The future of work will depend highly on soft skills. No matter how AI for recruitment and talent assessment is leveraged in the future, a candidate's high-order thinking and EQ will stay vital, something which the robots simply can't replace or automate! This accurate AI-powered tool (beyond IBM Watson) gives you full picture of a candidate's soft skill background (based on the Big 5 personality test, DISC OCEAN, mood graphs, sentiment analysis, digital footprint analysis, behavior score, and much more) to help recruiters spot and process the right'candidates' who would add to their diverse, inclusive company culture. Get a free assessment report, at: https://frrole.ai/deepsense-app/ You just need the twitter handle/ email ID of the individual to get started.

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.

The three adjectives served as parting wisdom for a dozen William & Mary students seated in McGlothlin-Street Hall. White was wrapping up the final class of the semester for his course "Neural Networks for Machine Learning." A 2017 Ph.D. graduate of W&M's Department of Computer Science, White returned to his alma mater to teach after he heard the department wanted to offer another course on machine learning, a key subset of artificial intelligence. "I believed neural networks could serve as the perfect backdrop for a class studying what learning from data means and how to do it well," White said. "Since the fundamentals draw from calculus, probability, statistics, and linear algebra, the first part of the course is pretty intense, but I was interested in returning to teach because I had some ideas on how to manage this complexity."

Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network . Significantly different from the previous deep models, inside the deep loopy neural network, there exist a large number of loops created by the extensive connections among nodes in the input graph data, which makes model learning an infeasible task. To resolve such a problem, in this paper, we will introduce a new learning algorithm for the deep loopy neural network specifically. Instead of learning the model variables based on the original model, in the proposed learning algorithm, errors will be back-propagated through the edges in a group of extracted spanning trees. Extensive numerical experiments have been done on several real-world graph datasets, and the experimental results demonstrate the effectiveness of both the proposed model and the learning algorithm in handling graph data.

Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238200 person instances manually labeled in over 47300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211200). We optimize four state-of-the-art deep learning approaches (Faster R-CNN, R-FCN, SSD and YOLOv3) to serve as baselines for the new object detection benchmark. In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the effect of the training set size, the dataset diversity (day- vs. night-time, geographical region), the dataset detail (i.e. availability of object orientation information) and the annotation quality on the detector performance. Finally, we analyze error sources and discuss the road ahead.

Technology may have brought the music industry to its knees 20 years ago, but these days pop stars and record labels are using computing power to find new audiences and take fresh creative decisions. The benefits and pitfalls of this new technology are being debated at The Great Escape music festival in Brighton, in a day-long conference. Here are some of the things we learned. Virtual reality could allow fans to experience Glastonbury from the comfort of their sofa, simply by plugging in a headset. In fact, Melody VR - a London-based tech start-up - launched earlier this month, offering concerts by stars like The Who, Royal Blood and Rag'N'Bone Man through VR sets like Oculus Go and Samsung's Gear VR.

Across consumer-facing industries including hospitality and quality ranking sites, customer service is one of the most critical metrics determining the value of a product against competitors. Planned standards are well and good, but if a process doesn't work or malfunctions, consumers want help, and they want that help to be easy to access, able to handle the problem and resolve it quickly. Moreover, they want anybody (including our often hated self-check-in machines), to be understanding, considerate, and happy to assist. For decades this "human touch" has been the one thing the digital age could not offer, even as the shopping path, and the products themselves have become more and more interwoven with computer and internet technology. The U.S. 3D design company Autodesk has teamed up with Soul Machines, a New Zealand developer of human-like avatars, to produce its first digital customer service agent. AVA (Autodesk virtual agent) is ready to interact with customers 24 hours a day to resolve their concerns.

Geography of locations mentioned in close proximity to the word "love" in global news coverage April 6, 2017 to May 4, 2018 as monitored by the GDELT Project Geography gives us an incredible lens through which to see the world. While we often think of maps in terms of information that already exists in the spatial domain, such as building addresses or GPS coordinates, the written word contains a tremendous volume of locative insights. What happens when we combine fulltext geocoding with the power of the cloud to analyze hundreds of billions of words of global news coverage to map the hidden geography of the language of news? Language is inextricably interconnected with geography. Each day as we consume the stream of information available about the world around us, we are inundated with mentions of location. The news media in particular is saturated with locative references, anchoring our understanding of the world in space.

Cameras--especially phone cameras--are terrible at taking pictures in the dark. The tiny image sensors in most modern cameras can only absorb a small amount of light, which often results in dark, grainy images. To try to solve this problem without inventing a new image sensor, researchers at Intel and the University of Illinois Urbana-Champlain taught an artificial intelligence algorithm how to take the data from darker images and reconstruct them so that they're brighter and clearer, according to research published this month and to be presented in June at an industry conference. To train the algorithm, the researchers showed it two version of more than 5,000 images taken in low-light scenarios: One set that was taken to be purposefully too dark, and one set that was taken with a longer exposure time, meaning the sensor is given more time to collect light and better expose the image. The Intel and UIUC team claims the algorithm can now amplify low-light images the equivalent of up to 300 times the exposure, without the same noise and discoloration that programs like Photoshop might introduce or having to take two separate images. While the team did build a custom algorithm to do the task, the most innovative aspect of the work is the dataset they created.

In this age of disinformation, where fake news thrives and the public has trust issues with technology, Google designed a machine that can deceive humans. Gosh, what could go wrong? Google's engineers used artificial intelligence to give birth to something called Duplex -- a digital assistant that can call a local business to make appointments on your behalf over the phone. And the receptionists on the other end of the call have no idea they are conversing back and forth with a robot. Not only is the typical robotic voice gone, but Google took extra steps to disguise the system to sound more like a human.