LEVERAGING AIML FOR ADVANCED DATA GOVERNANCE ENHANCING DATA QUALITY AND COMPLIANCE MONITORING

Authors

  • Sudeesh Goriparthi Senior software engineer, software architecture, Walmart, Dallas, USA. Author

Keywords:

AI, ML, DL, Data Governance

Abstract

Modern sophisticated robotics owes a great deal to the ground-breaking developments in AI, ML, and DL that have occurred in the past few years. By enhancing their intelligence, efficiency, and adaptability to complicated tasks and settings, AI, ML, and DL are revolutionizing the field of advanced robotics. Advanced robotics uses AI, ML, and DL for tasks such as predictive maintenance, object recognition and manipulation, autonomous navigation, and natural language processing. Collaborative robots (cobots) that can learn from humans and adjust to new activities and surroundings are another product of these technological advancements. Transportation firms and passengers alike can benefit from enhanced safety, efficiency, and convenience brought about by modern transportation systems that use AI, ML, and DL. Manufacturing assembly robots are also benefiting greatly from AI, ML, and DL, which are enhancing their intelligence, safety, and efficiency on the job. On top of that, they're useful in many areas of aviation management, where they can assist airlines in becoming more efficient, cutting expenses, and satisfying customers better. In addition, taxi firms may benefit from AI, ML, and DL to enhance client service by making it safer, more efficient, and more reliable. The research delves into the latest advancements in AI, ML, and DL as they pertain to sophisticated robotics systems. It also explores different ways these systems might be used to modify robots. Additionally, to address the inadequacies in the current literature, future research should investigate how advanced robotics systems might make use of AI, ML, and DL.

 

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Published

2022-09-06

How to Cite

LEVERAGING AIML FOR ADVANCED DATA GOVERNANCE ENHANCING DATA QUALITY AND COMPLIANCE MONITORING. (2022). INTERNATIONAL JOURNAL OF DATA ANALYTICS (IJDA), 2(1), 1-11. https://lib-index.com/index.php/IJDA/article/view/IJDA_02_01_001