Lately, it feels like everyone was born knowing exactly how AI works, while the rest are still trying to figure out the basics. If you feel like you missed the orientation memo, don’t worry. We’re going to break down the tech that’s suddenly everywhere — in plain English, over a coffee.
1. The Core Layers: Artificial Intelligence, Machine Learning, and Deep Learning
Think of these as circles within circles, each one getting more specific as you go deeper.
- Artificial Intelligence (AI): The broadest category. It refers to any technique that enables computers to mimic human behavior.
- Machine Learning (ML): A subset of AI. Instead of giving the computer manual “if-then” rules, you give it data and let it figure out patterns itself.
- Deep Learning (DL): A more advanced kind of ML that uses Neural Networks to handle complex tasks like recognizing faces or translating languages.
2. The Builders: Who Does What?
As the field has grown, the work has been split into a few specialized roles:
- AI Scientist: The inventors. They work in labs researching new mathematical theories and designing new model architectures. They care about why things work.
- Data Scientist: The detectives. They analyze massive datasets to find trends and insights that help businesses make decisions.
- AI Engineer: The builders. They take the models created by scientists and turn them into working products. They focus on making sure the app actually works for the end-user.
3. Predictive vs. Generative AI
- Predictive AI: This looks at past behavior to predict what happens next — like suggesting your next purchase or analyzing a medical scan to find a tumor.
- Generative AI: This uses what it has learned to create entirely new text, images, music, or code. Because it is creating from scratch rather than just labeling data, Generative AI requires significantly more computing resources to run.
4. Neural Networks and The GPT Secret
A Neural Network consists of layers of mathematical “neurons” that pass information to each other. Despite the name, these networks don’t actually simulate how a human brain works — they are just inspired by it.
The Training: You show the network millions of examples. Every time it gets an answer wrong, you adjust the internal “weights” (the importance of certain signals) until it gets it right. This is called backpropagation.
What GPT stands for
- Generative: It doesn’t just look up information; it generates entirely new text, code, or ideas from scratch.
- Pre-trained: It has already “read” a massive chunk of the internet to learn how human language works before you ever send your first prompt.
- Transformer: This is the specific neural network architecture that serves as the engine for modern AI. It allows the model to “pay attention” to context, no matter how far apart words are in a sentence.
AI is not only about neural networks
It’s easy to think neural networks are the only game in town, but AI is a diverse toolbox. While neural networks find patterns in messy data, Symbolic AI uses clear “if-then” logic for math or legal rules. Fuzzy Logic helps machines handle “gray areas” rather than just true or false. Today, the field is moving toward Neuro-Symbolic models, which combine pattern-spotting with hard reasoning.
5. LLMs, SLMs, and Frontier Models
To understand scale, we look at parameters. These are the “internal settings” a model fine-tunes during training. While more parameters allow for more complex information, they don’t strictly define intelligence; a smaller model trained on high-quality data can often outperform a larger, poorly trained one.
- SLM (Small Language Model): Scaled-down, efficient versions (1B to 10B parameters). They can often run directly on your phone without an internet connection.
- LLM (Large Language Model): The mainstream heavy-hitters (70B to hundreds of billions of parameters).
- Frontier Models: State-of-the-art models pushing the limits, often boasting 1 trillion parameters or more.
6. The “Foundation” and Customization
You don’t usually build an AI from scratch; you adapt a “Foundational” model:
- RAG (Retrieval-Augmented Generation): Like an “open book” test. The AI looks up facts in your private documents before answering.
- Fine-Tuning: Like sending the AI to grad school to learn specific terminology for a field like law or medicine.
7. The Newest Frontier: Agents and Reasoning
- AI Agents: Unlike simple chatbots, agents can use tools to act — browsing the web, booking flights, or writing code autonomously.
- System 2 Reasoning: Models now use “Chain-of-Thought” reasoning, pausing to verify their own logic before answering.
8. Where does an AI actually live?
While Frontier models require industrial power, many SLMs and mid-sized LLMs can be run locally on a laptop using tools like Ollama. Training a model from scratch requires thousands of GPUs, but you can Fine-Tune or use RAG on a local machine to customize an AI without a massive budget.
Why is a GPU so special for AI?
A CPU is a generalist; it handles tasks one by one in a sequence. A GPU is a specialist designed for parallel processing. Instead of a few dozen powerful cores, a GPU has thousands of simpler cores. Because AI involves trillions of simple calculations happening simultaneously, the GPU’s “divide and conquer” approach is thousands of times more efficient for these tasks.
9. The Physical Bill: Electricity, Water, and Hardware
AI runs on massive, heat-generating physical infrastructure. Generative AI requires significantly more electricity and water for cooling than traditional computing. To prevent hardware like GPUs from overheating, data centers consume vast resources, making the physical cost a vital part of the conversation.
The Bottom Line
AI is moving fast, and while it can feel overwhelming to keep up with the jargon, understanding it helps us move past the hype and start using these tools with confidence.