Authors : Raviraju Balappa D; Dr. Gautam Kumar Rajput

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/3dc86j6x

Scribd : https://tinyurl.com/53n75se7

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV378

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Matrices are foundational to artificial intelligence (AI), serving as critical tools for data representation, manipulation, and transformation across various applications. From machine learning algorithms to neural network architectures, matrix theory supports essential computational processes, enabling AI systems to manage vast datasets, detect intricate patterns, and execute complex transformations. This paper examines the integral role of matrices in AI, highlighting basic matrix operations in linear and logistic regression, as well as their applications in more advanced models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Key mathematical operations, including matrix decomposition and eigenvalue computations, are explored for their significance in data reduction and feature extraction, which enhance computational efficiency in fields like computer vision, natural language processing (NLP), and robotics. The paper also addresses the computational challenges associated with large-scale matrix operations, such as high-dimensional data processing, scalability, and numerical stability. To overcome these limitations, advancements in distributed matrix computation frameworks, GPU and TPU hardware acceleration, and sparse matrix techniques are discussed, showing how these innovations enhance the efficiency and scalability of AI models. Additionally, recent progress in quantum computing and matrix-specific hardware solutions offers promising directions for future research, with potential to revolutionize AI by achieving exponential speed-ups in matrix computations. Overall, matrices remain at the heart of AI’s computational power, providing a versatile and efficient framework that supports both current applications and emerging capabilities in artificial intelligence.

Keywords : Matrix theory, linear algebra, machine learning, artificial intelligence, singular value decomposition (SVD).

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