This paper develops a prudential framework for assessing the reliability of large language models (LLMs) in reinsurance. A five-pillar architecture--governance, data lineage, assurance, resilience, and regulatory alignment--translates supervisory expectations from Solvency II, SR 11-7, and guidance from EIOPA (2025), NAIC (2023), and IAIS (2024) into measurable lifecycle controls. The framework is implemented through the Reinsurance AI Reliability and Assurance Benchmark (RAIRAB), which evaluates whether governance-embedded LLMs meet prudential standards for grounding, transparency, and accountability. Across six task families, retrieval-grounded configurations achieved higher grounding accuracy (0.90), reduced hallucination and interpretive drift by roughly 40%, and nearly doubled transparency. These mechanisms lower informational frictions in risk transfer and capital allocation, showing that existing prudential doctrines already accommodate reliable AI when governance is explicit, data are traceable, and assurance is verifiable.

The concept of a fuzzy number is generalized to the case of a finite carrier set of partially ordered elements, more precisely, a lattice, when a membership function also takes values in a partially ordered set (a lattice). Zadeh's extension principle for determining the degree of membership of a function of fuzzy numbers is corrected for this generalization. An analogue of the concept of mean value is also suggested. The use of partially ordered values in cognitive maps with comparison of expert assessments is considered.

A concept of multi-valued cognitive maps is introduced in this paper. The concept expands the fuzzy one. However, all variables and weights are not linearly ordered in the concept, but are only partially-ordered. Such an ap- proach allows us to operate in cognitive maps with partially-ordered linguis- tic variables directly, without vague fuzzification/defuzzification methods. Hence, we may consider more subtle differences in degrees of experts' uncer- tainty, than in the fuzzy case. We prove the convergence of such cognitive maps and give a simple computational example which demonstrates using such a partially-ordered uncertainty degree scale.

America is facing a health care crisis primarily due to its aging population. Physician shortages have come to the forefront recently, as many hospitals are overwhelmed due to the COVID-19 pandemic. In truth, our looming physician shortage is a generation in the making, as baby boomer doctors retire in droves. This is all occurring as lifespans are increasing--hence, there are fewer doctors to treat more patients. Exacerbating the problem is that medical schools are not churning out medical students fast enough due to capacity constraints, and it takes 12 to 15 years to train a doctor. Today, more than half of active physicians are older than 55, and by the year 2032, the Association of American Medical Colleges projects a shortfall of 122,000 doctors in the United States.

Artificial intelligence (AI), as we have seen in the past, is already established in the enterprise. Some professions, like human resources, have taken to it easily while others, particularly regulated industries, have been slower to write AI into their future. The fact of the matter is that AI is still a very new technology and it is still not clear what it will bring to the enterprise, or if what it brings will be positive. In fact, it does not take much digging to find people that are cautious, or against the deployment of AI with many arguing that its negative aspects will outweigh its benefits. Gustavo Pezzi is a computer science lecturer at BPP University London and a fellow of the Higher Education Academy.

Computers and the Internet: Listening to Girls' Voices – Dorothy Ellen Wilcox concludes that "instead of socializing adolescent girls toward docility, non-hierarchical technology like the Internet may provide a discourse for development of higher-level cognitive skills and the ability to unmask inequities in power and politics."

IT and analytics managers struggling with all the data flooding into their organizations may find it hard to ignore the increased marketing push machine learning tools are getting from technology vendors. And for good reason: Running automated algorithms designed to learn on their own as they churn through large data sets can accelerate data mining and predictive analytics applications -- and give users information they might not get otherwise. But companies looking to take advantage of machine learning often face a substantial learning curve. For starters, a lot of big data infrastructure technologies -- Hadoop, the Spark processing engine and related open source software in particular -- typically underlie machine learning efforts. In many cases, that means building a suitable data processing and management architecture from scratch.