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Despite the awe-inspiring rate at which the technology and consumer electronics industries have grown and adapted over the years, the vast majority of products (including our iPhones and iPads) are still being built almost entirely by hand, according to one former Apple product design engineer, who recently opened up about her time working with the company in an exclusive interview. Moreover, she shared her personal experiences and applied them to how “modern manufacturing” and artificial intelligence-based machine learning will play a pivotal role in “revolutionizing factories” around the world.
Anna-Katrina Shedletsky, who during her tenure at Apple played an integral role in the development of products like the original Apple Watch, several generations of iPod, and more, indicated in her interview with Cultofmac’s Leander Kahney how she believes that AI-based machine learning will shortly begin shaking up the manufacturing sector as we know it. She argues that while the majority of electronic devices are still built by hand on Far East assembly lines, the days of “assembly by hand” are essentially numbered due to what she calls the impending “sea of change” wrought by robotics and machine learning.
Shedletsky argued that companies like Apple, in particular, would ultimately stand to benefit the most by “modernizing their manufacturing processes.” For example, by incorporating robotics and machine learning algorithms, she waged the case that companies like Apple can greatly improve the efficacy, accuracy, and consistency with which their new products are built, from start to finish.
Despite her optimistic overtures of a fully automated manufacturing future, however, Shedletsky acknowledge there are currently a myriad of obstacles standing in the way of that reality. Most notably, she outlined a number of issues concerning the advancement of products from prototype to production units, and how a “big part” of working out the kinks in a product is actually sampling and experimenting with it by hand before launching into large scale production. By blending certain aspects of software and hardware integration, however, current and future product design engineers will one day be able to “virtually disassemble” any problematic units — while advanced machine learning can dichotomously be used in the process of manufacturing to help learn errors so they don’t repeat themselves.
Of course, even when AI and machine learning begin taking over assembly lines, Shedletsky notes there will still be other challenges for the field of manufacturing, as a whole, to overcome. These include how to manage larger factories such as those currently in operation by Foxconn — Apple’s primary iPhone assembly partner. She noted that, at present, Foxconn is running like a small “city,” which is effectively built around the factory and its vast workforce, and specifically how bringing automation to the table might disrupt processes currently in place.
Still, while challenges may persist, Shedletsky remains optimistic that the benefits of automation significantly outweigh the hurdles standing in its way.