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Difference Between Artificial Intelligence-Machine Learning and Deep Learning?

We all are familiar with the term “Artificial Intelligence.” Because it been a popular phrase in movies such as The Terminator, Matrix, and I Robot. But you may have recently been hearing about other terms like “Machine Learning” and “Deep Learning,” which are sometimes used interchangeably with artificial intelligence. So the difference between artificial intelligence, machine learning, and deep learning can be very unclear. AI – Human Intelligence Exhibited by Machines. AI involves machines which can perform tasks that resembles characteristics of human intelligence. While this is rather general, AI includes things like planning, understanding language, recognizing objects and sounds, learning, and problem-solving. AI is classified into two categories, General and Narrow. General AI would have all of the characteristics of human intelligence, including the capacities mentioned above. Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI.

MACHINE LEARNING –

Machine learning is a way of achieving AI. That basically means you can get 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. To do this, we need to feed huge amounts of data to the algorithm allowing the algorithm to adjust itself and improve. To give an example, 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).
We gather many thousands or perhaps millions of pictures and then have humans tag them

Example:

Suppose Humans might tag pictures that have a cat in them versus those that do not. Now, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. When the accuracy level is high enough, the machine has now “learned” what a cat looks like.

Deep Learning — A Technique Used for Implementing Machine Learning

Deep learning is one of the various approaches to machine learning. 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 different 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.

AI and IoT are Inextricably tangled –

I think of the connection between AI and IoT very similar to the connection between the human brain and body. Our bodies collect sensory input like sight, sound, and touch. Our brains take that information and make sense of it,
turning light into recognizable objects and turning sounds into understandable speech. Human brains then make decisions, sending signals back out to the body to command movements like picking up an object or speaking. All of the connected sensors that make up the internet of Things are like our bodies, they supply the data of what’s happening within the world. AI is like our brain, creating a sense of that data and deciding what actions to perform and the connected devices of IoT are again like our bodies, carrying out physical actions or communication to others