Wild animals are experts at staying out of sight, but a new partnership between Google and the conservation organization Wildlife Insights will try to help scientists capture and analyze pictures of them in their natural habitat. The program will use an artificial intelligence program to sort through photographs taken by small sensor driven camera installations placed in wilderness areas around the world. Google's AI and Cloud services will help researchers analyse and archive the enormous volume of images captured through the program as part of an effort to improve animal conservation strategies all around the world. The camera traps were originally developed in 1990 and in the intervening years have been placed everywhere from Mexico to Madagascar. To date, 4.553 million pictures have been taken from 8,209 camera deployments.

The Russian space agency Roscosmos is planning to install a nuclear-powered observatory on its future moon base to held spot deadly Earth-threatening asteroids. Establishing a permanent presence near the lunar south pole has been a priority for Roscosmos ever since NASA announced plans to return to the moon earlier this year. The base's telescopes will work in tandem with spacecraft placed in orbit around the Earth to help provide humanity with a space-rock early warning system. In addition, the lunar facility's permanent crew will be made up of robots -- with cosmonauts only visiting to handle more complicated tasks. The plans to establish an observatory on the future moon base were announced by Alexander Bloshenko, Roscosmos' Executive Director for Science and Long-Term Programs, Russian news outlets RT and TASS reported.

Gay: As an astronomer, I have to admit, my day-to-day life is sitting at home writing software to help us better understand our universe. Then, as a communicator of science, it just makes me so excited to come out here and tell you about the kind of stuff I get to do. As an astronomer, I use data; images, spectra, photos but taken with cameras that are sometimes orbiting our world and other planets, moons, asteroids. For a lot of my career, everything I wanted to study, everything I wanted to learn, I could do with software, a database, and sometimes some really clumsy-linked lists because that was C in the 90s. Along the way though, I got curious about all these other areas of science that are different from mine. It was from the planetary-science community where I've somehow migrated over the years that I learned there are people - such as the folks who are today mapping out planet classic Pluto - that the way they do their analysis of the geological features on this world are to sit around round tables with a screen and a Wacom tablet. They draw by hand what they perceive to be the boundaries between different kinds of glaciers, different kinds of mountains, different features on this distant world. This is science by hand because humans and software don't know what to make of Pluto but the humans can at least guess. There's a lot of science that works this way. One of the most disturbing things I learned is there is a brilliant scientist Stuart Robbins who, as his PhD work at the University of Colorado, drew three million circles - again, with a Wacom tablet; go Wacom - three million circles on thousands and thousands of images of the surface of Mars. This ended up leading to a catalog of 600,000 craters. The reason he had to draw so many circles is he had to periodically remap regions to make sure that his bias hadn't changed over time. He had to map things at small scales, at big scales, at in-between scales, bridge across all of these, have overlapped between his image. Three million circles got him a PhD.

AI and Machines Learning Innovations from H2O.ai Drive Personalized and Better Experiences for Jewelers and Consumers H2O.ai, the open source leader in artificial intelligence (AI) and machine learning (ML), announced Jewelers Mutual, one of the United States' and Canada's most established and trusted providers of affordable and comprehensive insurance for jewelers and consumers, has chosen its award winning AI platforms to provide AI and machine learning capabilities to better serve its customers. As a leader in driving customer-focused innovation and providing the latest technology to a long-standing industry, Jewelers Mutual is using H2O-3 open source and H2O Driverless AI to deliver exceptional customer experiences, prevent losses, and provide better protection and policies for both jewelers and customers. "We have been in the jewelry insurance business for over 100 years, and our leadership team has been looking to raise the bar for technology-driven innovation in the industry. After two years of experimentation with AI and machine learning, we came to place a high value on model transparency and explainability. Our business end-users demanded it. The initial AI platform we used was lacking in this area so we began searching for a new platform," said Andrew Langsner, Senior Manager, Embedded Analytics at Jewelers Mutual.

Samsung Electronics has partnered up with Baidu to produce its new cloud-to-edge artificial intelligence (AI) chip, Kunlun, with mass production slated for early next year, the companies announced. It is the first such partnership between the South Korean tech giant and Chinese search behemoth. Kunlun will be built on Baidu's own XPU neural processor architecture for cloud, edge, and AI and will be made with Samsung's 14nm process. The South Korean company will also make the chips using its I-CubeTM, or interposed-cube, package solution. I-Cube integrates a System-on-a-Chip (SoC) and a High Bandwidth Memory (HBM) onto a silicon interposer to create a single package to increase electrical transference.

