Sparsity in AI involves the deliberate reduction of data or model complexity to increase computational efficiency and reduce storage requirements. This concept is pivotal in designing models that are both computationally inexpensive and memory efficient, particularly valuable in environments with limited hardware resources. Sparse representations are often used in machine learning models to highlight the most essential features of data, thereby improving model performance by focusing on the most relevant information. In neural networks, sparsity can be introduced through techniques such as pruning, where less important connections are removed, or by employing sparse activations, where most neurons remain inactive (zero output) at any given time.
The concept of sparsity has been prevalent in computational fields since the early days of machine learning, but it gained significant traction in the AI community around the 2000s with the advent of large-scale machine learning and the need for more efficient computations.
While many researchers have contributed to the development and application of sparsity in AI, notable figures include Stephen Boyd, whose work on convex optimization has laid theoretical foundations for sparse methods, and Yann LeCun, who has advocated for model compression and sparsity for efficient neural network design.