Deep learning algorithms run data through several "layers" of neural network algorithms, each of which passes a simplified representation of the data to the next layer. Most machine learning algorithms work well on datasets that have up to a few hundred features, or columns. However, an unstructured dataset, like one from an image, has such a large number of features that this process becomes cumbersome or completely unfeasible. A single 800-by-1000-pixel image in RGB color has 2.4 million features – far too many for traditional machine learning algorithms to handle. Deep learning algorithms learn progressively more about the image as it goes through each neural network layer.

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Fujitsu has developed a new artificial intelligence-based method of performing behavioral analysis on video footage, which it says can recognize a range of subtle and complex human activities without large amounts of training data, according to a company announcement. The new "Actlyzer" technology was developed by Fujitsu Laboratories and the Fujitsu Research and Development Center, and combines about 100 basic actions it is pretrained for modularly to identify more complex behaviors, such as acting suspiciously or considering a purchase. In contrast, Fujitsu says, deep learning technologies typically rely on huge amounts of video to train the recognition of individual behaviors, which means they take several months to be introduced in the field. The systems accuracy for recognizing the 100 basic actions is 90 percent or higher on average, according to Fujitsu. Suggested potential uses for the technology include automatic detection of suspicious activity, product interest surveys based on recognized purchase behavior, and training applications by comparing the skills of workers with different levels of experience in factories.

Whoever controls the strongest artificial intelligences controls the world. Artificial intelligence is the most important technology of the 21st century. It is therefore important to understand global ambitions and movements. In this article I examine the global artificial intelligence industry and in this context consider the aspects of politics, data, economy, start-ups, financing, research and infrastructure. I will only briefly discuss the current superpowers China and the USA, as I will dedicate a separate article to each of them. The question that we must ask ourselves in the end is how humanity will deal with the global challenges. So far, the first wave of digitization has developed without much government influence. Although there are now plans to break Google's monopoly (USA and Europe), for example by imposing European fines on Google and Facebook, politics is lagging behind the market by over a decade. As far as AI is concerned, for the first time in recent history I have observed a multitude of initiatives, strategies and actions by dozens of governments around the world - with very different goals and approaches. Artificial intelligence is and remains an issue that politicians and administrations of all nations have to deal with. AIs are relevant for climate protection and economic policy.

Every week, my team at Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Gaming is one of the areas in which AI has shown the most progress in the last few years. From checker to Go to StarCraft, AI programs has regularly achieved superhuman performance and shown signs of creativity.

At Redox our mission is to enable the frictionless adoption of technology in healthcare.To that end we have enabled a network of healthcare organizations and technology developers to connect through us to improve patient healthcare. Our customers have asked us to help minimize data duplication for various scenarios. An ideal candidate is a data science enthusiast and will solve complex data-related problems, using advanced statistical and machine learning tools, in a real-world setting with product managers and engineers. Candidates should demonstrate strong statistical, mathematical and technical skills, with proven capabilities to transition ideas into fully working projects. In addition, the candidate needs to have a creative and first principles mindset to examine our current processes and help us grow our Data Science muscle at Redox.

Expeditionary Artificial Intelligence is the theme for the May 2020 research and experimentation week during which Private Industry, Academia, and Non-Government Organizations will collaborate on developing and demonstrating Machine and Deep Learning, sensor, networked all-domain Autonomous Systems, and other technologies. The May 2020 experiment will be co-hosted by The Sea Land Air Military Research initiative (SLAMR) and Joint Interagency Field Experimentation (JIFX) Program. The initial TechOp Day at the Naval Postgraduate School (NPS) was part of the laying the groundwork with Private Industry and other partners. This video was produced by NPS and can be watched at NPS' YouTube channel at https://www.youtube.com/user/NPSvideo.

Variational Auto-Encoders have often been used for unsupervised pretraining, feature extraction and out-of-distribution and anomaly detection in the medical field. However, VAEs often lack the ability to produce sharp images and learn high-level features. We propose to alleviate these issues by adding a new branch to conditional hierarchical VAEs. This enforces a division between higher-level and lower-level features. Despite the additional computational overhead compared to a normal VAE it results in sharper and better reconstructions and can capture the data distribution similarly well (indicated by a similar or slightly better OoD detection performance).

One out of four children in India are leaving grade eight without basic reading skills. Measuring the reading levels in a vast country like India poses significant hurdles. Recent advances in machine learning opens up the possibility of automating this task. However, the datasets are primarily in English. To solve this assessment problem and advance deep learning research in regional Indian languages, we present the ASER dataset of children in the age group of 6-14. The dataset consists of 5,300 subjects generating 81,658 labeled audio clips in Hindi, Marathi and English. These labels represent expert opinions on the ability of the child to read at a specified level. Using this dataset, we built a simple ASR-based classifier. Early results indicate that we can achieve a prediction accuracy of 86 percent for the English language. Considering the ASER survey spans half a million subjects, this dataset can grow to those scales.

Physics-informed neural networks (PINNs) [31] use automatic differentiation to solve partial differential equations (PDEs) by penalizing the PDE in the loss function at a random set of points in the domain of interest. Here, we develop a Petrov-Galerkin version of PINNs based on the nonlinear approximation of deep neural networks (DNNs) by selecting the {\em trial space} to be the space of neural networks and the {\em test space} to be the space of Legendre polynomials. We formulate the \textit{variational residual} of the PDE using the DNN approximation by incorporating the variational form of the problem into the loss function of the network and construct a \textit{variational physics-informed neural network} (VPINN). By integrating by parts the integrand in the variational form, we lower the order of the differential operators represented by the neural networks, hence effectively reducing the training cost in VPINNs while increasing their accuracy compared to PINNs that essentially employ delta test functions. For shallow networks with one hidden layer, we analytically obtain explicit forms of the \textit{variational residual}. We demonstrate the performance of the new formulation for several examples that show clear advantages of VPINNs over PINNs in terms of both accuracy and speed.