Ayush

Welcome back to AI Squared, our two mindsets for one intelligent future.

I'm Ayush.

Today we're going one level deeper.

We're talking about the core idea behind most modern AI systems, from image recognition to chatbots.

It's all about neural networks.

You can think of it as a giant web of tiny decision makers called neurons.

Each neuron takes in some numbers, does a small calculation, and passes a result forward.

The word a neural comes from neurons in a brain.

But it's just an inspiration.

Real brains are way more complex.

Neural networks are just simplified mathematical versions.

Let's zoom in on a single neuron in a network.

You can imagine each weight as saying, how important is this output?

A bigger weight means that input matters more.

A smaller weight means that the input matters less.

The activation function decides whether the neuron fires strongly or weakly.

It also helps the neural network handle complex non-linear patterns instead of just straight lines.

Now, neurons are grouped in layers.

Then you have one or more hidden layers, called hidden because you don't directly see them.

They're where most of the pattern learning happens.