Listen : Audio version of this article
The problem with any exodus is that it sweeps everything that is in front of it. Data has been the exodus of this age and often, in this exodus to join the data revolution, people forget to distinguish between different technologies and often club them under the same category. However, it is hardly so and hence, the need to differentiate between three decisive technologies that will shape the future is necessary. Not only it gives a novice a head on regarding which is what, but also lets you separate the applications from one another.
What does machine learning broadly mean?
Machines are not humans, or they are yet to be. Hence, machines have to be taught certain principles before they can work. Such a principle would be computer programming. However, machine learning enables machines to learn by themselves. While it dates back to the 50s in terms of its existence, it has come of age of late. It is one of the most widely used technologies currently and pervades domain of analytics as well as data mining. Machine learning lets you describe, prescribe, predict and even correct the data trends that you observe in sales, patients and many more objects.
Deep learning- a derivative
Machine learning has a broad scope and can work in both supervised as well unsupervised manner. Python, Scala, R are some of the technologies used in machine learning. However, when you go deeper in to this technology, you arrive at deep learning where you start unravelling the untouched recesses of data and networks. Deep learning abstracts models from extremely complex data and can return results much faster than usual machine learning processes. It not only learns the changes, but also the priorities from the data itself so you don’t have to tell what is necessary or not.
Artificial intelligence- the application
Surely, AI is a known word to science-fiction lovers. But, recently, artificial intelligence is an umbrella term for any kind of operation that replicates human intelligence, and that includes big data. However, beyond the common usage, AI is all about doing things that an average human being can. That, however, demands a lot and AI is yet to reach that stage. Broadly, it is an application of deep learning that has gained currency over the years. However, big data has brought all of them together and any data-related problem is solved by using any of the three interchangeably.