DarwinAI
Assembly Line
Apple plans to automate 50% of iPhone assembly using AI and robotics
According to a new report, Apple is working on automating its iPhone assembly lines, aiming to reduce the human workforce by up to 50% in the coming years. This decision, driven by both operational challenges and the pursuit of greater efficiency, has far-reaching implications for the tech giant’s supply chain and the global labor market.
Apple’s automation strategy involves reviving and investing heavily in previously shelved projects due to high upfront costs. These efforts have already started to bear fruit, particularly in the production of the iPhone 15, where a significant amount of the assembly process has been automated. Peter Thompson, Apple’s vice president of operations, has been pivotal in these efforts, working closely with manufacturing partners like Foxconn, Luxshare Precision, and Pegatron.
Apple’s automation efforts have been bolstered by strategic acquisitions, such as DarwinAI and Drishti. DarwinAI specializes in inspecting components like printed circuit boards for defects, while Drishti provides technology to identify production bottlenecks in real-time. These acquisitions have enhanced Apple’s capability to implement and manage automated processes more effectively.
Apple Buys Canadian AI Startup as It Races to Add Features
Apple Inc. has acquired Canadian artificial intelligence startup DarwinAI, adding technology to its arsenal ahead of a big push into generative AI in 2024. The iPhone maker purchased the business earlier this year, and dozens of DarwinAI’s employees have joined Apple’s artificial intelligence division.
DarwinAI has developed AI technology for visually inspecting components during the manufacturing process and serves customers in a range of industries. But one of its core technologies is making artificial intelligence systems smaller and faster. That work that could be helpful to Apple, which is focused on running AI on devices rather than entirely in the cloud.
Deep Learning Boosts Robotic Picking Flexibility
Gripping and manipulating items of diverse shapes and sizes has long been one of the biggest challenges facing industrial robotics. The difficulty is perhaps best summed up by the Polanyi Paradox, which states that we “know more than we can tell.” In essence, while it may be easy to teach machines to exhibit a high level of performance on tasks that require abstract reasoning such as running computations, it is substantially harder to grant them the sensory-motor skills of even a small child in all but the most standardized and predictable environments.