All of us have become familiar with the term “Artificial Intelligence.” It is the most commonly used term in sci-fi movies and the tech world these days. Recently you may have been hearing other words like ‘Machine Learning’ and ‘Deep Learning.’ These are sometimes used interchangeably with artificial intelligence.
Therefore, this confuses when one has to understand the difference between artificial intelligence, machine learning, and deep learning. Let’s trying understanding the difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to clear the clouds of confusion.
John McCarthy coined the term Artificial Intelligence in 1956. AI involves machines that can perform tasks that are characteristic of human intelligence. While this is slightly generic, it includes things like understanding the language, identifying objects and sounds, planning, problem-solving and learning.
We can classify Artificial Intelligence into two categories, general and narrow. The General AI consists all the traits of human intelligence, including the competencies mentioned above. Narrow AI, on the other hand, exhibits some facet(s) of human intelligence and can do it exceptionally well. But it lacks efficiency in other areas. A machine that is efficient in only recognizing images and nothing else is an example of narrow AI.
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The term Machine Learning was coined not too long after A.I, in 1959 by Arthur Samuel. It is defined as, “the ability to learn without being directly programmed.” We can build AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.
So, instead of hard-coding software routines with specific instructions to accomplish a particular task, machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves feeding vast amounts of data to the algorithm and allowing the algorithm to adjust itself and improve.
For instance, machine learning has been used to make drastic improvements to computer vision (the ability of a machine to recognize an object in an image or video). You gather hundreds of thousands or even millions of pictures and then have humans tag them. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a photo as containing a cat or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a cat looks like.
Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Artificial Neural Networks (ANNs) are algorithms that mimic the biological structure of the brain.
In ANNs, there are “neurons” which have discrete layers and connections to other “neurons.” Each layer picks out a specific feature to learn, such as curves/edges in image recognition. It’s this layering that gives deep learning its name; depth is created by using multiple layers as opposed to a single layer.
Hope this article helped in clearing your confusion on the differences between AI, ML and DL.