by Gary Mintchell | Apr 24, 2024 | Generative AI
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).
by Gary Mintchell | Apr 24, 2024 | Generative AI
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.
by Gary Mintchell | Mar 25, 2024 | Generative AI, Operations Management
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
by Gary Mintchell | Mar 18, 2024 | Generative AI, Operations Management
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.
by Gary Mintchell | Feb 28, 2024 | Generative AI, Software
It had to happen sooner or later—GenerativeAI Large Language Model (LLM) for human-machine interface applications. Funny that nowhere in the press release do they mention HMI while using more awkward workaround phrasing. Maybe that is a Finish translation?
- Generative AI Large Language Model (LLM) technology for operational environments, bridging knowledge and language barriers between industrial workers and OT systems
- On-premise edge based MX Workmate solution enables connected workers to get contextually relevant real-time information and query OT-systems in a secure and reliable way using natural language
- OT-compliant MX Workmate automated IT/OT knowledge retrieval, eases interaction between workers and systems to drive efficiency, productivity and worker safety
MX Workmate leverages Generative AI (GenAI) and large language module (LLM) technologies to generate contextual, human-like language content based on real-time OT data, enabling workers to understand complex machines, get real time status information and industries to achieve greater flexibility, productivity, sustainability, as well as improve worker safety.
by Gary Mintchell | Feb 20, 2024 | Asset Performance Management, Generative AI, News, Operations Management, Software
Generative artificial intelligence (AI) popularized by ChatGPT is this year’s big buzz in industrial technology. Predictive maintenance seems to be one logical place where finding more powerful computation can be supportive.
Siemens has worked with Microsoft closely for decades. It has also recently acquired Senseye. Here is news about using GenerativeAI for enhancing a predictive maintenance solution.
In short:
- Enhancing proven machine learning capabilities with generative AI creates a robust, comprehensive predictive maintenance solution that leverages the strengths of both.
- Using a conversational user interface, manufacturers can take proactive actions easily, saving both time and resources.
- New generative AI functionality in Senseye Predictive Maintenance makes predictive maintenance conversational.
Siemens is releasing a new generative artificial intelligence (AI) functionality into its predictive maintenance solution – Senseye Predictive Maintenance. This advance makes predictive maintenance more conversational and intuitive. Through this new release of Senseye Predictive Maintenance with generative AI functionality, Siemens will make human-machine interactions and predictive maintenance faster and more efficient by enhancing proven machine learning capabilities with generative AI.
Senseye Predictive Maintenance uses artificial intelligence and machine learning to automatically generate machine and maintenance worker behavior models to direct users’ attention and expertise to where it’s needed most. Building on this proven foundation, now a generative AI functionality is being introduced that will help customers bring existing knowledge from all of their machines and systems out and select the right course of action to help boost efficiency of maintenance workers.
Currently, machine and maintenance data are analyzed by machine learning algorithms, and the platform presents notifications to users within static, self-contained cases. With little configuration, the conversational user interface (UI) in Senseye Predictive Maintenance will bring a new level of flexibility and collaboration to the table. It facilitates a conversation between the user, AI, and maintenance experts: This interactive dialogue streamlines the decision-making process, making it more efficient and effective.
In the app, generative AI can scan and group cases, even in multiple languages, and seek similar past cases and their solutions to provide context for current issues. It’s also capable of processing data from different maintenance software. For added security, all information is processed within a private cloud environment, safeguarded against external access. Additionally, this data will not be used to train any external generative AI. Data doesn’t need to be high-quality for the generative AI to turn it into actionable insights: With little to configure, it also factors in concise maintenance protocols and notes on previous cases to help increase internal customer knowledge. By better contextualizing information at hand, the app is able to derive a prescriptive maintenance strategy.
The new generative AI functionality in the Software-as-a-Service (SaaS) solution Senseye Predictive Maintenance will be available starting this spring for all Senseye users. The combination of generative AI and machine learning creates a robust, comprehensive predictive maintenance solution that leverages the strengths of both.
