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About this Project
"Dentro de AI" translates to "Inside AI". It is a private blog-style project to serve as educational resource to make Artificial Intelligence more understandable.
I'm a Senior IT Business Analyst with 30 years in tech - and a strong passion to simplify. Let me share what I discover - in plain English and suited for non-IT business people.
What to Expect
This site is new (launched April 2025), but has plenty of content already:
Future Content Ideas - Currently in Development
- Historic overview of AI and Machine Learning.
- Explanation of "Open Source" in the AI space (Open weights, Open Training, Closed).
- Proven Use Cases of AI installations for real world Business Solutions.
Easy to Use, Hard to Understand?
Artificial intelligence can feel deceptively easy. With just a few lines of code or a user-friendly tool, you can generate images, analyze data, or create text that feels surprisingly human. But beneath that simplicity lies a world of complexity: statistical models, neural networks, training data, biases, and constant evolution. This contrast is part of what makes AI both exciting and overwhelming. The goal of Dentro de AI is to bridge that gap - to demystify the underlying concepts without losing sight of the power and nuance behind them.
Why Simplicity Matters
For AI to be truly useful and widely adopted, people need to understand it - not just developers and data scientists, but business leaders, creatives, and everyday users. Easy-to-grasp explanations build trust, reduce fear, and open the door to real-world impact. This project is built on the idea that clarity is the foundation for meaningful use.
A Familiar Pattern
If you've been in tech for a while, today's AI boom might feel familiar. It's like when the first personal computers entered the corporate world - geeks first, then business, often without a clear strategy. The internet followed a similar path: early adopters made noise, businesses scrambled to catch up, and real value only emerged when understanding met purpose.
AI seems to be going through the same cycle. Tools are available, expectations are high, and many organizations are still figuring out what it all really means.
The technology behind AI is complex, fast-moving, and often hidden behind marketing terms or black-box models. But to make smart decisions, you don?t need to become a machine learning engineer. You just need a clear, working understanding of what's happening under the hood - concepts like models, training data, tokens, embeddings, and inference. Knowing these basics helps you ask better questions, spot opportunities, and have more informed conversations.
This space is here to help with that - not by simplifying things to the point of distortion, but by breaking them down into something you can actually follow. And by keeping pace as the field evolves.
Breaking Down the Jargon
If you're just getting started, the AI Glossary is a good first step. It covers core terms and ideas - explained simply, without dumbing them down. Whether you're trying to understand what a transformer does, what fine-tuning means, or why everyone is suddenly talking about tokens, you'll find practical, business-relevant explanations.
More is coming soon: timelines, blog posts, visual guides - all designed to help you (and me!) stay oriented in a fast-moving space.
Spot Something? Let Me Know
Have a term you'd like to see added? Spot something unclear? Let me know! Reach out on X/Twitter or LinkedIn. I'd love to hear from you.
