Artificial intelligence has evolved rapidly over the past decade, branching into distinct paradigms that serve different needs and industries. Among these, traditional AI, generative AI, and agentic AI stand out as the most discussed and transformative. In this post, we will explore these three types of AI, highlight their differences, and examine how they apply … Read more
The retail industry is undergoing a significant transformation with the advent of artificial intelligence. Among the most groundbreaking developments is agentic AI, a form of AI that acts with autonomy, makes decisions, and takes proactive steps to achieve complex goals. Unlike traditional AI models, agentic AI systems can reason, plan, and interact dynamically with their … Read more
In the rapidly evolving world of machine learning, two foundational approaches stand out: reinforcement learning (RL) and supervised learning. Both are powerful methods with distinct characteristics, applications, and learning strategies. If you’re building intelligent systems or training AI models, understanding the differences between these paradigms is critical. This article offers an in-depth comparison of reinforcement … Read more
Reinforcement learning (RL) has emerged as one of the most powerful and fascinating branches of machine learning, powering breakthroughs in robotics, game playing, autonomous vehicles, and more. But despite its growing popularity, one fundamental question continues to puzzle many newcomers and practitioners alike: Is reinforcement learning supervised or unsupervised? In this blog post, we’ll dive … Read more
In the machine learning lifecycle, labelled data is often regarded as gold standard—critical for training supervised learning models. However, obtaining and using labelled data comes with notable downsides. From high annotation costs to inherent biases and scalability issues, relying heavily on labelled datasets can constrain the development and deployment of AI systems. In this comprehensive … Read more
Image recognition technology has become a significant part of our daily digital interactions. From identifying plants to translating text in real-time, image recognition apps are transforming how we gather information from the world around us. Among the many options available, Google Lens stands out as one of the most popular and versatile tools. But how … Read more
With AI becoming a part of almost everything we do, keeping our data safe while using these smart systems is super important. When different AI models and apps need to talk to each other, they have to share info—but how do we make sure that info doesn’t fall into the wrong hands? That’s where the … Read more
The Model Context Protocol (MCP) is emerging as a crucial innovation for advancing AI integration across various systems. By enabling different AI models and applications to share context seamlessly, MCP enhances interoperability, efficiency, and adaptability. But beyond its technical appeal, how is MCP being used in the real world? This blog post explores some of … Read more
Artificial Intelligence (AI) continues to evolve rapidly, pushing the boundaries of what machines can achieve. However, as AI systems grow more complex and interconnected, ensuring smooth, efficient, and meaningful integration between different AI components, models, and applications remains a significant challenge. This is where the Model Context Protocol (MCP) comes into play. In this article, … Read more
Managing data platforms and real-time analytics often involves integrating powerful backend servers to handle complex computations and data flows. One such critical server type is the MCP server (Multi-Chip Package server), known for its high-performance capabilities. If you’re working with Cursor, a modern data processing and query engine, you might wonder how to add an … Read more
