Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarce. In this paper, we introduce JobHop, a large-scale public dataset derived from anonymized resumes provided by VDAB, the public employment service in Flanders, Belgium. Utilizing Large Language Models (LLMs), we process unstructured resume data to extract structured career information, which is then normalized to standardized ESCO occupation codes using a multi-label classification model. This results in a rich dataset of over 1.67 million work experiences, extracted from and grouped into more than 361,000 user resumes and mapped to standardized ESCO occupation codes, offering valuable insights into real-world occupational transitions. This dataset enables diverse applications, such as analyzing labor market mobility, job stability, and the effects of career breaks on occupational transitions. It also supports career path prediction and other data-driven decision-making processes. To illustrate its potential, we explore key dataset characteristics, including job distributions, career breaks, and job transitions, demonstrating its value for advancing labor market research.

Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skill extraction achieve strong performance, they depend on large language models (LLMs), which are computationally expensive and slow. In this paper, we propose \textbf{ConTeXT-match}, a novel contrastive learning approach with token-level attention that is well-suited for the extreme multi-label classification task of skill classification. \textbf{ConTeXT-match} significantly improves skill extraction efficiency and performance, achieving state-of-the-art results with a lightweight bi-encoder model. To support robust evaluation, we introduce \textbf{Skill-XL}, a new benchmark with exhaustive, sentence-level skill annotations that explicitly address the redundancy in the large label space. Finally, we present \textbf{JobBERT V2}, an improved job title normalization model that leverages extracted skills to produce high-quality job title representations. Experiments demonstrate that our models are efficient, accurate, and scalable, making them ideal for large-scale, real-time labor market analysis.

We introduce JobBERT-V3, a contrastive learning-based model for cross-lingual job title matching. Building on the state-of-the-art monolingual JobBERT-V2, our approach extends support to English, German, Spanish, and Chinese by leveraging synthetic translations and a balanced multilingual dataset of over 21 million job titles. The model retains the efficiency-focused architecture of its predecessor while enabling robust alignment across languages without requiring task-specific supervision. Extensive evaluations on the TalentCLEF 2025 benchmark demonstrate that JobBERT-V3 outperforms strong multilingual baselines and achieves consistent performance across both monolingual and cross-lingual settings. While not the primary focus, we also show that the model can be effectively used to rank relevant skills for a given job title, demonstrating its broader applicability in multilingual labor market intelligence. The model is publicly available: https://huggingface.co/TechWolf/JobBERT-v3.

In this paper, we explore the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, highlighting its potential to revolutionize industry practices and enhance operational efficiency. We delve into various applications of AI in manufacturing, with a particular emphasis on human-machine interfaces (HMI) and AI-powered milling machines, showcasing how these technologies contribute to more intuitive operations and precision in production processes. Through rigorous market analysis, the paper presents insightful data on AI adoption rates among German manufacturers, comparing these figures with global trends and exploring the specific uses of AI in production, maintenance, customer service, and more. In addition, the paper examines the emerging field of Generative AI and the potential applications of large language models in manufacturing processes. The findings indicate a significant increase in AI adoption from 6% in 2020 to 13.3% in 2023 among German companies, with a projection of substantial economic impact by 2030. The study also addresses the challenges faced by companies, such as data quality and integration hurdles, providing a balanced view of the opportunities and obstacles in AI implementation.

The Global Machine Learning as a Service (MLaaS) Market 2032 Industry Report is a professional and in-depth study on the current state of the Machine Learning as a Service (MLaaS) Market by QMI. The Machine Learning as a Service (MLaaS) Market is supposed to demonstrate a considerable growth during the forecast period of 2023 – 2032. The company profiles of all the key players and brands that are dominating the market have been given in this report. Their moves like product launches, joint ventures, mergers and acquisitions and the respective effect on the sales, import, export, revenue and CAGR values have been studied completely in the report. The scope of this Machine Learning as a Service (MLaaS) Market report can be expanded from market scenarios to comparative pricing between major players.

The Artificial Intelligence In Genomics Market Size is expected to reach USD Billion by 2027, at a CAGR of 53% during the forecast period from 2021 to 2027. This report covers a sub-market in this field the Artificial Intelligence In Genomics Market by offering type in detail, segmenting the market as Software, Services. The scope of the report covers technology segment which includes Machine Learning, Deep Learning, Supervised Learning, Reinforcement Learning, and Unsupervised Learning. The segment Functionality type segregated into Genome Sequencing Gene Editing Clinical Workflows Predictive Genetic Testing & Preventive Medicine. Moreover, it provides in-sights on Application that segregates into Diagnostics Drug Discovery & Development Precision Medicine Agriculture & animal Research Other Applications.

Global Artificial Intelligence In Insurtech Market is a new publication from MarketQuest.biz that examines current, historical, and evolutionary patterns in the Artificial Intelligence In Insurtech business. The market is broken down into five major regions in the research. This part contains an overview of the company, a segment and brand overview, financial performance, and advancements made by the company to keep ahead of the competition. The prospective opportunities in the Artificial Intelligence In Insurtech market are assessed. Several variables have had or are having a significant impact on the market, according to the research.

The latest Computer Vision in Artificial Intelligence (AI) market research report presents an in-depth analysis of all the important aspects such as …

The market report, titled "Video Analytics and Artificial Intelligence Market", is a broad research dependent on Video Analytics and Artificial Intelligence market, which examines the escalated structure of the present market all around the world. Planned by the sufficient orderly system, for example, SWOT investigation, the Video Analytics and Artificial Intelligence market report demonstrates an aggregate appraisal of overall Video Analytics and Artificial Intelligence market alongside the noteworthy players Allgovision, Honeywell, Ipsotek, Viseum, Intelligent Security Systems, Digital Barriers, Avigilon, Puretech Systems, 3VR, Briefcam, Qognify, Axis Communications, Genetec, Intellivision, Gorilla Technology, Delopt, Kiwisecurity, Cisco Systems, Iomniscient, Aimetis, Intuvision, IBM, Agent VI, Aventura, Verint, I2V of the market. The conjecture for CAGR (Compound Annual Growth Rate) is expressed by the Video Analytics and Artificial Intelligence Market report in the terms of proportion for the particular time length. This will likewise assist the client with understanding and settle on an exact decision based on an expected diagram. Furthermore, The report presents a detailed segmentation Video Analytics Hardware, Video Analytics Software, Artificial Intelligence Hardware, Artificial Intelligence Software, Market Trend by Application IBFSI, City Surveillance, Critical Infrastructure, Education, Hospitality and Entertainment, Manufacturing, Defense and Border Security, Retail and Consumer Goods, Traffic Management, Transportation, Others of the global market based on technology, product type, application, and various processes and systems.

A new market study is released on Global "Artificial Intelligence Market 2021" with data Tables for historical and forecast years represented with Chats & Graphs with easy to understand detailed analysis. The report also sheds light on present scenario and upcoming trends and developments that are contributing in the growth of the market. In addition, key market boomers and opportunities driving the market growth are provided that estimates for Global Artificial Intelligence Market till 2027. The authors of the Artificial Intelligence Market report have piled up a detailed study on crucial market dynamics, including growth drivers, restraints, and opportunities. The Global Artificial Intelligence Market accounted for USD 16.14 billion in 2017 and is projected to grow at a CAGR of 37.3% the forecast period of 2018 to 2025.