"The Boy Who Cried Wolf" has long been a staple on nursery room shelves for a reason: It teaches kids that joking too much about a possible threat may turn people ignorant when the threat becomes an actual danger. President Donald Trump has been warning about "fake news" throughout his entire political career putting a dark cloud over the journalism professional. And now the real wolf might be just around the corner that industry experts should be alarmed about. The threat is called "deepfaking," a product of AI and machine learning advancements that allows high-tech computers to produce completely false yet remarkably realistic videos depicting events that never happened or people saying things they never said. A viral video starring Jordan Peele and "Barack Obama" warned against this technology in 2018, but the message was not enough to keep Jim Carrey from starring in "The Shining" earlier this week.

This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI) classification. MPRI inherits the merits of the principle of relevant information (PRI) to effectively extract multiscale information embedded in the given data, and also takes advantage of the multilayer structure to learn representations in a coarse-to-fine manner. Specifically, MPRI performs spectral-spatial pixel characterization (using PRI) and feature dimensionality reduction (using regularized linear discriminant analysis) iteratively and successively. Extensive experiments on four benchmark data sets demonstrate that MPRI outperforms existing state-of-the-art HSI classification methods (including deep learning based ones) qualitatively and quantitatively, especially in the scenario of limited training samples. I. INTRODUCTION With the rapid development of hyperspectral imaging techniques, current sensors always have high spectral and spatial resolution [1]. This work was supported in part by the National Natural Science Foundation of China (Grant No. 61502195), and in part by the Office of Naval Research Science of Autonomy (Grant No. N000141812306). Yantao Wei is with School of Educational Information Technology, Central China Normal University, Wuhan 430079, China (email: yantaowei@mail.ccnu.edu.cn).

Experimental data is often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by their intricate dynamics. Modern machine learning methods are well-suited for modeling complex datasets, but to be effective in science, the result needs to be interpretable. We demonstrate an unsupervised learning technique for extracting interpretable physical parameters from noisy spatiotemporal data and for building a transferable model of the system. In particular, we implement a physics-informed architecture based on variational autoencoders that is designed for analyzing systems governed by partial differential equations (PDEs). The architecture is trained end-to-end and extracts latent parameters that parameterize the dynamics of a learned predictive model for the system. To test our method, we train the architecture on simulated data from a variety of PDEs with varying dynamical parameters that act as uncontrolled variables. Specifically, we examine the Kuramoto-Sivashinsky equation with varying viscosity damping parameter, the nonlinear Schr\"odinger equation with varying nonlinearity coefficient, and the convection-diffusion equation with varying diffusion constant and drift velocity. Numerical experiments show that our method can accurately identify relevant parameters and extract them from raw and even noisy spatiotemporal data (tested with roughly 10% added noise). These extracted parameters correlate well (linearly with $R^2>0.95$) with the ground truth physical parameters used to generate the datasets. Our method for discovering interpretable latent parameters in spatiotemporal systems will allow us to better analyze and understand real-world phenomena and datasets, which often have uncontrolled variables that alter the system dynamics and cause varying behaviors that are difficult to disentangle.

The Joint Enterprise Defense Infrastructure -- or JEDI -- will store and crunch vast amounts of classified data and let the military use artificial intelligence to speed up war planning and enhance fighting capabilities. Think of it as "The Terminator" meets your iCloud. The robots aren't taking over just yet, but the Pentagon could be bringing us one step closer, thanks to a new project dubbed the "War Cloud." The official name is JEDI: Joint Enterprise Defense Infrastructure, a plan by the Pentagon to build a cloud computing system for the military. The so-called "War Cloud" will store and crunch vast amounts of classified data and let the military use artificial intelligence to speed up war planning, and enhance fighting capabilities. It can also be used for strategic war games, playing out potential conflilcts in a virtual world to give us an early look at what the outcome could be.

Deepfakes are video manipulations that can make people say seemingly strange things. Barack Obama and Nicolas Cage have been featured in these videos. "The Boy Who Cried Wolf" has long been a staple on nursery room shelves for a reason: It teaches kids that joking too much about a possible threat may turn people ignorant when the threat becomes an actual danger. President Donald Trump has been warning about "fake news" throughout his entire political career. And now the real wolf might be just around the corner.

Businesses are increasing the pace of investment in AI systems to defend against the next generation of cyberattacks, a new study from the Capgemini Research Institute has found. Two thirds (69%) of organizations acknowledge that they will not be able to respond to critical threats without AI. With the number of end-user devices, networks, and user interfaces growing as a result of advances in the cloud, IoT, 5G and conversational interface technologies, organizations face an urgent need to continually ramp up and improve their cybersecurity. AI-enabled cybersecurity is now an imperative: Over half (56%) of executives say their cybersecurity analysts are overwhelmed by the vast array of data points they need to monitor to detect and prevent intrusion. Accordingly, almost half (48%) said that budgets for AI in cybersecurity will increase in FY2020 by nearly a third (29%).

Artificial intelligence can help transform health care by improving diagnosis, treatment, the delivery of patient care, and the efficiency of healthcare systems. But AI is only as good as the data it's built on. AI developers in the health sector are facing multiple challenges including limited access to health data, poor data quality, and concerns over the ethical use of data for AI. The Office of the Chief Technology Officer (CTO) in the U.S. Department of Health and Human Services (HHS) is now exploring the potential for a departmentwide AI strategy to facilitate the development of AI for health. As one important first step, the HHS Office of the CTO and the Center for Open Data Enterprise (CODE) co-hosted a roundtable in April to gather input from stakeholders outside of HHS.

It violates The Fair Housing Act to advertise in ways that deny particular segments of the housing market information about housing opportunities. It also violates New York law. But what happens when you use an advertising medium that discriminates on its own? We may just find out. New York Governor Andrew Cuomo recently directed the New York Department of Financial Services to investigate reports of housing providers harnessing Facebook's abilities to target users with precision.

Whilst the'special relationship' between the UK and the US has been put to the test this week following a diplomatic dispute, it's clear that there are still mutually beneficial investment opportunities for the two nations - particularly in the field of robotics and artificial intelligence (RAI). That was the view of a cohort of leading experts from the UK's RAI research, start-up and enterprise communities, who were taken out to California and Texas in March of this year on a mission to meet with some of the US's leading aerospace, marine and cross-cutting technology companies. The cohort included people from D-RiskQ, Shadow Robot, Rolls Royce, London Southbank University, Brunel University, Soil Machine Dynamics, Forth Engineering, Autonomous Devices, Headlight.AI and HyBird. Their feedback was presented at an event yesterday in central London, alongside the publication of their findings in new Knowledge Transfer Network (KTN) report - 'USA Robotics & AI in Extreme Environments 2019'. I'd recommend reading the report for a full overview of the mission and the RAI opportunities available.

Artificial Intelligence (AI) is nothing new. The field of AI research was founded in 1956. To date, this field has been always covered with huge expectations. Surely, we are in such a hype phase, but Google CEO Sundar Pichai is also right, when he says AI is bigger than the invention of fire and electricity. As one of the largest industrial nations, Germany addresses this big promise and published a AI strategy this month (Nov, 2018).