The advertising world loves big, shiny, techy things. Agency and client ears perk right up when they hear about virtual reality kiosks, gadget-filled activations and holograms of dead rock stars. But then there are the tech innovations that sound a bit, or a lot, less sexy. Deep learning is a subset of machine learning that essentially teaches computers to find patterns in sounds, images and other data. And while that may not seem like much fun to your average social marketer or copywriter, the tech giants--the Facebooks, Apples, Googles, Netflixes, Microsofts and Baidus of the world--are investing massive sums of money in it.

Microsoft is making some bold promises with Story Remix, its recently announced app for the Windows 10 Fall Creators update. Together with the company's deep learning technology, it can automatically craft your photos and videos into short films. Story Remix resembles Apple Clips and Google's Photo Assistant, but it goes a bit farther with the ability to analyze everything on a pixel level-basis to detect people, objects and the overall setting. If it works as advertised, it could be a transformational app for consumers fed up with their ever-growing libraries of digital media. It's the latest attempt by Microsoft to make your life easier by predicting what you want.

Given the ubiquity of fakes among re-sellers, buyers often examine pre-owned fashion to deduce authenticity, often analyzing the stitching, font size and interior labels. But sometimes, a copy is just so well-made that the human eye can't tell it from the original. Entrupy is a portable scanning device that instantly detects imitation designer bags by taking microscopic pictures that take into account details of the material, processing, workmanship, serial number, and wear/tear. It then employs the technique of deep learning to compare the images against a vast database that includes top luxury brands and if the bag is deemed authentic, users immediately get a Certificate of Authenticity. After launching as a paid service in September 2016, the New York-based venture now has over 130 paid customers, almost all of whom are American businesses drawn to the 97.1 percent accuracy rate, explained Entrupy CEO Vidyuth Srinivasan.

If you give a computer enough photos and the right algorithm, it can learn to see. And if the photos show damaged eyes, the computer can learn to diagnose eye disease even better than humans can. People with diabetes frequently suffer from a condition called diabetic retinopathy, where the tiny blood vessels at the back of their eyes (the retina) become damaged and start to leak. About one in three diabetics have this kind of damage, and if left untreated it can cause permanent blindness. The problem is that many people don't have access to an ophthalmologist who can diagnose them.

Translating from one language to another is hard, and creating a system that does it automatically is a major challenge, partly because there are just so many words, phrases and rules to deal with. Fortunately, neural networks eat big, complicated data sets for breakfast. Google has been working on a machine learning translation technique for years, and today is its official debut. The Google Neural Machine Translation system, deployed today for Chinese-English queries, is a step up in complexity from existing methods. Here's how things have evolved (in a nutshell). A very simple technique for translating -- one a kid or simple computer could do -- would be to simply look up each word encountered and switch it with the equivalent word in another language.

Your speech-recognizing friends at Nuance are back with a major update to their flagship app, Dragon. The popular productivity software is now in its 15th version, an update that promises some substantial improvements in accuracy courtesy of the company's own deep learning tech, which forms the basis of its speech engine. According to Nuance, this latest upgrade brings better accuracy "upwards of 24 percent," with improved ability to recognize and learn accents and voice patterns, while adapting to the acoustics of the speaker's environment. Here's a quote from the company's CTO, "Training such Deep Neural Net models typically requires large amounts of training data and a high-performance computing environment. However, our new Dragon portfolio includes our latest breakthrough that allows Dragon's Deep Neural Nets to continuously learn from the user's speech during use on a standard personal computer, and drive accuracy rates in some instances up to 24 percent higher." Version 15 also brings a simplified UI, formatting improvements, more support for audio transcription and optimization for a number of touchscreen Windows PCs.