Integrating DataOps, artificial intelligence (AI) and machine learning (ML), Hitachi Vantara is touting big data technology in a Tuesday announcement of its Lumada Manufacturing Insights, a suite of Industrial Internet-of-Things (IoT) products. DataOps, which is an "automated, process-oriented methodology, used by analytic and data teams," according to a Wikipedia article, appears to be a major emphasis for Santa Clara, Calif.-based Hitachi Vantara, a wholly owned subsidiary of Japan's Hitachi, Ltd. "We know DataOps," pops out in large white letters on the first page of the company Web site. After asking if the Web visitor knows about the methodology, the site proclaims: "You know there's value in your data. But you've only scratched the surface. To get the full value out of your data, you need to get the right data to the right place at the right time. DataOps helps you do that."

On August 29, 2019 I put on a workshop on Emerging Memories and Artificial Intelligence at Stanford University put on by the Stanford Center for Magnetic Nanotechnology and Coughlin Associates. We had several interesting speakers talking about various types of artificial intelligence and the role that new non-volatile memories will play in both training AI models and implementing them in the field using inference engines. This piece will talk about some of the material presented at this workshop. Dr. Shan Wang, co-organizer of the event gave a introduction, talking about emerging non-volatile memories and in particular on Magnetic Random Access Memory (MRAM). He spoke about how various new memories work--in particular Resistive RAM (RRAM), Phase Change Memory (PCM), MRAM and Ferrroelectic RAM (FRAM).

Data extraction from printed forms is by now a tried and true technology. Form Recognizer extracts key value pairs, tables and text from documents such as W2 tax statements, oil and gas drilling well reports, completion reports, invoices, and purchase orders. However, real-world businesses often rely on a variety of documents for their day-to-day needs that are not always cleanly printed. We are excited to announce the addition of handwritten and mixed-mode (printed and handwritten) support. Starting now, handling handwritten and mixed-mode forms is the new norm.

Machine learning algorithms can take important decisions, sometimes legally binding, about our everyday life. In most cases, however, these systems and decisions are neither regulated nor certified. Given the potential harm that these algorithms can cause, qualities such as fairness, accountability and transparency of predictive systems are of paramount importance. Recent literature suggested voluntary self-reporting on these aspects of predictive systems -- e.g., data sheets for data sets -- but their scope is often limited to a single component of a machine learning pipeline, and producing them requires manual labour. To resolve this impasse and ensure high-quality, fair, transparent and reliable machine learning systems, we developed an open source toolbox that can inspect selected fairness, accountability and transparency aspects of these systems to automatically and objectively report them back to their engineers and users. We describe design, scope and usage examples of this Python toolbox in this paper. The toolbox provides functionality for inspecting fairness, accountability and transparency of all aspects of the machine learning process: data (and their features), models and predictions. It is available to the public under the BSD 3-Clause open source licence.

In this feature, Dr Doron Myersdorf, CEO of StoreDot, explores how AI might just hold the key to solving these issues, and the potential of this approach for the future of energy storage and EVs. By 2030, it is expected that the total sales of electric vehicles (EV) worldwide will surpass 30 million, and the cost parameters will finally come in line with the internal combustion engine. The EV represents a seismic shift in the automotive industry, and its potential impact on business and the environment is compelling. Today, the debates about adoption and longevity of EVs is rife and none more so than the potential performance of EV batteries. For scientists, many challenges still remain - from battery safety, to energy density, charging capabilities, and their performance within a car.

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Universal mCloud is creating a more efficient future with the use of AI and analytics, curbing energy waste, maximizing energy production, and getting the most out of critical energy infrastructure. Through mCloud's AI-powered AssetCare platform, mCloud offers complete asset management solutions to three distinct segments: smart facilities, power generation, and process industries including oil and gas. IoT sensors bring data from connected assets into the cloud, where AI and analytics are applied to maximize their performance. Headquartered in Vancouver, Canada with offices in locations worldwide including Calgary, San Francisco, and Beijing, the mCloud family includes an ecosystem of operating subsidiaries that deliver high-performance IoT, AI, 3D, and mobile capabilities to customers, all integrated into AssetCare. With over 100 blue-chip customers and more than 35,000 assets connected in thousands of locations worldwide, mCloud is changing the way energy assets are managed.

DNV GL operates globally across a range of industries to provide trust and help stakeholders understand and manage risks. The company has over 2,200 experts providing advice for renewable generation, transmission, distribution and energy management and efficiency. The work they do ranges from testing high voltage grid components to bankability assessments to allow solar farms to be financed. Their latest research showcases the insight related to how drone technology and computer vision can help with inspections while also providing a global outlook to 2050. We wanted to learn more about the energy innovations DNV GL is exploring and utilizing, so we talked with Elizabeth Traiger, Senior Researcher, Power & Renewables DK & GB at DNV GL – Energy.

Tesla's battery research partner has released a new paper on a battery cell that could last over 1 million miles, which they say is going to be particularly useful in'robot taxis' -- something that Tesla wants to bring to market. When talking about the economics of Tesla's future fleet of robotaxis at the Tesla Autonomy Event, Tesla CEO Elon Musk emphasized that the vehicles need to be durable in order for the economics to work: The cars currently built are all designed for a million miles of operation. The drive unit is design, tested, and validated for 1 million miles of operation. But the CEO admitted that the battery packs are not built to last 1 million miles. Earlier this year, Musk said that they built Model 3 to last as long as a commercial truck, a million miles, and the battery modules should last between 300,000 miles and 500,000 miles.

Since late last year, a group of workers within Amazon have been organizing to push the company to radically reduce its carbon emissions. Yesterday, they announced a major new action: on 20 September, Amazon workers around the world will walk out of their offices to join the Global Climate Strike. So far, over 1,000 workers have pledged to participate. The organizers have three demands. They want the company to commit to zero emissions by 2030, to have zero custom cloud computing contracts with fossil fuel companies, and to spend zero dollars on funding climate-denying lobbyists and politicians. I spoke to one of the walkout's organizers, a 28-year-old Amazon employee in Seattle named Rebecca Sheppard.