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2,388 papers
Findings indicate that the scale is both valid and reliable, providing a robust tool for measuring preservice teachers' attitudes toward AI technology, and has a generally acceptable level of validity.
Methodologically, it advances BIM-enabled resilience intelligence by integrating causal reasoning, multiplex graph learning, Bayesian uncertainty quantification and XAI within a unified framework for proactive disaster-resilient infrastructure governance and decision support.
The effectiveness of immune checkpoint inhibitors (ICIs) heavily depends on the complex spatial interactions of cells within the tumor microenvironment (TME). However, existing predictive biomarkers generally lack spatial resolution and interpretability, making it difficult to guide clinical decisions or reveal actiona...
It is argued for shifting the field's focus from methods to models, along two complementary lines: existing tools let us characterize and compare what different models represent and compute, and whether a model can actually be understood by the humans who rely on it.
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Scholarly knowledge is typically encountered in stabilized form, while the process histories through which research objects, claims, and contributions acquire form remain largely hidden. This study examines how human-AI scholarly collaboration develops under matched starting conditions and whether those conditions stab...
This work finds that reasoning operations are separable in held-out representations, with separability peaking in middle layers, and verify that this structure is not explained by lexical or positional confounds.
A unified learning paradigm is proposed that jointly optimizes predictive performance and modality-specific explainability by identifying the most informative elements inside each modality that contribute to diagnostic decisions by employing a matrix-based Renyi's $\alpha$-order entropy functional under the assumption...
Delayed closure is developed as a practice of keeping recognized interpretations revisable and five public requirements concerning materials and versions, evidential roles, failure, contract revision, and responsibility are proposed.
The divergence between algorithmic and radiologist agreement demonstrates that benchmark performance against NLP-derived labels is insufficient, highlighting the need for radiologist-labeled ground truth before clinical deployment.
The resulting framework explains both the appeal and the limits of compressionist accounts of understanding while shedding light on the inscrutability of AI systems.
It is proved in this paper that several classes of abductive explanations, remain hard to compute even for Ordered Binary Decision Diagrams, one of the most tractable subsets of the knowledge compilation map.
This paper introduces the Corporate Language Model (CLM), a framework transforming a firm's structured, unstructured, multimodal, and tacit knowledge into an ontology-grounded enterprise foundation upon which reasoning and governed execution are composed.
GPT-Lab
· gpt-lab.eu
Oct 14, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Professor Deb Roy contends there should be someone who is “answerable” for every piece of text in public life, no matter its origins.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 12, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
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