SmartBell: Weight Tracking and Augmentation; Artificial Intelligence and Machine Learning: Framework for Underregulated Technology
Kakeh, Hamza, School of Engineering and Applied Science, University of Virginia
Neeley, Kathryn, EN-Engineering and Society, University of Virginia
Powell, Harry, EN-Elec/Computer Engr Dept, University of Virginia
For an occupation whose median wage is below twenty dollars an hour, the probability that career could be automated is greater than eighty percent. This problem will cause the largest trade adjustment period in modern history. The STS topic is loosely coupled to the technical topic as data collection and analysis are logical next steps for the technical project. My technical project produced a device to provide dynamic feedback on a user’s form while performing an athletic lift as well as recording statistics about repetitions, sets, idle time, active time, and calories burned. My STS research explores how artificial intelligence (AI) and machine learning (ML) are distinctive amongst technological innovations and analyzes their effects on labor in order to establish a definitive course of action that does not result in income inequality post labor reconstruction.
The technical portion of my thesis was an exercise in the minimization of resources in manufacturing and construction as well as the efficiency in power use, size, and economic feasibility. In concept, the device, or SmartBell, would attach to any barbell unobtrusively and record statistics for the user as well as provide instantaneous feedback on balance and orientation of the weights relative to the ground. This meant designing as small a circuit board as possible and using a microprocessor and sensors capable of tracking motion and recording a sizeable set of data in memory, all on a low power battery pack. The entire apparatus was housed in a ten square inch 3D printed and custom designed case. The SmartBell reduced the total cost of production below the market price of similar products and produced a working prototype in a single-semester capstone class directed by Professor Harry Powell.
While loss of labor in inevitable when labor is automated, profits created by the advances in a given field are often reintroduced to the economy, resulting in demand for new products and services. However, due to the unknown pace of this process, movement of capital is not easily predictable. My analysis of AI and ML as underregulated technologies explored private sector engagement in innovation and possible avenues to mitigate trade readjustment as well as use the near limitless profitability of their implementation to support education and cultivation of post fourth industrial revolution labor viability. The current speed of development and adoption of AI and ML poses challenges to policymakers but maintaining the status quo for regulating these transformative technologies and not fostering skills for future jobs through improved education will only stifle innovation and restrain a possible evolution of societal norms.
My STS and technical research projects combined to illustrate the pervasiveness of AI and ML in modern technical industries. The technical project poses a design process for innovation aimed at future ML integration, but creates a common ethical dilemma of job displacement. This dilemma was fully explored in the STS research analyzing popular innovations such as autonomous vehicles and their preeminent challenges to government regulatory regimes, engineering codes of ethics for operation, and possible career replacement. There must be navigable paths to new and useful trades for the inevitably displaced work force. Without such considerations to the social impacts as well as the potential economic benefits, AI and ML innovation could be more destructive than productive. Imperatively, the workforce and the individuals in any system must be malleable actors, tailored to current labor and societal necessities and empowered by social programs insuring a twenty first century standard of living.
BS (Bachelor of Science)
Social Construction of Technology, Underregulated Technology, Artificial Intelligence, Machine Learning, Income Inequality, Automation
School of Engineering and Applied Sciences
Bachelor of Science in Computer Engineering
Technical Advisor: Harry Powell
STS Advisor: Kathryn Neeley
Technical Team Members: XiaoChuan Ding, Nathan Park, Daniel Wu, Kevin Zheng
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