Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.

AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Information collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The early detection of the change (drift) is crucial for updating the model's knowledge, which is challenging especially in scenarios where the ground truth associated to the stream data is not readily available. Among many other techniques, the estimation of the model's confidence has been timidly suggested in a few studies as a criterion for detecting drifts in unsupervised settings. The goal of this manuscript is to confirm and expose solidly the connection between the model's confidence in its output and the presence of a concept drift, showcasing it experimentally and advocating for a major consideration of uncertainty estimation in comparative studies to be reported in the future.

Organizations need to consider the general workflow required to build a usable AI. The very first step is to gain access to all relevant historical data. Which may show up in the form of multiple files or databases with the required information. This huge collection of data is what we call the "Data lake". This lake contains all forms of unstructured and structured data. Which generally is in its raw format i.e. no preprocessing of the data has taken place.

Few areas in computer science have, over the years, repeatedly created as much interest, promise, and disappointment, as the field of artificial intelligence. The manufacturing industry, now the latest target application area of "AI", puts much hype on AI for predictive maintenance. Will AI deliver this time, or is disappointment inevitable? In engineering, the development of AI was arguably driven by the need for automated analysis of image data from air reconnaissance (and later satellite) missions at the height of the Cold War in the 1960s. A novel class of algorithms emerged that applied back-propagation to non-binary decision trees to force convergence of input data towards previously undefined output clusters.

As technology continues to move globally towards Industry 4.0, the need for digitalization is at an all-time high. With demand for great innovation to meet this need, it is abundantly clear that startups are trailblazing the path toward a more digital, innovative, and sustainable future. Although it is easy to see startups as the industry leaders of tomorrow, that does not mean their journey is without its set of unique challenges. To better support startups, Siemens launched the Frontier Partner Program. This program allows us to partner with and support early-stage software and hardware startups looking to build their groundbreaking solutions on top of our advanced software and transform the product design and manufacturing landscape.

Matt Kirchner spent his career running manufacturing companies. Now, he shares his knowledge with listeners of Webinar Wednesday and the TechEd Podcast. In this article, Matt gives us 11 predictions for how artificial intelligence will totally transform manufacturing. To view this article as a video presentation, click here. You know, there's a handful of things that have differentiated world class manufacturers from average ones in the last several decades. The organizations that embraced change surged ahead, while the others were left behind.

The Hailo-8TM inference chip expands the number of industrial Artificial Intelligence (AI) applications possible for a wide range of industrial applications including optimization of production, processes, track & trace, logistics, quality, machine functions, and predictive maintenance by eliminating inherent limitations of server and cloud solutions with processing at the edge. The Hailo-8TM inference chip expands the number of industrial Artificial Intelligence (AI) applications possible for a wide range of industrial applications including optimization of production, processes, track & trace, logistics, quality, machine functions, and predictive maintenance by eliminating inherent limitations of server and cloud solutions with processing at the edge. Server and cloud AI solutions are suitable for a wide range of applications, but compute costs, network communication speed and latency factors pose limitations for many real time industrial and process applications. The Hailo-8TM inference chip and plugin modules expand the number of feasible applications. Hailo was established in Israel in 2017 by members of the Israel Defense Forces' elite technology unit, Hailo developed AI processors for edge devices.

Emergent Insight: We all are recipients of the untiring work of artificial intelligence but we don't take time to acknowledge it. Industrial businesses certainly are leveraging the powerful technology as noted in this post by Michael Sharp at Metrology News. In your company tasks and objectives, take a moment to consider how rules-based AI or machine learning can improve productivity, safety or more. Machines, devices and computers usually take over tasks that are mundane and laborious and don't really require a human to do. Why not let AI do the work and switch the human employees to more satisfying roles?

As the digital age moves forward, it's becoming impossible to avoid interacting with artificial intelligence (AI) systems. Computer assistants and AIs perform an ever-growing range of tasks that are broadly intended to improve our quality of life. This extends to industry as well. But first, what do we mean by artificial intelligence? In simple terms, it's any machine (usually a computer) that does things normally associated with human intelligence, such as reasoning, learning and self-improvement.

Artificial Intelligence (AI) can be applied to a lot of industrial environments to save costs and to improve processes. This industrial Artificial Intelligence does not only include the smart algorithms and Big Data concepts that reside in the virtual space inside the computer systems, but it consists of the physical devices themselves too. Data has to be captured with sensors. Commands have to be sent to actuators and control systems. This whole chain and flow of information, wireless or via cables, goes through places with extreme conditions.