A team of researchers at Yonsei University and École Polytechnique Fédérale de Lausanne (EPFL) has recently developed a new technique that can recognize emotions by analyzing people's faces in images along with contextual features. They presented and outlined their deep learning-based architecture, called CAER-Net, in a paper pre-published on arXiv. For several years, researchers worldwide have been trying to develop tools for automatically detecting human emotions by analyzing images, videos or audio clips. These tools could have numerous applications, for instance, improving robot-human interactions or helping doctors to identify signs of mental or neural disorders (e.g.,, based on atypical speech patterns, facial features, etc.). So far, the majority of techniques for recognizing emotions in images have been based on the analysis of people's facial expressions, essentially assuming that these expressions best convey humans' emotional responses. As a result, most datasets for training and evaluating emotion recognition tools (e.g., the AFEW and FER2013 datasets) only contain cropped images of human faces.

A robot pilot is learning to fly. It has passed its pilot's test and flown its first plane, but it has also had its first mishap too. Unlike a traditional autopilot, the ROBOpilot Unmanned Aircraft Conversion System literally takes the controls, pressing on foot pedals and handling the yoke using robotic arms. It reads the dials and meters with a computer vision system. The robot can take off, follow a flight plan and land without human intervention. ROBOpilot is a drop-in system meaning that the pilot's seat is removed and replaced with the robot.

Human "exceptionalism" is for many people an unquestioned assumption. For the religious, it is a God-given fact; for humanists, it is a celebration of our unique mental capacities.

It could eventually be used to make brain surgeries less invasive. Yoonho Kim and his colleague Xuanhe Zhao at Massachusetts Institute of Technology created the robot out of a polymer with small magnetic particles embedded throughout, meaning it can be directed using a magnet. It is coated in a self-lubricating material and is less 0.6 millimetres in diameter. The pair tested the robot on a silicone model of a human brain, which contained a substance that mimics blood. When controlled with a magnet held outside the brain, the robot could worm its way through hard-to-reach blood vessels.

A former Google engineer was charged Tuesday with stealing closely guarded secrets that he later sold to Uber as the ride-hailing service scrambled to catch up in the high-stakes race to build robotic vehicles. The indictment filed by the U.S. Attorney's office in San Jose, California, is an offshoot of a lawsuit filed in 2017 by Waymo, a self-driving car pioneer spun off from Google. Uber agreed to pay Waymo $245 million to settle the case, but the federal judge overseeing the lawsuit made an unusual recommendation to open a criminal probe. Uber considered having self-driving technology crucial to survive. Anthony Levandowski, a pioneer in robotic vehicles, was charged with 33 counts of trade secrets theft.

Book a flight now, and Google Flights' says they'll make sure you get the best price. The indictment filed by the U.S. attorney's office in San Jose, California, is an offshoot of a lawsuit filed in 2017 by Waymo, a self-driving car pioneer spun off from Google. Uber agreed to pay Waymo $245 million to settle the case last year, but the federal judge overseeing the lawsuit made an unusual recommendation to open a criminal probe after seeing enough evidence to conclude a theft may have occurred. Uber considered having self-driving technology crucial to survive and counter potential competitive threats from Waymo and dozens of other companies working on robotic vehicles. Uber wants to build self-driving cars so it can eliminate the need to have a human behind the wheel, one of the biggest expenses in its still-unprofitable ride-hailing service.

JERUSALEM – The long shadow war between Israel and Iran has burst into the open in recent days, with Israel allegedly striking Iran-linked targets as far away as Iraq and crash-landing two drones in Hezbollah-dominated southern Beirut. These incidents, along with an air raid in Syria that Israel says thwarted an imminent Iranian drone attack, have raised tensions at a particularly fraught time. Israeli Prime Minister Benjamin Netanyahu is looking to project strength three weeks before national elections, while Iran has taken a series of provocative actions in recent months aimed at pressuring European nations to provide relief from crippling U.S. sanctions. Hassan Nasrallah, leader of the Iran-backed Hezbollah, vowed to retaliate after a drone crashed on the militant group's Beirut media office and another exploded midair early Sunday. Israeli forces along the border with Lebanon are on high alert, raising fears of a repeat of the 2006 war.

The BBC is preparing to launch a rival to Amazon's Alexa called Beeb, with a pledge that it will understand British accents. The voice assistant, which has been created by an in-house BBC team, will be launched next year, with a focus on enabling people to find their favourite programmes and interact with online services. While some US-developed products have struggled to understand strong regional accents, the BBC will this week ask staff in offices around the UK to record their voices and make sure the software understands them. The BBC currently has no plans to launch a standalone physical product such as Amazon's Echo speaker or a Google Home device. Instead, the Beeb software will be built into the BBC's website, its iPlayer app on smart TVs, and made available to manufacturers who want to incorporate the public broadcaster's software.

StyleSnap is Amazon's latest attempt to use machine learning to peddle fashion. How it works: Announced at Amazon's AI and robotics conference re:MARS 2019, the tool lets you upload photos and screenshots of clothes and accessories you like. It then uses machine-learning algorithms to match them to similar items on Amazon. Accredited "influencers" for Amazon are encouraged to get their followers on social media to use StyleSnap to take a screenshot of outfits they've modeled. The influencer will then earn commission on any subsequent sales. The big picture: It's the company's latest crack at one of the few areas it has yet to dominate in retail.

The same artificial intelligence method used in facial recognition systems could help improve the prediction of hailstorms and their severity, according to researchers at the National Center for Atmospheric Research. National Center for Atmospheric Research (NCAR) researchers have found that the same artificial intelligence method used in facial recognition systems could help improve the prediction of hailstorms and their severity. The researchers used machine learning to train a convolutional neural network to recognize features of individual storms that affect the formation of hail. The model was trained on images of simulated storms, along with information about temperature, pressure, wind speed, and direction. Once trained, the model was able to determine which features of the storm correlate with whether or not it will hail, and how big the hailstones are likely to be.