Large Language Models (LLMs) and other generative forms of artificial intelligence are rapidly moving into enterprise environments. Challenges surround ensuring the security of such models, managing risks associated with their use, ensuring that all use meets organizational and external compliance requirements, and ens...
Kishan Raj Bellala· International Journal of Sci...· 0 citations
Human-centric identity systems deployed in real-world environments continuously encounter non-stationary conditions, such as changing illumination, background noise, device transitions, and physiological aging. These environmental and temporal variations introduce distribution shift, degrading system performance, eleva...
Moien Shaik· Zenodo (CERN European Organi...· 0 citations
Human-centric identity systems deployed in real-world environments continuously encounter non-stationary conditions, such as changing illumination, background noise, device transitions, and physiological aging. These environmental and temporal variations introduce distribution shift, degrading system performance, eleva...
Moien Shaik· Zenodo (CERN European Organi...· 0 citations
Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect a model's true decision process and may therefore mislead clinicians. We investigate this problem using a Vision Transformer-based breast MRI classifier trained on the OD...
Peachapong Poolpol, Henrik H. J. Detjen, Eike Petersen· 0 citations
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Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality. Despite this, learning from those abductive signals is largely ignored in artificial intelligence. While explainable AI (XAI) increasingly draws on...
Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig et al.· 0 citations
Chain-of-thought (CoT) monitoring is only meaningful if written reasoning causally constrains the answer. We introduce continuation-based causal testing, an ablation-patch intervention that perturbs one reasoning step, truncates the chain, and forces the model to continue from the corrupted prefix. It measures how load...
An AI-Augmented Active Aging Framework that explains how conversational AI affordances, responsiveness, personalization, accessibility, and availability, interact with personal, social, environmental, and system-level conversion factors and argues that the primary value of conversational AI lies not in its ability to s...
Chong-Tang Zhou, Hong-Yu Wu, Gao-Hang Li et al.· Frontiers in Psychology· 0 citations
Purpose: This study validated and refined a context-specific framework explaining meaningful use behavior of PictureThis artificial intelligence (AI) for plant identification among undergraduate students in Ugandan public universities. Unlike an earlier analysis of predictors of behavioral intention from the same docto...
John Bukenya, Paul Birevu Muyinda, Ghislain Maurice Nobert Isabwe et al.· American Journal of Educatio...· 0 citations
INTRODUCTION: Due to the development of the Industrial Internet of Things (IIoT), it has been possible to make the predictive maintenance of the manufacturing industry using data. Nevertheless, recent developments in manufacturing systems are prone to higher levels of disruptions caused by sensor degradation, data anom...
Yi-Ting Bai· ICST Transactions on Scalabl...· 0 citations
This study develops a comprehensive understanding of AI leadership by examining how traditional leadership theories and psychological constructs explain effective leadership behaviors in AI-intensive organizational contexts by synthesizing theories from leadership studies, cognitive psychology, organizational behavior,...
D. Upadhyay· Leadership & Organizatio...· 0 citations
Biostatistics literacy is essential for evidence-based medicine, yet no instrument specifically measures this competency in medical students. This study aimed to develop a measure of self-perceived biostatistics literacy and conduct an initial psychometric evaluation in samples comprising predominantly early-stag...
N. M. Konar, Aslı Suner, D. Özyürek et al.· BMC Medical Education· 0 citations
Background Population aging and the growing burden of chronic diseases have increased the need for more accessible and equitable primary healthcare. Artificial intelligence (AI)-assisted hierarchical diagnosis and treatment has been increasingly introduced into primary care to support patient guidance, triage, follow-u...
Hang Cheng, Li Jia· Frontiers in Public Health· 0 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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