Abstract--The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized toplayer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features of high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layers. We also present the results for evaluation of lossless deep feature compression with four benchmark data compression methods, which provides meaningful investigations and baselines for future research and standardization activities. ECENTLY, deep neural networks (DNNs) have demonstrated the state-of-the-art performance in various computer vision tasks, e.g., image classification [1], [2], [3], [4], image object detection [5], [6], visual tracking [7], visual retrieval [8]. In contrast to the handcrafted features such as Scale-Invariant Feature Transform (SIFT) [9], deep learning based approaches are able to learn representative features directly from the vast amounts of data. For image classification, which is the fundamental task of computer vision, the AlexNet model [1] has achieved 9% better classification accuracy than the previous handcrafted methods in the 2012 ImageNet competition [10], which provides a large scale training dataset with 1.2 million images and one thousand categories. Inspired by the fantastic progress of AlexNet, DNN models continue to be the undisputed leaders in the competition of ImageNet. In particular, both VGGNet [2] and GoogLeNet [11] announced promising performance in the ILSVRC 2014 classification challenge, which demonstrated that deeper and wider architectures can bring great benefits in learning better representations via large scale datasets. In 2016, He et al. also proposed residual blocks to enable very deep learning structure [3]. With the advances of network infrastructure, cloud-based applications are springing up in recent years. In particular, the front-end devices acquire information from users or the physical world, which are subsequently transmitted to the cloud end (i.e., data center) for further process and analyses. In particular, for visual analysis, the front-end devices deployed in the real world such as surveillance cameras and wearable devices acquire massive visual data which are transmitted to the cloud side for analyses, as shown in Figure 1.

As facial recognition technology use generates intense scrutiny, a new system unveiled at Washington's Dulles airport is being touted as a "user friendly" way to help ease congestion for air travelers. Officials at Dulles unveiled two new face recognition systems Thursday, one to meet legal requirements for biometric entry-exit records, and a second to help speed processing of travelers arriving on international flights by matching their real-time images with stored photos. The growing use of facial recognition has ignited debate over surveillance and privacy around the world, but officials told media this system was a way to help reducing annoying lines and wait times without compromising security. "The technology works," US Customs and Border Protection Commissioner Kevin McAleenan told reporters at an airport unveiling. And we believe it will change the face of international travel."

Despite its promise, the growing field of Artificial Intelligence (AI) is experiencing a variety of growing pains. In addition to the problem of bias I discussed in a previous article, there is also the'black box' problem: if people don't know how AI comes up with its decisions, they won't trust it. In fact, this lack of trust was at the heart of many failures of one of the best-known AI efforts: IBM Watson – in particular, Watson for Oncology. Experts were quick to single out the problem. "IBM's attempt to promote its supercomputer programme to cancer doctors (Watson for Oncology) was a PR disaster," says Vyacheslav Polonski, PhD, UX researcher for Google and founder of Avantgarde Analytics.

Artificial Intelligence is helping both attackers and their targets – shifting the nature of attack surfaces without being able to weaken the intention of those wishing to benefit from its crashes and failures. Cyber defense expert will perhaps become the ultimate profession in charge of continuously understanding and monitoring what's happening under the hood with AI. Should we expect a digital world entirely administered by so-called "strong" Artificial Intelligence, capable of ensuring its own infrastructure's security and reliable enough to fully ensure that of human users? A true singularity moment in which there would be absolutely no interaction of the human operator with the artificial brain – whether for its maintenance or its evolution. Before that horizon, which seems far away and would not necessarily be socially desirable, the human operator – user, administrator, or designer – will unfortunately remain in capacity of compromising (intentionally or not) the integrity and the efficiency of the multiple AI processes in charge of our digital security.

