AI might seem mysterious, even intimidating—but it doesn’t have to be. The key to understanding it is simple: real-world analogies and practical examples. This article kicks off a series that will explore AI step by step, following a clear roadmap to cut through the noise and focus on what truly matters.
I’ll share my approach to understanding the AI market —not the only way, and maybe not the perfect one for you, but a useful place to start if you’re curious about AI and want to understand it from a practical, simple, and human-centered perspective. No complex technical terms here.
So, where do we start? Let's break down AI’s evolution using a simple and familiar analogy: GPS navigation. No jargon, no fluff—just a clear path forward
TL;DR
Imagine AI’s journey as the evolution of navigation—how humans went from using simple maps to AI-powered GPS systems that can think ahead. Just like AI, our ability to navigate the world started with basic tools and evolved into something intelligent and dynamic.
Paper Maps (Electrical Circuits) – Fixed, static, no thinking.
Compasses & Road Signs (Computer Logic) – Following strict rules.
GPS That Learns (Neural Networks) – Adapting to real-time changes.
AI Pilots (LLMs) – Predicting, creating, and planning new paths.
Now, AI isn’t just helping us navigate information—it’s leading the way into the future.
1. Electrical Circuits - Paper maps
When AI followed fixed paths without adaptation.
A long time ago, people didn’t have navigation tools. They made simple paper maps to mark where mountains, rivers, and cities were. These maps didn’t change, they were just a static set of instructions to follow.
This is how AI started—with electrical circuits, which were like simple paper maps: they could store information and follow fixed routes, but they couldn’t think or change by themselves.
A machine at this stage could turn on a light or do simple math, but it was just following a fixed path, like someone following a hand-drawn treasure map.
2. Computer logic - Compasses and road signs
When AI started following strict logical rules.
As explorers improved, they started using compasses and road signs. Now, instead of just looking at a map, they could follow clear rules to navigate:
"The compass always points north."
"If you see a river, turn left."
Computers at this stage worked the same way—they could follow strict rules like "If the number is 5, multiply by 2." This made them useful for calculations, organizing data, and even playing basic games. 🎲
But just like road signs, these rules never changed—if a road was blocked, a compass wouldn't tell you what to do. Computers still couldn’t adapt or learn—they just followed logic
3. Neural networks - GPS that learns
When AI began recognizing patterns and adapting to new data.
Now imagine GPS systems. Unlike a paper map or a compass, GPS learns from real-time traffic. It sees where people are driving, detects accidents, and even suggests faster routes.
This is how Neural Networks changed AI. Instead of just following rules, AI started learning from patterns—just like GPS learns from millions of drivers.
For example, if AI sees millions of cat pictures, it learns to recognize a cat—even if it’s a new type of cat it has never seen before.
This was a game-changer because AI no longer just followed static instructions—it started adapting and improving over time.
4. Large Language Models (LLMs) - AI Pilot
When AI moved from recognition to prediction and creativity.
Now, imagine an AI-powered autonomous spaceship 🚀. Not only does it use GPS, but it also predicts where new obstacles might appear, understands the best path to take, and even suggests entirely new routes that no one has thought of before.
This is what LLMs (Large Language Models) have done for AI. They don’t just recognize patterns—they generate ideas and create new things.
For example, instead of just identifying words in a book, LLMs can write their own books, summarize knowledge, or even answer deep questions in a natural way.
Now, AI is no longer just navigating the world as it is—it is charting new paths, like an explorer predicting the future
5. The Future – AI as the ultimate explorer?
As AI continues to evolve, it may become an independent explorer—capable of solving real-world problems without human guidance. Imagine AI:
Exploring new planets and finding the best survival strategies.
Helping doctors predict diseases before they happen.
Creating entirely new inventions by analyzing millions of ideas.
We’ve come a long way from paper maps (basic circuits) to AI pilots (LLMs). Where will AI take us next?
Next steps: Keep learning and exploring AI
Curious to learn more? Take the next step. Here’s a more technical article on the evolution of AI, written by one of my favorite authors,
. Let’s dive deeper and continue exploring AI from a more advanced technical perspective.
In the next post, I’ll introduce the second framework in the AI learning roadmap, helping you further understand this rapidly evolving landscape. Stay curious, and let's keep learning together!
Share this post