September 22, 2020

How industrial AI will power the refining industry in the future

The ultimate vision for the industry is the self-optimising, autonomous plant – and the increasing deployment of artificial intelligence (AI) across the sector is bringing the reality of this ever closer. However, while refining has been an early adopter of many digital tools, the industry is yet to fully realise the potential of industrial AI. That is, in no small part, because AI and machine learning are too often looked at in isolation, rather than being combined with existing engineering capabilities – models, tools and expertise, to deliver a practical solution that effectively optimises refinery assets. ... Machine learning is used to create the model, leveraging simulation, plant or pilot plant data. The model also uses domain knowledge, including first principles and engineering constraints, to build an enriched model — without requiring the user to have deep process expertise or be an AI expert. The solutions supported by hybrid models act as a bridge between the first principles-focused world of the past and the “smart refinery” environment of the future. They are the essential catalyst helping to enable the self-optimising plant.

Microsoft's new feature uses AI to make video chat less weird

Eye Contact uses the custom artificial intelligence (AI) engine in the Surface Pro X's SQ1 SOC, so you shouldn't see any performance degradation, as much of the complex real-time computational photography is handed off to it and to the integrated GPU. Everything is handled at a device driver level, so it works with any app that uses the front-facing camera -- it doesn't matter if you're using Teams or Skype or Slack or Zoom, they all get the benefit. There's only one constraint: the Surface Pro X must be in landscape mode, as the machine learning model used in Eye Contact won't work if you hold the tablet vertically. In practice that shouldn't be much of an issue, as most video-conferencing apps assume that you're using a standard desktop monitor rather than a tablet PC, and so are optimised for landscape layouts. The question for the future is whether this machine-learning approach can be brought to other devices. Sadly it's unlikely to be a general-purpose solution for some time; it needs to be built into the camera drivers and Microsoft here has the advantage of owning both the camera software and the processor architecture in the Surface Pro X.

Digital transformation: 5 ways the pandemic forced change

Zemmel says that the evolution of the role of the CIO has been accelerated as well. He sees CIOs increasingly reporting to the CEO because they increasingly have a dual mandate. In addition to their historical operational role running the IT department, they now are also customer-facing and driving revenue. That mandate is not new for forward-looking IT organizations, but the pandemic has made other organizations hyper-aware of IT’s role in driving change quickly. CIOs are becoming a sort of “chief influencing officer who is breaking down silos and driving adoption of digital products,” Zemmel adds. Experian’s Libenson puts it this way: “The pandemic has forced us to be closer to the business than before. We had a seat at the table before. But I think we will be a better organization after this.” The various panelists gave nods to the role of technology, especially the use of data; Zemmel describes the second generation of B2B digital selling as “capturing the ‘digital exhaust’ to drive new analytic insights and using data to drive performance and create more immersive experiences.”

Diligent Engine: A Modern Cross-Platform Low-Level Graphics Library

Graphics APIs have come a long way from a small set of basic commands allowing limited control of configurable stages of early 3D accelerators to very low-level programming interfaces exposing almost every aspect of the underlying graphics hardware. The next-generation APIs, Direct3D12 by Microsoft and Vulkan by Khronos are relatively new and have only started getting widespread adoption and support from hardware vendors, while Direct3D11 and OpenGL are still considered industry standard. ... This article describes Diligent Engine, a light-weight cross-platform graphics API abstraction layer that is designed to solve these problems. Its main goal is to take advantages of the next-generation APIs such as Direct3D12 and Vulkan, but at the same time provide support for older platforms via Direct3D11, OpenGL and OpenGLES. Diligent Engine exposes common C/C++ front-end for all supported platforms and provides interoperability with underlying native APIs. It also supports integration with Unity and is designed to be used as graphics subsystem in a standalone game engine, Unity native plugin or any other 3D application. The full source code is available for download at GitHub and is free to use.

Supporting mobile workers everywhere

It is amazing how quickly video conferencing has been accepted as part of the daily routine. Such is the success of services like Zoom that CIOs need to reassess priorities. In a workforce where people are working from home regularly, remote access is not limited to a few, but must be available to all. Mobile access and connectivity for the mobile workforce needs to extend to employees’ homes. Traditional VPN access has scalability limitations and is inefficient when used to provide access to modern SaaS-based enterprise applications. To reach all home workers, some organisations are replacing their VPNs with SD-WANs. There is also an opportunity to revisit bring-your-own-device (BYOD) policies. If people have access to computing at home and their devices can be secured, then CIOs should question the need to push out corporate laptops to home workers. While IT departments may have traditionally deployed virtual desktop infrastructure (VDI) to stream business applications to thin client devices, desktop as a service (DaaS) is a natural choice to delivering a managed desktop environment to home workers. For those organisations that are reluctant to use DaaS in the public cloud, as Oxford University Social Sciences Division (OSSD) has found (see below), desktop software can easily be delivered in a secure and manageable way using containers.

Secure data sharing in a world concerned with privacy

Compliance costs and legal risks are prompting companies to consider an innovative data sharing method based on PETs: a new genre of technologies which can help them bridge competing privacy frameworks. PETs are a category of technologies that protect data along its lifecycle while maintaining its utility, even for advanced AI and machine learning processes. PETs allow their users to harness the benefits of big data while protecting personally identifiable information (PII) and other sensitive information, thus maintaining stringent privacy standards. One such PET playing a growing role in privacy-preserving information sharing is Homomorphic Encryption (HE), a technique regarded by many as the holy grail of data protection. HE enables multiple parties to securely collaborate on encrypted data by conducting analysis on data which remains encrypted throughout the process, never exposing personal or confidential information. Through HE, companies can derive the necessary insights from big data while protecting individuals’ personal details – and, crucially, while remaining compliant with privacy legislation because the data is never exposed.

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