Building Context with Neurons

We’ve seen how word embeddings like Word2Vec and GloVe represent words as dense vectors, capturing their basic meaning and relationships better than frequency-based methods. This was a huge step forward in natural language processing.

But embeddings alone are static. They tell us what a word usually means, not how its meaning shifts in context. For example, “The movie was fantastic” is clearly positive, while “The price was fantastic” could imply something very different. The word “fantastic” is the same, yet the interpretation changes with context.

To move beyond static meaning, we need models that can learn how words interact within sentences. This is where neural networks enter the picture.

Let’s test your knowledge. In the widget below, type out your answer to the following question:

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 that underlie 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 idea of neural networks began in the 1940s and 1950s, when Warren McCulloch and Walter Pitts proposed simple mathematical models of neurons. These early attempts mimicked the brain’s basic function: taking inputs, processing them, and producing outputs.

In the late 1950s, the Perceptron became one of the first practical models. It could separate data with a straight line, like dividing pepperoni from mushrooms on a pizza if they’re neatly split. However, if the toppings are mixed together, a single straight cut won’t work. Likewise, the Perceptron struggled with problems that required more complex decision boundaries, which limited its usefulness.

For decades, neural networks remained obscure due to limited theory and computing power. The breakthrough came in the 1980s with the popularization of backpropagation, a method for adjusting weights based on errors. This allowed multi-layer networks, or “deep” networks, to learn non-linear patterns far beyond the single-layer Perceptron.

As computing power and training techniques improved in the 1990s and 2000s, neural networks grew deeper and more capable, powering advances in speech recognition, image classification, and natural language processing. Today, they are the engines behind nearly every major AI achievement, including modern generative AI.

What is a neuron?

At the core of a neural network is the artificial neuron, inspired by how biological neurons work. Think of it as a small decision-making unit.

A neuron takes multiple inputs, each with a weight that sets its importance, like a volume knob that turns some inputs up and others down. It adds these weighted inputs together, then adjusts the result with a bias, similar to adding a fixed amount of seasoning to a dish, no matter the ...