In the early 2000s, Eduardo Arino de la Rubia worked as a contractor for a number of manufacturing companies in the United States, specializing in automation technologies and working with an engineering team that built conveyors, tooling, and machines. A constant thread in the customers and engagements was a desire by manufacturers to build “smart” systems. Factories owned by global conglomerates faced constant pressure from corporate headquarters and offshore or South American divisions to ruthlessly cut costs while retaining high quality. Whether the pressure and competition came from inside or outside, the message was the same: you must do more with less.
During this time, Eduardo implemented a number of solutions at a wide range of manufacturers using a broad array of machine-learning approaches. Genetic algorithms for job shop scheduling, classifiers for automated component quality assurance, vision systems for defect tracking, neural networks for RFID signal disambiguation—a wide assortment of ML techniques found their way into factories. The goal was survival of the factory, not forever, but for a little bit longer. The workers at these factories knew time was limited and competition was fierce, and they would often work side by side with Eduardo, helping teach the algorithms everything they could about the job. Often, however, when someone who had spent their entire lives mastering a process saw a piece of software better them just moments after being trained, their reactions were darker.
Eduardo discusses the repercussions of injecting advanced techniques such as ML and AI into decidedly blue-collar manufacturing environments and explains how a workforce reacted to the knowledge that a machine understood their jobs often better than they did. Eduardo concludes by exploring what might be coming for a large number of the jobs that remain unautomated. . .for now.
Eduardo Arino de la Rubia is chief data scientist at Domino Data Lab. Eduardo is a lifelong technologist with a passion for data science who thrives on effectively communicating data-driven insights throughout an organization. He is a graduate of the MTSU Computer Science Department, General Assembly’s Data Science Program, and the Johns Hopkins Coursera Data Science Specialization. Eduardo is currently pursuing a master’s degree in negotiation, conflict resolution, and peacebuilding from CSUDH. You can follow him on Twitter at @earino.
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