Autonomous cars use a variety of technologies like radar, lidar, odometry and computer vision to detect objects and people on the road, prompting it to adjust its trajectory accordingly. To tackle this problem, electrical engineers from University of California, San Diego used powerful machine learning techniques in a recent experiment that incorporated so-called deep learning algorithms in a pedestrian-detection system that performs in near real-time, using visual data only. The findings, which were presented at the International Conference on Computer Vision in Santiago, Chile, are an improvement over current methods of pedestrian detection, which uses something called cascade detection. This traditional form of classification architecture in computer vision takes a multi-stage approach that first breaks down an image into smaller image windows. These sub-images are then processed by whether they contain the presence of a pedestrian or not, using markers like shape and color.
Nvidia has announced the launch of EGX Edge Supercomputing Platform designed to let organisations easily deploy the hardware and software necessary for high-performance, low-latency AI workloads. Instead of being deployed inside big data centres, an EGX deployment is designed to sit at the edge of the cloud which, Nvidia believes, makes it ideal for the next generation of use cases. "We've entered a new era, where billions of always-on IoT sensors will be connected by 5G and processed by AI," Jensen Huang, Nvidia founder and CEO, said at a keynote ahead of MWC Los Angeles earlier this week. "Its foundation requires a new class of highly secure, networked computers operated with ease from far away. "We've created the Nvidia EGX Edge Supercomputing Platform for this world, where computing moves beyond personal and beyond the cloud to operate at planetary scale," he added. The EGX stack includes an Nvidia driver, Kubernetes plug-in, Nvidia container runtime, and GPU monitoring tools, delivered through the Nvidia GPU Operator, which allows you to standardise and automate the deployment of all necessary components for provisioning GPU-enabled Kubernetes systems. Nvidia will certify hardware as'NGC Ready for Edge' that customers will be able to buy from partners such as Advantech, Altos Computing, ASRock RACK, Atos, Dell Technologies, Fujitsu, GIGABYTE, Hewlett Packard Enterprise, Lenovo, MiTAC, QCT, Supermicro, and TYAN. Nvidia says EGX is already being used by customers. At Walmart's Intelligent Retail Lab in Levittown, New York, for example, EGX enables real time processing of more than 1.6 terabytes of data generated each second to "automatically alert associates to restock shelves, open up new checkout lanes, retrieve shopping carts, and ensure product freshness in meat and produce departments." The EGX platform features software to support a wide range of applications, including Nvidia Metropolis, which can be used to power smart cities and build intelligent video analytics applications. The city of Las Vegas, for example, is using EGX to capture vehicle and pedestrian data to make its streets safer. San Francisco's Union Square Business Improvement District is using EGX to capture real-time pedestrian counts for local retailers. "We use our smartphones sporadically -- we type into it, or watch a movie now or then -- and frankly there are only seven and a half billion of us," Huang said. "In the case of sensors, it will be streaming all the time.
The CES trade show is powering up again in Vegas. Most of the biggest names in tech and stacks of start-ups you've never heard of will compete for attention over the next week. Some products may launch new categories - past events presented a first look at video cassette recorders (VCRs), organic light-emitting diode (OLED) TVs and Android tablets. But many more will flop or never even make it to market. We've scoured the internet for hints about what will be on show... One of the biggest developments at the last few CES expos has been Amazon Alexa and Google Assistant's rival efforts to extend their reach in the home and beyond.
Department of Computer Science and Engineering Texas A&M University College Station, TX 77840, USA Abstract PyODDS is an end-to-end Py thon system for O utlier D etection with Database Support. It provides various outlier detection algorithms which meet the demands for users in different fields, with or without data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches. Keywords: anomaly detection, end-to-end system, outlier detection, deep learning, machine learning, data mining, full stack system, data visualization 1. Introduction Outliers refer to the objects with patterns or behaviors that are significantly rare and different with the rest of majorities.