Creating Sparse, Multitask Neural Networks

The use of Machine Learning (ML) has become increasingly central to the acquisition and interpretation of data. As improved methodologies for training ML models are created, the performance of models trained with these methodologies are inevitably compared to the field's moonshot: Artificial General Intelligence. However, today's models fall far short of the ability to generalize robustly on multiple tasks, let alone be fully generalizable. To address the shortcomings of modern ML architectures, we've turned for inspiration to the human brain. Specifically, we sought to emulate the sparse, hierarchical structure of the human cortex, which enables the flexible learning and multitask performance that we humans are lucky enough to possess.