Gesture Controller Smartwatch; AI Automation and the Displacement of Human Jobs

My STS thesis explores the capabilities of current AI technologies, namely in machine and deep learning, and their potential to disrupt many human roles in the workforce by offering superior accuracy and efficiency in the automation of tasks. I analyze both positive and negative economic forces on employment observed in previous waves of technological automation, and reevaluate the extents of their influence in context of recent AI innovations. I also present the theory that most skills and knowledge which are widely considered exclusive to human workers may actually be in danger of being automated by emerging AIs, putting the future value of human labor into question. Alternatively, I also discuss glaring social issues and technical limitations of current machine learning AIs, supporting the continued need for human involvement in certain instances of machine automation.

My ECE Capstone project consists of designing a wearable wristwatch device that tracks the motion of hand and arm gestures in order to remotely control devices via Bluetooth. By constantly sensing gyroscopic and accelerometer data, directional gestures such as hand waves can be detected as corresponding commands on a Bluetooth-paired device, such as advancing through a slideshow. Since there is no machine learning or AI involved in the device’s software, it is not intended to be related to my STS topic in any way. Still, such a technology could arguably be considered as a case opposing machine automation, as its value is based on the niche use of natural body motions to perform particular tasks. However, this is only one specific and narrow context of human engagement, which is mostly irrelevant to my thesis’s focus on the cognitive fundamentals of productivity.

School of Engineering and Applied Science

Bachelor of Science in Computer Engineering

Technical Advisor: Harry Powell

STS Advisor: Michael Gorman

Technical Team Members: Pearak Tan, Edward Ryan