IMAGE: Map colors indicate estimates of migration traffic from measurements at 143 radar stations. Locations are indicated by white circles, with size proportional to migration traffic at the station. On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services' network of constantly scanning weather radars, until recently these data have been mostly out of reach for bird researchers. That's because the sheer magnitude of information and lack of tools to analyze it made only limited studies possible, says artificial intelligence (AI) researcher Dan Sheldon at the University of Massachusetts Amherst.

Fremont, CA: Cybersecurity, otherwise known as information technology security, refers to the act of protecting the data, systems, networks, and programs from digital attacks. In the existing connected world, the necessity to secure systems is rising. It is evident from the prevailing cyberattacks in different industry sectors like health care, government agencies, education institutions, and energy. According to the UK government's Cyber Security Breaches Survey 2019, 32% of businesses have identified cybersecurity breaches or attacks in the past 12 months. Besides, with the advent of new devices that overhaul people, security risks are evolving, and cyberattackers are becoming more innovative in implementing these attacks.

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. In Nigeria, AI has the power to reduce election fraud, improve infrastructure design and provide greater government security for intelligence, military and foreign relations. It would create jobs and expand job growth as it is expected to become more deeply embedded in science, healthcare, global business and trade. AI will evolve with greater transparency to resolve trust issues and lack of understanding. AI has the ability to "learn" when information is acquired.

Burleigh County Sheriff's Department receives federal waiver to fly drones over people

One of the most troubling trend narratives on the rise today is that of the direction intersection of tech algorithms and machine learning with that of housing and credit checks. Whether we use our residences as a much-needed retreat after work or that which is leveraged for both home and work, this area is critical because it provides our very foundation, literally and figuratively. Concerns around housing, whether redlining or other barriers, have always been issues in this country, however the volume on the topic is growing because we are relying more and more emerging technology such as artificial intelligence to make very final decisions on crucial life plays. Such usage is important to examine because technology never exists in vacuum. Tech intersects with cultural trends, various business agendas, subconscious and conscious cultural bias, therefore, it can and is taking harmful shape across a growing number of demographics.

Laura Nolan is a computer programmer who resigned from Google over Project Maven. She is now a member of the International Committee for Robot Arms Control (ICRAC) a founding member of the Campaign to Stop Killer Robots. There are many ethical, political and legal reasons to oppose autonomous weapons, which can strike without a direct human decision to attack. These are good reasons to worry about autonomous weapons, but I am a software engineer and I also oppose the development and use of autonomous weapons on technological grounds. Testing cannot find and eliminate them all.

When you post a photo on Facebook, and the platform automatically tags the people in the image, you might not give much thought to the technology behind the convenience. However, when you discover that facial recognition technology could track you without your permission while you walk down a street in London, it might make you question the invasion of your privacy. Just like with any other new technology, facial recognition brings positives and negatives with it. Since it's here to stay and expanding, it's good to be aware of the pros and cons of facial recognition. What is facial recognition, and how does it work? Facial recognition is a biometric technology that uses distinguishable facial features to identify a person.

There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Automated Driving (AD) features. These range from a need for road determination (<5 meters), lane determination (<1.5 meters), and determining where the vehicle is within the lane (<0.3 meters). This work examines the performance of Global Navigation Satellite Systems (GNSS) on 30,000 km of North American highways to better understand the automotive positioning needs it meets today and what might be possible in the near future with wide area GNSS correction services and multi-frequency receivers. This includes data from a representative automotive production GNSS used primarily for turn-by-turn navigation as well as an Inertial Navigation System which couples two survey grade GNSS receivers with a tactical grade Inertial Measurement Unit (IMU) to act as ground truth. The latter utilized networked Real-Time Kinematic (RTK) GNSS corrections delivered over a cellular modem in real-time. We assess on-road GNSS accuracy, availability, and continuity. Availability and continuity are broken down in terms of satellite visibility, satellite geometry, position type (RTK fixed, RTK float, or standard positioning), and RTK correction latency over the network. Results show that current automotive solutions are best suited to meet road determination requirements at 98% availability but are less suitable for lane determination at 57%. Multi-frequency receivers with RTK corrections were found more capable with road determination at 99.5%, lane determination at 98%, and highway-level lane departure protection at 91%.

Remote Sensing Images from satellites have been used in various domains for detecting and understanding structures on the ground surface. In this work, satellite images were used for localizing parking spaces and vehicles in parking lots for a given parcel using an RCNN based Neural Network Architectures. Parcel shapefiles and raster images from USGS image archive were used for developing images for both training and testing. Feature Pyramid based Mask RCNN yields average class accuracy of 97.56% for both parking spaces and vehicles

Existing computational methods for the analysis of corpora of text in natural language are still far from approaching a human level of understanding. We attempt to advance the state of the art by introducing a model and algorithmic framework to transform text into recursively structured data. We apply this to the analysis of news titles extracted from a social news aggregation website. We show that a recursive ordered hypergraph is a sufficiently generic structure to represent significant number of fundamental natural language constructs, with advantages over conventional approaches such as semantic graphs. We present a pipeline of transformations from the output of conventional NLP algorithms to such hypergraphs, which we denote as semantic hypergraphs. The features of these transformations include the creation of new concepts from existing ones, the organisation of statements into regular structures of predicates followed by an arbitrary number of entities and the ability to represent statements about other statements. We demonstrate knowledge inference from the hypergraph, identifying claims and expressions of conflicts, along with their participating actors and topics. We show how this enables the actor-centric summarization of conflicts, comparison of topics of claims between actors and networks of conflicts between actors in the context of a given topic. On the whole, we propose a hypergraphic knowledge representation model that can be used to provide effective overviews of a large corpus of text in natural language.