Linear algebra is fundamental to artificial intelligence, especially in fields such as machine learning, computer vision, and deep learning. It deals with vectors, matrices, and linear transformations, providing the mathematical framework for optimizing and solving equations in high-dimensional spaces. Key operations like matrix multiplication, determinant calculation, eigenvalues and eigenvectors, and singular value decomposition are critical for algorithms that perform tasks such as image recognition, natural language processing, and predictive modeling. The ability to handle large, complex datasets and perform operations efficiently in these spaces makes linear algebra indispensable in the AI toolkit.

Historical overview: The development of linear algebra as a coherent discipline dates back to the mid-19th century, though many of its concepts were used implicitly in earlier mathematical works. It gained significant importance in the 20th century as computers enabled the practical application of its theories to problems in physics, engineering, and, subsequently, computer science.

Key contributors: Significant figures in the development of linear algebra include Arthur Cayley and James Joseph Sylvester in the 19th century, who introduced matrix theory that later became central to the subject. More contemporary applications to AI and machine learning have been shaped by researchers in computer science and statistics, though no single individual dominates this vast field's contribution to AI.