“In a realm where science requires matter to prove the nature, we find ourselves adrift in the mysteries that transcend matter, much like the enigmatic nature of consciousness.”

David Chalmers in one of the most fascinating Ted Talks aptly puts it: “On the one hand, it’s a datum that we’re conscious. On the other hand, we don’t know how to accomodate it into our scientific view of the world. So I think consciousness right now is a kind of anomaly, one that we need to integrate into our view of the world, but we don’t yet see how”. Ever since encountering his famous Hard Problem of Consciousness - how physical processes transform into a unique, non-replicable subjective reality, qualia - I have always been captivated by the topic of consciousness. Meanwhile, as a Data Scientist having worked across many industries, I explore the dynamic and rapidly changing landscape of Artificial Intelligence (AI). My curiosity is not just confined to algorithms and data; it runs in parallel into the profound realms of consciousness, cosmology, and philosophy.

Social media platforms are abuzz with debates on how Artificial General Intelligence (AGI) is right around the corner, risks or not to the very existence of humanity, and current AI developments having emergent or perhaps hallucination properties, which model has performed the best on which benchmark, etc. These debates often touch upon the existential questions, the complexities of data governance, ethics, and cybersecurity. As we stand in this transformative era of AI’s dawn, I find myself deeply immersed in understanding and contributing to this transformation while contemplating the intangible questions related to consciousness. I wish a near future where the AI community might develop some commonly agreed, quantified definitions or degrees of AGI, moving beyond subjective terms like “superintelligence” or “beyond human intelligence” that stem from our own subjective realities.

I am also intrigued by philosophical perspectives, such as those offered by Advaita Vedanta. These teachings, which explore the nature of perception and reality, resonate with the challenges we face in AI regarding understanding and interpreting complex human behaviors and cognitions. They remind us that our perception of the world is often filtered and interpreted through various lenses, a concept that has fascinating parallels in the world of AI. These philosophical approaches always enrich my perspective, allowing me to approach data science not just as a technical field, but as one deeply interconnected with our understanding of human cognition and perception, our attempt to quantify those aspects of the world around us.

Apart from the above topics, here I would also take the opportunity to share the practical experiences I have learnt across various industries in the field of Machine Learning that could be helpful for anyone. Its been almost 5 years working as a Senior Data Scientist, after my Ph.D. in Computer Science. After countless lines of codes, reports, meetings with stakeholders from engineering to business and clients, I see my technical work along the three axis of:

• Use Case: what problem am I trying to solve, why, how would it benefit a business or the end-user.
• Performance: if performance of any application, code, or work can be improved and why we need it to be performant.
• Scalability; good performance is always translatable to possible scalability.

My technical interests are in topics of Natural Language Processing (NLP), Time Series Forecasting, Anomaly Detection, and Product Development.

Constructive criticism, questions, suggestions, and sharing of thoughts are always welcome in this space-time. I am lucky to live in the era of Open Source where knowledgde is so freely available and there is a huge gratitude always towards the open source community.

Lets connect!