Now, almost 25 years later, Facebook is gearing up to introduce a similar artificial intelligence system that could change the way you get dressed in the morning forever. Facebook AI researchers have created Fashion, an #AI tool that learns from sample images and then recommends easy changes to a person's outfit to make it more stylish. Fashion, Facebook's digital "style editor" for your everyday wardrobe choices, operates in a way that's more constructive criticism and less Simon Cowell. According to the official beta website, the program will make minimal edits to your'fit and suggest adjustments catered to what's considered stylish. "The system uses a discriminative fashionability classifier that is trained on thousands of publicly available images of outfits that have been judged to be stylish. These serve as ground truth examples of fashionable outfits, and unfashionable examples are then bootstrapped by swapping garments on the fashionable examples with their least similar counterparts. Once the classifier is trained, our system gradually updates the outfit in order to make it more fashionable. An image-generation neural network renders the newly adjusted look, using a variational auto-encoder to generate the silhouette and a conditional generative adversarial network (cGAN) to generate the color and pattern. The latent encodings learned by this generator are further used to identify which garments in its inventory will best achieve the style."

Every day, more than 1 million people in the United States require physical assistance to get dressed, whether because of injury, permanent disability, age, or other debilitating factors. To alleviate the problem, researchers from Georgia Tech built a deep learning-equipped robot that can help people get dressed. "What the robot is trying to do is to take the person's perspective of what a person is feeling during assistance," said Zachary Erickson, a robotics Ph.D. Student at Georgia Tech. "When the robot is doing this, it's using what it feels on its fingertips or its gripper and saying, what do I think a person is feeling while being dressed?" The robot, named PR2, was trained using NVIDIA Tesla V100 GPUs on the Amazon Web Services cloud with the cuDNN-accelerated Keras and TensorFlow deep learning frameworks.

If you're reading this article, chances are that you're pretty aware of the fact that many things are moving from the physical world to the digital one -- from editorial content to retail. But sometimes, those online experiences still leave something to be desired; a trend we've noticed over the past couple of years is retailers harnessing technology in an attempt to mimic the level of customer service and personalization you might get from a really good, attentive salesperson IRL. While shopping online is supposed to be convenient, it can often be overwhelming. Many online retailers boast tens or hundreds of thousands of brands and SKUs, and if you don't know exactly what you're looking for, and how it will fit you, the experience can be pretty frustrating. To mitigate that, retailers with the resources to do so are working to use data collection and, in some cases, AI to create more personalized shopping experiences -- i.e., showing you products it thinks you will like based on what you've purchased before, sort of like fashion's version of Spotify Discover Weekly or Apple Music's For You tab.

Building a quantum computer has been called the'space race of the 21st century' – a difficult and ambitious challenge, with the potential to deliver revolutionary tools. Now an invention by engineers in Australia may have brought us one step closer to achieving the goal. The team designed a new kind of quantum bit, which can retain information for 10 times longer than ever previously achieved. The new quantum bit, known as a'dressed qubit' has been designed by researchers at the University of New South Wales, and is made up of the spin of a single atom in silicon, merged with an electromagnetic field (artist's impression) Quantum computing takes advantage of the ability of subatomic particles to exist in more than one state at any time. In traditional computers, data is expressed in one of two states – known as binary bits – which are either a 1 or a 0. But quantum computers use quantum bits, or qubits.