Industrial processes must be robust and adaptable, as environments and tasks are often unpredictable, while operational errors remain costly and difficult to detect. AI-based control systems offer a path forward, yet typically depend on supervised learning with extensive labelled datasets, which limits their ability to generalize across variable and data-scarce industrial settings. Foundation models could enable broader reasoning and knowledge integration, but rarely deliver the quantitative precision demanded by engineering applications. Here, we introduceControl and Interpretation of Production via Hybrid Expertise and Reasoning (CIPHER): a vision-language-action (VLA) model framework aiming to replicate human-like reasoning for industrial control, instantiated in a commercial-grade 3D printer. It integrates a process expert, a regression model enabling quantitative characterization of system states required for engineering tasks. CIPHER also incorporates retrieval-augmented generation to access external expert knowledge and support physics-informed, chain-of-thought reasoning. This hybrid architecture exhibits strong generalization to out-of-distribution tasks. It interprets visual or textual inputs from process monitoring, explains its decisions, and autonomously generates precise machine instructions, without requiring explicit annotations. CIPHER thus lays the foundations for autonomous systems that act with precision, reason with context, and communicate decisions transparently, supporting safe and trusted deployment in industrial settings.

Root Cause Analysis (RCA) in the manufacturing of electric vehicles is the process of identifying fault causes. Traditionally, the RCA is conducted manually, relying on process expert knowledge. Meanwhile, sensor networks collect significant amounts of data in the manufacturing process. Using this data for RCA makes it more efficient. However, purely data-driven methods like Causal Bayesian Networks have problems scaling to large-scale, real-world manufacturing processes due to the vast amount of potential cause-effect relationships (CERs). Furthermore, purely data-driven methods have the potential to leave out already known CERs or to learn spurious CERs. The paper contributes by proposing an interactive and intelligent RCA tool that combines expert knowledge of an electric vehicle manufacturing process and a data-driven machine learning method. It uses reasoning over a large-scale Knowledge Graph of the manufacturing process while learning a Causal Bayesian Network. In addition, an Interactive User Interface enables a process expert to give feedback to the root cause graph by adding and removing information to the Knowledge Graph. The interactive and intelligent RCA tool reduces the learning time of the Causal Bayesian Network while decreasing the number of spurious CERs. Thus, the interactive and intelligent RCA tool closes the feedback loop between expert and machine learning method.

AI technologies are quickly maturing as a viable means to enabling and supporting essential business functions. But creating business value from artificial intelligence requires a thoughtful approach that balances people, processes and technology. AI comes in many forms: machine learning, deep learning, predictive analytics, natural language processing, computer vision and automation. Companies must first start with a solid foundation and realistic view to determine the competitive advantages that an AI implementation can bring to their business strategy and planning. "Artificial intelligence encompasses many things, and there is a lot of hyperbole and in some cases exaggeration about how intelligent it really is," said John Carey, managing director at business management consultancy AArete.

Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution. 1 1. The first two revolutions introduced programmable logic controllers and distributed control systems, which enabled plant-wide data collection and automation. The third revolution--advanced process controls--further abstracted automation into high-level models, allowing for increasingly dynamic plant operation. For more on the latest innovations in process controls, see Stephan Görner, Andy Luse, Naman Maheshwari, Ravi Malladi, Lapo Mori, and Robert Samek, "The potential of advanced process controls in energy and materials," November 23, 2020. AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever.

Cologne – LANXESS is broadening its use of artificial intelligence (AI) in product development. The specialty chemicals company has launched a project aimed at expanding its range of prepolymers. The goal is to offer customers tailor-made polyurethane systems with even shorter lead times, including for entirely new applications with different requirements. The Urethane Systems business unit is using the potential of AI and has brought materials AI company Citrine Informatics on board as a project partner. LANXESS data specialists and process experts used the Citrine Platform for artificial intelligence to add further data points to the company's formulation database.

In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts. The classifier nodes jointly soft-partition the input-output space based on the natural separateness of multimodal data. This enables simple leaf experts to be effective for prediction. Further, we develop a probabilistic framework for the HRME model, and propose a recursive Expectation-Maximization (EM) based algorithm to learn both the tree structure and the expert models. Experiments on a collection of regression tasks validate the effectiveness of our method compared to a variety of other regression models.

AI Business did an interview with Rulex's Andrea Ridi about the areas of challenge in implementing AI, and how he believes the professional services industry will change by adopting the technology. Rulex provides revolutionary AI software that enables business and process experts to embed automated real time predictive intelligence in applications, infrastructure, and IoT edge apps. Rulex's proprietary machine learning algorithms automatically learn and extract predictive if-then logical rules from raw data with no need for speculative data exploration or iterative scientific experimentation. Unlike the math-based predictive models produced by conventional machine learning algorithms, Rulex's logic-based models are compact and efficient, and can be easily used for making predictions on highly distributed systems and low cost, low power IoT devices. With the Rulex platform, business analysts and solution developers can easily create a new class of advanced applications for automated decision making, self-managing networks, and real-time native prediction on IoT edge devices.

Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.