How Does AI Artificial Neural Network Actually Work

How Does This Thing Actually Work? (No Math Degree Required)

Let’s talk about artificial neural networks — the engine inside almost every impressive AI you see today.

Imagine the human brain: billions of neurons connected by synapses. When one neuron fires strongly enough, it triggers others. That’s oversimplified biology, but good enough.

An artificial neural network is a crude copy:

You have layers of “neurons” (just math equations)
Each neuron takes inputs, does some simple math, and decides whether to “fire”
Early layers detect simple patterns (edges in images, common word pairs in text)
Deeper layers combine those into complex concepts (faces, sentences, ideas)

Training = showing the model millions of examples and tweaking billions of tiny knobs (parameters) until the output is correct. Each tweak is tiny, but billions of tweaks add up.

Where do the billions of parameters live? In today’s biggest models (GPT-4 class, Llama 3 405B, etc.) there are hundreds of billions of numbers that took weeks or months and millions of dollars in GPUs to adjust perfectly.

The magic trick: transformers In 2017 a Google paper called “Attention Is All You Need” introduced the transformer architecture. That single idea is why ChatGPT can write essays instead of just finishing the next word like a super-charged autocomplete.

Attention = the model can look back at every previous word in the sentence (or every pixel in the image) and decide which ones are most relevant right now. That’s how it keeps context over long conversations or generates coherent 2,000-word articles.

Tokens: the LEGO blocks of language models Every model breaks text into tokens (roughly 3–4 characters each). “Project Manager” is probably two tokens. The average model in 2025 can handle 128,000–1 million tokens of context — that’s an entire novel.

Training vs Inference Training = expensive, one-time (or infrequent), done by big labs. Inference = running the finished model. That’s what you do when you chat with Grok or Claude — cheap and fast.

Fine-tuning and RAG Most companies don’t train from scratch (too expensive). They take a base model and fine-tune on their data, or use Retrieval-Augmented Generation (RAG) — basically Google search inside the model so it can cite current info instead of hallucinating 2023 knowledge.

That’s the entire engine room. No calculus required.