You don’t get to vote on whether AI is going to have an impact on your life! It already is a part of your life!
Remember when you first learned to ride a bike? It started with a tiny, wobbly splash—just a new idea, a new way of getting around. First, it was a slow, methodical, meticulous effort to learn a new skill. And then came the ripples, those little improvements, until suddenly, it felt like a full-blown storm of change. That’s what’s happening with Artificial Intelligence, or AI.
For many of us, the term “Artificial Intelligence” might sound like something out of a science fiction movie—talking robots, flying cars, and supercomputers taking over the world. While those are certainly part of the fun, the reality of AI today is much more grounded and, frankly, fascinating. It’s woven into the fabric of our daily lives, making things simpler, smarter, and more efficient.
This blog is the first in a series called “AI for All,” where we’ll journey together to understand this powerful technology, one simple step at a time. I promise to leave the complex jargon behind and focus on real-world examples that you, your kids, or your grandparents can easily relate to. Let’s start with the basics!
What is Artificial Intelligence?
Look at the image above. It’s a great way to visualize the relationship between all these tech buzzwords you might have heard. At the very top, the largest circle represents the big umbrella: Artificial Intelligence.
Although the term ‘Artificial Intelligence’ sounds complicated, its main goal is beautifully simple: to build machines (Artificial) that can ‘think’ and ‘act’ (Intelligence) just like us. But what’s the secret to thinking and acting like a human? At its heart, it’s all about making good decisions. And where does our ability to make good decisions come from? Where do we get that ‘Spark’? It’s all thanks to our education and our experiences. So, if we want artificial intelligence to truly mimic human thought, then it needs to learn and gain experience just like we do. This is the foundation for everything from solving complex problems to understanding language and recognizing images.
Real-life example: Imagine a smart home system. When it learns to automatically adjust the temperature based on your daily routine, or to turn on the lights when you enter a room, that’s a form of AI. It’s a machine performing a task that, in the past, required a human to think and act.
Zooming In: The Role of Machine Learning
Imagine Artificial Intelligence (AI) as a giant universe. Floating inside it is a smaller, but incredibly powerful planet called Machine Learning (ML). This is where the magic happens—where computers stop being passive tools that only do what’s instructed to them and start learning from experience, just like we do.
For example, imagine teaching a child to recognize a cat. You don’t give them a giant list of rules (“a cat has two pointy ears, a long tail, whiskers, and says ‘meow'”). That would be impossible! Instead, you show them many pictures of different cats. Eventually, the child learns to recognize a cat in any new picture, whether it’s black, orange, sleeping, or running.
That’s exactly what Machine Learning does. We feed it massive amounts of data—pictures, numbers, words—and over time, it begins to spot patterns and make its own decisions. The more data it sees, the smarter and more accurate it gets.
Two Ways Machines Learn: With a Teacher or Without One
Just like kids in school, machines can learn in different ways:
Supervised Learning: Learning With a Teacher
Imagine showing a child flashcards of animals and saying, “This is a cat. This is a dog.” After enough examples, the child starts pointing correctly on their own. That’s supervised learning.
In the machine world, we do the same. We give the computer labelled data—like thousands of emails marked “spam” or “not spam.” It studies the patterns and learns to spot spam in new emails. It’s how your inbox stays clean without you lifting a finger.
Unsupervised Learning: Learning Without a Teacher
Now imagine giving a child a box of toys and saying, “Sort these however you like.” They might group by colour, size, or type—whatever patterns they notice. That’s unsupervised learning.
Computers do this too. Give them customer shopping data, and they that might discover that people who buy bread also tend to buy milk, or that certain customers have similar buying habits. This is the secret behind those personalized recommendations you get from Netflix or Spotify. They’re not just guessing; they’re using Unsupervised Learning to find patterns in what you and others enjoy, helping them suggest your next favourite song or movie.
Going Deeper: What Is Deep Learning, Really?
This is where things get really clever.
