Blog Archive

When most people think about training artificial intelligence, they think of massive datasets that need to be labeled. It could be thousands of annotated images, carefully tagged text, or structured tables telling the model exactly what each piece of data represents. But labeling data like this is expensive, time-consuming, and sometimes downright impossible. That’s where self-supervised learning comes in. 

When people think of artificial intelligence, they often picture massive, complex neural networks powering self-driving cars or language models that can write like humans. But at the foundation of all these systems lies a much simpler idea: the feedforward neural network (FNN). It’s the original blueprint for how machines can learn from data. Over time, this concept has evolved into what we now call deep neural networks (DNNs), which are larger, more powerful versions capable of tackling far more complex tasks.

Training models are a bit like teaching children. There's a lot of trial-and-error and mistakes. But with guidance, a nod of approval here, a small correction there, they begin to understand not just what works, but why it works. Over time, they start making decisions that align with your expectations without you having to spell everything out.