Anyone can Build Omniverse Applications?

 Though still a skeptic of the current Omniverse,   I admit that NVIDIA being involved can make it move. 

Anyone Can Build Metaverse Applications With New Beta Release of NVIDIA Omniverse

By Frank DeLise

Digital Twin & Metaverse, News, Omniverse, open source, Universal Scene Description

The new beta release of NVIDIA Omniverse is now available with major updates to core reference applications and tools for developers, creators, and novices looking to build metaverse applications.

Each of the core components of the Omniverse platform have been updated to make it even faster, more accessible, and more flexible for collaborative workflows across applications. These updates empower developers of any background to easily build their custom applications, connections, and extensions anywhere. Learn more about how to develop on NVIDIA Omniverse.

Powered by support for new NVIDIA Ada Generation GPUs and advances in NVIDIA simulation technology, this new beta release focuses on maximizing ease of ingesting large, complex scenes from multiple third-party applications, and maximizing real-time rendering, path tracing, and physics simulation.

Graphic of the five core components of NVIDIA Omniverse: Nucleus, Connect, Kit, Simulation, and RTX Renderer.

Figure 1. The five core components of NVIDIA Omniverse

Nucleus, the central database and collaboration engine of NVIDIA Omniverse, now enables faster live collaboration and copying between servers. Nucleus Navigator 3.2 makes it possible to move files and folders seamlessly between servers located on-premises and in the cloud. It also adds enhanced search functionality to quickly retrieve images, objects, and other assets. OmniObjects with Omniverse Live 2.0 allows faster collaboration between Connectors.

New and updated Connectors for popular apps are available through Omniverse Connect, the libraries that allow you to create Connectors from your favorite apps to the Omniverse platform. The beta release includes new and updated Connectors for PTC Creo, Autodesk Alias, Kitware ParaView, Siemens JT, and Autodesk Maya, among others.

PhysX 5, the flagship tool of Omniverse Simulation, has been open sourced so you can easily modify, build, and distribute your own physics simulation applications. The new version of PhysX comes with exciting new features like support for multiple scenes, collision-triggered audio, and an inspector for robotic applications. Experience Omniverse Simulation by downloading Omniverse and testing technical demos in Omniverse Showroom to see the power of PhysX 5 and real-time RTX Rendering.

New features and capabilities across Omniverse applications are driven by Omniverse Kit 104, which now allows novice or experienced Python and C++ developers to more easily develop, package, and publish their own custom metaverse applications and extensions to accelerate industry-specific workflows.  ... ' 

11 Examples of Blockchain Use

Lisa Morgan  in InformationWeek  ... 

Blockchain Gets Real Across Industries

Blockchain use cases are exploding across industries because these days trust is 'a thing.' Here are 11 examples.

Bitcoin arguably put blockchain technology on the map for the general public. Of course, without blockchain technology, Bitcoin wouldn't exist.

Few remember the early "digital cash" initiatives that emerged not long after the debut of the World Wide Web. Back then, the fatal flaw was operating outside the established financial ecosystems. Blockchain, because it provides permanent, immutable records of transactions, was apparently the missing piece.

While many organizations have launched blockchain initiatives, multinational professional services network KPMG is now approaching it differently.

"Blockchain is now at a point where it's just part of the overall technology stack and that's how we're approaching it," said Tegan Keele, managing director of enterprise innovation at KPMG.

Up until about a month ago, Keele's team was branded as "the blockchain team." Part of that team focuses on crypto asset services that are about making blockchain useful for large financial services companies and helping crypto-native companies understand regulatory control Know Your Customer (KYC) transaction monitoring. Keele's focus is climate monitoring -- specifically, climate accounting that enables companies to prove their emissions footprint and their progress toward Net Zero and other climate-related goals.  ... ' 

Digital Twins

Useful piece on Digital Twins and their definition and use.   Adds to my current examination and use cases.

The Multiple Faces of Digital Twins   By Alex Woodie  in Datanami

Digital twins are emerging as a hot technology, particularly among manufacturers and companies involved with the Industrial Internet of Things. Depending on the use cases, though, customers may opt for one type of digital twin over another.

To a certain extent, every digital twin is a unique creation. The ability to create a digitized copy of an actual physical asset, such as a wind turbine or a locomotive, and measure how that model responds and reacts to different inputs is the fundamental breakthrough that is driving adoption of digital twin technologies.

But there are a few broad categories of digital twins, and companies that are considering adopting a digital twin would do well to explore how their use cases match up to these types.

According to Philipp Wallner, the industry manager for MathWorks, there are two general types of digital twins: physics-based twins and data-based twins.

The physics-based digital twin is a detailed reproduction of a well-understood piece of machinery that behaves in a predictable manner, such as a robot or a piece of equipment on a manufacturing line. In some cases, these physics-based digital twins are created by importing CAD files into a simulation platform.

Data-based twins, on the other hand, are models that machineries or processes that are not as well understood and have very complex interactions, such as a compressor. While the physics driving these machines are generally understood, the high number of variables involved, including the shapes of vessels, precludes basing the model directly on physics. However, users do have a large amount of data describing the machine’s behavior, which becomes the basis for the model.

MathWorks helps its customers build both types of digital twins. One of its customers, Krones, developed a digital twin in MathWorks’ software based on CAD drawings of its bottle-handling robot.

“What we offer is a CAD import functionality,” Wallner says. “So you take these CAD models, and you import them. And then you already have a pretty good basis for your model. You still have to add additional parameters and some of the functionality that is not captured in the CAD model.”  ... ' 

Why Your AI Project May Fail

This is true, if you don't have ready understanding/access into the local architecture, its much harder to get the data to train models in context.    And certainly also very hard to implement them into any sort of an deployed  model.  This is true whenever you hope to get anything used by a client, no necessarily just an AI project though.   To e clear though, you usually understand this fairly early on.   And people who are already there will usually tell you

The Dumb Reason Your AI Project Will Fail
by Terence Tse , Mark Esposito , Takaaki Mizuno and Danny Goh  in the HBR

Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the time, money, and effort necessary to achieve resounding success with their proof of concept and demonstrate how the use of artificial intelligence will improve their business. Then everything comes to a screeching halt — the company finds themselves stuck, at a dead end, with their outstanding proof of concept mothballed and their teams frustrated.

What explains the disappointing end? Well, it’s hard — in fact, very hard — to integrate AI models into a company’s overall technology architecture. Doing so requires properly embedding the new technology into the larger IT systems and infrastructure — a top-notch AI won’t do you any good if you can’t connect it to your existing systems. But while companies pour time and resources into thinking about the AI models themselves, they often do so while failing to consider how to make it actually work with the systems they have.

The missing component here is AI Operations — or “AIOps” for short. It is a practice involving building, integrating, testing, releasing, deploying, and managing the system to turn the results from AI models into desired insights of the end-users. At its most basic, AIOps boils down to having not just the right hardware and software but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems. Evolved from a software engineering and practice that aims to integrate software development and software operations, it is the key to converting the work of AI engines into real business offerings and achieving AI at a large, reliable scale.  ... "