Diving into the World of AI and Computer Vision!

The Data Behind AI Models

I’ve been thinking about how much data is needed to train AI models, how our daily lives shape this data and the future of technology.

When you hear about AI, huge language models like the one you’re reading right now, it’s easy to overlook how much data goes into making these systems work.

Training a large language model requires vast amounts of text data—millions or even billions of words—to help the AI understand language and context. But data alone isn't enough.

Training a model also requires a massive amount of computing power. Think about the servers, energy, and time needed to process all that data. It’s an expensive and time-consuming process. And even after training, the model still needs to be tested with more data to ensure it works properly. This is how we ensure the AI can perform well when it’s used in the real world.

What Is Computer Vision?

Shifting gears a bit, let’s talk about computer vision. In simple terms, computer vision is about teaching computers to “see"—meaning to interpret and understand images. Computer vision makes this possible, such as identifying objects in a picture, detecting faces, or analysing video feeds.

Years ago, when computer vision was first being developed, engineers had to manually draw boxes around objects in pictures, a process known as “dense captioning.” These “bounding boxes” helped the computer learn what objects were in an image. Today, thanks to advances in machine learning, computers can do this on their own much more quickly and accurately.

Google Lens is a use case of Machine Learning.

One of the most popular examples of computer vision is Google Lens. If you’ve ever pointed your phone at a dog or a landmark and had it instantly identify what you were looking at, you’ve used computer vision. Behind the scenes, a machine learning model has been trained to recognise specific image patterns and features. The more data the system gets, the better it identifies things accurately. You can point your phone at almost anything—a dog, a tree, or even a book—and it will determine what you're looking at.

This is a perfect example of machine learning in action. Over time, the system has been trained with massive datasets of images, allowing it to recognise patterns and features. When you point the camera at something, it compares what it sees to everything it’s learned to predict what’s in front of it.

It’s not just about identifying objects, though. The technology behind it also helps classify those objects and understand more about them. For example, Google Lens doesn’t just tell you that something is a dog; it can also tell you the breed, or if it's a famous landmark, it can provide additional information.

My Computer Vision Learning Journey

For me, learning about computer vision has been a hands-on experience. I’ve been diving into platforms like Microsoft Cloud Foundry to build projects that use AI for image detection. These tools help you create and train your machine-learning models.

I’ve learned that building these models isn’t just about writing code — it’s about collecting the correct data, preparing it for training, and then testing the models to see how well they work. It’s a process that requires patience, but the rewards are enormous when you see the results in action. It’s also amazing to see how fast the technology is evolving. What once took hours or even days to process can now be done in minutes, thanks to the power of cloud computing and better algorithms.

The Future of AI and Computer Vision

As AI and computer vision evolve, thinking about where we’ll be in the next few years is exciting. We already see incredible advancements, from self-driving cars to more innovative health diagnostics. The potential for these technologies is limitless, and it will only get better.

For anyone just starting to learn about machine learning and computer vision, the best advice I can give is to jump in and start experimenting. The tools are more accessible than ever, and you can learn by doing. If you can understand how computers “see” and recognise patterns, you’ll be amazed at what you can create.

Join me, and let’s look ahead together.

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