Sai kiranmai Vemula

This Raksha Bandhan, I was reminded of the power of technology to bring us closer, even when we're miles apart. My brother, Aarav, and I have always shared a special bond, but with him in Silicon Valley and me in India, I wondered how we could keep our traditions alive. Aarav, ever the innovator, surprised me with an app he developed called “RakhiBond.” Through it, I designed a digital rakhi that he received instantly on his smartwatch. The app’s AI even analyzed my emotions as I created it, delivering a personalized message that captured the depth of our connection. But the real magic happened when Aarav sent me a pair of VR glasses, allowing me to tie a virtual rakhi on his holographic wrist in a digital version of our childhood home. This experience showed me that while distance might separate us, technology can bridge the gap, preserving and even enhancing the traditions that matter most. It’s a beautiful reminder that innovation isn’t just about the latest gadgets—it’s about f...

As I sat in class, my professor once again brought up the Iris dataset. It was the third time this week, and I couldn't help but wonder—what is it about this dataset that makes it such a staple in our machine learning curriculum? It wasn’t the first time I had encountered it, either. In fact, the Iris dataset had been a constant companion throughout my journey in data science, popping up in textbooks, online courses, and now, in my classroom at ISB. But why? What makes this particular dataset so significant that it finds its way into almost every discussion about machine learning? Curious to find out, I decided to dig a little deeper. The Roots of a Legend: The story of the Iris dataset begins long before any of us were grappling with algorithms and models. It was 1936, and a British statistician and biologist named Ronald A. Fisher introduced the dataset in a paper that would become a cornerstone of statistical analysis. Fisher wasn’t just working with numbers; he was laying the g...

In the rapidly advancing field of artificial intelligence, the connection between AI memory systems and human selective memory is a topic of growing interest. As we develop more sophisticated machine learning models, drawing parallels between human cognitive processes and AI memory architectures can offer valuable insights into how these systems manage and prioritize information. What Is Human Selective Memory? Human selective memory is a critical cognitive function that allows us to filter and retain information based on relevance, emotional significance, and contextual importance. This process helps us focus on the most important data, making it accessible when we need it. For instance, emotionally charged experiences are often remembered more vividly due to the brain's complex memory mechanisms. This selective process ensures that our mental resources are efficiently allocated, helping us navigate everyday life with greater ease. How AI Controllable Memory Mimics Human Memory? A...