Understanding Neural Networks: The tech behind today's AI

Hey folks,

I've been getting a lot of messages asking how to learn AI as a beginner. And honestly, it made me realize there's no point talking about AI news and trends if you don't understand what's actually happening under the hood.

So I'm starting a weekly learning series. Every issue will break down one AI fundamental. We're starting with Neural Networks, basically the foundation of everything in AI.

Let's dive in!

What are Neural Networks?

Before going to definitions, here’s a simple real world example.

Your phone's Face ID uses a Neural Network to detect and recognize your face. It doesn't follow a checklist of rules, it learned by seeing thousands of faces.

History: Neural networks as a concept originated in the 1950s, but the modern deep learning era began in 2012 when AlexNet revolutionized computer vision and triggered the AI explosion we see today.

Fun historical note: The first neural network was called The Perceptron (1958). It was an actual physical machine with:

-> 400 photo-sensor cells

-> Electro-mechanical motors that adjusted weights

-> Ability to learn and recognize letters

Definition: A neural network is a computational model inspired by the way neurons work in the human brain. It consists of interconnected units (“neurons”) that process information by adjusting weights based on data, enabling the model to learn tasks like classification, prediction, and pattern recognition.

How it works (simple version): Think of a neural network like a learning student. You show it thousands of Face ID images labeled "you" or "not you". The network starts guessing randomly. When it gets it wrong, it adjusts its internal connections (weights) a tiny bit. After seeing thousands of examples, these small adjustments add up and suddenly it can recognize your face.

That's the core idea: learn from examples by adjusting internal connections, not by following explicit rules.

Next week we'll dive into exactly how those adjustments happen (neurons, weights, biases). But for now, just know: neural networks learn patterns from data instead of following a checklist.

Why this matters

Unlike traditional programming where we write explicit rules, neural networks learn patterns from data. They made it possible to build systems that recognize faces, understand speech, classify images, and generate text.

This is how AI evolved:

• Early image recognition systems
• Speech recognition & voice assistants
• Modern AI models like GPT-4/ChatGPT, built on Transformer-based neural networks

Today, almost every AI system uses neural networks -> from medical diagnostics to self-driving cars.

Generative Models (Text)

ChatGPT, Claude, and Grok use Transformer-based neural networks. The key idea is the attention mechanism. It lets the model focus on different parts of your input when generating each word. Transformers are really good at processing text and became the standard for language tasks.

Diffusion Models (Images)

DALL-E, Midjourney, and Stable Diffusion use Diffusion Models. How do they work? They start by adding noise to an image, then learn to remove that noise step by step. They usually work with Transformers (like CLIP) to understand your text prompt. Some use U-Net architecture -> basically a CNN optimized for removing noise from images.

Other Important Neural Network Types

• Convolutional Neural Networks (CNNs) handle computer vision tasks. They look at small parts of an image and detect patterns. You see them in medical imaging (finding tumors), face recognition, self-driving cars (spotting pedestrians), and satellite imagery.

• Recurrent Neural Networks (RNNs) including LSTMs and GRUs handle data that comes in sequence. Before Transformers took over, these were used for time-series prediction, speech recognition, and translation. Still useful when you need to process data step by step.

• Graph Neural Networks (GNNs) work with connected data -> social networks, molecules, recommendation systems. LinkedIn uses them to suggest connections. Researchers use them to understand how atoms bond.

• Reinforcement Learning Networks combine neural networks with learning from rewards. AlphaGo uses them, Robots use them. They learn by trying things and getting feedback.

• Autoencoders compress data into a smaller form, then expand it back. Good for finding unusual patterns (anomaly detection) or squeezing data into smaller files.

In short: Neural networks power everything in AI today.

📰 This Week in AI