Imagine Artificial Intelligence (AI) as a giant brain playground. Inside this playground, one of the star attractions is the neural network. Think of it like a bunch of friends (neurons) connected via walkie-talkies. There are the input friends (who start the message), the hidden friends (who pass the secret along), and the output friend (who announces the final answer). If there’s more than one hidden friend group, it becomes a deep neural network by adding more secret relay teams in a giant game of telephone.
Now, what do these friends talk about? Pixels! A pixel is the tiniest Lego block of an image. The more pixels you have, the smoother your picture looks. Fewer pixels make it look blocky, like old-school Pac-Man. Pixels are coded with 1s and 0s, where 1 means “color on” and 0 means “color off.” By piecing together millions of these little on/off switches, the network can recognize things, which explains how Face ID works on your phone!
But how do neural networks learn? Enter supervised learning, which is like having a teacher show the network flashcards. The teacher says, “This is a cat. This is a dog.” At first, the network guesses randomly, but it starts adjusting weights – little volume knobs that decide how loudly each pixel’s voice is heard. The sigmoid activation function is like a filter that decides whether a neuron’s signal is strong enough to pass on or should be kept quiet.
Sometimes the guesses are wrong – oops! That’s where backpropagation comes in. Imagine the teacher yelling back through the walkie-talkies, “Wrong answer – try again!” The friends adjust their volume knobs until the signal gets clearer. This whole guessing and adjusting loop is called training.
To make sure the network doesn’t wander aimlessly, it uses gradient descent – like rolling a marble down a hill until it finds the lowest valley, the best spot where mistakes are minimized. Slowly but surely, the network learns to make smarter predictions, just like a student getting better with practice.
These neural links/AI are often compared to neurons/brain because they process incoming information from the world around us. You can think of each neuron as a tiny biological computer – millions of them working together to create thought, movement, and emotion. But while the brain and AI share some similarities, they’re built very differently. Our brains run on just about 20 watts of power, which is roughly the same as a dim lightbulb – yet they outperform even massive AI systems in flexibility and common sense.
Neurons, however, are slower than electronics. A neuron’s recovery time is about 4 milliseconds, and brain signals travel at only 2–3 meters per second, compared to the lightning-fast 2 million meters per second in electronic circuits. Still, neurons communicate through electrical “spikes” that can be measured by their timing or frequency. A burst of parallel spikes might signal pain, and each neuron can represent different types of information. In a sense, these spikes act like the brain’s version of digital signals – just far more complex and alive.
OpenAI. (2025, September 23). ChatGPT [Large language model]. https://chat.openai.com/