Artificial Intelligence: Towards Dynamic Predictive Framework
In the present digital era, several corporate firms are concentrating more on the development of advanced artificial intelligence tools, and startups are investing in the same to compete with established organizations. In the year 1956, John McCarthy coined the term AI for the first time as “science and engineering technology for developing intelligent machines”.
The main theme behind the development and success of artificial intelligence is its capability to guide machines in performing several cognitive-based activities, by capturing human intelligence aspects such as voice or text recognition, visual identification, and problem-solving functions. These data will be initially pre-processed for extracting unique features of captured data and forwarded to training and testing for verification.
Through the knowledge gained from the historical data and experiences, several corporate firms are performing predictive analysis to increase their productivity by studying their present strength. But traditional predictive analytics techniques are found to be less accurate due to the lack of analytical skills and manual processes. The advancements in Artificial Intelligence have led researchers to focus on deploying AI-based algorithms for predictive analytics and have gained huge popularity.
In recent years, machines are trained with dynamic predictive analysis for smart thinking and act quickly at a particular situation. Several well-known organizations such as Google, Apple, and Microsoft are investing a huge amount in research and development of AI tools to achieve excellent customer service along with minimizing the drawbacks.
In the year 2017, CEO of Google, Sundar Pichai announced a new initiative Google.ai, which utilizes latest machine learning algorithms for a search engine to access and search contents with better processing speed. Employing predictive analytics and artificial intelligence tools in Google.ai helps in determining the shortest path between the stations or nearby places with less time consumption.
The new site is in progress from the Google development team, where it employs AutoML in which the neural network can develop another neural network through reinforcement learning. This helps to achieve auto draw application, where unskilled artists will provide their ideas through Google, and Artificial Intelligence tries to recognize the particular drawing.
In a nutshell, the embedding technologies of artificial intelligence and predictive analysis are leading people towards advanced technological aspects through futuristic strategies and scalable processes. It provides a new way of handling huge amount of data and to achieve optimal results through the intervention of humans and humanoid machines. In future, the safety parameters should be ascertained during the design and development for achieving accurate results.
By Tom Farrah, CIO & SVP, Dr Pepper Snapple Group
By George Evans, CIO, Singing River Health System
By John Kamin, EVP and CIO, Old National Bancorp
By Phil Jordan, CIO, Telefonica
By Elliot Garbus, VP-IoT Solutions Group & GM-Automotive...
By Dennis Hodges, CIO, Inteva Products
By Bill Krivoshik, SVP & CIO, Time Warner Inc.
By Gregory Morrison, SVP & CIO, Cox Enterprises
By Alberto Ruocco, CIO, American Electric Power
By Sam Lamonica, CIO & VP Information Systems, Rosendin...
By Sven Gerjets, SVP-IT, DIRECTV
By Marie Blake, EVP & CCO, BankUnited
By Lowell Gilvin, Chief Process Officer, Jabil
By Walter Carvalho, VP & Corporate CIO, Carnival Corporation
By Mary Alice Annecharico, SVP & CIO, Henry Ford Health System
By Bernd Schlotter, President of Services, Unify
By Bob Fecteau, CIO, SAIC
By Jason Alan Snyder, CTO, Momentum Worldwide
By Jim Whitehurst, CEO, Red Hat
By Marc Jones, Distinguished Engineer, IBM Cloud Infrastructure