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2,696 papers
The Observational Incompleteness Framework is an AI-based research programme deriving quantum-mechanical structure from the premise that observation is a proper subsystem of a deterministic whole. The archive contains three kinds of content: Technical papers (papers/) — the authoritative technical content, including th...
Artificial Intelligence (AI) systems are increasingly being applied to complex social and environmental challenges, but their implementation across different application domains requires appropriate combinations of computational techniques, data sources, deployment environments, user interfaces, and responsible-AI mech...
In contemporary higher technical education, academic institutions affiliated with large engineering universities face significant hurdles in monitoring student learning trajectories, diagnosing root conceptual deficiencies, and providing timely faculty interventions. Traditional examination systems rely predominantly o...
Single-node measurement note. Two-day campaign measuring three as-deployed LLM inference stacks (model + runtime + quantization: Qwen3.8-Flash-Next on halogen, GLM-5.3 on ExLlamaV3, DeepSeek-V4-Flash on ember) on one 128 GB unified-memory AMD Ryzen AI Max+ 395 node, under one measurement contract and one frozen corpus....
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Background The rapid integration of artificial intelligence (AI) and deep learning technologies into education creates new opportunities for personalized learning in early childhood education (ECE). Yet limited research has examined how educators interpret and enact AI-responsive curriculum decisions in preschool setti...
Machine learning systems that make automated decisions, including fraud flags, loan denials, and safety interlocks, typically generate an explanation (for example via SHAP) in the same trust domain as the model itself, leaving no independent proof that the explanation shown to an auditor is the one the model actually p...
The use of artificial intelligence (AI) is growing in pharmaceutical manufacturing, but regulatory assimilation has been slow due to difficulties in interpretability, uncertainty calibration and sustainability assessment. This work proposes a quantitative framework for assessing trustworthy AI through a multi-objective...
The explosion of data from candidate-job interactions due to digitization in recruiting has exceeded the ability of manual curation and exposed the inadequacies of lexicon-based applicant tracking systems. In this paper, we develop a new AI-driven recruitment analytics paradigm that reimagines the candidate-job matchin...
This paper critically examines the persistent responsibility gap in explainable artificial intelligence (XAI), arguing that current approaches fail to adequately bridge it. While XAI is often promoted as a remedy for the opacity of AI systems—the so-called “black box problem”—and as a way to enable human accountability...
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Generative AI
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
A Blog post by Dharma-AI on Hugging Face
Health & Bioscience
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