The rapid growth of digital pathology and advances in self-supervised deep learning have enabled the development of foundational models for various pathology tasks across diverse diseases. While multimodal approaches integrating diverse data sources have emerged, a critical gap remains in the comprehensive integration of whole-slide images (WSIs) with spatial tran-scriptomics (ST), which is crucial for capturing critical molecular heterogeneity beyond standard hematoxylin & eosin (H&E) staining. We introduce SPADE, a foundation model that integrates histopathology with ST data to guide image representation learning within a unified framework, in effect creating an ST-informed latent space. These authors contributed equally to this work. Pre-trained on the comprehensive HEST-1k dataset, SPADE is evaluated on 20 downstream tasks, demonstrating significantly superior few-shot performance compared to baseline models, highlighting the benefits of integrating morphological and molecular information into one latent space. Introduction High-resolution whole slide images (WSIs) have propelled the development of powerful deep learning foundation models in computational pathology, demonstrating robust performance across diverse tissue types and tasks [1, 2, 3, 4]. These models are typically trained using self-supervision, enabling learning from large unlabeled datasets and producing embeddings robust to institutional variations, including differences in staining procedures and other image-quality factors [5, 6, 7, 8]. By visually capturing cellular arrangement, WSIs enable the study of spatial organization and disorganization of cells in tissues, characterizations that are especially relevant in cancer research [9, 10]. In clinical settings, WSIs are commonly stained with hematoxylin & eosin (H&E), a two-color stain that highlights nuclei and cytoplasm but offers a limited view of molecular-level heterogeneity [11]. As tumor tissues are known to exhibit high variability within and across patients, deciphering the heterogeneity at the molecular level is critical for improving deep learning applications that can more precisely inform diagnosis, treatment, and prognosis [12, 13]. While H&E provides crucial morphological insights, its inability to capture molecular heterogeneity limits its utility in fully characterizing tissue complexity. Spatial transcriptomics addresses this gap by providing spatially resolved gene expression data, allowing for additional molecular context for a given tissue specimen. Although both ST and H&E data have independently proven useful in various applications, their combined potential for creating a more comprehensive representation learning framework remains unexplored. To this end, we introduce SPADE, a vision-ST foundation model that uses a mixture of experts, each trained via contrastive learning, to unify ST data and H&E images to produce slide representations that encompass both modalities.

As AI becomes more common in everyday living, there is an increasing demand for intelligent systems that are both performant and understandable. Explainable AI (XAI) systems aim to provide comprehensible explanations of decisions and predictions. At present, however, evaluation processes are rather technical and not sufficiently focused on the needs of human users. Consequently, evaluation studies involving human users can serve as a valuable guide for conducting user studies. This paper presents a comprehensive review of 65 user studies evaluating XAI systems across different domains and application contexts. As a guideline for XAI developers, we provide a holistic overview of the properties of XAI systems and evaluation metrics focused on human users (human-centered). We propose objectives for the human-centered design (design goals) of XAI systems. To incorporate users' specific characteristics, design goals are adapted to users with different levels of AI expertise (AI novices and data experts). In this regard, we provide an extension to existing XAI evaluation and design frameworks. The first part of our results includes the analysis of XAI system characteristics. An important finding is the distinction between the core system and the XAI explanation, which together form the whole system. Further results include the distinction of evaluation metrics into affection towards the system, cognition, usability, interpretability, and explanation metrics. Furthermore, the users, along with their specific characteristics and behavior, can be assessed. For AI novices, the relevant extended design goals include responsible use, acceptance, and usability. For data experts, the focus is performance-oriented and includes human-AI collaboration and system and user task performance.

