“Engineers at the tech giants built tools years ago that could put a name to any face but, for once, Silicon Valley did not want to move fast and break things.”
Source : The Technology Facebook and Google Didn’t Dare Release – The New York Times
Étiquette : image recognition (Page 1 of 2)
“With a unified model for a large number of languages, we run the risk of being mediocre for each language, which makes the problem challenging. Moreover, it’s difficult to get human-annotated data for many of the languages. Although SynthText has been helpful as a way to bootstrap training, it’s not yet a replacement for human-annotated data sets. We are therefore exploring ways to bridge the domain gap between our synthetic engine and real-world distribution of text on images”.
Source : Rosetta: Understanding text in images and videos with machine learning – Facebook Code
“Built by creative agency Redpepper, There’s Waldo zeroes in and finds Waldo with a sniper-like accuracy. The metal robotic arm is a Raspberry Pi-controlled uArm Swift Pro which is equipped with a Vision Camera Kit that allows for facial recognition. The camera takes a photo of the page, which then uses OpenCV to find the possible Waldo faces in the photo. The faces are then sent to be analyzed by Google’s AutoML Vision service, which has been trained on photos of Waldo. If the robot determines a match with 95 percent confidence or higher, it’ll point to all the Waldos it can find on the page”.
Source : This robot uses AI to find Waldo, thereby ruining Where’s Waldo – The Verge
«We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class».
«In (very, very unpublishable) clips shared with Engadget, the system was able to identify both the names of the performers in a scene, and what they were doing. Tags such as « blowjob, » « doggy, » « cowgirl, » and « missionary » floated on screen with the corresponding action. The system is also capable of, for instance, identifying blonde performers and adding the requisite tags».
« White-Collar Offender Tends to have a low self-esteem, a high IQ and charisma. Anxious, tensed and frustrated, competitive, ambitious and dominant. Usually loves to take risks and have a dry sense of humor » – Description of White-Collar Offender…
Source : FACEPTION | Our Technology
“We don’t know whether it knows something that we haven’t noticed, or if it’s just plain wrong,”
Source : Meet Penny, an AI That Predicts a Neighborhood’s Wealth From Space | WIRED
« Commencez par dessiner une forme, puis le logiciel prendra la relève pour compléter lui-même votre dessin. Grâce à ses expériences précédentes, le logiciel cherchera à prédire quel est l’objet que vous avez cherché à dessiner ».
Source : Ce réseau neuronal développé par Google anticipe vos dessins – Tech – Numerama
« Learned relationships between abstract concepts, explored using latent vector arithmetic ».
Our checkout-free shopping experience is made possible by the same types of technologies used in self-driving cars: computer vision, sensor fusion, and deep learning. Our Just Walk Out technology automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt.
Source : Amazon.com: : Amazon Go