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.
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.
- 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.
Quantum Computing has been making some headlines and the tech world is fluttering about AI. Dario Gil, SVP of Research at IBM provides a cogent explanation of these technologies. Perhaps the best I’ve heard. It’s on Guy Kawasaki’s podcast. Well worth a listen.
Robotic systems provide as much news as AI and cybersecurity over the past year. This news regards funding. I don’t follow the whole VC arena closely, but having been in entrepreneurial start-up mode a few times in my life, I know well the value of the life blood of money.
Naturally, this news combines AI and robotic software solutions. Might as well hit two trends at once. Xaba, a Toronto based developer of AI-powered cognitive software to automate programming and deployment of robotics and CNC machines, has announced it has raised US$2 million in a seed extension round of funding to bring to market AI-driven fabrication processes and intelligent autonomous machines.
It says the funding will enable it to democratize robotic automation and drive sustainable manufacturing.
The funding round was led by BDC Capital’s Deep Tech Venture Fund with participation from Hitachi Ventures and existing investor Hazelview Ventures. The investment will be used to establish and staff a new robotics lab and accelerate the delivery of two Xaba manufacturing platforms.
Xaba is augmenting industrial robots and cobots with xCognition, an artificial intelligence (AI)-powered software solution. xCognition leverages innovative proprietary machine learning algorithms to model the elasto-mechanical-dynamic behavior of industrial robotics and cobots, as well as workpiece variances. This includes variances in location and shape, using proprietary rule-based language models to eliminate coding of robotics programs. xCognition solves the challenges of deploying industrial robots by completely automating how they are programmed and adopted. This solution not only increases accuracy, consistency, and throughput, but also significantly reduces the time and costs of robotics deployments.
Xaba has two manufacturing platforms – xCognition and xTrude – which use proprietary, state-of-the-art industrial artificial intelligence (AI) to provide AI-powered cognitive industrial automation, consistency, robustness and high execution quality. Its platforms eliminate the need for constant human supervision, reprogramming, and waste – factors that significantly impact the return on investment of any major manufacturing or construction process.
xCognition is Xaba’s AI-powered software solution. This industrial robotics digital twin captures and models the true physics of any industrial robotics system (elastic, dynamic, mechanical, tooling). This includes workpiece variances both in location and shape, leveraging rules-based language models and multi-modal datasets that capture legacy data and best practices to enable any robotics system to execute tasks such as drilling, welding (MIG, TIG & Laser), assembling, riveting, laser, data acquisition, and more with maximum accuracy, repeatability, and minimum human supervision.
While I am on a Siemens run, here is recent news about a collaboration with Amazon Web Services. Collaborations are the second way large companies in our market are growing, innovating, and expanding. This one uses GenerativeAI to assist software development.
- Siemens to integrate Amazon Bedrock into its Mendix low-code development platform to allow customers to create new and upgrade existing applications with the power of generative AI
- Access to Amazon Bedrock’s advanced generative AI technologies will help customers accelerate digitalization and tackle skilled labor shortages
- Mendix is an industry leader in low-code development with 50M end-users and more than 200,000 applications running on AWS across industrial, finance and other sectors
Siemens and Amazon Web Services (AWS) are strengthening their partnership and making it easier for businesses of all sizes and industries to build and scale generative artificial intelligence (AI) applications. Domain experts in fields such as engineering and manufacturing, as well as logistics, insurance or banking will be able to create new and upgrade existing applications with the most advanced generative AI technology. To make this possible, Siemens is integrating Amazon Bedrock – a service that offers a choice of high-performing foundation models from leading AI companies via a single API, along with security, privacy, and responsible AI capabilities – with Mendix, the leading low-code platform that is part of the Siemens Xcelerator portfolio.
The combination will enable customers to select the generative AI model that best suits their specific use case and quickly and securely incorporate that model into their applications. This will make their development simpler, faster, and more efficient. Previously, when developers wanted to integrate generative AI models, they had to obtain access credentials, and write specialized function code. With the new Mendix-Amazon Bedrock integration, this can now be done with just a few clicks. Teams can create smart, industry-hardened applications without dedicated programming knowledge and users can interact with information easily via a graphical interface and the simplicity of a drag and drop commands.
