MX Workmate OT-compliant GenerativeAI Solution for Connected Workers

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.

Siemens Generative AI and Predictive Maintenance

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.

When Your Boss Asks for an AI Strategy

I use Hey for my email client. It is a new take on doing email from 37 Signals the developers of Basecamp. They’ve also released a new approach to calendars I’m trying. 

They also added a newsletter piece to the app that is not as obnoxious about collecting data like MailChimp or Constant Contact. It is called Hey World. I publish there occasionally and send a newsletter. You can sign up for the newsletter by clicking the envelop on the web page or click this link.

My last email plays with the question of what happens when your boss calls you and asks for an AI strategy.

You can help support my work at Buy Me a Coffee.

Emory Institute Harnesses AI To Improve Health

Despite all the hype, augmented intelligence (AKA artificial intelligence or AI) is and has been a real thing in programming. The power does continue to grow—just like all technology. Two areas ripe for improvement through the use of AI are education and healthcare. This news relates to healthcare.

Emory University is embarking on a new initiative that will unite the power of machine learning and big data to transform the ways in which health care systems prevent, diagnose, treat and cure diseases on a global scale.

Launching this month under the umbrella of Emory’s AI.Humanity initiative, the Emory Empathetic AI for Health Institute will utilize artificial intelligence (AI) and computing power to discern patterns in vast amounts of data and make predictions that improve patient health outcomes in diseases such as lung, prostate and breast cancer, heart disease, diabetes and more. While AI is already being deployed to improve diagnoses and treatment for numerous health conditions, the resounding impact AI can have on health care is just beginning.

As Georgia’s first institute of its kind, Emory AI.Health will foster the development of accessible, cost-effective and equitable AI tools by developing an ecosystem of multidisciplinary experts from Emory, the Atlanta VA Medical Center, the Georgia Institute of Technology and others, and seeking public-private partnerships to propel new research forward. It will then serve as an engine to deploy those tools to the patient’s bedside, initially within Emory Healthcare and ultimately across the globe.

“AI will transform society and at Emory, we want to use these powerful technologies to save and improve lives,” says Emory President Gregory L. Fenves. “We see the power AI has to facilitate healing while improving equitable access to health care. Dr. Madabhushi is a trailblazer in health-focused AI and the ideal person to lead the Empathetic AI for Health Institute.” 

Emory AI.Health will be led by Anant Madabhushi, PhD, a Robert W. Woodruff professor in the Wallace H. Coulter Department of Biomedical Engineering at Emory and Georgia Institute of Technology, a member of the Cancer Immunology research program at Winship Cancer Institute and a research career scientist with the Atlanta VA Medical Center.

A Peek Under the Covers of ChatGPT and Similar AI Models

This may sound surprising (although it shouldn’t). General media promotes a lot of hype and dire warnings and smoke-and-mirrors about large language models (LLM)—the latest type of augmented (artificial) intelligence. Do you think that if you could even get a peek into the math and technology that you could at least have a better grip on risk and reward?

I have just the book for you. The publicists sent a review copy. I was fascinated.

More than a Chatbot by Mascha Kurpicz-Briki, a professor for data engineering, enables readers to understand and be part of the exciting new development of powerful text processing and generation tools. After reading this book, the reader will be confident enough to participate in public discussions about how new generations of language models will impact society and be aware of the risks and pitfalls of such technologies.

Mascha Kurpicz-Briki is professor for data engineering at the Bern University of Applied Sciences in Biel, Switzerland, and co-leader of the research group Applied Machine Intelligence.

In particular, the book discusses the following questions: How did the field of automated text processing and generation evolve over the last years, and what happened to allow the incredible recent advances? Are chatbots such as ChatGPT or Bard truly understanding humans? What pitfalls exist and how are stereotypes of the society reflected in such models? What is the potential of such technology, and how will the digital society of the future look like in terms of human-chatbot-collaboration?

The book is aimed for a general audience, briefly explaining mathematical or technical background when necessary. After having read this book, you will be confident to participate in public discussions about how this new generation of language models will impact society. You will be aware of the risks and pitfalls these technologies can bring along, and how to deal responsibly when making use of tools built from AI technology in general.

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