Difference Between Artificial Intelligence ,Machine Learning and Deep Learning

In this post you will learn what is AI and Machine Learning and Difference between them. As we reached the digital era,where computers became an integral part of the everyday life style. Have you heard about Artificial Intelligence? How about Machine Learning? These words are familiar to us and can be used interchangeably, The more people used it, the more confusing it gets.

What is Artificial Intelligence?

AI is the like creating intelligence artificially. Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI has had some success in limited, or simplified, domains. It includes all kinds of tasks such as doing creative work, planning , moving around, speaking, recognizing objects and sounds, performing social or business transactions and a lot more. 

What is Machine Learning?

A type of AI that can include but isn’t limited to neural networks and deep learning. Generally, it is the ability for a computer to output or do something that it wasn’t programmed to do. More and more plans to try different approaches to use AI leads to the most promising and relevant area which is the Machine Learning. The most common way to process Big Data is called Machine Learning.

Difference Between AI ,Machine Learning and Deep Learning:

 Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart.we can consider Machine Learning as a subset of AI. When Artificial intelligence is the whole idea of a technology that behaves like a human, machine learning algorithms are about finding patterns and invariant in big data. These self-learning algorithms enable the machines to learn from data sets (inferential statistics) and they are a series of steps that lead to the completion of a task.

Artificial intelligence encompasses other areas apart from machine learning such as knowledge bases, natural language processing, robotics etc. Also symbolic logic (rules engines, knowledge bases, expert systems etc.), Bayesian statistics, robotics and evolutionary algorithms can be described as AI, however none of them are machine learning.

Digital era, brought an explosion of data in all forms and from every region of the world. This data, known simply as Big data, is drawn from sources like social media, internet search engines, e-commerce platforms etc.. This enormous amount of data is readily accessible and can be shared through various applications like cloud computing. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to Artificial Intelligence(AI) systems for automated support.

The most common way to process Big data is called Machine Learning. It is a self-adaptive algorithm that gets better and better analysis and patterns with experience or with newly added data.

For example, if a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensure, while a deep learning non-liner technique to weeding out a fraudulent transaction would include time, geographic location, IP address, type of retailer, and any other feature this is likely to make up a fraudulent activity.