{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/118508"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/118508","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"How and why robotics automate work : analyzing automation of tasks using machine learning suitability assessment metric","abstract":"As we are at the beginning of the Second Machine Age, where Al, Machine Learning, and Robotics technologies are increasingly influencing this revolution, we are experiencing significant automation changes in many industries such as warehousing and distribution centers. Many of the jobs in these industries aren't just being transformed but also partially or fully automated, often replacing the lowest skilled workers. Even though the core technologies driving automation today are improving exponentially, there are still many areas where human workers exceed and thrive. Some of the jobs might be automated, but there are some tasks which prove to be difficult for machines to perform. The research tries to understand how technology is automating tasks within warehousing jobs right now? By applying rigorous metrics, developed by Erik Brynjolfsson and Tom Mitchell to jobs within warehouses, the thesis aims to show which tasks within these jobs have the highest suitability for machine learning and robotics automation. The research includes the analysis of the not automated tasks and the possible reasons and opportunities for automation.","abstract_html":"As we are at the beginning of the Second Machine Age, where Al, Machine Learning, and Robotics technologies are increasingly influencing this revolution, we are experiencing significant automation changes in many industries such as warehousing and distribution centers. Many of the jobs in these industries aren't just being transformed but also partially or fully automated, often replacing the lowest skilled workers. Even though the core technologies driving automation today are improving exponentially, there are still many areas where human workers exceed and thrive. Some of the jobs might be automated, but there are some tasks which prove to be difficult for machines to perform. The research tries to understand how technology is automating tasks within warehousing jobs right now? By applying rigorous metrics, developed by Erik Brynjolfsson and Tom Mitchell to jobs within warehouses, the thesis aims to show which tasks within these jobs have the highest suitability for machine learning and robotics automation. The research includes the analysis of the not automated tasks and the possible reasons and opportunities for automation.","abstract_has_math":false,"creators":["Witoszko, Izabela"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Engineering and Management Program","school":null,"contributors":[],"advisors":["Erik Brynjolfsson and Julie A. Shah."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:22:26Z","subjects":["Engineering and Management Program.","Integrated Design and Management Program."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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