November 30, 2022. News was buzzing about a new “AI” tool called ChatGPT. Curious, I finally gave it a spin a few weeks later. Yes, it was cool. It could spit out convincing answers, like a polite parrot with a PhD. I remember asking it to explain quantum physics or relativity as if I were five years old. It did a reasonable job—though, let’s be honest, explaining relativity to a five-year-old is a tall order even for a seasoned teacher.
Was this revolutionary? Hardly, at first. The media largely treated it as a sophisticated form of auto-complete—something your phone does, but shinier. Then again, there were whispers in the air that this was only the beginning, that the technology under the hood was something genuinely new and worth paying attention to.
I was left in a limbo of curiosity and scepticism. I’m not a computer engineer, even with an IT degree tucked away from the past. That degree gave me a healthy dose of scepticism about anything labelled “AI.” Yet something nagged at me: how did this thing actually work? What were the fundamental ideas behind a tool that could chat, summarize, draft, and reason to some degree? I’ve always prided myself on grasping the nuts and bolts of any technology—understand the principles, feel the physics, reason through the problems. ChatGPT, on the other hand, felt like magic, but I knew magic needs a secret mechanism behind it.
And so I realized I had missed an (r)evolution happening right under my nose. Neural networks, machine learning, computer vision—these weren’t new fads; they’d been building quietly for years. Elements I had either dismissed or overlooked—Google Translate, voice assistants, image recognition—suddenly snapped into sharper focus. Life was busy, and in my professional bubble they didn’t touch the day-to-day in a dramatic way, so I kept moving.
The mystery deepened. How could I explain to my mom, my partner, or even myself what ChatGPT could do, what it couldn’t do, and whether it was a clever gimmick or a portal to something more? Could this be the beginning of a machine age where humans and machines partner or even cede ground to automation? Were the robots finally coming for us? A touch dramatic, perhaps, but the concern wasn’t entirely unfounded.
I decided to do something about it. The first half of 2023 was a slog of learning. YouTube tutorials, blog posts, endless searches—a flood of information that often felt contradictory or overly technical. The field evolved at a breakneck pace: new models, new start-ups, new capabilities, and a steady stream of hype. On the personal front, my company underwent a merger, and life threw me a few curveballs, so “AI” sat on the back burner more often than not.
Then a turning point: a blended online/offline course advertised as a solid foundation in Data Analysis and AI. It promised rigor without losing touch with real-world applications. I signed up, and it proved to be one of the best decisions I’ve made. By mid-2024, I still hadn’t mastered the nitty-gritty of how ChatGPT works under the hood, but the gaps in my understanding had started to shrink. I became comfortable with terms like neural networks, transformers, and backpropagation. I reconnected with programming through Python, enjoying the command line, GitHub, Linux basics, and databases. By the end of 2024 I even completed a short MIT online course on Machine Learning, diving deeper into neural networks and building blocks in PyTorch. I wasn’t about to become a hacker overnight; it was about building a working mental model and exposure to practical tools.
Today, a good three years on, I feel much more confident with the topic. I’ve begun to experiment with the technology, and I’ve learned to separate the noise from the signal. Without it, I wouldn’t have launched this website or these posts. I’m eager to share resources, crush myths, and point you toward trusted voices in the space.
What’s next for this blog? I’ll keep posting reflections, experiments, and practical tips—grounded in what I’ve learned, and tested in real projects. I’ll share the sources and influencers who helped me along the way, and I’ll invite you to join the conversation. If you’re curious, sceptical, or somewhere in between, I think you’ll find something valuable here.
A few notes I’ve found helpful on this journey:
- Start with the fundamentals: a basic understanding of machine learning, neural networks, and transformers pays off more than chasing every new tool or hot app.
- Practice matters: hands-on work—Python, Jupyter notebooks, GitHub projects, and small experiments—builds confidence faster than theoretical reading alone.
- Filter the noise: there’s a lot of hype. Seek reliable, beginner-friendly explanations, then incrementally deepen your knowledge.
- Keep a critical eye: AI tools are impressive, but they have limitations, biases, and ethical considerations that deserve attention.
If you’ve got questions, suggestions, or resources you love, I’d be happy to hear from you. Here’s to more learning, fewer misfires, and plenty of thoughtful experimentation as we navigate this evolving landscape together.