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AI isn’t one thing anymore. It’s a battle between traditional AI that analyzes and predicts—and generative AI that creates. The stakes? Trillions of dollars in future value. McKinsey projects generative AI alone could add $4.4 trillion annually to the global economy. Yet, MIT found 95% of enterprise AI projects fail to deliver measurable impact. So […]

Reinforcement learning (RL) sounds like magic. You set a goal, give an agent some rules, and it “learns” to achieve it on its own. Sounds easy, right? Here’s the reality: most RL projects fail before they even leave the lab. Agents need millions of trials, training eats thousands of GPU hours, and real-world deployment? That’s

Ever launched a machine learning project that worked perfectly in a pilot… and then completely fell apart when you tried to scale it across the enterprise? You’re not alone. In fact, around 87% of enterprise ML projects never make it past the pilot stage. That’s a staggering number—and it’s not because the technology fails. In

AI is everywhere. Headlines promise it will run businesses, write code, and even think like humans. But here’s the truth: most of it is overhyped. In this post, I’m going to show you the hidden flaws behind AI’s shiny promises. You’ll see exactly where it fails, why businesses overpay for it, and what to watch

What if I told you 90% of machine learning projects that start with reinforcement learning never make it past a prototype? Meanwhile, most successful AI products you use daily — from spam filters to credit scoring — run on simple supervised learning. That’s the gap we’re closing today. This post isn’t another theory dump. You’ll

You’ve probably heard people say, “AI is about to replace humans.” But is that really true? The truth: AI and human intelligence are completely different—not just in how they work, but in what they even are. Here’s a fact: In 2024, AI solved complex chess problems in seconds, but still can’t understand sarcasm or make

Ever feel like you understand nothing every time you Google “Supervised vs Unsupervised Learning”? You’re not alone.Over 70% of beginner ML learners misinterpret the difference, according to a Kaggle forum poll.And guess what? Most business blogs get it wrong too. Let me give it to you straight — Supervised Learning is for when you know