Pure Storage (NYSE: PSTG), a leading independent all-flash data platform vendor for the cloud era, announced significant customer momentum for FlashBlade, the system purpose-built for modern analytics. Since general availability in January 2017, FlashBlade has gained traction among organizations running and innovating with emerging workloads, specifically modern analytics, artificial intelligence (AI) and machine learning (ML). Data is at the center of the modern analytics revolution. Large amounts of data must be delivered to the parallel processors, like multi-core CPUs and GPUs, at incredibly high speeds in order to train machine learning and analytic algorithms faster and more accurately. Today, most machine learning production is undertaken by hyperscalers and large, web-scale companies.
Neural networks, and particularly deep learning research, have obtained many breakthroughs recently in the field of computer vision and other important fields in computer science. Deep neural networks, especially in the field of computer vision, object recognition and so on, have often a lot of parameters, millions of them. It's a quite recent model that achieved remarkable performances on object recognition tasks with very few parameters, and weighting just some megabytes. I added a recurrent layer to the output of one of the first densely connected layers of SqueezeNet: the network now takes as input 5 consecutive frames, and then the recurrent layers outputs a single real-valued number, the steering angle.
With the help of Microsoft, last year Toyota created a new data analytics division called Toyota Connected to bring Internet-connected services into the car. Earlier this year, Renault-Nissan inked a deal to leverage Microsoft's Connected Vehicle Platform and its Azure cloud architecture to collect vehicle sensor and usage data in order to develop "connected driving experiences." Ford recently invested $182 million in Pivotal, a cloud-based software company, in part to create analytics tools and a cloud platform to support the automaker's Smart Mobility initiative. Cadillac introduced the first production vehicle-to-vehicle communication system on its 2017 models, and last year, Audi launched a Traffic Light Information vehicle-to-infrastructure system that lets its cars know how long a light will stay red or green to help improve traffic flow.
Intel's Necati Canpolat argues that 5G and Wi-Fi will see increasing impacts on each other and require integration to make the best use of each technology. Cadence's Paul McLellan shares highlights from EDPS, where machine learning in EDA tools was a hot topic, plus a look at the challenges facing test. Editor in Chief Ed Sperling examines what happens if enough people can't afford or don't want driverless cars. Editor in Chief Ed Sperling finds more companies assessing pre-built and pre-verified circuits as a way of reducing time to market.
On Tuesday, NVIDIA unveiled the world's first artificial intelligence (AI) computer designed to drive fully autonomous vehicles by mid-2018. The new system, named Pegasus, extends the NVIDIA Drive PX AI computing platform to operate vehicles with Level 5 autonomy--without steering wheels, pedals, or mirrors. New types of cars will be invented, resembling offices, living rooms or hotel rooms on wheels. "The company hasn't claimed to have developed all the software, hardware, and data needed for automated driving; it's merely announced that it plans to market a chip that in theory could support the hardware and software envisioned for such a system," Walker Smith said.
Smart grids, connected to each other via the cloud, and utilising the IoT, big data analytics and machine learning, can significantly increase the energy efficiency of the existing grid. The result is advanced production optimised for resource consumption and cost including energy, raw materials and water, whilst also enabling connection with customer devices to optimise lifespan performance. Wider 4IR technologies incorporated by the IIoT platform include Virtual Reality product simulators to optimise smart product design, sensor-driven computing, industrial big data analytics, energy efficient robotics, and intelligent machine applications. IoT, sensors, AI and cloud-enabled'precision agriculture' can use on-farm sensors and connected machinery to access real-time data for farmer smart devices that can optimise how much water, energy, fertiliser and feed to use, increasing productivity whilst reducing energy use and product waste.
As companies rush to apply AI to high-value industrial tasks such as predictive maintenance or performance optimization, we are seeing a rush of investment in AI technologies. At many steps in the manufacturing process, AI will yield results based on its ability to understand its environment, analyze data, draw conclusions, and learn from experience to achieve continuous improvements. Using these methods, AI-powered hardware can visually inspect and provide superior QC on various products such as machined parts, painted car bodies, textured metal surfaces and more. While some companies are working to develop predictive forecasting and replenishment tools internally, others are turning to established vendors like Blue Yonder, which offer AI techniques capable of optimizing forecasting and replenishment while simultaneously adjusting pricing.
In order to navigate, autonomous vehicles must incorporate and analyze data from multiple sources, including internal vehicle sensors that monitor conditions such as engine performance and tire pressure, as well as external input from cameras, lidar, radar and wireless connections. Oracle's IoT cloud upgrade includes support for smart factories, another major application of machine learning technology. To facilitate the pursuit of this goal, the Industrial Internet Consortium recently launched the Smart Factory Machine Learning for Predictive Maintenance Testbed, a platform for exploring and evaluating machine learning techniques that can support predictive maintenance. By empowering mobile devices, autonomous vehicles and smart factories, machine learning innovations are boosting both the capability of the devices connected to the Internet of Things as well as the potential of the IoT as a whole.
SAE International has created the now-standard definitions for the six distinct levels of autonomy, from Level 1 representing only minor driver assistance (like today's cruise control) to Level 6 being the utopian dream of full automation: naps and movie-watching permitted. Many of the features of AI-assisted driving center around increased safety, like automatic braking, collision avoidance systems, pedestrian and cyclists alerts, cross-traffic alerts, and intelligent cruise control. A connected vehicle could also share performance data directly with the manufacturer (called "cognitive predictive maintenance"), allowing for diagnosis and even correction of performance issues without a stop at the dealer. Although it may not at first appear directly tied to automotive AI, the health and medical industry stands to experience some significant disruptions as well.
Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative's new "Federal Automated Vehicle Policy" as well as similar policy just emerging in Germany. Just today (9/6/17) the US House of Representatives released its 116 page "Federal Automated Vehicles Policy". Equally as interesting is that just two weeks ago the German federal government published its guidelines for Highly Automated Vehicles (HAV being the new name of choice for these vehicles). On the 6 point automation scale in which 0 is no automation and 5 is where the automated system can perform all driving tasks, under all conditions, the new policy applies to level 3 or higher (though the broad standards also apply to the partial automation in levels 1 and 2).