Despite the terms often being used interchangeably, machine learning and AI are separate and distinct concepts. As we’ve already mentioned, machine learning is a type of AI, but not all AI is, or uses, machine learning. Even though there is a large amount of overlap (more on that later), they often have different capabilities, objectives, and scope.
The broader aim of AI is to create applications and machines that can simulate human intelligence to perform tasks, whereas machine learning focuses on the ability to learn from existing data using algorithms as part of the wider AI goal.
AI can solve a diverse range of problems across various industries — from self-driving cars to medical diagnosis to creative writing. Sometimes these problems are similar, but often they are wildly different.
Machine learning, on the other hand, is much more limited in its capabilities. The algorithms are great at analyzing data to identify patterns and make predictions. But it can’t solve broader problems or be adapted in the same way as AI.
The best way to look at the difference between them is that machine learning is a single (but important) cog in the bigger AI machine. That machine might be a push-bike, or it could be a space rocket. It might not be as dynamic, but it’s a vital part that can’t be overlooked or taken for granted.
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