Telecom Business Review

Utpal Chakraborty ¹

Affiliations
1 Head of Artificial Intelligence, Yes Bank, Mumbai, Maharashtra, IN

Abstract

Artificial Intelligence has created immense hype in the last decade and the credit for the same goes to the groundbreaking breakthrough named “Deep Learning” or “Deep Neural Network”. Although “Artificial Neural Network”, the foundation of Deep Learning as a concept has been prevalent since 1958 but the actual implementation for solving real business use cases have only been possible over the last decade. Deep Neural Networks has demonstrated significant results in the fields of computer vision, speech recognition, and machine translation, and outperformed human brain in many instances. Artificial Neural Network, as it is inspired from human biological neural superstructure has few structural similarities but not possible in terms of how the human brain or biological neural network works because we have still limited information on its functioning. Nevertheless, Deep Neural Network paved the way for many possibilities and currently it is the most promising technology that we have in the field of Artificial Intelligence.

Keywords

Deep Neural Network, Artificial Intelligence, Artificial Neural Network, Layered Approximation.

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Utpal Chakraborty ¹

Affiliations
1 Head of Artificial Intelligence, Yes Bank, IN

Abstract

Speaking broadly, an AI framework consists of six main layers starting with Data Integration layer. All AI applications need some kind of integration with the input data sources from enterprise applications. So, it is essential that AI framework has the capability to integrate with different data sources for seamless data exchange. This study is an overview of the building blocks of an AI Framework to deal as an analytic tool for the manager’s tussle with AI’s influence on their industries. The core of the framework is an “AI Ecosystem” where all AI, ML & NLP capabilities or “AI Assets” will reside and will have the option to pick and choose the best of the breed capabilities for building AI, ML applications. These AI capabilities can be versatile, like cognitive text processing, speech, computer vision, cognitive search, etc. Also, the framework should be able to connect to different hosting applications or channels to host AI applications or solutions. It is also advisable to have a Framework Management layer wherein features like setup & configuration, monitoring of different services, monitoring and reporting can be embedded. As security is one of the key elements of any framework, it should also be accounted for while designing a sustainable AI framework. Such a framework is going to provide flexibility and agility while building any AI solution. As AI is purely data-driven, this study intends to provide an insight into an enterprise-wise policy or data standard to designing and assembling an AI system.

Keywords

AI Framework, Data Integration Layer, AI Ecosystem, Computer Vision and Cognitive Search

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