I. Backpropagation: How AI Learns.

Post 1/10 in the AI Basics Series.

Author

Jairo J. Niño Perez
July 26, 2024

Before we start: What this series is about.

In today’s rapidly evolving tech landscape, understanding AI is no longer optional—it’s essential. As professionals and decision-makers across industries grapple with the impact of AI on their businesses, many find themselves asking: "How can I cut through the buzzwords and truly understand what matters?" This series is designed to do just that based on my experience both in teaching and consulting in the field. Whether you're a seasoned tech expert or just beginning to explore the world of AI, this 10-post-series will walk you through the key concepts that drive the most transformative technology of our time. No need to dive deep into technical jargon—just the foundational knowledge you need to stay ahead of the curve and confidently lead in the age of AI. Let’s get started.

Backpropagation: How AI Learns

In the journey of teaching machines how to think, backpropagation has been one of the essential building blocks. But don’t let the technical name intimidate you—at its core, backpropagation is just a fancy way of saying "learning from mistakes."

Let’s take a closer look at what backpropagation really does, why it's important for AI, and how this seemingly simple concept revolutionized machine learning as we know it.

The Basics: What Is Backpropagation?

Imagine you’re trying to learn how to throw a basketball into the hoop. You throw the ball, watch where it goes, and based on that feedback, you adjust your next shot. Too short? Throw harder. Too far? Ease up a bit. This trial and error, guided by feedback, is essentially what backpropagation does for AI models.

When a neural network makes a prediction, it compares that prediction with the actual result and calculates the error (just like missing that basketball shot). Backpropagation is the process of sending that error back through the network to adjust the model’s parameters—specifically, the weights that connect different neurons in the network.

The goal? To minimize the error over time, so the model gets better and better at making accurate predictions. This is where gradient descent comes into play—a method to gradually reduce the error with each iteration.

Why Is Backpropagation So Important?

Before backpropagation, training neural networks was slow, inefficient, and often impractical. AI models struggled with "credit assignment"—how do we know which parts of the network are responsible for the errors? Backpropagation solved this by systematically adjusting the weights across all layers of the network, making learning faster and more scalable.

Without backpropagation, the deep learning revolution that powers modern AI models like GPT, self-driving cars, and image recognition systems simply wouldn’t exist.

How It Works: A Step-by-Step Breakdown

Here’s a simplified view of how backpropagation works:

  1. Forward Pass: The network makes a prediction based on the current weights. For instance, in a handwriting recognition task, it might predict that a certain digit is a “3” when it’s actually a “5.”

  2. Calculate the Error: The prediction is compared to the actual label. The difference between the two is the error (how far off the prediction was).

  3. Backward Pass: This is the "back" in backpropagation. The error is propagated backward through the network, starting from the output layer and moving toward the input layer.

  4. Weight Adjustment: Each neuron’s contribution to the error is calculated, and the network adjusts its weights accordingly, trying to minimize the error for the next prediction.

  5. Repeat: This process is repeated thousands or even millions of times until the network makes consistently accurate predictions.

Challenges and Limitations

While backpropagation is powerful, it’s not without its challenges. For one, it relies heavily on having a large amount of labeled data, which isn’t always easy to come by. Additionally, if the network is too deep (too many layers), the gradients can become very small as they are propagated backward, making it difficult for the network to learn—this is called the vanishing gradient problem.

Thankfully, researchers have developed solutions to many of these challenges, such as better activation functions and architectures like ResNets that help with deeper networks.

Final Thoughts

Backpropagation may seem like a small cog in the machine, but it’s the reason neural networks have been able to scale and tackle more complex tasks. Understanding this process gives us a glimpse into how machines learn from their mistakes and continually improve.

By mastering this key concept, we open the door to understanding more advanced models and algorithms that form the backbone of modern AI.

More in this series: