Hi! My name is Krishna Rupanagunta. A big part of this blog is devoted to the art and science of decision making: from how to transform problem solving in Enterprises to the massive opportunity that all things data presents in today’s business environment. I have always been in the problem solving space: from optimization and simulation all the way to the current AI/ML explosion. Here’s my professional journey: http://www.linkedin.com/in/krishna-rupanagunta
My other (real?) passion is to explore the endlessly fascinating complexities of the human experience at the confluence of Technology, Economics, and Psychology: what that means to how we as individuals and societies make choices, big and small. I will share my reading list and whenever I can, write up my review of the book as well. And if you are up for a book discussion, you can always reach out to me.
I would love to hear from you – feel free to leave a review/comments on my posts, and if you do want to go beyond that, my email: rsrikrishna1@gmail.com.
The Cognitive Enterprise
Data and AI is transforming the Enterprise like never before. And I am fortunate to be in the thick of how this is playing out in companies, big and small.
Random Musings
My thoughts on a wide variety of topics, that try and explore the human mind: how we think and how we make decisions
Book List
I read – and by conventional measures, read a lot. The list of books that I am reading/have read with my own little review
The Difference That Makes a Difference: The case for Data in Enterprise AI
In 1909, biologist Jakob von Uexkull coined the term umwelt — the perspective-dependent universe of salient information that an organism uses to navigate the world. Every species constructs its own. The electromagnetic spectrum is vast, but humans perceive only a narrow band that we call ‘light’. That is our umwelt: it defines our experience of…
#82: How do we evaluate Agentic AI Systems? Part-2
Continuing from my previous post, this is an attempt to define metrics for measuring the quality of Agentic AI Systems
#81: How do we evaluate Agentic AI Systems? Part-1
I recently met with the Head of Data Science at one of the largest media conglomerates and as often happens these days, evaluation and quality of AI Agents came up. One of the most important problems he has been trying to solve is how to evaluate the quality of AI agents, and once in production,…
Celebrating the learning and curious mindset 