Deep Learning is inspired by how our brains work. Just like our brains have billions of tiny cells called neurons that help us think, deep learning uses neural networks—a fancy term for digital layers that talk to each other and learn from data.
Picture it like a row of sieves, each one catching finer details until only the most important patterns remain. The more layers it has, the more complex patterns it can understand. That’s why it’s called “deep” learning—not because it’s mysterious, but because it goes many layers deep.
Do you want to know a fun fact? You’ve probably seen deep learning in action without even knowing it. For example:
- Face Unlock on Your Phone: When your phone scans your face to unlock, it’s not just looking at your nose or eyes. A deep learning system has studied millions of faces and learned how to recognize yours—even if you’ve changed your hairstyle or lighting is dim.
- Voice assistants: When you say something like “What’s the weather today?”, a deep learning model converts your speech into text, understands the meaning behind your words, and responds naturally. Each layer of the model filters sound, context, and intent—like sieves—until it delivers the most accurate and helpful reply.
- Driverless cars: A neural network processes camera images to detect pedestrians, traffic signs, and lane markings. Each layer filters and refines the data—like sieves—until the car understands what’s around it and makes safe driving decisions in real time.
The AI That Creates: The Newest Stars of the Show
Let’s look at the smallest, most recent additions to our chart. The innermost circles in the image is labelled Gen AI, with a part of it intersecting with a circle labelled LLM. There’s even a little arrow pointing to the very centre, noting “ChatGPT, Gemini, Copilot etc.” This is the part of AI that has been making the biggest headlines lately.
Alright, let’s put on our thinking hats and break this down together. Grab your favourite mug—this is the kind of chat that’s best enjoyed with a warm cup of coffee ☕ in hand.
What’s a Large Language Model (LLM)?
What’s a Large Language Model (LLM)?Think of an LLM like a super-smart parrot that’s read millions of books, articles, and websites. But instead of just repeating words, it understands how we talk—our grammar, tone, and even our quirks. It’s evolving at a rapid rate, and can now even understand sarcastic comments. It’s called “large” because it’s trained on a massive amount of text. That’s why it can write poems, answer questions, or even help you draft an email that sounds just like you.
What’s Generative AI?
What’s Generative AI?Generative AI is the umbrella term. It’s like the creative cousin of AI—it doesn’t just analyse things, it makes things. Text, images, music, videos—you name it.
Think of it this way: if older AI was a brilliant librarian who could sort and find books for you, Generative AI is a talented author who can write a brand-new book on any topic you ask for. It’s the AI that doesn’t just analyse and classify—it creates.
If you’ve ever asked an app to draw a picture of a cat wearing sunglasses or write a bedtime story for your niece, you’ve used generative AI.
Why Generative AI and LLMs Overlap
Why Generative AI and LLMs OverlapBut now the question arises: Why there’s an overlap between the Gen AI and LLM in the diagram? The answer is simpler. It might seem confusing, but they are actually two pieces of the same puzzle.
We are very well aware that Generative AI is a powerful artist who can create incredible things—pictures, music, even new stories. But this artist only understands one language: computer code.
Now, imagine you want this artist to draw a picture for you. You don’t speak their language of code. So, you need a translator.
That’s where the Large Language Model (LLM) comes in. The LLM is the brilliant translator who understands your words and the artist’s code. You simply tell the LLM what you want by typing a sentence, like, “Draw a happy robot planting a flower.” The LLM takes your words, figures out exactly what you mean, and then translates that idea into a set of instructions that the Generative AI artist can follow.
Artificial Intelligence is no longer a distant concept reserved for tech labs and sci-fi scripts—it’s here, woven into the fabric of our everyday lives. From the way we search, shop, and speak, to how we learn, create, and connect, AI is quietly reshaping the world around us. And as we’ve seen, understanding its foundations—from machine learning to generative models—isn’t just possible, it’s empowering. This is just the beginning of our journey into the heart of AI. In the next chapters, we’ll explore how these technologies are transforming industries, education, and personal creativity. So stay tuned—there’s so much more to uncover, and you won’t want to miss what’s coming next.
