Abstract
What are the sociotechnical implications of machine learning (ML)? ML has major implications in many fields, including e-commerce and transportation.
For the capstone paper, the writer of this paper made a proposal, which explores how tech companies use promises about ML, 5G, and other state-of-the-art technology to revive the optimism for autonomous vehicles. To pursue the paper’s goal, the writer plans to research, read, and gather information from credible media sources and articles to find reasons for the lack of trust in ML, its inherent danger, and the transformation it seeks to bring to e-commerce and transportation. In regard to the anticipated results, the research will help tech companies to streamline their production schedule, improve their apps, and ensure their apps and AVs are ready for e-commerce. Also, the anticipated results will provide a new meaning for how a driver is defined, allow engineers to learn new vehicle behavior to improve safety, highlight the ethical use of consumer data, and lay the foundation for policy-making to integrate AVs into Americans’ traditional modes of transportation and e-commerce. The capstone paper was very successful since the paper gave a detailed analysis and reached its goal.
Since 2018, as optimism about the near-term possibilities of so-called “autonomous vehicles” (AVs) has reduced, how have tech companies used promises about state-of-the-art technology to revive the optimism? Tech companies and automakers invoke machine learning (ML), 5G, lidar, and other state-of-the-art technology in an effort to sustain optimism about connected and automated vehicles. According to some companies, ML may finally make autonomous vehicles practical. Participants include automakers such as Honda and Tesla, tech companies such as Waymo and Aurora, and trade associations such as the Autonomous Vehicle Industry Association (AVIA), Partners for Automated Vehicle Education (PAVE), and the Coalition for Safe Autonomous Vehicles and Electrification (SAVE Coalition).