Both Reuters and the Wall Street Journal have learned that the Trump administration is likely to formally announce its latest tariffs on Chinese goods within the next few days (possibly as soon as September 17th). Imports for "internet technology products," circuit boards and other electronics are still likely to become more expensive, although the tariff level is reportedly set at 10 percent, not the originally proposed 25 percent also used for earlier tariffs. The administration may have lowered the tariffs to reduce the chances that companies would instantly raise prices to make up for the higher costs. As before, the tariffs are meant to pressure China into curbing trade policies deemed unfair, including attempts to acquire US technologies and subsidize tech categories like AI and robotics. There are hints of the two sides resuming talks that could mitigate or end the trade war, but Trump hasn't been willing to wait for these talks before imposing new tariffs.

Big pharma is taking an AI-first approach. Apple is revolutionizing clinical studies. We look at the top artificial intelligence trends reshaping healthcare. Healthcare is emerging as a prominent area for AI research and applications. And nearly every area across the industry will be impacted by the technology's rise. Image recognition, for example, is revolutionizing diagnostics. Recently, Google DeepMind's neural networks matched the accuracy of medical experts in diagnosing 50 sight-threatening eye diseases. Even pharma companies are experimenting with deep learning to design new drugs. For example, Merck partnered with startup Atomwise and GlaxoSmithKline is partnering with Insilico Medicine.

As an adjunct to AI initiatives being jointly developed by IT World Canada, CATAAlliance and SalesChoice, we are now providing the community with an AI (Artificial Intelligence) Canada Group on Linkedin. AI (Artificial Intelligence) Canada is a collaborative Forum, created and managed by IT World Canada, CATAAlliance and SalesChoice, where executives access research, share information, provide advocacy guidance and meet peers committed to advancing Canada's AI leadership. Part of our a shared community approach and mission is to cultivate every advantage and eliminate every barrier to Canada's AI competitiveness, and to facilitate AI adoption across all business and public sectors. AI (Artificial Intelligence) Canada is fully moderated and part of a multi faceted AI products and services leadership and branding program developed by IT World Canada, CATAAlliance & SalesChoice. Please review the Benefits of this Program and then for more information contact us at: jreid@cata.ca

Traffic wastes time and money almost everywhere on the planet, so congestion is the bogeyman many transportation planners hope to defeat. Attendees at the most-recent Transportation Techies Meetup – held at Mobility Lab in Arlington, Va., and focused on traffic solutions – got a taste of several early-stage tech/planning options. "Data and technology are becoming more and more crucial in planning for safer streets. This becomes even more important as autonomous vehicles begin to come online," said Paul Mackie, Mobility Lab's communications director. How are DOTs handling data for projects like AVs and Vision Zero?

The British installation at the London Design Biennale is an international project that demonstrates how victims of human rights violations around the world can gather proof of their own experiences. Plastic bottles, digital cameras and kites, just some of the low-cost items in the exhibition, are being used in the Sinjar region of northern Iraq to gather the remaining evidence of Isis's 2014 treatment of the Yazidi ethnic minority, treatment that survivors and their supporters have called genocide and hope to prosecute in the international courts. Not only do they say thousands were killed by the terrorist group and thousands more displaced, but Yazidi cultural and religious heritage sites were destroyed and their temples were used as mass graves. Four years later, the region is still dangerous, littered with landmines and booby-traps left by the militants as they retreated. So when Yazda, a global rights organisation established by the Yazidi diaspora, sought help in supplementing their documentation efforts from Forensic Architecture, an independent research agency based at Goldsmiths, University of London, its team of architects, photographers, software developers, lawyers and archaeologists adapted their investigative methods to provide ways for Yazidis to gather video and data without entering the most hazardous areas.

Abstract--Extended Kalman Filtering (EKF) can be used to propagate and quantify input uncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on the input distribution. This methodology yields results comparable to existing methods of uncertainty propagation for DNNs while lowering the computational overhead considerably. Additionally, EKF allows model error to be naturally incorporated into the output uncertainty. This question tends to come up during confidence scoring in areas such as automatic speech recognition where things like background noise can distort the input signal [1]. However, it can be approximated by a Gaussian and modified later if necessary [2].