As we train more and more AI models on "big" data, that bigness starts to become a curse. If a dataset is big enough, we can spot-check it at best. Sometimes, people don't even do that. This makes it extremely dangerous to work with large datasets
Eisenhower and his ambitious interstate highway system transformed the US into a nation with infrastructure primarily designed around cars and large machinery. As we roll out more autonomous vehicles and implement machine vision more broadly in public spaces, we’re going to again redefine who public spaces are designed for.
This is slightly old news by now, but owners of older Haas machining centers are revolting over lack of product support on older tools (like, 90s and aughts). I have some sympathy for Haas here, but I’m also aware that manufacturers of heavy equipment like Caterpillar and John Deere
This one is back from 2009 and not entirelty a bug, but still relevant and came in up conversation recently (I’m working with a team to implement an ERP at my day job): Shane Co. signed a contract with SAP AG in 2005 for a “highly sophisticated point-of-sale and
I went out to a Chinese restaurant with a friend and their back-of-house online order processing setup was in the middle of the hallway, right across from the bathroom. Small New York restaurants, amiright? They had the whole wall covered in different iPads and android tablets. Every company they worked