This innovation allows Mendix customers to apply generative AI to drive productivity within their workforce. For instance, using generative AI, a factory worker can find machine documentation faster, generating relevant visualizations without a need to manually search a database, manuals, and records. A production engineer could also use generative AI to suggest machine adjustments to improve yield, and get suggestions on equipment adjustments, maintenance, or even spare parts to maximize a factory’s productivity. Customers do not need to build their own AI infrastructure and will be able to harness the power of their company’s data with the highest possible security and privacy, maintaining full control of their data.
Generative AI technology can supercharge applications with features like summarizing and analyzing lengthy technical or legal documents, translating content into different languages, or recognizing images. Financial businesses can integrate automatic fraud detection in their software, while workers in a car factory can improve quality based on AI analysis of millions of data points in the manufacturing line. With access to a choice of foundational models on Amazon Bedrock, users can easily select the best model for their specific task and integrate it with just a few clicks.
The collaboration expands on the long-established partnership between AWS and Siemens to help streamline the use of IT and cloud technology so it can be easily integrated in applications and machine workflows, making it seamless to engage with.
Today, more than 50 million end users worldwide work with more than 200,000 applications built with Mendix’s low-code platform, available as part of the Siemens Xcelerator portfolio. Low-code platforms are expected to grow substantially over the next years. The technology enables developers to create applications by drag and drop with reusable components and software building blocks, which means they can build more software faster and with smaller teams.
Amazon Bedrock is a fully managed service that offers easy access to a choice of industry-leading large language models and other foundation models from AI21 Labs, Amazon, Anthropic, Cohere, Meta, and Stability AI, along with a broad set of capabilities that customers need to build generative AI applications—simplifying development while supporting privacy and security. Users can also apply Guardrails to filter undesired content, adhere to responsible AI policies, or finetune their models using Knowledge Bases for Amazon Bedrock to give contextual information from private data sources and more relevant, accurate, and customized responses. The Mendix-Amazon Bedrock integration complements AWS’s other generative AI services, like Amazon CodeWhisperer, a machine learning (ML)–powered service that helps improve developer productivity by generating code recommendations based on developers’ comments in natural language and their code. Together, the services extend the benefits of generative AI to developers and enterprise users regardless of their programming abilities.
Once upon a time surveys were the purview of analyst firms and media. None were mathematically rigorous. Most do show trends and yield ideas for thought.
Digital transformation is top of mind for companies who develop and market software solutions but maybe not so much for customers. This survey is from iBase-t. I knew them as an MES supplier, but now the are the company “that simplifies how complex products are built and maintained.” In other words, MES. That’s OK. My background in that application goes back decades.
This original survey of more than 100 discrete manufacturing executives in the U.S. found that a lack of a clearly defined roadmap is the biggest challenge for manufacturers looking to digitally transform their operations.
None of this surprises me. Many studies have found similar statistics. Upper management in manufacturing organizations “know” these problems. They don’t seem to know how to go about implementing solutions. Or, they don’t want to spend the money!
In brief, their study revealed:
- 60% of manufacturers don’t have a clear understanding of the model-based enterprise
- 67% of manufacturers say that less than half their operations are digital
A full 60% of respondents said they did not have a clear understanding of the model-based enterprise (MBE), which employs CAD systems, Product Lifecycle Management (PLM) systems and Manufacturing Execution Systems (MES) to help manufacturers fully digitize their operations.
Respondents confirmed that although paperless manufacturing and digital transformation are very important priorities, more than two-thirds (67%) of manufacturers reported that less than half of their operations are digital.
The survey found that more than half (54.5%) of respondents lack the interoperability across operations to adopt an MBE strategy. An additional 55% said that their manufacturing systems are not mature enough to support MBE.
Other Key findings:
- According to the survey, 62% of total respondents said that they believe paperless manufacturing is “very important” to their organization.
- The top four goals for manufacturers heading into 2024 are efficiency (66%), on-time delivery (66%), done-right first time (49%) and profitability (47%). An MBE strategy empowers manufacturers to reach all of these goals.