What is a neural network, and how does it actually work?

A researcher recently demonstrated a working neural network built entirely inside a medieval strategy game using goats as binary switches. The stunt is funny, but the underlying point is serious: a neural network is a computational structure, not a mind — and understanding that distinction shapes every decision you make when building with AI.

Why this matters now

As AI tools move into production workflows, the temptation to treat model outputs as evidence of reasoning grows stronger. Fluent, confident language feels like understanding. But the architecture underneath has no awareness of what it is doing — it is executing learned statistical operations. Professionals who internalize what a neural network actually is make better decisions about when to trust model outputs, how to design evaluations, and where human verification is non-negotiable. The goat-based neural network makes this concrete: the same logic that powers a large language model can, in principle, run on medieval farm animals. Sentience is not a prerequisite.

How it works

A neural network is a layered system of nodes connected by weighted edges. Each node takes inputs, applies a simple mathematical transformation, and passes an output forward. Learning happens by adjusting those weights based on the difference between the network's output and the correct answer — a process called backpropagation. Repeat this over enough examples and the network encodes statistical regularities from the training data into its weights.

Output layer ···················
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Hidden layers (N deep) ·········
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Input layer ····················
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Raw data ·······················

The key mechanism is next-token prediction in language models: given a sequence of tokens, the network assigns probability scores to every possible next token and selects the most likely one. Do that repeatedly and you get fluent, coherent text. Nothing in this process requires the model to understand meaning. It requires that the training data contained patterns the weights can reproduce.

The goat experiment illustrates this at the smallest possible scale. Two XNOR gates and one AND gate, each built from game scripting logic and binary goat positions, implement the logical AND function. That is a real neural network. It learns. It produces correct outputs. It knows nothing.

Real-world applications

Neural networks underpin nearly every AI capability professionals encounter today. Image classifiers, speech recognition, recommendation engines, and large language models are all variants of the same core architecture, differentiated by scale, training objective, and the structure of their layers. Transformer architecture — the foundation of modern LLMs — extends this base with attention mechanisms that weigh relationships between tokens across long sequences.

For working professionals, the practical implications are consistent regardless of use case. A model that produces a confident, well-structured answer is demonstrating pattern reproduction, not verified reasoning. This means evaluation design should test outputs against ground truth, not against whether the response sounds authoritative. It also means that consequential tasks — medical triage, legal summarization, financial decisions — require structured human review, not because the model is unreliable in general, but because fluency is not the same as correctness.

Understanding the architecture also clarifies what fine-tuning, retrieval-augmented generation, and prompt engineering actually do: they shape the statistical context the model operates within, steering probability distributions rather than programming explicit logic.

Where to go deeper

If this framing sharpens your thinking, the natural next step is understanding how scale transforms these basic principles into the capabilities you see in production systems. Courses on transformer architecture and foundation models will show you how attention mechanisms and massive parameter counts extend the core neural network idea. Tokenization explains how raw text becomes the numerical input these networks actually process. Generative AI and large language model courses connect the architecture to the tools you are already using — and help you build the habit of testing what models actually do rather than assuming what they appear to do.