Amputation is a leading cause of disability, and prosthetic devices are commonly accepted treatment options to restore functional capabilities. However, current prosthetic devices still cannot fully match the functionality of their natural counterparts. This talk focuses on the progress made in the development and control of bionic limbs for individuals with limb loss. The first portion of the talk provides an overview of the development, testing and commercialization of pattern recognition control systems for prosthetic arms, including their operation with advanced surgical techniques, such as targeted muscle reinnervation. A significant emphasis of this work has been on evaluation based on real user feedback, ensuring that the developed technologies meet the actual needs and preferences of end users. The second portion focuses on the application of these approaches (ie statistical pattern recognition and finite state-machines) to controlling powered leg prostheses. Finally, I will discuss our recent work in using deep-learning coupled with benchmark datasets (some collected at Georgia Tech) to remove the reliance of finite-state machines from our overall control approach.