I dunno man, when I got into learning CNN it becomes very easy to see how neural networks work at an intuitive level and how that plays into its "intelligence".

I dunno man, when I got into learning CNN it becomes very easy to see how neural networks work at an intuitive level and how that plays into its "intelligence". For example, early layers of the network respond very well to simple features like edges and simple patterns, while later layers respond really well to more and more abstract things, like the shape of a person or a wheel etc. The craziest part is that this is all emergent from a random initialized state, all these patterns and abstractions develop with no manual intervention, they just happen to be the result of backpropagation consistently lowering a cost function. The biggest thing is that computing is just now finally able to have enough data and large enough networks to really start to create more generalized models. With a computer 20 years ago you might be able to do squeeze out simple pattern recognition, but for every layer and every neural node you add to a neural network, the more complex the model becomes and the more edge cases it can fold into itself. Take a look at Universal approximation theorem and how, with enough nodes in a neural network, you can solve pretty much any problem given the right weights.