By far the greatest danger of AI is that people conclude too early that they understand it ??

It’s almost harder to understand all the acronyms around?Artificial?Intelligence (AI) than the technology itself.

AI vs Machine Learning vs Deep Learning?– These terms are often carelessly mixed together. But what are actually the differences? This article may be the simplest introduction to all Three fields, because even though there is overlap, they differ.

It should be important for you to know these differences, as each discipline describes different stages of a data analysis pipeline.

In the following figure, it is schematically shown the individual fields in their context. As you can see, the individual disciplines surround each other and form an onion-like layered model.

The figure clearly shows that there are relationships between individual disciplines. AI is to be understood as a generic term and thus includes the other fields. The deeper you go in the model, the more specific the tasks become. In the following, we will follow this representation and work our way from the outside to the inside.

All disciplines are encompassed by the term AI. It is a science that explores ways to build intelligent programs and machines that can perceive, reason, act, and solve problems creatively. To this end, it attempts to model how the human brain works.

The following figure shows that AI can basically be divided into two categories.

Classification is about measuring the performance of AI based on how well it is able to replicate the human-like brain. In the Based on Functionality category, AI is classified based on how well it matches the human way of thinking. In the second category, it is evaluated based on human intelligence. Within these categories, there are still some subgroups that correspond to an index.

So what is the first subcategory Machine Learning and how does it differ from AI? What distinguishes Machine Learning vs Deep Learning, and versus Deep Neural Learning?

You can find below a couple of great graphs to grasp differences and options. For a more detailed view, please refer to the original article here.

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