Google no longer understands how its “deep learning” decision-making computer systems have made themselves so good at recognizing things in photos.
This means the internet giant may need fewer experts in future as it can instead rely on its semi-autonomous, semi-smart machines to solve problems all on their own.
The claims were made at the Machine Learning Conference in San Francisco on Friday by Google software engineer Quoc V. Le in a talk in which he outlined some of the ways the content-slurper is putting “deep learning” systems to work.
"Deep learning" involves large clusters of computers ingesting and automatically classifying data, such as pictures. Google uses the technology for services like Android voice-controlled search, image recognition, and Google translate, among others. […]
What stunned Quoc V. Le is that the machine has learned to pick out features in things like paper shredders that people can’t easily spot – you’ve seen one shredder, you’ve seen them all, practically. But not so for Google’s monster.
Learning “how to engineer features to recognize that that’s a shredder – that’s very complicated,” he explained. “I spent a lot of thoughts on it and couldn’t do it.” […]
This means that for some things, Google researchers can no longer explain exactly how the system has learned to spot certain objects, because the programming appears to think independently from its creators, and its complex cognitive processes are inscrutable. This “thinking” is within an extremely narrow remit, but it is demonstrably effective and independently verifiable.
Will we ever know the truth about the Kennedy assassination? In a film by Errol Morris, Josiah “Tink” Thompson returns to what has haunted him for 50 years: Frame #313 of the Zapruder film.
A gif of characters from my new print: http://www.tomgauld.com/index.php?/shop/noisy-alphabet-print/
"Their collection not only includes great images but also data about each image," Pinterest product manager Michael Yamartino wrote in a blog post. "This can include who took the image, when, where, and what’s in the picture. We think this will be really valuable, especially when pin descriptions and links are not as helpful as we’d hope."
Imagine the writer as a meme machine, writing works with the intention for them to ripple rapidly across networks only to evaporate just as quickly as they appeared. Imagine a poetry that is vast, instantaneous, horizontal, globally distributed, paper thin, and, ultimately, disposable.
Amazon must rely on barcodes and human hands to find the ordered items and drop them into the proper bins — without robots, Amazon utilizes a system known as “chaotic storage,” where products are essentially shelved at random.
By storing items randomly instead of categorically, the warehouse has a much better flow of material. Even without robots or automation, Amazon can compile a “picking list” that locates where each item needs to be taken off the shelf and scanned again before it can be shipped.
The real advantage to chaotic storage is that it’s significantly more flexible than conventional storage systems. If there are big changes in a product range, the company doesn’t need to plan for more space, because the products or their sales volumes don’t need to be known or planned in advance if they’re simply being stored at random.
Furthermore, free space is much better utilized in a chaotic storage system. In a conventional system, free space may go unused for quite a while simply because stock is low or there aren’t enough products to begin with. Without any kind of fixed positions, available shelf space is always being used.