Retrieval-Augmented Generation (RAG) has emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge. However, deploying RAG-based tools in Small and Medium Enterprises (SMEs) remains a challenge due to their limited resources and lack of expertise in natural language processing (NLP). This paper introduces EASI-RAG, Enterprise Application Support for Industrial RAG, a structured, agile method designed to facilitate the deployment of RAG systems in industrial SME contexts. EASI-RAG is based on method engineering principles and comprises well-defined roles, activities, and techniques. The method was validated through a real-world case study in an environmental testing laboratory, where a RAG tool was implemented to answer operators queries using data extracted from operational procedures. The system was deployed in under a month by a team with no prior RAG experience and was later iteratively improved based on user feedback. Results demonstrate that EASI-RAG supports fast implementation, high user adoption, delivers accurate answers, and enhances the reliability of underlying data. This work highlights the potential of RAG deployment in industrial SMEs. Future works include the need for generalization across diverse use cases and further integration with fine-tuned models.

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.

The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster, being less sensitive to false negative noises in other clusters. At inference time, we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely, the samples in one cluster should be semantically similar, but the number of data experts should still be reasonable for training and inference. As such, we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less ($<$35\%) training cost. Meanwhile, MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available at https://github.com/facebookresearch/MetaCLIP/tree/main/mode.

Is conversational AI a blessing or curse for data? If you follow the tech industry, you’ve heard about ChatGPT. Whether you think it’s the future of chatbot technology or you’re erring on the side of caution, if you know about it, you’re bound to have an opinion. As Google confirms it’s launching a rivalling service, interacting with AI will soon become commonplace in our personal and professional lives. But what does that mean for data and analytics? Here, Jonathan Hedger, co-founder of the UK’s only data jobs board, Only Data Jobs, explores. Launched late in 2022, ChatGPT has quickly become

Among those technologists who work with artificial intelligence (A.I.) and data, a higher percentage of women have advanced degrees than men. However, that doesn't translate into comparable salaries, according to a new study by O'Reilly: Instead, women who work with A.I. and data make significantly less than their male counterparts. The O'Reilly breakdown suggests that men working in A.I. and data make an average of $150,000 per year, while women make $126,000. That's significantly less, and the gap persists regardless of education levels (according to the study, 16 percent of women involved in A.I. and data had a doctorate, versus 13 percent of men; 47 percent of women had a master's degree, compared to 46 percent of men). "Women's salaries also lagged men's salaries when we compared women and men with similar job titles," the report stated.

Transparency in AI's working can be headache-inducing for organizations that incorporate the technology in their daily operations. So, what can they do to put their concerns surrounding explainable Artificial Intelligence (AI) requirements to rest? AI's far-reaching advantages in any industry are pretty well-known by now. We are aware of how artificial intelligence helps thousands of companies around the world by speeding up their operations and allowing them to use their personnel more imaginatively. Additionally, the long-term cost and data security benefits of AI incorporation have also been documented countlessly by several tech columnists and bloggers.

When it comes to organizational transformation and turning AI and machine learning from science fiction into a reality that drives day-to-day decisions, it's much easier said than done. Our report, Adopting AI in organizations, which surveyed more than 2,000 decision makers worldwide, is a case in point: 99 percent of respondents claimed to have faced challenges implementing AI and analytics initiatives across all three categories studied: technology, organization, and people/culture. Another significant finding was that 87 percent of respondents faced more people/culture challenges than technology or organizational challenges. The road to AI adoption hasn't been easy, and it's certainly not over. AI and machine learning (ML) initiatives are increasing in the boardroom, as are the opportunities for the average employee -- including business and domain experts, for example, customer support engineers -- to actually leverage them to make better decisions in their day-to-day work.

Transparency in AI's working can be headache-inducing for organizations that incorporate the technology in their daily operations. So, what can they do to put their concerns surrounding explainable artificial intelligence (AI) requirements to rest? AI's far-reaching advantages in any industry are pretty well-known by now. We are aware of how artificial intelligence helps thousands of companies around the world by speeding up their operations and allowing them to use their personnel more imaginatively. Additionally, the long-term cost and data security benefits of AI incorporation have also been documented countlessly by several tech columnists and bloggers.