from the standpoint of formal training, a degree does not seem important. However, it is of paramount importance that the foundations are clear, which then comes from proper college training. After gaining theoretical knowledge, the next step is to understand the practical applications, which comes with hands-on projects, hackathons and such. Practicing these techniques as part of an industry or academia provides a broad perspective on applications which result in out of the box solutions. 

AIM: With so many patents to your name, how were you able to come up with such ideas?

Debdoot: It is all part and parcel of working in a research lab. The goal of researchers is to look for and develop ideas that have a significant impact. One is also expected to drive this impact in both the business world and academic world. Overtime, one does get a playbook on how to convert ideas into patents. 

AIM: How does Meesho leverage AI/ML in its business ?

Debdoot: The mission statement of Meesho is to use AI/ML as a sort of enabler to all pillars of e-commerce platforms, marketplace trends, and such. There are a lot of applications on the demand side, like the consumer side where people discover products—be it the feed that one landed on, or opening a different category listing pages on an app or sifting through the search interface itself—AI is being integrated in features like computer vision, virtual assistants, search enablement to improve the user experience. We are also working on the preempt mechanism and, with time and history of user preferences, we will be able to recommend certain products that a user will need in the future. However, a lot of this is serendipitous discovery, where, based on the depth of understanding, the user can be recommended a lot of products, without having a clear shopping intent in that category. Now from the supply side, the scenario is not that different. A lot of applications are largely led by recommendation systems and ranking monitors on a variety of touch points. 

AIM: Your vision for the future?

Debdoot: In this day and age, AI has become a prerequisite for a successful business as a major part of the business process has AI/ML techniques integrated into them. However, there are many other industries where AI adoption is still in its infancy. Artificial intelligence has the power to not only transform businesses but also society at large. AI can do well in some variability of large and structured data sets but it struggles to replicate intuition. Natural Language Processing, object detection and image generation are some of the challenges that research institutes and scientists are working to crack. My vision is that AI/ML models create solutions that humans can utilise in various tasks, but not replace humans in any manner.

AIM: What is your point of view on AGI? Have we achieved it yet?

Debdoot: I’m pretty sure that we haven’t achieved it yet. However, sentience in its essence is fairly subjective, like emotion, perception and so on. AI has not reached the level of human intelligence as a lot of these machines still fall short in comparison to the human brain. Keeping that in mind, the next phase of development is mimicking the workings of the human brain. The metric might not be the same and for most cases, AI requires a lot of data, pre-conditions, and such. One must look into nature for answers. The solution is natural and causal. So far, the end result has been very good. But, we need to fundamentally change the approach and then you can think of getting closer to AGI.