Why We Need Artificial Neurons: A Guide to Neural Networks in AI

In this post, we’ll delve into neural networks—not the biological ones from our school days, but the artificial neural networks (ANNs) powering today’s AI. I assume you’re familiar with biological neurons, but let’s quickly recap.

In a biological neuron, we have:

● Nucleus: The “brain” of the neuron
● Dendrites: Extensions that receive signals from other neurons
● Axon: Sends signals to other neurons
● Synapses: The junctions where signals are transmitted between neurons. Biological neurons are complex structures, and we’ve attempted to replicate some of this complexity in artificial neurons to mimic human brain functions.

Why Artificial Neurons?

So, why do we need artificial neurons in AI? And why are these advanced deep neural networks gradually replacing traditional statistical machine learning models? Let’s explore this by first looking at traditional machine-learning models.

Traditional Machine Learning Models

Without going into too much technical detail, think of a machine learning model as a “black box.” You input data, which could be any structured information, and the model makes predictions by performing computations and mathematical operations on it. Machine learning models are very effective with structured data, typically presented in tables with rows (horizontal lines) and columns (vertical lines), each storing specific types of information.

For years, traditional models performed well with such structured, tabular data. However, as the world has evolved, so has our data. Today, we handle massive amounts of unstructured data, such as images, audio, and text, where traditional machine learning struggles to find meaningful patterns. For this type of complex data, deep learning models perform much better.

Introduction to Artificial Neurons and Deep Neural Networks

To understand the artificial neuron, we need to go back to the 1950s, when a model called the Perceptron was introduced by Frank Rosenblatt. The perceptron is not a person—it’s an artificial neuron and the foundational building block of neural networks designed to mimic the behavior of a biological neuron.

The Perceptron

The perceptron has a simple structure but plays a vital role in neural networks. Here’s how it works:
● Input Data: The perceptron takes inputs, represented as X₁, X₂, and X₃, which collectively form the input layer.
● Weights: Each input has a corresponding weight (W₁, W₂, W₃). Weights determine the influence or importance of each input.
● Summation: The perceptron multiplies each input by its weight and then sums them up. But there’s one more crucial component here—bias.
○ Bias: Think of bias as a “starting push” that helps the perceptron make better decisions. It provides an extra boost that can make the model more flexible and accurate.
● Activation Function: After computing the weighted sum, the result passes through an activation function, a kind of decision-maker. The activation function determines whether the result is strong enough to produce an output. For example, you can think of the activation function as a gate. If the combined input is high enough, the gate “opens” (output is 1); if not, it “stays closed” (output is 0). This mechanism helps the perceptron decide whether to activate and send a signal. This is the basic training process for a single perceptron. When multiple perceptrons are connected in multiple layers, they form what we call a deep neural network. Structure of a Deep Neural Network A deep neural network consists of:
1. Input Layer: Receives data inputs.
2. Hidden Layers: Layers between input and output layers, where the network can be made more complex by adding more layers. These layers enable the network to learn and represent complex patterns.
3. Output Layer: Produces the model’s predictions, which can be either:
○ Classification (predicting categories, like “cat” vs. “dog”), or
○ Regression (predicting continuous values, like price or age).

In Summary

We’ve explored the differences between traditional machine learning models and the more advanced deep neural networks. While traditional models work well with structured, tabular data, deep neural networks excel with unstructured data such as images, audio, and text. Starting from the perceptron as the basic unit, these networks have evolved to enable complex data processing and high-level AI applications. Artificial neural networks are transforming the AI landscape, offering powerful ways to solve problems that were previously out of reach for traditional machine learning. And this is only the beginning!