Traditional AI, focuses on performing a specific task intelligently depending on given inputs as per a set pattern.  These systems have the capability to learn from data and make decisions or predictions based on that data.

A simple way to understand AI is to look at its processing format and functions. You may know how to create graphs from data given in charts using software such as Excel or Numbers. Graphical representation of data makes charts more visually attractive and user-friendly than a tabular display. Hundreds and thousands of such graphs are used by students, educational institutes, media houses, corporates, stock analysts, bankers, investment advisors, defense analysts, manufacturers and tech majors every day to describe their data story. Many of them are mass-produced by AI through what is known as descriptive analytics. 

Descriptive analytics tells you what happened in the past, based on the statistics and data collected. It can take raw data and summarize it to show you trends over time. The format is usually visually expressive so that the user can understand it without scanning the data tables. For example, a sales report may include graphs that summarize past performance based on company data. It is the basic form of data interpretation where two user needs are simultaneously addressed. Firstly, the collected data is presented in a visually attractive format with easy-to-follow charts, and secondly, the data is put in context to tell a story. 

Penn Medicine, a multi-hospital healthcare organization in Pennsylvania, uses an electronic dashboard for real-time descriptive analytics, monitoring and intervention by its nursing staff. It developed an application for ‘awakening and breathing coordination’ called ABC that minimized the time ICU patients spent on a mechanical ventilator by more than 24 hours. During the COVID-19 pandemic, all of the data put out by the CDC, the NHS, the ICMR and the WHO on a daily basis that instantly tabulated inputs from thousands of hospitals across geographies about numbers admitted, patients recovered and deaths were AI-driven. Such real-time data representation is possible only because of the large-scale deployment of descriptive analytics in data analysis, through millions of globally networked computers. 

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