Clarifying AI, Machine Learning, Deep Learning, Data Science with Venn Diagrams
Harnessing the capabilities of artificial intelligence, machine learning, deep learning, and data science will be instrumental in the future; however, the average person likely only has a surface level understanding of how these four terms are related. Therefore, the purpose of this article is to clarify each of these terms utilizing Venn Diagrams. We will begin with artificial intelligence, which can be thought of as human intelligence exhibited by a machine.
Humans can only perform specific tasks after learning them, and we improve our execution of these tasks through practice. Artificial intelligence works the same way and we refer to this process as machine learning. In terms of a venn diagram, machine learning is a subset of AI: We teach a specific task to the machines, they learn it and improve their execution of it -ultimately faster than human capabilities- until the task becomes integrated into their overall intelligence. Let’s include Machine Learning into the picture.
Now we’ve understood AI and ML, what is deep learning? Before we get to that, it is important to note that reading and analyzing images is a task that that machines are currently not very efficient at. Deep learning is necessary in these cases because while machine learning is restricted to structured or semi structured data, deep learning allows machines to input images, voices and videos. We’ll take a glance at the picture with deep learning.
We have looked at AL, ML and DL. So where does Data Science fit in? Is it not a completely separate field? This is actually a misconception because oftentimes the roles of a Data Scientist and Machine Learning Engineer overlap. Data science involves analyzing data for the purpose of business goal, but many times data pools are too vast for humans to manually go through. As a result, data scientists must be able to harness artificial intelligence and all its processes because not only can…