The AI Thought Process: Understanding how AI works from a human perspective

When we talk about artificial intelligence, it's tempting to imagine a sharp line separating how machines "think" and how humans make judgments. We often hear phrases like "humans rely on intuition" or "AI only crunches data," as though the two operate on completely different planes. But the truth is more nuanced. In fact, the way humans form judgments has striking parallels to how AI systems process information. At the heart of human decision-making is experience. From childhood, we gather countless examples-how people react when we smile, what happens if we touch something hot, the tone of voice that signals danger versus kindness. These experiences accumulate into patterns that our brains store and recall when making new choices. When faced with uncertainty, we don't compute from scratch; instead, we lean on prior examples and fill in the gaps with learned associations. That's remarkably similar to how AI models, especially those trained on massive datasets, operate. AI systems don't "understand" in a human sense; they recognize patterns and make predictions based on prior training. Of course, humans have something AI doesn't: context shaped by biology, culture, and emotions. We don't just draw on stored data but also factor in feelings, instincts, and values that have been wired into us through evolution. Still, if you zoom out, both humans and AI are essentially predictive engines. Given input, both attempt to produce the most likely or useful output based on prior knowledge. A toddler learning that fire burns and a neural network learning that certain pixels represent a cat are not so different in structure-they're both adjusting internal parameters in response to feedback. Interestingly, this similarity raises questions about bias. Just as AI models can reflect the biases present in their training data, humans inherit biases from personal experiences, cultural norms, and social influences. A person who grows up in a community with limited diversity may unconsciously adopt skewed views, just as an AI model trained on incomplete or biased data can make unfair predictions. Both systems are shaped by what they're exposed to, and both require mechanisms-whether human reflection or algorithmic fairness adjustments-to correct for blind spots. Where humans still diverge, at least for now, is in creativity and moral reasoning. We don't just predict; we imagine, improvise, and weigh decisions against ethical frameworks. But even here, AI is beginning to inch closer, generating new ideas, artworks, and even strategies that surprise its creators. It may not "feel" like we do, but the mechanics of pattern generation echo our own creative leaps. Perhaps the most humbling realization is this: if we reduce judgment to pattern recognition, feedback, and adaptation, then AI isn't a foreign way of thinking-it's a mirror. It reflects, in a distilled form, how we ourselves navigate the world. Rather than treating AI as alien, we might see it as a reminder that our human judgments, though wrapped in emotion and meaning, are built on structures not so different from the machines we've created. This book tries to explain how AI works from a human perspective. Examples are provided in plain languages and in Python code to show you step by step how AI find answers to your questions.