Recent innovations in deep learning theory and implementation have enabled neural nets to achieve what was once unthinkable: beat humans at complex image recognition skills, safely pilot cars over chaotic road systems, and overwhelm Grandmaster Lee Sedol in the game of Go, a challenge previously thought immune to AI because of the gameâs near-infinite complexity. Clostra has applied a machine learning solution to automatically label/segment ML datasets. While training neural networks are computationally intensive and requires specialized hardware, execution is computationally inexpensive and can be implemented with very modest CPU and memory requirements. Phase 1 of the project determines feasibility by testing and training a sophisticated segmentation/localization/classifier allowing for large datasets to be automatically label for use in training neural networks