DALLAS, TX / ACCESSWIRE / December 17, 2019 / Trintech, a leading global provider of integrated Record to Report software solutions for the office of finance, today announced its newest Artificial Intelligence (AI) investments, AI Risk Rating for Journal Entries and Risk Intelligent Inspect powered by MindBridge Ai. Each of these investments leverage Financial Controls AI, a type of Artificial Intelligence developed specifically for the complex needs of the office of finance to identify errors and anomalies in financial data. It uses a risk-based approach to help financial professionals optimize global controls and automate workflow. "Artificial Intelligence is playing a powerful role in helping organizations analyze financial data, identify insights and ultimately remove risk in their balance sheet as far down as each individual transaction," said Michael Ross, Chief Product Officer at Trintech. "As the risk of fraudulent activity and misstatement continues to rise, we are continuing to invest in our AI strategy to better provide our customers with solutions that efficiently and effectively reduce risk throughout their financial close process."

I will also discuss the common technical challenges of executing A/B tests on ML algorithms, such as infrastructure requirements, connecting online and offline metrics, and handling ramp up periods for online learning algorithms. Overall, the goal of this talk will be to motivate ML practitioners to use A/B testing when evaluating their algorithms and provide them with high-level guidelines on how to do it. Profile Pavel Dmitriev is a Vice President of Data Science at Outreach, where he works on enabling data driven decision making in sales through experimentation and machine learning. He was previously a Principal Data Scientist with Microsoft's Analysis and Experimentation team, where he worked on scaling experimentation in Bing, Skype, and Windows OS. Pavel co-authored numerous papers at top-tier data mining and machine learning conferences, such as WWW, ICSE, KDD, has given keynotes and tutorials at WWW, SIGIR, SEAA, and KDD.

The perception system of an autonomous vehicle is responsible for mapping sensor observations into a semantic description of the vehicle's environment. 3D object detection is a common function within this system and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, limited field-of-view and low-point density of the sensor data cannot be reliably and cost-effectively addressed by multi-modal sensing from a single point of view. Alternatively, cooperative perception incorporates information from spatially diverse sensors distributed around the environment as a way to mitigate these limitations. This paper proposes two schemes for cooperative 3D object detection. The early fusion scheme combines point clouds from multiple spatially diverse sensing points of view before detection. In contrast, the late fusion scheme fuses the independently estimated bounding boxes from multiple spatially diverse sensors. We evaluate the performance of both schemes using a synthetic cooperative dataset created in two complex driving scenarios, a T-junction and a roundabout. The evaluation show that the early fusion approach outperforms late fusion by a significant margin at the cost of higher communication bandwidth. The results demonstrate that cooperative perception can recall more than 95% of the objects as opposed to 30% for single-point sensing in the most challenging scenario. To provide practical insights into the deployment of such system, we report how the number of sensors and their configuration impact the detection performance of the system.

An Intrusion Detection System (IDS) is a key cybersecurity tool for network administrators as it identifies malicious traffic and cyberattacks. With the recent successes of machine learning techniques such as deep learning, more and more IDS are now using machine learning algorithms to detect attacks faster. However, these systems lack robustness when facing previously unseen types of attacks. With the increasing number of new attacks, especially against Internet of Things devices, having a robust IDS able to spot unusual and new attacks becomes necessary. This work explores the possibility of leveraging generative adversarial models to improve the robustness of machine learning based IDS. More specifically, we propose a new method named SIGMA, that leverages adversarial examples to strengthen IDS against new types of attacks. Using Generative Adversarial Networks (GAN) and metaheuristics, SIGMA %Our method consists in generates adversarial examples, iteratively, and uses it to retrain a machine learning-based IDS, until a convergence of the detection rate (i.e. until the detection system is not improving anymore). A round of improvement consists of a generative phase, in which we use GANs and metaheuristics to generate instances ; an evaluation phase in which we calculate the detection rate of those newly generated attacks ; and a training phase, in which we train the IDS with those attacks. We have evaluated the SIGMA method for four standard machine learning classification algorithms acting as IDS, with a combination of GAN and a hybrid local-search and genetic algorithm, to generate new datasets of attacks. Our results show that SIGMA can successfully generate adversarial attacks against different machine learning based IDS. Also, using SIGMA, we can improve the performance of an IDS to up to 100\% after as little as two rounds of improvement.

There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. Most of the work is limited to either independent and identically distributed setting, or time series with independent and/or (sub-)Gaussian innovations. We extend current literature to a broader set of innovation processes, by assuming that the error process is non-sub-Gaussian and conditionally heteroscedastic, and the generating process is not necessarily sparse. This setting covers fat tailed, conditionally dependent innovations which is of particular interest for financial risk modeling. It covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications.