Robotic Process Automation

Robotic process automation is an application of artificial intelligence which configures a robot (software application) to interpret, communicate and analyse data. This discipline of artificial intelligence helps to automate partially or fully manual operations that are repetitive and rule based.

Natural language generation

Natural language generation is a trendy technology that converts the structured data into native language. The machines are programmed with algorithms to convert the data in a desirable format to the user. Natural language is a subset of artificial intelligence which helps the content developers to automate content and deliver in the desired format.

The content developers can use the automated content to promote on various social media platforms, and other media platforms to reach the targeted audience. Human intervention will significantly reduce as data will be converted into desired formats. The data can be visualized in the form of charts, graphs etc..

Speech recognition

Speech recognition is another important subset of artificial intelligence which converts human speech into a useful and understandable format by computers. Speech recognition is a bridge between human and computer interactions. The technology recognizes and converts human speech in several languages. Siri of iPhone is a classic example of speech recognition.

Virtual Agents

A virtual agent is a computer application that interacts with humans. Web and mobile applications provide chatbots to their customer service agents to interact with humans to answer their queries. Google Assistant helps to organize meetings, and Alexia from Amazon helps to make your shopping easy. A virtual assistant also acts like a language assistant, which picks cues from your choice and preference. The IBM Watson understands the typical customer service queries which are asked in several ways. Virtual agents act as software-as-a-service too.

Biometrics

Deep learning another branch of artificial intelligence which functions based on artificial neural networks. This technique teaches computers and machines to learn by example just the way humans do. The term “deep” is coined because it has hidden layers in neural networks. Typically, a neural network has 2-3 hidden layers and can have a maximum of 150 hidden layers.

Deep learning is effective on huge data to train a model and a graphic processing unit. The algorithms work in a hierarchy to automate predictive analytics. Deep learning has spread its wings in many domains like aerospace and military to detect objects from satellites, helps in improving worker safety by identifying risk incidents when a worker gets close to a machine, helps to detect cancer cells etc.

Machine learning

Machine learning is a division of artificial intelligence which empowers machine to make sense from data sets without being actually programmed. Machine learning technique helps businesses to make informed decisions with data analytics performed using algorithms and statistical models.

Enterprises are investing heavily in machine learning to reap the benefits of its application in diverse domains. Healthcare and medical profession need machine learning techniques to analyse patient data for the prediction of diseases and effective treatment. The banking and financial sector needs machine learning for customer data analysis to identify and suggest investment options to customers and for risk and fraud prevention. Retailers utilize machine learning for predicting changing customer preferences, consumer behaviour, by analysing customer data.