Hexagon Partners with Microsoft Using Cloud Technology for Team Collaboration

Partnering with Microsoft continues to be an important part of manufacturing software development. This news is from Hexagon partnering with Microsoft to integrate engineering with Microsoft 365 to foster data collaboration among engineers. There’s a lot of marketing overkill in the release, but the essence is they hope to improve innovation through engineers and designers using improved collaboration tools.

  • Hexagon has contributed significantly to the open-source Fluid Framework data architecture that connects any manufacturing system and will integrate with Microsoft 365 creating agile, simplified workflows and productive collaboration using engineering and productivity software
  • Hexagon will roll-out applications that integrate the Microsoft Azure OpenAI Service to empower experienced employees to be more productive and assist less skilled users
  • These innovations form a significant foundation for new real-time co-engineering applications that combine Hexagon’s digital twin technologies with Microsoft Azure

Hexagon and Microsoft have partnered closely on the development and scaling of the open-source Fluid Framework and Azure Fluid Relay service to support the real-time sharing of data across a wide range of manufacturing industry processes and systems, allowing data created in one system to be immediately available to any other person or machine operating in another. Under the new partnership, the Microsoft 365 ecosystem will plug into this data layer, enabling customers to connect their day-to-day office documents and processes with manufacturing tools. This gives teams the freedom to innovate with the tools they already use; for example, tooling cost data from a Microsoft Excel worksheet could be easily shared with a CAM programmer, so simplifying work practices and decision-making between roles.

Microsoft Teams calls can become interactive working sessions, with CAD, simulations or metrology point clouds seamlessly visualised from the source data to allow on-the-spot collaboration and fast, iterative teamwork across disparate engineering and manufacturing functions. Hexagon has already demonstrated this capability in its 3D Whiteboard Nexus tool, which is also now available as an native app in Teams.

Hexagon is working with Microsoft to integrate generative AI models into its manufacturing software, helping users to make better use of their capabilities and analysing existing datasets to learn and suggest the best practices for achieving desired outputs. These AI experiences include contextual advisors, offering expert users productivity-boosting automation while also helping new users to upskill faster and achieve good results with less supervision – a valuable tool as the industry faces a growing skills shortage in many essential roles.

SymphonyAI announces IRIS Foundry, an AI-powered Industrial Data Ops Platform

Now that ChatGPT has been out for a while, people playing with it have discovered the shortcomings. Today at MIT, Sam Altman acknowledged the shortcomings of GenerativeAI. But that doesn’t stop companies from jumping on the GenAI bandwagon. Yes, they are using it. I’d just suggest doing a test drive or asking a lot of questions to discover just what it can do for you.

This is news from a company using Generative AI for predictive applications. Check it out.

SymphonyAI, a leader in predictive and generative enterprise AI SaaS, announced IRIS (Industrial Reasoning and Insights Service) Foundry, an industrial data operations platform for the rapid creation of robust digital industrial applications that improve process efficiency, reduce unscheduled asset downtime, and enhance connected worker capabilities. IRIS Foundry, powered by SymphonyAI’s award-winning predictive and generative EurekaAI platform, uses AI-enabled data contextualization at enterprise scale and is both open and composable.

IRIS Foundry provides the differentiating building blocks of industrial data management and governance needed to deploy AI-embedded manufacturing solutions at enterprise scale. IRIS Foundry has prebuilt connectors to extract data from IT, OT, and enterprise data sources into polyglot dataops storage to ensure versatile handling and integration of multiple data contexts. Data is organized into a structured asset hierarchy using AI-powered P&ID ingestion or through an existing asset historian framework. This process, enhanced with sophisticated contextualization services, automatically maps data into a unified namespace. The result is a dynamic industrial knowledge graph, simplifying access to and navigation of information. The IRIS Foundry knowledge graph is a foundational layer for enriched analysis and insights, empowering IRIS copilots for user-based interactions and guiding the exploration and understanding of complex data landscapes. Industrial applications built on IRIS Foundry adhere to data governance, audit, and security standards.

IRIS Foundry offers a low-code, drag-and-drop user experience, easy integration with programming tools, and an ability to deploy in various modes ranging from SaaS to customer-hosted models in a private cloud. Built on a lightweight architecture with cloud and edge computing in scope, the install footprint is synergistic with manufacturers’ operational technology (OT), information technology (IT), and external data ecosystems and contains hundreds of prebuilt connectors, reducing the effort to unify industrial data.

Guardrails—Guiding Human Decisions

A personal development speaker I often listen to delivers a set of talks on developing personal guardrails designed to prevent us from going off the deep end emotionally and relationally. Similarly as we explore this new age of artificial intelligence (AI) people are recognizing that we could use a set of guardrails to help guide our collective decisions using this new technology.

Collective guardrails generally include social norms, laws, and rules. Do we have any existing guardrails that will help us navigate AI? Where might they come from? What guardrails might work? Which might fall short?

Guardrails: Guiding Human Decisions in the Age of AI by Urs Gasser and Viktor Mayer-Schönberger came out recently. I promised to read and review it a couple of months ago. It got buried amongst other reading, plus it is not one of those “skim through” business books. This book has real meat. Based on the latest insights from the cognitive sciences, economics, and public policy, Guardrails offers a novel approach to shaping decisions by embracing human agency in its social context.

