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2,125 papers
JAZB (Judiciary AI Zero-Trust Broker) is an Authority-centric, human-sovereign enterprise governance framework and architecture for artificial intelligence designed to govern the establishment, delegation, interpretation, exercise, assurance, and revocation of organizational Authority. JAZB applies Zero Trust, least pr...
The present study investigates how AI-driven customer analytics and personalised marketing influence customer experience, customer loyalty and purchase intention in digital markets, applying a Stimulus-Organism-Response framework extended with privacy concerns as a boundary condition. Data were drawn from a quantitativ...
Install & first launch macOS — Apple Silicon: …_aarch64.dmg · Intel: …_x64.dmg (requires macOS 13+) Developer ID-signed and notarized. Open the DMG and drag Open Science into Applications. When you use an existing project in place, allow access to its folder if macOS asks. Windows — …_x64-setup.exe (start here) · …_x64...
Abstract Organisations, governments and regulated industries operate under large, continuously revised bodies of policy. As these documents evolve across versions, editions and jurisdictions, overlapping, contradictory or silently modified clauses accumulate and create compliance, governance and legal risk that manual...
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This systematic literature review examines the relationship between artificial intelligence (AI) anxiety and employee performance, with particular attention to job insecurity, threat–challenge appraisal, coping, career resilience, job crafting, leadership, training, organisational support, and human–AI collaboration. T...
D.R.O.N.E. (Dynamic Responsive Optimized Neural Engine) is a small artificial intelligence written from scratchin C. It uses no outside libraries and no pre-trained model, and it runs offline on an ordinary CPU. Each AI builton the engine is called a drone.The design splits the work of answering into two parts. An exac...
Goal Recognition infers an actor’s goal from observed actions and is critical for human-AI cooperation in areas like robotics, healthcare, and autonomous driving. Traditional Goal Recognisers (GRs) predict likely goals but rarely explain their reasoning, limiting their application. This research develops a framework fo...
In saturated expert markets, professional recognition does not track substantive competence. Senior experts with decades of operational experience routinely lose position to less qualified but more precisely perceived peers. Existing accounts (personal branding, thought leadership, self-marketing) describe this phenome...
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23129468. Summary This paper attacks what it calls the compliance fiction: the industry practice of treating regulatory conformity as a binary verdict declared at deployment time, w...
In this paper, explainable artificial intelligence (XAI) based prediction is used for gate all around (GAA) MOSFET to predict its electrical behaviour. The data set is produced using TCAD simulations by changing device parameters like channel length (L g ), Radius of silicon pillar (R), work function (Φ m ), doping con...
Abstract Introduction: The Athlete Biological Passport (ABP) is an anti-doping tool that monitors athletes’ biomarkers over time in an effort to detect atypical variations associated with potential doping. However, the classical ABP analysis relies on adaptive Bayesian models that may have limitations in detecting subt...
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
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.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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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