Artificial Intelligence (AI) vs Machine Learning (ML) vs Deep Learning (DL)

I often hear the words AI, ML, and DL used interchangeably, and while they all fall under the same umbrella, the technics and use cases might differ, In this article, I’ll briefly try to resolve the confusion.

Intelligence is an attribute associated with humans and their cognitive functions, and when you try to mimic this ability in machines, it’s called Artificial Intelligence, or AI for short. AI can be a pile of if-then statements, just rules that tell the machine to behave in a certain way in a defined situation. Or, AI can be a complex statistical model mapping raw sensory data to symbolic categories. When the goal is to create a machine program that can alter itself when exposed to data, we call it Machine learning, or ML for short. ML is dynamic and does not require human intervention to make specific changes. That makes it less brittle, and less reliant on human experts. Deep learning or DL for short, is, on the other hand, ML with more accuracy, more math, and more computing, it is a subset of ML in which we explore unstructured data such as blobs of pixels or text. and perform exceptionally well in tasks that involve unstructured data such as blobs of pixels or text. Usually, when people use the term deep learning, they are referring to deep artificial neural networks. Deep is a technical term. It refers to the number of layers in a neural network. Deep learning uses a multi-layered structure of algorithms called neural networks.

So, in summary, AI is mimicking human behavior by any means, whereas ML is a subset of AI that does not use explicit programming which means that it is not using any fixed rules but requires features from humans. DL is a subset of ML that does not require any feature definition from humans, it is able to decide by itself which features are relevant and train itself on the given data.

Enjoy 🖖.