International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 07 | July 2024
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p-ISSN: 2395-0072
Synergy of Artificial Intelligence and Mathematics: Revolutionizing Research and Discovery Chandrasekhar Karnam University of Southern California, USA ---------------------------------------------------------------------------***---------------------------------------------------------------------------
Abstract: This article explores the transformative impact of Artificial Intelligence (AI) on mathematical research and education. It examines various areas where AI has significantly contributed, including optimization, automated theorem proving, datadriven mathematics, AI-enhanced software, and collaborative platforms. The paper presents concrete examples and statistics demonstrating AI's ability to accelerate problem-solving, uncover new mathematical concepts, and democratize access to advanced mathematical tools. While highlighting the remarkable advancements, the article also addresses the challenges faced in integrating AI into mathematics, such as the interpretability of AI-generated proofs and potential biases in AI systems. At the end of the article, future directions for combining AI and mathematics are discussed. These include quantum-enhanced AI, cognitive AI for mathematical intuition, and the creation of ethical frameworks for AI use in mathematical research. Keywords: AI-Mathematics Integration, Automated Theorem Proving, Data-Driven Mathematical Discovery, AI-Enhanced Mathematical Software, Collaborative AI Platforms in Mathematics
1. Introduction: Integrating Artificial Intelligence (AI) into mathematical research has ushered in a new era of discovery and problem-solving. This synergy between AI and mathematics has led to groundbreaking advancements across various subdisciplines, challenging traditional approaches and accelerating mathematical innovation [1]. The impact of AI on mathematics has been profound and multifaceted, with significant progress observed in areas such as automated theorem proving, computational algebra, and data-driven mathematical modeling.
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