Peter Song 
The finance industry is undergoing a transformative shift with the integration of advanced artificial intelligence technologies. Among the most promising innovations is Agentic AI, a new paradigm of AI systems that exhibit autonomy, proactivity, and goal-directed behavior. Unlike traditional AI, which is often reactive and rule-based, Agentic AI can make decisions, plan actions, and adapt … Read more
In the world of machine learning, working with high-dimensional datasets is common, especially in domains like genomics, text mining, image analysis, and finance. While more features may intuitively seem beneficial, high dimensionality often leads to overfitting, increased computational cost, and poor model interpretability. That’s where feature selection techniques for high-dimensional data come into play. This … Read more
As AI continues to evolve beyond simple automation and content generation, the spotlight is now shifting toward agentic AI—intelligent systems capable of reasoning, planning, and autonomously executing tasks. These AI agents don’t just answer questions or run models; they operate with a sense of agency, breaking down goals, taking actions across tools and APIs, and … Read more
The banking industry is undergoing a technological revolution, and one of the most disruptive innovations at the forefront is Agentic AI. Unlike traditional AI systems that are reactive and limited to single-turn tasks, agentic AI is capable of autonomous decision-making, dynamic planning, tool usage, and long-term goal pursuit. This makes it especially powerful in complex, … Read more
Artificial intelligence has come a long way since its inception, evolving from early rule-based expert systems to today’s highly autonomous and adaptive agentic AI systems. These two paradigms represent vastly different generations of AI—with expert systems grounded in human-defined rules, and agentic AI representing the frontier of goal-driven, self-directed agents. In this post, we’ll explore … Read more
As artificial intelligence continues to evolve at a breakneck pace, new terms and concepts are emerging to capture the expanding capabilities of machines. Two such terms—Agentic AI and Agent AI—are gaining attention, especially in the context of autonomous systems, intelligent assistants, and human-like digital workers. Although they sound similar, Agentic AI and Agent AI differ … Read more
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
