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Verizon details network enhancements aimed at improving GPS accuracy

ZDNet

Verizon on Thursday released details around its initiative to enhance the GPS accuracy of the phones, drones, and IoT devices that run on its network. The company said it's been building and deploying Real Time Kinematics (RTK) reference stations to its network that are meant to provide hyper-precise location information for connected devices. Verizon said RTK's pinpoint-level location data is a building block to bring to scale emerging technologies like driverless cars, drone delivery, and IoT. As part of its efforts, Verizon is also working with mapping provider HERE Technologies and automated mobility specialist Renovo on scaling autonomous vehicle technology that can address future autonomy needs and pedestrian safety issues. On the mapping side, the companies are pairing HERE's HD Map and HD Positioning technologies with intelligent RTK algorithms, and making that scalable.


The Emergence Of Hardware As A Key Enabler For The Age Of Artificial Intelligence

#artificialintelligence

Over the past few decades, software has been the engine of innovation for countless applications. From PCs to mobile phones, well-defined hardware platforms and instruction set architectures (ISA) have enabled many important advancements across vertical markets. The emergence of abundant-data computing is changing the software-hardware balance in a dramatic way. Diverse AI applications in facial recognition, virtual assistance, autonomous vehicles and more are sharing a common feature: They rely on hardware as the core enabler of innovation. Since 2017, the AI hardware market has grown 60-70% annually, and is projected to reach $65 billion by 2025.


Ibeo's LiDAR systems to provide higher autonomy to autonomous vehicles - Geospatial World

#artificialintelligence

Germany's Ibeo Automotive Systems, which specializes in lidar systems for autonomous driving, has signed a contract to provide China's Great Wall Motor Company (GWM) with its latest solid-state design. Ibeo said that it has commissioned key partner ZF Friedrichschafen – which in 2016 acquired a major stake in Ibeo – to produce the sensors and control unit for the "Level 3" system, which will provide partial autonomy. GWM has contracted one of its own subsidiaries to develop the system, which will be based around vertical cavity surface-emitting lasers (VCSELs) produced by Austria's AMS. Ibeo points out that, after signing a letter of intent in 2019, it has already been in pre-development with GWM for a year. Officially, the project started with the signing of an additional contract by the two parties last month.


Apes Spotted Flying Drone and Smiling

#artificialintelligence

In a new short video that has surfaced on TikTok, apes have been spotted flying drones. The drone is an Autel Robotics Evo and the apes are located in a Myrtle Beach Safari in South Carolina. The video was taken by photographer Nick B. and shows two apes flying a drone. One is standing up using the drone's controller while the other sits beside him holding the drone's case. The video is particularly impressive as the ape seems very much in control of the drone.


Know About Data Annotation

#artificialintelligence

Data annotation consists, text annotation, image annotation, and video annotation using the various techniques as per the project requirements and machine learning algorithms compatibility. Data annotation is done to create the training data sets for AI and ML while image annotation is a very important type of image annotation. A task of marking and outlining objects and entities on an image and offering various keywords to classify it which is readable for machines. Presently, Image annotation is growing very fast as image annotation is a very important task as this data helps to create accurate datasets that help computer vision models work in a real-world scenario and get effective results. We annotate & tag images with respective labels & keywords for easy and accurate categorization & help you in creating your customized image annotation services.


How AI can create self-driving data centers

#artificialintelligence

Most of the buzz around artificial intelligence (AI) centers on autonomous vehicles, chatbots, digital-twin technology, robotics, and the use of AI-based'smart' systems to extract business insight out of large data sets. But AI and machine learning (ML) will one day play an important role down among the server racks in the guts of the enterprise data center. AI's potential to boost data-center efficiency – and by extension improve the business – falls into four main categories: Put it all together and the vision is that AI can help enterprises create highly automated, secure, self-healing data centers that require little human intervention and run at high levels of efficiency and resiliency. "AI automation can scale to interpret data at levels beyond human capacity, gleaning imperative insights needed for optimizing energy use, distributing workloads and maximizing efficiency to achieve higher data-center asset utilization," explains Said Tabet, distinguished engineer in the global CTO office at Dell Technologies. Of course, much like the promise of self-driving cars, the self-driving data center isn't here yet.


Hyundai unveils its self-driving car company Motional that will launch as robotaxis by 2022

Daily Mail - Science & tech

Self-driving cars often use a combination of normal two-dimensional cameras and depth-sensing'LiDAR' units to recognize the world around them. However, others make use of visible light cameras that capture imagery of the roads and streets. They are trained with a wealth of information and vast databases of hundreds of thousands of clips which are processed using artificial intelligence to accurately identify people, signs and hazards. In LiDAR (light detection and ranging) scanning - which is used by Waymo - one or more lasers send out short pulses, which bounce back when they hit an obstacle. These sensors constantly scan the surrounding areas looking for information, acting as the'eyes' of the car.


Hyun Kim, CEO and Co-Founder, Superb AI – Interview Series

#artificialintelligence

Huyn Kim is the CEO and Co-Founder of Superb AI, a company that provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows. What initially attracted you to the field of AI, Data Science and Robotics? As an undergraduate majoring in Biomedical Engineering at Duke, I was passionate about genetics and how we can engineer our DNA to cure diseases or create genetically engineered organisms. I remember one wet-lab experiment distinctly that kept failing for like 6 months straight. The most frustrating part of it was that there was a lot of repetitive manual work, and in hindsight that was probably the root of some many potential errors.


Autonomous Self-driving Cars Working Principles 3D Animation

#artificialintelligence

Self-driving cars rely on hardware and software to drive down the road without user input. The hardware collects the data; the software organizes and compiles it.This animation explains the basic operation of self-driving vehicles. Self-driving cars combine a variety of sensors to perceive their surroundings, such as radar, lidar, sonar, GPS, odometry and inertial measurement units. The challenge for driverless car designers is to produce control systems capable of analyzing sensory data in order to provide accurate detection of other vehicles and the road ahead. Modern self-driving cars generally use Bayesian simultaneous localization and mapping (SLAM) algorithms,[63] which fuse data from multiple sensors and an off-line map into current location estimates and map updates.


Data Annotation- Types, Tools, Benefits, and Applications in Machine Learning

#artificialintelligence

In this article, we have mentioned what data annotation or labeling is, and what are its types and benefits. Besides this, we have also listed the top tools used for labeling images. The process of labeling texts, images, and other objects help ML-based algorithms to improve the accuracy of the output and offer an ultimate user experience. A reliable and experienced machine learning company holds expertise on how to utilize these data annotations for serving the purpose an ML algorithm is being designed for. You can contact such a company or hire ML developers to develop an ML-based application for your startup or enterprise. Read More: How does Machine Learning Revolutionizing the Mobile Applications?