These days, it’s almost impossible to go anywhere without hearing about artificial intelligence (AI) or machine learning (ML). From marketing tools to self-driving cars, it seems like everything claims to be powered by AI. But what exactly is it, and why all the hype?
There’s a lot to AI, but in this post, we’ll focus on one key computing system: neural networks. As the name suggests, neural networks are designed to mimic the way biological brains, like those of humans and animals, process information and solve problems.
To put it in perspective, the human brain has around 100 billion neurons (or nerve cells). At birth, the brain is essentially a blank slate in terms of knowledge. A newborn doesn’t know the difference between a pineapple and a mango—it must first learn what fruits are and how they look. Over time, through repeated exposure, the child’s brain learns to distinguish between the two. This process is essentially pattern recognition: the child is fed information, processes it, and eventually learns to make correct identifications.
Neural networks work in much the same way, aiming to recognize patterns in data. In this context, however, you need to know the patterns you’re looking for in advance (this is called supervised learning). Just like neurons in the brain, which are either activated or inactivated, neurons in a neural network exist in an ON/OFF state, represented by 0/1 in software. The strength, or weight, of the connections (synapses) between these neurons determines whether they are activated.
In a simplified neural network, neurons are arranged in layers: an input layer, one or more hidden layers, and an output layer. You feed the data—like the image of a pineapple—into the input layer (on the far left). Activated neurons in this layer pass signals to the neurons in the next layer (the hidden layers). Eventually, the signal reaches the output layer (on the far right), where the network makes a prediction: “Yes, it’s a pineapple” or “No, it’s not.” If the network gets the prediction wrong, it adjusts itself (through a process called backpropagation) until it starts making accurate predictions.
Neural networks have a wide range of real-world applications. For instance, if Facebook wanted to ban images of hot dogs 🌭, they could use a trained neural network to analyze uploaded pictures and detect whether they contain a hot dog. So, if you post a picture of your Aunt in Mexico, the network would determine if a 🌭 is present or not.
Of course, identifying hot dogs is just scratching the surface of what neural networks can do. From facial recognition to medical diagnostics, neural networks are revolutionizing industries and reshaping how we interact with technology.
