Honor, a leading smartphone e-brand under the Huawei Group signed up to work with Central Saint Martins (CSM) students to explore the concept of colour and its emotional significance within the history of design, as well as the relationship between art, design and technological innovation. Collaborating to find beauty in Artificial Intelligence (AI), selected CSM students are to be awarded the Honor Art prize, which is an award given for a piece of final degree work that celebrates the innovative use of colour and technology in artistic practice. This year, the inaugural prize was awarded to MA Fine Art student Marco Pantaleoni for his series of works in 3D scanning, photography and painting. Receiving his award, Marco said: "I feel honoured to be awarded this prize. Technology is an essential part of my practice, so to be recognised by a technology brand like Honor, not only reinforces some of the concepts behind my work, it also really resonates with the way I create."

Medibloc said Tuesday that it has signed a memorandum of understanding with PolicyPal Network to build a platform that can invigorate insurance products and create a medical information ecosystem. MediBloc is a patient-centered healthcare platform based on block-chain technology. The company's platform stores integrated medical information such as the patient's medical record and lifelog, and provides the data to doctors when visiting medical institutions. Also, researchers can use Medibloc's platform to collect medical data regardless of country or region. PolicyPal, launched in 2016, is an insurance application that utilizes machine learning and artificial intelligence. The company later launched the PolicyPal Network, which combines block-chain technology.

You've seen the ways AI has been leveraged in recent years: Tesla has self-driving cars, chatbots answer customer service queries, and Siri can send a text for you or set a reminder. In 2018 we're likely to see AI permeate our daily lives in much more real ways. Everything from the way we make purchases to the way we create content is likely to see a significant transformation. Here are five artificial intelligence advancements that will work their way further into your life in 2018. Sure, you can already unlock the latest iPhones and confirm some payments with your face.

YOU'VE heard of it in movies or in passing conversations. Maybe your workplace uses it, or you're considering using it yourself. As technology continues to make ripples across the workplace, AI has become increasingly prevalent. Through AI, companies are able to analyse large amounts of data, which will allow them to better engage with customers. Today, AI is easily accessible.

Federal prosecutors have charged a former Apple employee with stealing trade secrets related to Apple's autonomous vehicle program. Xiaolang Zhang allegedly worked on Apple's secretive self-driving car project. Zhang left Apple in April saying he was going to work for a Chinese electric vehicle company called Xpeng Motors. He is accused of copying more than 40GB of Apple intellectual property to his wife's laptop before leaving the company, according to court documents. The documents do not accuse Xpeng Motors of wrongdoing.

SAN FRANCISCO – A former Apple Inc. engineer was arrested on charges of stealing driverless car secrets for a Chinese startup after he passed through the security checkpoint at San Jose International Airport to board a flight to China. Xiaolang Zhang was accused by U.S. prosecutors of downloading files containing proprietary information as he prepared to leave the iPhone maker in April and start work for Guangzhou-based Xiaopeng Motors, according to a criminal complaint filed Monday in federal court in San Jose, California. A hardware engineer for Apple's autonomous vehicle development team, Zhang was granted access to confidential company databases, according to the complaint. After he took paternity leave, he told Apple in April he was moving back to China to work at Xmotors. Apple grew more suspicious after seeing his increased network activity and visits to the office before he resigned, according to the complaint.

A new approach to operating a building's cooling system using machine learning techniques and Internet of Things (IoT) data can help to drive down energy consumption and costs, as the global demand for energy increases. The buildings sector is one of the largest energy-consuming entities, accounting for a staggering 40 percent of global energy consumption today1. What makes this statistic grim is that building energy consumption is projected to increase by 50 percent by 2050, unless energy efficiency strategies are actively embraced to curb the growth. To put this in perspective, the 50 percent increase is equivalent to the combined energy consumption of Russia and India today2. Global pressure for improving environmental sustainability, combined with increasing electricity prices across several nations worldwide, are pushing corporations to reduce the energy consumption incurred in operating their buildings.

WASHINGTON--(BUSINESS WIRE)--Jul 10, 2018--The following is an opinion editorial provided by Naveen Rao of Intel Corporation. This press release features multimedia. Naveen Rao was the founder of Nervana and is now corporate vice president and general manager of the Artificial Intelligence Products Group at Intel Corporation. Most people agree that artificial intelligence (AI) will transform modern society in positive ways. From autonomous cars that will save thousands of lives, to data analytics programs that may finally discover a cure for cancer, to machines that give voice to those who can't speak, AI will be known as one of the most revolutionary innovations of mankind. But this fantastic future is a long way off, and the path to get us there is still under construction.

Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quarantined by mapping (e.g., GPS) or via beacons that delineate a no-entry area. We propose a delineation method where the industrial vehicle utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to detect passive beacons and model-predictive control to stop the vehicle from entering a restricted space. The beacons are standard orange traffic cones with a highly reflective vertical pole attached. The LiDAR can readily detect these beacons, but suffers from false positives due to other reflective surfaces such as worker safety vests. Herein, we put forth a method for reducing false positive detection from the LiDAR by projecting the beacons in the camera imagery via a deep learning method and validating the detection using a neural network-learned projection from the camera to the LiDAR space. Experimental data collected at Mississippi State University's Center for Advanced Vehicular Systems (CAVS) shows the effectiveness of the proposed system in keeping the true detection while mitigating false positives.

Recognizing apparel attributes has recently drawn great interest in the computer vision community. Methods based on various deep neural networks have been proposed for image classification, which could be applied to apparel attributes recognition. An interesting problem raised is how to ensemble these methods to further improve the accuracy. In this paper, we propose a two-layer mixture framework for ensemble different networks. In the first layer of this framework, two types of ensemble learning methods, bagging and boosting, are separately applied. Different from traditional methods, our bagging process makes use of the whole training set, not random subsets, to train each model in the ensemble, where several differentiated deep networks are used to promote model variance. To avoid the bias of small-scale samples, the second layer only adopts bagging to mix the results obtained with bagging and boosting in the first layer. Experimental results demonstrate that the proposed mixture framework outperforms any individual network model or either independent ensemble method in apparel attributes classification.