Daily Tech Digest - 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.

When -- and when not -- to use cloud native security tools

Cloud native security tools like Amazon Inspector and Microsoft Azure Security Center automatically inspect the configuration of common types of cloud workloads and generate alerts when potential security problems are detected. Google Cloud Data Loss Prevention and Amazon Macie provide similar functionality for data by automatically detecting sensitive information that is not properly secured and alerting the user. To protect data even further there are tools, such as Amazon GuardDuty and Azure Advanced Threat Protection, that monitor for events that could signal security issues within cloud-based and on-premises environments. ... IT teams use services like Google Cloud Armor, AWS Web Application Firewall and Azure Firewall to configure firewalls that control network access to applications running in the cloud. Related tools provide mitigation against DDoS attacks that target cloud-based resources. ... Data stored on the major public clouds can be encrypted electively -- or is encrypted automatically by default -- using native functionality built into storage services like Amazon S3 and Azure Blob Storage. Public cloud vendors also offer cloud-based key management services, like Azure Key Vault and Google Key Management Service, for securely keeping track of encryption keys.

Four Case Studies for Implementing Real-Time APIs

Unreliable or slow performance can directly impact or even prevent the adoption of new digital services, making it difficult for a business to maximize the potential of new products and expand its offerings. Thus, it is not only crucial that an API processes calls at acceptable speeds, but it is equally important to have an API infrastructure in place that is able to route traffic to resources correctly, authenticate users, secure APIs, prioritize calls, provide proper bandwidth, and cache API responses.  Most traditional APIM solutions were made to handle traffic between servers in the data center and the client applications accessing those APIs externally (north-south traffic). They also need constant connectivity between the control plane and data plane, which requires using third-party modules, scripts, and local databases. Processing a single request creates significant overhead — and it only gets more complex when dealing with the east-west traffic associated with a distributed application.  Considering that a single transaction or request could require multiple internal API calls, the bank found it extremely difficult to deliver good user experiences to their customers.

Building the foundations of effective data protection compliance

Data protection by design and default needs to be planned within the whole system, depending on the type of data and how much data a business has. Data classification is the categorization of data according to its level of sensitivity or value, using labels. These are attached as visual markings and metadata within the file. When classification is applied the metadata ensures that the data can only be accessed or used in accordance with the rules that correspond with its label. Businesses need to mitigate attacks and employee mistakes by starting with policy - assessing who has access. Then they should select a tool that fits the policy, not the other way round; you should never be faced with selecting a tool and then having to rewrite your policy to fit it. This will then support users with automation and labelling which will enhance the downstream technology. Once data is appropriately classified, security tools such as Data Loss Prevention (DLP), policy-based email encryption, access control and data governance tools are exponentially more effective, as they can access the information provided by the classification label and metadata that tells them how data should be managed and protected.

Q&A on the Book Fail to Learn

People often fear failure because of the stakes associated with it. When we create steep punishment systems and “one-strike-you’re-out” rules, it’s only natural to be terrified of messing up. This is where we need to think more like game designers. Games encourage trial and error because the cost of starting over in a game is practically nothing. If I die playing Halo, I get to respawn and try again immediately. We need to create more “respawn” options in the rest of our lives. This is something that educators can do in their course design. But it’s also something we can encourage as managers, company leaders, or simply as members of society. The best way to do this is to start talking more about our mistakes. These are things we should be able to celebrate, laugh over, shake our collective heads at, and eventually grow from. ... If we go back to people like Dyson and Edison, you see failure-to-success ratios that reach five-thousand or even ten-thousand to one. A venture capitalist who interviewed hundreds of CEOs arrived at the same ratio for start-up companies making it big: about a 10,000:1 failure-to-success ratio. Now, we probably don’t need that many failures in every segment of our lives, but think about how far off most of us are from these numbers.

Quote for the day:

"Leaders need to be optimists. Their vision is beyond the present." -- Rudy Giuliani

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