The "little black dress" has been considered a staple in women's fashion since the designer Coco Chanel popularized it in the 1920s. Since then, it's seen many iterations, most recently, by machine-learning software developed by two recent MIT graduates, called Glitch. Pinar Yanardag and Emily Salvador met at MIT while taking a course called "How to Generate (Almost) Anything," which encouraged students to use deep learning software for creative projects. In that course, they dabbled with creating AI-generated art, perfume and jewelry, and were inspired to start Glitch, a new clothing company that sells pieces designed by AI. "The'little black dress' is considered an essential item that should be in any woman's wardrobe," said Yanardag. "Sooner or later, AI is going to be an essential tool for any person in computing, so we thought the'little black dress' was a good place to start."

A couple weeks ago we discussed how to perform multi-label classification using Keras and deep learning. Today we are going to discuss a more advanced technique called multi-output classification. And how are you supposed to keep track of all these terms? You can even combine multi-label classification with multi-output classification so that each fully-connected head can predict multiple outputs! If this is starting to make your head spin, no worries -- I've designed today's tutorial to guide you through multiple output classification with Keras. It's actually quite easier than it sounds. That said, this is a more advanced deep learning technique we're covering today so if you have not already read my first post on Multi-label classification with Keras make sure you do that now. From there, you'll be prepared to train your network with multiple loss functions and obtain multiple outputs from the network.

All women will be familiar with that rush of excitement that comes when you lay eyes on the perfect LBD or pair of heels. But what if technology could harness that instinctive surge of emotion to help you make the right purchase - without so much as a click of a button? The unstoppable rise of online shopping means we are overwhelmed by choice - a search for'black dress' turns up 35 million results - but increasingly, retailers are using neuroscience to predict our needs and help us filter through the myriad options available at our fingertips. Eventually, online retailers may even be able to automatically place items into our shopping cart based on our brain signals. Now, the site has partnered with brain technology company MyndPlay, using electroencephalogram (EEG) headsets to research the effects of shopping on the brain.

Just under a year after their $12.5 million Series A funding round, Israeli Artificial Intelligence for ecommerce startup Twiggle announced today the release of their Semantic API product, bringing their accumulated expertise in search to the wider online shopping market. Twiggle was co-founded in December of 2013 by CEO Dr. Amir Konigsberg, previously one of the members of Google's emerging markets operations, and Dr. Adi Avidor, a former engineering tech lead at Google. In the time since their Series A, they have picked up another $5 million from Alibaba, doubled their team, and moved shop to new offices overlooking Tel Aviv. Describing what they have built in short, this company has made search for ecommerce usable to the point that it becomes a near enjoyable experience. Their engine processes through lists of products, understanding the attributes that make them what they are.

As product catalogs continue to get larger and larger, e-commerce sites need to provide shoppers better ways to navigate through their inventory. One of the most common methods applied right now is product tagging. Generally, product tagging operations are completed by huge teams of interns or crowdsourcing providers. Those people are given instructions and definitions and set off to tag various characteristics of every individual product in a site's catalog. In essence, these tags add additional metadata to a product database which in turn give users more ways to find they're looking for.