Machine Learning with Artificial Intelligence: And Towards a Broad Consensus

Abstract

This study explores the complex interplay between machine learning (ML) and artificial intelligence (AI), highlighting the pressing requirement for a broad consensus framework. This study uses the Jupyter Notebook for analysis and a secondary research methodology to examine the current state of machine learning (ML) in AI applications. It carefully examines the benefits, and difficulties, along with ethical considerations inherent in this integration, illuminating the complexity of AI research. This research also highlights critical research gaps and provides doable tactics to encourage cooperation among many stakeholders, including academia, business, government, and civil society. It emphasizes the crucial part that these consensus frameworks play in offering moral direction for ethical AI innovation as well as deployment. This research acts as a crucial compass for decision-makers, developers, researchers, as well as ethicists in a world that is being progressively altered by AI. When society navigates this revolutionary technological environment, it reinforces the value of ethical concerns and cooperative efforts. It provides insightful information about the dynamic junction of AI and ML.

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