福特公司雇佣了人工智能,解雇了人类员工。结果适得其反。
阅读原文· the-independent.com福特因激进采用AI质检系统导致成本损失数十亿美元,三年内返聘350多名资深工程师(内部称“gray beards”),负责质量审查并帮助改进AI。首席运营官Kumar Galhotra承认自动化系统未达预期,经验丰富的工程师能预先发现故障点。返聘后,福特在J.D. Power年度新车质量调查中16年来首次获得主流品牌排名第一。公司表示不会放弃AI,但未来将结合人类监督与经验使用该技术。
Ford has admitted to rehiring hundreds of human workers after its aggressive AI adoption strategy backfired.
The US automaker hired over 350 veteran engineers, referred to internally as “gray beards”, over the past three years in order to address mistakes made by automated systems.
The staff will lead quality reviews after the automation issues cost the company billions of dollars, Bloomberg reported, while some workers will also help improve and train the AI systems.
“We had been relying more and more on automated quality systems and not getting the desired results,” said Kumar Galhotra, Ford’s chief operating officer.
“We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor.”
Ford had been increasingly relying on AI-driven inspection systems to streamline production and address quality control issues, however the firm acknowledged that AI lacked the nuanced judgement when it came to complex problems.
After rehiring experienced engineers, Ford experienced a marked improvement in its quality standards.
According to the latest J.D. Power Initial Quality Survey, an annual automotive benchmark that measures the quality of new vehicles, Ford ranked top among mainstream brands – the first time it has achieved that milestone in 16 years.
Ford continues to have quality issues with its older vehicles, and remains the most recalled automaker in the US, though executives blamed this on past issues involving automation, rather than the rehiring of humans.
The company said it would not abandon its use of AI, but plans to now use it in conjunction with human oversight and experience.
“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” said Charles Poon, Ford’s vice president of vehicle hardware engineering.
“Over prior years, we didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles.
“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product.”