Neural Networks

Neural networks are computer models inspired by how the human brain works. They have lots of connected parts called “neurons,” just like our brain does.

This is important because it makes them really good at spotting complex patterns.

As computers got faster and more powerful, these models got better.

They could take in more information and find more patterns in language. This helped them “understand” language in a deeper way. It made their responses to input more believable. A little more human.

It finally started to feel like we could actually talk to computers.

Like statistical models, neural networks could read one word at a time and guess the next one. But unlike those earlier models, neural networks had a little bit of memory. They could remember what came before in a conversation.

This made it feel like the model could carry on a real chat - good for things like chatbots, right?

But early neural networks had a problem: they had limited memory. They forgot things pretty quickly. If the input was too long, the model would lose track of earlier parts. That made conversations feel choppy or disconnected - not so good for chatbots.

So researchers needed to fix that forgetfulness.