Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assessment framework built on ontological blocks of meaning-discrete, interpretable units that encode ethical principles such as fairness, accountability, and ownership. By integrating these blocks with FAIR (Findable, Accessible, Interoperable, Reusable) principles, the framework supports scalable, transparent, and legally aligned ethical evaluations, including compliance with the EU AI Act. Using a real-world use case in AI-powered investor profiling, the paper demonstrates how the framework enables dynamic, behavior-informed risk classification. The findings suggest that ontological blocks offer a promising path toward explainable and auditable AI ethics, though challenges remain in automation and probabilistic reasoning.

Wikipedia is the world's largest online encyclopedia, but maintaining article quality through collaboration is challenging. Wikipedia designed a quality scale, but with such a manual assessment process, many articles remain unassessed. We review existing methods for automatically measuring the quality of Wikipedia articles, identifying and comparing machine learning algorithms, article features, quality metrics, and used datasets, examining 149 distinct studies, and exploring commonalities and gaps in them. The literature is extensive, and the approaches follow past technological trends. However, machine learning is still not widely used by Wikipedia, and we hope that our analysis helps future researchers change that reality.

The original Chapter 10 lab made use of keras, an R package for deep learning that relies on Python. Getting keras to work on your computer can be a bit of a challenge. Installation instructions are available here. RStudio has recently released a new R package for deep learning, called torch, that does not require a Python installation. Daniel Falbel and Sigrid Keydana, two of the torch developers, translated our keras version of the Chapter 10 lab to torch.

Attitude is omnipresent in almost every type of text. There has yet to be any relevant research on attitudinal shifts in self-translation. The Chinese version of Between Tears and Laughter is a rare case of self-translation and co-translation in that the first 11 chapters are self-translated by Lin Yutang, and the last 12 chapters by Xu Chengbin. The current study conducted a word frequency analysis of this book's English and Chinese versions with LIWC and AntConc, and made comparative research into Lin Yutang's attitudinal changes. The results show that due to different writing purposes and readerships, there is less anger in Lin's self-translation (M=0.7755, SD=0.3861), which is a significant difference (t=2.2892, This attitudinal change is also reflected in the translations of some n-grams containing anger words. In contrast, there is no significant difference (t=1.88, This paper believes that corpus tools can help co-translators keep their translation consistent in attitude. Introduction Lin Yutang (1895 - 1976), the author of My Country and My People and Moment in Peking, was a world-renowned Chinese writer publishing novels, essays, translations, textbooks, and Chinese-English dictionaries, making outstanding achievements in literature, translation, and language research. He even invented a Chinese typewriter. He wrote more than 30 books in English, most of which describe China's cultural aspects. According to the biography of Lin Yutang written by his daughter Lin Taiyi, Lin was nominated twice for the Nobel Prize for Literature (Lin 1989).

Studies show dramatic increase in elderly population of Western Europe over the next few decades, which will put pressure on healthcare systems. Measures must be taken to meet these social challenges. Healthcare robots investigated to facilitate independent living for elderly. This paper aims to review recent projects in robotics for healthcare from 2008 to 2021. We provide an overview of the focus in this area and a roadmap for upcoming research. Our study was initiated with a literature search using three digital databases. Searches were performed for articles, including research projects containing the words elderly care, assisted aging, health monitoring, or elderly health, and any word including the root word robot. The resulting 20 recent research projects are described and categorized in this paper. Then, these projects were analyzed using thematic analysis. Our findings can be summarized in common themes: most projects have a strong focus on care robots functionalities; robots are often seen as products in care settings; there is an emphasis on robots as commercial products; and there is some limited focus on the design and ethical aspects of care robots. The paper concludes with five key points representing a roadmap for future research addressing robotic for elderly people.

In Quebec City, Canada, Andriy Burkov works as a machine learning specialist. He earned his doctorate in artificial intelligence eleven years ago, and for the past eight years, he has been in charge of a group of machine learning engineers at Gartner. The study of natural language is his area of expertise. His team uses shallow learning and deep learning techniques to develop cutting-edge multilingual text extraction and normalization systems for production. Andrew Ng's Machine Learning Yearning is an excellent textbook for practitioners. It is similar to "The Hundred-Page Machine Learning Book" in its comprehensive coverage of machine learning and its application to AI but is written more in a comment-to style. The book is also written in a logical order that closely mimics the typical process that a data scientist or machine learning engineer would follow when working on an end-to-end machine learning project, along with discussing key considerations and trade-offs. The book has 4.3 ratings with over 40 reviews on Goodreads.com.

"Give Me a break" Statement 70

"Give Me a break" Statement 70

I first came across this book when I was reading analysts' review of President Xi Jinping's New Years' address during the turn of the year and this book was apparently one of the two books on AI and robotics that was on the Chinese President's bookshelf. Piqued by this revelation, I then subsequently learnt that this book was also on Bill Gates' recommended reading list. The book's full title, "The Master Algorithm – How the Quest for the Ultimate Learning Machine will Remake our World," provided the necessary hyperbole that helped me make my decision to read it. Whilst I had some rudimentary of what algorithms do, how AI will impact the world we live in, and how machine learning is being used across various industries from healthcare, to education to security. At the heart of machine learning is the ability of learners to use algorithms to collate data, create meaningful and actionable insights from the data and determine or execute next steps or tasks.