This post Corresponds to learning obejctive 2: Clearly explain the essence of what it means to be digital.

Digital Essence: A neural network is an “interconnected group of nodes” that takes in data, which for an image, is “turned into numbers (pixels with values).” This process of converting real-world information (like an image) into discrete, numerical, and machine-readable data (bits and bytes) is the essence of what it means to be digital.

AI itself is a digital construction, a software system running on digital hardware, designed to simulate human intelligence. Understanding a neural network’s architecture (layers, nodes, weights) and training process (backpropagation) is crucial to grasping the digital logic that enables this simulation.

One of the most important tools in AI is neural networks. They’re inspired by how the human brain works and form the backbone of many modern AI systems, including chatbots like ChatGPT.

An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.

What is a Neural Network?

A neural network is made up of nodes (also called neurons). These are arranged in layers:

• Input layer: takes in the data (like pixels in an image).

• Hidden layers: do the “thinking” by combining and transforming inputs.

• Output layer: gives the final result (for example, “this is a cat”).

When a network has many hidden layers, it’s called a deep neural network, and that’s where the term deep learning comes from.

How Does It Learn?

Neural networks don’t just “know” things—they learn from data. This process is called training:

1. The network starts with random guesses (random “weights”).

2. It makes predictions—for example, whether an image is an odd or even number.

3. If it’s wrong, it adjusts the weights slightly to do better next time.

4. This adjustment process is called backpropagation.

The network repeats this thousands of times until it gets very accurate.

A Simple Example

Imagine teaching a network to spot if a digit (like 2, 3, or 9) is odd or even.

• Each image is turned into numbers (pixels with values).

• The network checks patterns in those pixels.

• Over time, it learns which shapes look like odd digits and which look like even ones.

At first, it makes mistakes—but with training, it becomes very good at predicting correctly.

If you’re just starting, think of a neural network like a student: it studies examples, makes mistakes, learns from them, and eventually becomes skilled at the task.

Sources

OpenAI. ChatGPT (GPT-5) Response to “AI and neural networks” Video. 23 Sept. 2025, https://chat.openai.com/.

Data Professor. “AI and Neural Networks.” YouTube, 28 June 2021, www.youtube.com/watch?v=GHDSwZsVrHQ.