### Detailed Explanation:

Matrix multiplication (MatMul) involves the product of two matrices, resulting in a third matrix where each element is the dot product of corresponding row and column vectors from the input matrices. This operation is crucial in many fields, especially in AI and machine learning, where it underpins operations in neural networks, enabling the transformation and combination of input data through layers of neurons. Efficient implementation of MatMul is critical for the performance of AI systems, given its computational intensity. Optimizations like parallel processing on GPUs or specialized hardware like TPUs (Tensor Processing Units) are often employed to handle the large-scale matrix operations typical in deep learning models.

### Historical Overview:

The concept of matrix multiplication dates back to the 19th century, with significant formalization occurring in the early 20th century. The computational importance of MatMul became prominent in the mid-20th century with the advent of digital computers, and it gained further prominence with the rise of machine learning and neural networks in the late 20th and early 21st centuries.

### Key Contributors:

Key contributors to the development and optimization of matrix multiplication include mathematicians such as Arthur Cayley, who formalized matrix algebra in the 19th century, and computer scientists like Volker Strassen, who developed the Strassen algorithm in 1969, reducing the computational complexity of matrix multiplication. More recently, contributions from researchers at companies like Google have advanced the implementation of efficient MatMul operations on specialized hardware like TPUs.