Second, our regulatory regime makes it more difficult to build things in the United States and sell them to other countries, creating a market for foreign competitors who would otherwise not stand a chance. For years, the United States curbed exports of encryption technology and basic processors. This only led international competitors to fulfill demand, creating a market for themselves. When U.S. allies like Saudi Arabia, Pakistan, the United Arab Emirates, and Turkey needed access to unmanned aerial systems to prosecute the war on terror, these requests were delayed or denied. We have since lost almost all of these markets to Chinese exports and indigenous development.

Nearly all of the deep learning based image analysis methods work on reconstructed images, which are obtained from original acquisitions via solving inverse problems. The reconstruction algorithms are designed for human observers, but not necessarily optimized for DNNs. It is desirable to train the DNNs directly from the original data which lie in a different domain with the images. In this work, we proposed an end-to-end DNN for abnormality detection in medical imaging. A DNN was built as the unrolled version of iterative reconstruction algorithms to map the acquisitions to images, and followed by a 3D convolutional neural network (CNN) to detect the abnormality in the reconstructed images. The two networks were trained jointly in order to optimize the entire DNN for the detection task from the original acquisitions. The DNN was implemented for lung nodule detection in low-dose chest CT. The proposed end-to-end DNN demonstrated better sensitivity and accuracy for the task compared to a two-step approach, in which the reconstruction and detection DNNs were trained separately. A significant reduction of false positive rate on suspicious lesions were observed, which is crucial for the known over-diagnosis in low-dose lung CT imaging. The images reconstructed by the proposed end-to-end network also presented enhanced details in the region of interest.

This paper presents an automated approach for interpretable feature recommendation for solving signal data analytics problems. The method has been tested by performing experiments on datasets in the domain of prognostics where interpretation of features is considered very important. The proposed approach is based on Wide Learning architecture and provides means for interpretation of the recommended features. It is to be noted that such an interpretation is not available with feature learning approaches like Deep Learning (such as Convolutional Neural Network) or feature transformation approaches like Principal Component Analysis. Results show that the feature recommendation and interpretation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in time to develop a solution. It is further shown by an example, how this human-in-loop interpretation system can be used as a prescriptive system.

In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize both the block-sparsity of the coefficients and the statistical dependency between neighboring coefficients of the common row sparsity MMV signals. Unlike many other methods, the proposed method is able to automatically capture the block sparse structure of the unknown signal. Our method is developed using an expectation-maximization (EM) framework. Simulation results show that our proposed method offers competitive performance in recovering block-sparse common row sparsity pattern MMV signals.

Bayesian nonparametrics (BNP) models appear to be perfectly suited for the era of big data (Jordan, 2011), in which ever-expanding databases of high-dimensional data cannot be dealt with simplistically. Generative processes priors like the Dirichlet process (Ferguson, 1973) or the Indian buffet process (Griffiths and Ghahramani, 2011) allow for modeling latent variables like clusters or otherwise unobservable features in our data and adapting the complexity of the model in accordance to the complexity of the data. Even if we had some understanding of the latent structure in the data, we would not necessarily know their exact forms and implications in the model a priori. The BNP solution, which divides the data into discrete features and clusters, fosters interpretable models that would naturally lead to new hypotheses about the information in such databases (Kim et al., 2015). For example, in a general medical records dataset containing billions of observations, a cluster (or feature) composed of 0.001% of the population still includes tens of thousands of people.

Density ratio estimation (DRE) [18, 11, 27] is an important tool in various branches of machine learning and statistics. Due to its ability of directly modelling the differences between two probability density functions, DRE finds its applications in change detection [13, 6], twosample test [32] and outlier detection [1, 26]. In recent years, a sampling framework called Generative Adversarial Network (GAN) (see e.g., [9, 19]) uses the density ratio function to compare artificial samples from a generative distribution and real samples from an unknown distribution. DRE has also been widely discussed in statistical literatures for adjusting nonparametric density estimation [5], stabilizing the estimation of heavy tailed distribution [7] and fitting multiple distributions at once [8]. However, as a density ratio function can grow unbounded, DRE can suffer from robustness and stability issues: a few corrupted points may completely mislead the estimator (see Figure 2 in Section 6 for example).

The multimodal web elements such as text and images are associated with inherent memory costs to store and transfer over the Internet. With the limited network connectivity in developing countries, webpage rendering gets delayed in the presence of high-memory demanding elements such as images (relative to text). To overcome this limitation, we propose a Canonical Correlation Analysis (CCA) based computational approach to replace high-cost modality with an equivalent low-cost modality. Our model learns a common subspace for low-cost and high-cost modalities that maximizes the correlation between their visual features. The obtained common subspace is used for determining the low-cost (text) element of a given high-cost (image) element for the replacement. We analyze the cost-saving performance of the proposed approach through an eye-tracking experiment conducted on real-world webpages. Our approach reduces the memory-cost by at least 83.35% by replacing images with text.

Sophia is the first artificial intelligence to be granted a citizenship. The world reacted with equal parts shock and awe when the news broke that the Kingdom of Saudi Arabia had given citizenship to Sophia, an Artificial Intelligence designed by Hanson Robotics. Some celebrated this momentous achievement in human history, while many others reacted with fear. Regardless of how you feel about artificial intelligence, Sophia is a huge step forward in artificial intelligence technology, showcasing the potential impact this technology will have on humankind. Hanson Robotics is determined to create AIs that are benevolent, and they believe the best approach to doing this is with SingularityNET, a decentralized, token-based artificial intelligence economy.

Inc. Echo Spot, from left, Echo, Echo Plus, and Fire TV devices sit on display during the company's product reveal launch event in downtown Seattle, Washington, U.S., on Wednesday, Sept. 27, 2017. According to a new report, Singapore is on the cusp of the voice technology revolution. Nearly half of the population has tried voice technology services such as Apple's Siri, Samsung's S Voice and Google Assistant, and a quarter of them use such services monthly. With Amazon entering the market this year, the potential for further uptake is high as more advanced products and applications hit the market. Voice technology has been with us for many years now – from automated voice recognition phone systems that failed to understand accents, to simple voice-to-text dictaphones that produced inaccurate copy – but the failings of these systems prevented widespread uptake.

NEW DELHI: Tech Mahindra will train an additional 10,000 employees on automation this year as the country's fifth largest IT firm expands its focus on new technologies to become more competitive. Automation is an area where Tech Mahindra is heavily focused on with an aim of making company competitive, Tech Mahindra Vice Chairman Vineet Nayyar said on an investor call. "...so far around 11,000 employees have been training in automation technologies while company intends to train an additional 10,000 associates during the year," he added. Nayyar said during the first two quarters, Tech Mahindra generated "productivity worth over 3,200 persons in different projects", encompassing close to half of the employees based in IT. At the end of September 2017 quarter, Tech Mahindra had a total headcount of 1,17,225 people. Of these, 75,587 people were in software business, while 35,287 were part of BPO operations.