Building Context with Neurons
Learn how neural networks process word embeddings through layered computation, enabling advanced generative AI capabilities.
We have discovered how word embeddings—such as Word2Vec and GloVe—turn words into dense vectors that capture their basic meaning and relationships more effectively than traditional frequency-based methods. These embeddings revolutionized natural language processing by allowing us to work with words in a more nuanced numerical form.
However, embeddings alone only tell us what words “are” in a static sense. They don’t capture how words interact in a specific context. For example, “fantastic” may have a positive connotation in “The movie was fantastic,” but if paired with a product’s price, “The price was fantastic,” the sentiment might shift or need further clarification. Neural networks address this by learning to interpret each word with its neighbors, forming deeper, context-dependent representations beyond the static meaning of individual word embeddings.
Word embeddings are learned from existing data. How do you think a model handles brand-new slang or words that didn’t appear in its training data, and what challenges might that create?
In this lesson, we will trace the origins of neural networks—from simple perceptrons to the deep architectures underlying today’s large-scale generative AI models. We’ll explore how these networks learn and process embeddings and why they are so effective at tasks that once seemed impossible for machines.
How did neural networks come to be?
The story of neural networks begins with a dream—to mimic how the human brain works. In the 1940s and 1950s, pioneering researchers such as Warren McCulloch and Walter Pitts introduced simple mathematical models of neurons. These early models were designed to replicate the brain’s basic function: receiving inputs, processing them, and producing an output. Although these initial ideas were rudimentary, they laid the groundwork for decades of research.
One of the earliest practical models was the “Perceptron,” developed in the late 1950s. The Perceptron was designed to perform simple binary classifications—essentially drawing a straight line to separate two groups (like apples from oranges). Despite its promise, the Perceptron had serious limitations. It could not solve problems that required more complex decision boundaries (for example, distinguishing overlapping classes) and was eventually overshadowed by alternative methods.
Imagine you’re tasked with dividing a pizza into two sections: one half should have mostly pepperoni and the other mostly mushrooms. If the toppings are neatly separated—pepperoni on one side and mushrooms on the other—a single straight cut works perfectly. However, if the toppings are sprinkled in a more complex, intertwined pattern, a single straight slice won’t do the job; you’d need a flexible, curving knife that can meander through the clusters to separate them accurately. This is similar to the Perceptron: it can only draw one straight decision line, which works well when the data is clearly divided but fails when the boundaries are more intricate or overlapping
For many years, research on neural networks remained relatively obscure due to theoretical challenges and limited computational power. It wasn’t until the 1980s, with the re-discovery and popularization of the backpropagation algorithm, that multi-layer neural networks (or “deep” neural networks) began to show real promise. Backpropagation provided a systematic way to adjust the weights in a network based on the error of its output, letting deeper architectures learn non-linear relationships and tackle a much wider range of problems than the single-layer Perceptron ever could. As computational resources grew and training techniques improved throughout the 1990s and early 2000s, neural networks evolved from simple, shallow architectures into deep models capable of handling tasks like speech recognition, image classification, and natural language processing. Today, neural networks are at the heart of virtually every major AI breakthrough, forming the foundation upon which modern generative AI systems are built.
What is a neuron?
To understand how neural networks refine word embeddings, we first need to understand their fundamental building block: the artificial neuron. Inspired by the workings of biological neurons, an artificial neuron processes information very simply. Imagine a single neuron as a tiny decision-making unit. ...