MIT researchers introduced the Lottery Ticket Hypothesis, showing that neural networks contain smaller subnetworks that can perform as well as the full model. Studies found that up to 90 percent of parameters can be pruned without reducing accuracy, meaning most of the network is redundant.
This pruning reduces model size, lowers compute cost, and improves inference efficiency. It also reveals that effective learning depends on specific “winning” subnetworks rather than the full architecture.
This insight has major implications for building efficient AI systems, enabling faster, cheaper, and more scalable deployment of large models across real world applications.
