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Digital Transformation from Leveraging Blockchain Technology, Artificial Intelligence, Machine Learning and Deep Learning

  • N. Chandrasekaran
  • Radhakhrishna SomanahEmail author
  • Dhirajsing Rughoo
  • Raj Kumar Dreepaul
  • Tyagaraja S. Modelly Cunden
  • Mangeshkumar Demkah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)

Abstract

These are exciting times as new software development paradigms are fast emerging to cope up with the shift in focus from “mobile first” to “AI first” approach being adapted by Google, Facebook, Amazon and others. This can mainly be attributed to the stability of the cloud computing platform and the developments in search capabilities which have extended from traditional text and web pages to achieving voice and vision recognitions relating to images and videos. Continued research focus has brought the error rate in image recognition by machine to converge sharply with that of the human. Apart from developments in big data analytics, artificial intelligence, machine learning and deep learning, break throughs in peer to peer distributed ledgers with a blockchain technology platform, which incorporates multiple levels of strong encryptions, have created massive developmental interests. Most of the “popular apps” that we use today, are being built using AI algorithms. To achieve this, changes are being incorporated to computational architecture to make them compatible with “AI first” data centers equipped with AI driven features. Tensor Processing Unit (TPU), which powered Google’s developments in ML and AI, has now become part of cloud computing service. Anticipating cost related issues, new hardware developments are focusing on moving from the cloud to the edge with the new “Edge TPU”. Digital transformation is further augmented by the fact that block chain platforms, which are built on de-centralized tools and technology, are exhibiting greater maturity by the day. The paper highlights several blockchain applications to deliver on several of the promises. The paper also discusses the fundamentals of Neural network to demonstrate how well these concepts that are incorporated in deep learning have decreased error rates by tenfold compared to previous technologies.

References

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • N. Chandrasekaran
    • 1
  • Radhakhrishna Somanah
    • 1
    • 2
    Email author
  • Dhirajsing Rughoo
    • 1
  • Raj Kumar Dreepaul
    • 1
  • Tyagaraja S. Modelly Cunden
    • 1
  • Mangeshkumar Demkah
    • 1
  1. 1.Université des MascareignesRose HillMauritius
  2. 2.University of MauritiusReduitMauritius

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