The basis of GDPR is the privacy of data for citizens and consumers of EU countries and the methods needed to track and enforce GDPR will need to have intelligent methods to process the large amount of data that will need to be sorted to identify GDPR compliance or non-compliance. According to Dr. Shane Archiquette, CTO – Global Communications, Media & Entertainment, Global Markets and Technology Architecture, AI-assisted auditing will most likely be the method used to look for patterns across multiple disparate datasets that businesses will need to produce and process. In addition, AI will provide a composable method to machine-learn new areas that are identified by GDPR legal precedence as new cases are opened and closed. According to a recent report, European Union Institute researchers created AI-enabled software to scrutinize the privacy policies of 14 major technology companies for violations of the new GDPR. They found that 1/3 rd of the clauses contained "insufficient information," with 11% of the policies' sentences using "unclear language."

The first word in AI may stand for "artificial," but the belief in its potential for cybersecurity in government and business circles is very real. In fact, industry sources say efforts to make use of artificial intelligence could drive a more flexible approach to cyber-related regulation, particularly in the finance sector. Former White House cybersecurity coordinator Rob Joyce, now back at the National Security Agency, called AI a "key element" in cybersecurity strategy in a recent speech. "The point about AI being a key element of the future, I think there is so much that AI can do to clean out anomalies, to move the speed of cyber, in setting up those defenses," Joyce said. Makers of financial-technology products are looking into and promoting the possibilities, a topic discussed extensively at the recent Securities Industry and Financial Markets Association "FinTech" conference in New York City.

Addressing the "memory wall" and pushing for a new architectural solution enabling highly efficient performance computing for rapidly growing artificial intelligence (AI) applications are key areas of focus for Leti, the French technology research institute of CEA Tech. Speaking to EE Times at Leti's annual innovation conference here, Leti CEO Emmanuel Sabonnadière said there needs to be a highly integrated and holistic approach to moving AI from software and the cloud into an embedded chip at the edge. "We really need something at the edge, with a different architecture that is more than just CMOS, but is structurally integrated into the system, and enable autonomy from the cloud -- for example for autonomous vehicles, you need independence of the cloud as much as possible," Sabonnadière said. He commented on the Qualcomm bid for NXP being a key pointer as a driver for more computing at the edge. "Why do you think Qualcomm is buying NXP? It's for the sensing, and to put digital behind the sensing."

From the critical, like law enforcement, healthcare, and humanitarian aid, to the mundane, like dating and shopping, artificial intelligence (AI) seems to be the answer to all our problems. AI is a catch-all phrase for a wide-ranging set of technologies most of which apply learning techniques from statistics to find patterns in large sets of data and make predictions based on those patterns. It seems like there are meetings every other week, organised by representatives from industry, government, academia, and civil society to address the perils of AI and formulate solutions to harness its potential. But who is driving the regulatory agenda and what do they stand to gain? This question needs to be answered because letting industry needs drive the AI agenda presents real risks.

However, it never scratches far beneath the surface, or tries to answer the many questions it raises. That makes "The Future of Work" a quick read but ultimately leaves the reader wanting, like a computer with too little data to draw any conclusions. The second section of the book, for example, addresses the need for enormous societal change, including a shift to lifetime learning, a need for a new social contract and new definitions of work. These are laudable goals, but how do we achieve them? The notion of a universal basic income (in which everyone receives enough money to live) gets mentioned and defined. But the huge cost and attendant challenges are barely addressed.

An image from the product page of Amazon's Rekognition service, which provides image and video facial and item recognition and analysis. SAN FRANCISCO -- The Orlando Police Department in Florida is planning to continue its test of a facial recognition program from Amazon, despite outcry from civil rights and privacy groups that law enforcement and government agencies could abuse the technology. OPD announced last month that the trial proof of concept run of the software had expired, but OPD public information officer, Sgt. Eduardo Bernal, said in a release Monday that the department will continue its testing of the program. Two years ago, Amazon built the facial and product recognition tool, called Rekognition, as a way for customers to quickly search a database of images and look for matches.

Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards the over-represented class where windows remained closed. In addition, they require tuning for each occupant which can not be efficiently scaled to the increased number of occupants. This paper presents a window opening model for commercial buildings using deep learning methods. The model is trained using data from occupants from an office building in Germany. In total the model is evaluated using almost 20 mio. data points from 3 independent buildings, located in Aachen, Frankfurt and Philadelphia. Eventually, the results of 3100 core hours of model development are summarized, which makes this study the largest of its kind in window states modeling. Additionally, the practical potential of the proposed model was tested by incorporating it in the Modelica-based thermal building simulation. The resulting evaluation accuracy and F1 scores on the office buildings ranged between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 % points in case of sparse input data, while the F1 score remained high.

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.

The NOAA's Okeanos Explorer wrapped up its mission to investigate the poorly understood waters off the southeast US last week, revealing a stunning glimpse into life thousands of feet beneath the surface. From the adorable wide-eyed'deep sea dumpling' to the ferocious-looking lizardfish, scientists operating a remote vehicle stumbled upon all sorts of beautiful and unusual creatures during the 17 'Windows to the Deep' dives. The mission came to a close on July 2 after more than a month mapping and observing the US Southeast Continental Margin, which stretches from Florida to North Carolina. On July 1, the last day of the dive, the researchers caught a glimpse of the eerie lizardfish at around 1,771 meters (5,810 feet) deep. Dives kicked off on May 22, beginning a two-part ocean exploration mission in the deepwater areas off the southeast, which is said to contain'some of the least explored areas' of the East Coast.

Top Republican lawmakers on the House Energy and Commerce Committee sent letters Monday asking Apple and Google for more information on how extensively their smartphones track people's locations and record snippets of their conversations. The questions from lawmakers come amid a broader scrutiny from Capitol Hill into how the underlying, everyday practices of technology companies may infringe on Americans' privacy. Congressional hearings with Facebook chief executive Mark Zuckerberg revealed several lawmakers were troubled by the amount of data that the social network collects on a regular basis. In the letters to Google and Apple, lawmakers said the committee is "reviewing the business practices that may impact the privacy expectations of Americans." The letters ask Larry Page -- the chief executive of Google's parent company Alphabet -- and Tim Cook, Apple's CEO, for more specific information on how their phones collect location information at times when many people may not expect.