The authors with meticulous research lead us through technology approaches and social approaches through laws and regulations revealing the benefits but also the shortcomings of each.

From the press release: In this visionary book, Urs Gasser and Viktor Mayer-Schönberger show how the quick embrace of technological solutions can lead to results we don’t always want and explain how society itself can provide guardrails more suited to the digital age, ones that empower individual choice while accounting for the social good, encourage flexibility in the face of changing circumstances, and ultimately help us to make better decisions as we tackle the most daunting problems of our times, such as global injustice and climate change.

They conclude, “We hope that our readers—and everyone in governments, companies, and communities tasked with confronting some of humanity’s biggest challenges—will embrace this timely opportunity to think about and experiment with smarter guardrails to work toward better, fairer, and more sustainable futures.”

Urs Gasser is professor of public policy, governance, and innovative technology and dean of the School of Social Sciences and Technology at the Technical University of Munich. His books include (with John Palfrey) Born Digital: How Children Grow Up in a Digital Age. Viktor Mayer-Schönberger is professor of internet governance and regulation at the University of Oxford. His books include Delete: The Virtue of Forgetting in the Digital Age (Princeton).

Aras Study Finds 80% of Industrial Companies Unprepared for the Use of Artificial Intelligence

 I went to the Aras customer conference for the first time this year. Interesting company, good products, innovative customers. But, sorry, I’m hardly shocked that a survey of 835 “executive level experts” say their companies are not prepared to use Artificial Intelligence. We are all still feeling our way along the path toward discovering if there is a use or not. AR and VR are much farther along the hype curve and still haven’t really found a place.

However, you can check out all the details here.

Aras, a leader in product lifecycle management (PLM) and digital thread solutions, announced today findings from its report, “Spotlight on the Future 2024,” highlighting that nearly 80% of industrial companies lack the knowledge or capacity to successfully use artificial intelligence (AI).

Oh, PLM users seem to be the best positioned to benefit. You can pick up a few ideas from my interview with CTO Rob McAveney.

Despite this unpreparedness, 84% of companies expect AI to provide new or better services, while 82% expect an increase in quality. These findings come from Aras’ recent global industry study in which 835 executive-level experts across the United States, Europe, and Japan were surveyed.

“Adapting and modernizing the existing IT landscape can remove barriers and enable companies to reap the benefits of AI,” said Roque Martin, CEO of Aras. Current gaps in the industry according to Aras’ global study, include capacity bottlenecks 79%, lack of knowledge 77%, reliance on isolated IT applications 75%, and existing data quality concerns 70%.

The findings from the report suggest that augmenting product lifecycle management (PLM) with AI leads to improved effectiveness. Some 75% of respondents noted AI’s influences on their PLM strategy, while 2/3 of respondents said that their current PLM platform and data infrastructure is well-prepared for AI technologies.

Martin added, “Companies that are already using a flexible and modern PLM are much better prepared for the challenges of new, data-intensive technologies, leveraging AI to their benefit.”

Study participants rely primarily on datasets such as product data, quality control data, production data, or customer data. Many survey respondents acknowledge their data quality is not enough to achieve their company’s goals. As a result, 51% of respondents are intensifying their efforts to improve production, while 46% are looking at services data, and 45 percent are paying special attention to research and development datasets. These findings show a growing recognition of the important role that high-quality data plays in driving successful AI use within enterprises.

Generative AI Is Seemingly Everywhere

Generative AI is all the rage. When it hits all the main-stream media, though, it be already past its prime according to the Gartner Hype Cycle. NVIDIA chips power the GenAI surge. It takes a lot of compute and a lot of electricity to power this technology. Especially so when everyone wants into the act.

NVIDIA has announced some pretty good sales and earnings. Not one to let an opportunity pass by, It has recently inked agreements (all the companies expect to accelerate digital disruption, of course) with these industrial companies:

Hitachi Collaborates with NVIDIA to Accelerate Digital Transformation with Generative AI 

Schneider Electric Collaborates with NVIDIA on Designs for AI Data Centers

Siemens and NVIDIA Expand Collaboration on Generative AI for Immersive Real-time Visualization 

Rockwell Automation to Increase Scale and Scope of AI in Manufacturing with NVIDIA

Seeq Announces Generative AI Capabilities with Seeq AI Assistant

I sincerely hope you are not tired of reading about generative AI, because that will be the news for the rest of the year as each company introduced it as part of their software solution. This one comes from Seeq.

Seeq unveiled the Seeq AI Assistant, a generative AI (GenAI) resource embedded across its industrial analytics platform. The Seeq AI Assistant provides real-time assistance to users across the enterprise, empowering them to accelerate mastery of the Seeq platform, build advanced analytics, machine learning, and AI skills and knowledge, and accelerate insights to improve decision making in pursuit of operational excellence and sustainability.

The Seeq AI Assistant provides organizations with the opportunity to further de-bottleneck their most precious resource – the people at the frontlines of their processes and decisions.

This paragraph is a bit confusing, but I think realistic. You can try out GenerativeAI and play with it, but as far as trusting—well, you’d better double check results.

GenAI is a type of artificial intelligence capable of generating new content, such as text, images, and code in response to prompts entered by a user. GenAI models are trained with existing data to learn patterns that enable the creation of new content. While GenAI is a powerful technology, it isn’t innately capable of generating information and guidance applicable within the complexity and context of an industrial production environment.

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