Neural networks are the mathematical brains behind modern AI - think of them as simplified versions of how your actual brain processes information.
- They're made up of interconnected "nodes" that work together to find patterns in data
- Unlike traditional algorithms that follow rigid rules, neural networks learn from examples
- They're the foundation behind everything from ChatGPT to image recognition
- The "neural" part is inspired by how brain cells connect, but they're much simpler than the real thing
Neural networks have become the dominant approach in AI because they excel at handling messy, complex data that breaks traditional programming approaches.
What is a neural network?
A neural network is a type of machine learning algorithm that's inspired by how brain cells (neurons) connect and communicate with each other. But before you get too excited about artificial brains, let's be clear: they're much simpler than the real thing.
That's why ML algorithms can be as simple as linear regression — which you may have learned about in Statistics 101 — or as complex as a neural network with millions of nodes. The kinds of models that have made headlines recently are mind bogglingly complex, and took the work of hundreds of people (not to mention decades of collective research).
Think of a neural network like a really sophisticated pattern-matching machine. If you showed a traditional computer program a picture and asked "is this a cat?", you'd have to write thousands of lines of code describing what makes a cat a cat (pointy ears, whiskers, four legs, etc.). A neural network, on the other hand, learns what a cat looks like by studying thousands of cat photos until it figures out the patterns on its own.
How do neural networks work?
Neurons are the basic building blocks of AI architectures, modeled after the actual biological neurons that transmit signals throughout the human brain. Remember, AI models are essentially pattern investigators; they find the underlying pattern in the data. You can think of these neurons as the mathematical functions that are doing this hard investigative work, getting into the weeds of the data and figuring out what’s going on.
The math performed by individual neurons is actually pretty simple – it’s usually just basic multiplication and addition that you could do with a calculator. So how are AI models able to capture such complex patterns, like the ones involved in language and vision? The trick is to string together a lot of neurons – like hundreds of millions of them.
This stringing together is where our first “decision” – and thus the early stages of an architecture – starts to come into play. Researchers can combine neurons in two ways.
First, neurons can be lined up in a sequence, so the output of one becomes the input of the next.
Neurons can also be stacked in layers, where they don’t interact directly but take the same input values.
Some special neurons can even accept their own output and use it to update their internal function, in a kind of simulated memory. This is helpful when you’re handling a sequence of data inputs, like a bunch of frames from the same video, and you want your model to use knowledge from earlier frames to contextualize what’s happening in later frames.
Put these configurations together, and you’ve got an Artificial Neural Network – the most basic model architecture. Neural networks are just layers (stacks of neurons) arranged in a sequence.
Different networks might follow different rules: in the setup above, a neuron accepts input from every single neuron in the layer preceding it. This is what computer scientists call a fully connected network (actually, the configuration pictured has an even more specific name: the Feedforward Neural Network, or FNN). But networks can also be partially connected, meaning that neurons selectively accept input from neurons in the previous layer.
You might notice that – just like an individual neuron – a neural network takes an input and returns an output. The architecture itself can be treated like a big mathematical function, and used as part of an even larger architecture.
What's the difference between neural networks and traditional algorithms?
Though neural networks have been around for almost 100 years, they are far from the only algorithm used for AI and machine learning. When I was coming up through my undergrad Data Science degree, the most popular algorithms at the time were things like Linear Regression, Decision Trees, K-Means Clustering, etc. All of these algorithms have a time and place…but for GenAI, neural networks have won out as the primary architecture.
One way to think about why is how general purpose neural networks are. Throw enough data and compute at them, and they’ll be able to find patterns in pretty much any type of data; whereas some of the more traditional algorithms are best fit on a specific type of data or problem.
Neural networks have essentially become the foundation of modern AI for one simple reason: they work incredibly well with the kinds of complex, unstructured data that we actually care about. Want to understand human language? Neural networks. Want to recognize objects in photos? Neural networks. Want to predict what song you'll like next? You guessed it.
The breakthrough came when researchers figured out how to make these networks much bigger and train them on vastly more data than ever before. Turns out, the bigger and more data you feed them, the better they get. And with the internet providing essentially unlimited training data, neural networks have become scarily good at tasks that used to be impossible for computers.
How do neural networks learn?
So far, our architectures have just been arrangements of many indistinguishable computing units (neurons). But not all neurons are created equal. Researchers can create neurons that do, well, anything, each with a specific problem or data type in mind. These neurons or units can be attached to the front or tail end of an existing neural network to form even more complex and powerful models.
The learning process itself happens during a phase called training. When you train a neural network, you feed it data and let it make predictions or guesses. The network compares its output to the correct answer, measures the difference, and adjusts its internal parameters to perform better next time. This adjustment process is repeated millions of times until the network becomes very good at recognizing patterns.
Training relies on a process called backpropagation, which is essentially how the network teaches itself. Backpropagation works by calculating how much each neuron contributed to the final error, then slightly tweaking the neuron’s internal weights to reduce that error on the next pass. Over time, this fine-tuning lets the network minimize mistakes and generalize from examples to new data it has never seen before.
Once trained, these networks can perform tasks ranging from recognizing images and translating languages to writing coherent paragraphs. Their power comes from the fact that each layer of neurons learns to represent the world a little differently, with deeper layers capturing more abstract patterns and relationships.
Frequently Asked Questions About Neural Networks
How many nodes does a neural network have?
Neural networks can range from dozens of millions of nodes (and parameters) to billions or even trillions of parameters, depending on the model architecture and purpose. With GPT-5, while OpenAI has not publicly disclosed an exact parameter count, most independent estimates place it in the low-trillion range or higher. Some analyst write-ups suggest around 1.7–1.8 trillion parameters for a dense-style version, while others argue that if a “mixture-of-experts” (MoE) architecture is used, the total across all experts could reach tens of trillions.
So instead of “about 175 billion connections,” you’re looking at potentially thousands of billions of connections—trillions—for GPT-5. That’s far beyond the star count of the Milky Way, but the exact figure remains a guarded secret. Despite the scale, engineers still manage to run it on server farms, and yes, it can still write you a poem about your cat.
Are neural networks actually like the human brain?
Not really, despite what the marketing materials might suggest. They're inspired by how brain cells connect, but real neurons are vastly more complex and mysterious. Think of neural networks as a very simplified cartoon version of brain function - useful for getting computers to recognize patterns, but about as similar to a real brain as a paper airplane is to a Boeing 747.
Do neural networks think?
This is where things get philosophical real quick. Neural networks are really good at pattern matching and can produce outputs that seem intelligent, but whether that constitutes "thinking" is the kind of question that keeps philosophers and AI researchers up at night. What we can say is they process information in ways that can be surprisingly human-like, even if the underlying process is just math.
How long does it take to train a neural network?
Anywhere from your lunch break to several months, depending on what you're building. Training ChatGPT took weeks on thousands of powerful computers and cost millions of dollars. A simple network for recognizing handwritten digits might train in an hour on your laptop. The general rule: the more impressive the AI, the more expensive and time-consuming it was to create.
Can neural networks make mistakes?
Oh absolutely, and sometimes in spectacular ways. They might confidently identify a chihuahua as a muffin, or translate "hydraulic ram" as "water sheep" (true story). Neural networks are only as good as their training data, and they can be fooled by adversarial examples or situations they've never encountered before.
What happens when neural networks get bigger?
Generally, they get better at their tasks, but with diminishing returns and exponentially increasing costs. There's also an interesting phenomenon called "emergence" where larger networks suddenly develop capabilities that smaller ones don't have — like the ability to reason through multi-step problems or understand context in surprisingly sophisticated ways.