**Why AI's Get Fooled by Some Simple Games** You've probably heard about the incredible abilities of Google's AI-powered game-playing machines, known as Alpha series. These AI's have mastered complex games like chess and Go, beating even the best human players. But what happens when these AI's encounter games that are seemingly simple, yet stump them? In a recent study, researchers from Google's DeepMind discovered that their AI's have a blind spot when it comes to certain types of games. They found that games like Nim, a simple game involving matchsticks and a pyramid-shaped board, can easily defeat an AlphaGo-like AI, even if the human player is a newbie. So, what's going on here? The answer lies in the way these AI's are trained. You see, AlphaGo and AlphaChess use a technique called self-play training, where they play against themselves millions of times to learn the game. But this method has limitations. For example, it can't handle "impartial games" like Nim, where both players use the same set of rules and pieces. Nim is a great example of an impartial game. Players take turns removing matchsticks from a pyramid-shaped board, with the goal of being the last one left with a legal move. It's a simple game that kids can learn in no time, but it's also a critical example of an impartial game. In fact, researchers have shown that any position in an impartial game can be represented by a configuration of a Nim pyramid. This discovery is significant because it highlights the limitations of current AI training methods. It shows that AI's can be fooled by very simple games, which can have real-world implications as we increasingly rely on AI for problem-solving. By studying these blind spots, researchers can improve AI training methods, making them more robust and effective. In Nigeria, where technology is rapidly advancing, this study has implications for the development of AI-powered systems. As we build more complex AI systems, we need to ensure that they're robust and can handle a wide range of scenarios, including simple games like Nim. This study is a reminder that there's still much to be learned about AI, and that there's always room for improvement.