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Ford has rehired approximately 350 veteran engineers, drawn both from former employees and supplier staff, after concluding that its growing reliance on automated AI-driven quality systems had failed to meet the company’s standards. Chief Operating Officer Kumar Galhotra said Ford had increasingly leaned on automated quality systems in recent years, but that the results fell […]
Read full articleThe US motor company found that the hundreds of AI cameras being used for design and manufacturing checks were prone to pitfalls Name: “Greybeards.” Age: There’s a clue in the name. Continue reading...
Ford has rehired approximately 350 experienced engineers after the company’s investment in AI and automated quality control systems failed to meet expectations, according to Bloomberg. In short, the technology did not detect enough problems. “We mistakenly believed that we could create a high-quality product simply by introducing artificial intelligence and inputting our design requirements,” Charles Poon, head of Ford’s hardware development, told Bloomberg. The rehired quality inspectors — known internally as “gray beard” engineers for their experience and years with the company — are now working to identify defects before components reach the factories. Still, Ford is not abandoning AI completely; the experienced engineers will be asked to help train younger employees and improve the company’s AI tools.
"Mistakenly we thought that by just introducing artificial intelligence ... that would produce a high-quality product.”
To celebrate its new status as No. 1 in JD Power's initial quality ranking among mainstream automakers, Ford is opening up about the challenges it has faced in recent years, especially around its reliance on automated systems in production and design. It turns out that those automated systems were not as robust as previously assumed, requiring Ford to hire experienced technicians - sometimes bringing back former employees - to correct errors made by the company's robots. In Ford's view, AI is both powerful and prone to pitfalls. Its effectiveness depends entirely on the quality of the data used to train the AI models. In addition, the auto … Read the full story at The Verge.
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