Artificial Intelligence is the broader umbrella under which Machine Learning and Deep Learning come. Deep Learning and Machine Learning are the subset of each other. It's all about tremendous increase in data so results can't be predicted accurate, hence AI comes into the picture and now it is talk of the town.
The term artificial intelligence was firstly coined in the year 1956. The concept is pretty old but it has gained its popularity recently. The reason behind this is that earlier we have very small amount of data, the data we had was not enough to predict the accurate result. But now there is a tremendous amount of increase in data statistics suggests that by 2020 the accumulated amount of data will increase from 4.4ZB to 44ZB roughly. Along with such enormous amount of data now we have more advance algorithms and high computing storage that can deal with such large amount of data. As a result it is expected that 70% of enterprise will implement AI over the next twelve months.
Artificial Intelligence is a technique which enables machines to mimic human behavior or in other words Artificial Intelligence is the technique which enables the machine to act as human- beings. With AI it is possible to machine learn from the experience. The machines just add their responses based on their new input day by performing human like task. Artificial intelligence can be trained to accomplish specific task by processing large amount of data and recognizing pattern in them. So as artificial intelligence researchers should think ourselves as a humble brick makers was job to study how to build components and many more so on that someday somewhere and someone will integrate into the intelligence system. Some of the examples of artificial intelligence from our day to day lives are Apple series just playing computer and test less self-driving car.
Machine Learning came into the existence in late 80's and early 90's. In statistics: How to efficiently train large complex models?
Machine Learning is a subset of Artificial Intelligence technique which uses statistical methods to enable machines to improve with experience. But what were the issues with the people which make the machine learning come into the existence. Let us discuss each one by one like in the field of statistics, the problem was how to efficiently train the large complex model. In the field of artificial intelligence and computer science is how to train the more robot version of the artificial intelligence system. While in the field of neuroscience problem faced by the researchers was how they design the operational model of the brain. So, these were some of the issues which had the largest influence and led to the existence of machine learning. And machine learning shifted its focus from symbolic approaches which has inherited from artificial intelligence.
The concept of deep learning is not new but recently its hype has increased and deep learning is getting more attention. This field is a particular king of Machine Learning that is inspired by the functionality of our brain cells called neurons which led to the concept artificial intelligence neural network. We can also say that deep learning is also a machine learning. Hence, most specifically it is the next evolution of the machine learning. Deep Learning, we can consider as rocket engine and its fuel as data which feeds the algorithms.