“We don’t just want this to be an academically interesting result – we want it to be used in real treatment. So our paper also takes on one of the key barriers for AI in clinical practice: the “black box” problem. For most AI systems, it’s very hard to understand exactly why they make a recommendation. That’s a huge issue for clinicians and patients who need to understand the system’s reasoning, not just its output – the why as well as the what. Our system takes a novel approach to this problem, combining two different neural networks with an easily interpretable representation between them. The first neural network, known as the segmentation network, analyses the OCT scan to provide a map of the different types of eye tissue and the features of disease it sees, such as haemorrhages, lesions, irregular fluid or other symptoms of eye disease. This map allows eyecare professionals to gain insight into the system’s “thinking.” The second network, known as the classification network, analyses this map to present clinicians with diagnoses and a referral recommendation. Crucially, the network expresses this recommendation as a percentage, allowing clinicians to assess the system’s confidence in its analysis”.
“Once, the Internet was fun. It’s time to move on. We’ve built up archives of our past selves online over the years — tweets, social media, message-board posts, live journals or, ahem, deadjournals. And, increasingly, those past selves have become liabilities. Multiple professional baseball players have now apologized for ugly old tweets containing racist and anti-gay slurs. The tweets, written while they were teens, resurfaced online”.
“Mr. Vassilev, 32, does not provide the views himself. His website, 500Views.com, connects customers with services that offer views, likes and dislikes generated by computers, not humans. When a supplier cannot fulfill an order, Mr. Vassilev — like a modern switchboard operator — quickly connects with another. “I can deliver an unlimited amount of views to a video,” Mr. Vassilev said in an interview. “They’ve tried to stop it for so many years, but they can’t stop it. There’s always a way around.””
“Lyu says a skilled forger could get around his eye-blinking tool simply by collecting images that show a person blinking. But he adds that his team has developed an even more effective technique, but says he’s keeping it secret for the moment. “I’d rather hold off at least for a little bit,” Lyu says. “We have a little advantage over the forgers right now, and we want to keep that advantage.””
“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”.
“Josh Mitchell, qui travaille pour la société Nuix, a testé des modèles construits par cinq entreprises (Vievu, Patrol Eyes, Fire Cam, Digital Ally, et CeeSc) et y a découvert d’importantes failles de sécurité. Chez les cinq constructeurs, il a démontré qu’il était possible d’obtenir des informations sensibles, comme la géolocalisation des caméras ou leur identifiant unique. Quatre des modèles testés comportent également des failles qui peuvent permettre, à distance, d’accéder aux images filmées, de les supprimer ou de les modifier à l’insu de l’utilisateur. Dans certains cas, il est également possible pour un pirate de consulter en direct les images filmées par la caméra”.
“Thanks to the technology supported by the architecture, including the new DLAA anti-aliasing tech, Turing can improve rendering times of real-time ray tracing over Pascal “by a factor of six.””
“Bridle conclu son propos en rappelant les propos de l’écrivain suédois, militant de la non-violence, Sven Lindqvist qui s’adressait ainsi à ses lecteurs en évoquant les violences coloniales : « Vous en savez assez. Moi aussi. Ce n’est pas la connaissance qui nous manque. Ce qui nous manque, c’est le courage de comprendre ce que nous savons et d’en tirer les conclusions ». De combien de lanceurs d’alertes aurons-nous besoin pour nous rendre compte qu’il y a quelque chose de pourri au royaume de la Silicon Valley ?”
“Social Mapper is a Open Source Intelligence Tool that uses facial recognition to correlate social media profiles across different sites on a large scale. It takes an automated approach to searching popular social media sites for targets names and pictures to accurately detect and group a person’s presence, outputting the results into report that a human operator can quickly review”.
“Ce compte, dont l’activité frénétique peut faire penser à celle d’un robot, semble bien tenu par un humain, une humaine en l’occurrence : contactée, l’auteure explique consacrer beaucoup de temps à son activité sur Twitter, et notamment à relayer des informations issues d’autres comptes.
Une vérification plus poussée de son activité et de ses échanges tend à confirmer qu’il s’agit bien d’une personne réelle et non d’un robot ou d’un espion russe. On obtiendra la même réponse en se penchant sur plusieurs possesseurs de compte, souvent « fiers », d’ailleurs, du rythme de leurs retweets, là encore, en général plusieurs centaines par jour”.
