Artificial Intelligence for Business by Rajendra Akerkar

Author:Rajendra Akerkar
Language: eng
Format: epub, pdf
ISBN: 9783319974361
Publisher: Springer International Publishing

Applications of Deep Learning in Business

Analytics has been changing the bottom line for businesses for quite some time. Now that more companies are mastering their use of analytics, they are delving deeper into their data to increase efficiency, gain a greater competitive advantage and boost their bottom lines even more. That is why companies are looking to implement machine learning and artificial intelligence; they want a more comprehensive analytics strategy to achieve these business goals. Learning how to incorporate modern machine learning techniques into their data infrastructure is the first step. For this many are looking to companies that already have begun the implementation process successfully.

Precisely, businesses in the customer engagement space utilize AI and machine learning to analyse conversations, both those that end in a sale and those that do not, and to automatically identify the language that naturally leads to a sale or that predicts when a sale will occur.

An important problem is whether to utilize the entire big data input corpus available when analysing data with deep learning algorithms. The general focus is to apply deep learning algorithms to train the high-level data representation patterns based on a portion of the available input corpus, and then utilize the remaining input corpus with the learnt patterns for extracting the data abstractions and representations. In the context of this problem, a question to explore is what volume of input data is generally necessary to train useful (good) data representations by deep learning algorithms which can then be generalized for new data in the specific big data application domain.

One of the most widely discussed deep learning business applications right now is with self-driving cars – a concept every big player is getting on, from Volkswagen to Google. These systems use sensors and a neural network to process a vast amount of data. The car learns how to recognize obstacles and react appropriately, increasing its knowledge through use beyond its factory programming. The self-driving car systems use sensors and a neural network to process a vast amount of data.

Eventually, given enough data, the machines learn how to drive better than humans.

Another common use is image detection and object classification, as seen today with Facebook. The company has more than enough data on images to work with, making deep learning for image detection very accessible. Currently, Facebook can classify different objects in an image with a very high accuracy.

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