Artificial Intelligence and the Indian Media Industry: the Future is Now

https://doi.org/10.55529/jaimlnn.26.24.31

Authors

  • Dr. Rubaid Ashfaq Assistant Professor, Amity University, Noida, India.
  • Ms. Zeba Nabi Assistant Professor, Lovely Professional University, Jalandhar, India.
  • Dr. Rohit Assistant Professor, Amity University, Noida, India.

Keywords:

AI, NLP, Indian Media, News Channels, Automated Journalism.

Abstract

The Indian media industry is currently undergoing a significant transformation with the advent of Artificial Intelligence (AI). AI has the potential to revolutionize the way news stories are produced and consumed, providing new opportunities for innovation and growth. However, it is also important to consider the challenges that come with implementing AI in the Indian media industry. This research aims to provide a comprehensive examination of the future of the Indian media industry in the age of AI. The study will analyse the current state of AI in the Indian media industry, focusing on its impact on efficiency, cost-effectiveness, personalization, multilingual news production, real-time news generation, and combat against misinformation. Additionally, the research will also explore the challenges that come with the adoption and implementation of AI in the Indian media industry, such as job losses, ethical concerns, lack of understanding, data, and infrastructure. The study will use both primary and secondary data sources, including interviews with industry experts, case studies of media organizations that have implemented AI, and a review of relevant literature. The research will provide valuable insights for media organizations, policymakers, and researchers on the opportunities and challenges of AI in the Indian media industry, and how it is shaping the future of the industry.

Published

2022-11-29

How to Cite

Dr. Rubaid Ashfaq, Ms. Zeba Nabi, & Dr. Rohit. (2022). Artificial Intelligence and the Indian Media Industry: the Future is Now. Journal of Artificial Intelligence,Machine Learning and Neural Network , 2(06), 24–31. https://doi.org/10.55529/jaimlnn.26.24.31

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