Most of the companies are making a colossal investment in artificial intelligence to gain the competitive advantage. The canopy of artificial intelligence is so broad that different technologies have a distinct purpose. Most of the times, it is challenging to understand which solution is right for a particular work process.
One of the famous siblings of artificial intelligence is robotic process automation (RPA) for automating repetitive tasks, machine learning to give systems the power to learn by itself. Another non-famous siblings of artificial intelligence are the natural language processing (NLP), natural language understanding (NLU) and natural language generation (NLG).
Natural language processing (NLP) is the oldest and is prominently used in reading text and finding what is useful. When a sentence is typed in google, NLP helps to generate relevant search results. NLP analyze keywords and is a factor in making web pages searchable. Also, NLP helps in solving the problem of spamming of emails in the inbox.
Natural language understanding (NLU) is the young and the nascent sibling of artificial intelligence. NLU followed and learned the path of natural language processing, until it got a big break in the voice of Siri on the iPhone, followed by the voice of Amazon’s Alexa and google assistant. When conversing with a computer in speech type, NLU makes humans feel talking to other humans. As NLU is undergoing training by conversing with millions of humans each year, it gets better each time.
Natural language generation is widely used in machine communication in human language and it can seamlessly write thousands of pages of human-seeming language every second. NLG does this by combining three things, first a base template of what written output is supposed to resemble. Second text and data that feed into the template, with accompanying logic to inform how and when that data should appear. Third, the elements of variability in the template such as the use of synonyms, alternative phrasing idioms to reflect the way of human writings.
In the media industry, NLG is used for the automated generation of financial news items. A wider use of NLG is a tool developed which is used to translate business data into decision-making insights. Usually, a platform is developed where a framework of rules and parameters are created where unstructured data is fed into the platform in order to get output reports, paragraphs and emails. For the generation of sports post-match recap and player notes, automated insights-wordsmith was developed but now the company is progressing towards making intelligence reports. These intelligent reports include market analysis and significant insights derived from data straight into a firm’s dashboards enabling the entire report to be understood by everyone in the organisation without explanation. In this way, NLG helps in automating the manual work of the firm’s method of collecting data and converting it into a form understandable by all.
NLG has advanced over a couple of years but there is a lot to work around customization and advancement. In the introspective, humans will be able to witness NLG voice systems to recognize customer’s tone and respond accordingly or a situation could arise where NLG can provide financial advice to people.