When people hear I’m pursuing a PhD in Artificial Intelligence, the reactions are nearly predictable: “You must be incredibly smart,” or “Wow, working on the future of humanity?” The assumptions are flattering—but often far from accurate. The truth? Research in AI isn’t some linear march toward breakthrough innovation. It’s a nuanced, uncertain, often messy process […]
PhD Diaries: Research Isn’t What You Think Read More »
Artificial Intelligence continues to redefine industries, promising automation, efficiency, and unprecedented insights. From self-driving cars to generative language models, AI is being positioned as a revolutionary force capable of transforming business and society. Yet, as impressive as these advancements are, there is one fundamental truth that often gets overlooked: technology is only as smart as
Tech Is Only as Smart as the People Behind It Read More »
As we move into the second quarter of 2025, it is clear that AI and data streaming are evolving at an unprecedented pace. With businesses increasingly relying on real-time insights, AI-driven automation, and event-driven architectures, the way we handle data is undergoing a fundamental transformation. While Generative AI continues to dominate discussions, the true advancements
AI and Data Streaming Trends in 2025 Read More »
If you’ve spent any time on social media recently, you’ve probably seen the trend: people uploading selfies to AI services that render them as retro action figures sealed in plastic blister packs. The results are undeniably creative—quirky accessories, heroic stances, vintage fonts. It’s nostalgia wrapped in AI-powered novelty. But beneath the fun lies a growing
Artificial Intelligence has become synonymous with Generative AI in recent years. Whenever AI is discussed in public forums, mainstream media, or boardroom meetings, the focus is almost exclusively on chatbots, image generation, and large language models like ChatGPT. While these technologies are indeed impressive, they represent only a fraction of the vast AI landscape. AI
Not All AI is ChatGPT! Read More »
The world of investing has always been a numbers game, but in the age of AI, those numbers are being processed at an unprecedented scale and speed. Financial markets generate vast amounts of data every second—price movements, trading volumes, earnings reports, economic indicators, and news sentiment. AI is transforming how investors analyze this information, identify
How AI Powers Smart Investing Read More »
The phrase “data is the new oil” has become a common metaphor in the digital economy, emphasizing data’s immense value as a driver of business and technological innovation. However, like crude oil, raw data in its unprocessed form is not inherently useful. Just as oil must undergo refining before it can power industries, data must
Data is the New Oil, but Not All Oil is Refined Read More »
In today’s digital world, real-time data streaming is no longer a luxury—it’s a necessity. Businesses and AI-driven applications thrive on instant insights, requiring robust data pipelines that can process and react to events as they happen. Apache Kafka and Apache Flink form the backbone of modern real-time data processing, enabling seamless data flow, low-latency analytics,
As we have explored in the previous parts of this series, high-quality and real-time data are essential for AI and ML applications. Now, let’s take a deeper look into how to implement real-time data products effectively using Apache Kafka and Apache Flink. This part focuses on two crucial features of Flink that enable reliable and
Implementing Real-Time Data Products with Apache Kafka and Apache Flink (Part 3) Read More »
Building and maintaining data products for AI and ML is not just about collecting data—it is about ensuring data quality, scalability, and accessibility. Without addressing these challenges, AI models will produce unreliable results, and organizations will struggle to use data effectively. Two of the biggest challenges in this area are data quality and scalability. Ensuring
Challenges in Building and Maintaining Data Products for AI and ML (Part 2) Read More »