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2,181 papers
Digital finance has enabled faster payments, lending and banking and investment service, but it has also created a larger attack surface for financial crime, anomaly abuse, money laundering and fraud. This chapter explores intelligent defence mechanisms, which integrate machine learning, deep learning, graph analytics,...
This paper presents a complete framework for feature-driven Android malware detection that combines predictive modeling with explainable artificial intelligence to improve classification accuracy, interpretability, and operational dependability. The system initiates by examining network-flow metrics, including Source P...
Traditional 12-lead ECGs offer comprehensive insights into the electrical activity of the heart, but require clinical settings and expert interpretation, which limits their accessibility. Smartwatch 1-lead ECGs can be recorded at home, allowing more frequent and rapid monitoring, opening opportunities for early adver...
Ad hoc teamwork requires an agent to collaborate with previously unknown teammates (human, AI) without prior coordination. State of the art methods address this primar- ily as a learning problem, using large datasets of prior observations to model teammate behavior and determine the actions of the ad hoc agent. Such da...
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Schema evolution at enterprise scale: Amazon Web Services study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract The engineering problem behind schema evolution at enter...
Slides from the invited keynote delivered at the 30th International Conference on Theory and Practice of Digital Libraries the 23rd of September, 2026 (https://tpdl2026.ualg.pt/keynotes/) The FAIR principles have significantly improved the publication and reuse of digital resources, promoting data that are Findable, Ac...
Security operations centers rely on machine learning in increasing amounts when conducting network intrusion detection, but the inability to comprehend, trust, and act on alerts caused by black-box models is a significant issue under time pressure. Explainable AI (XAI) solutions such as SHAP and LIME provide solutions...
A $2.3M AI platform launch resulted in zero adoption after 90 days. David Ohnstad explains the critical mistake: building infrastructure before understanding what problems it actually solves. The real roadmap starts with user demand, not infrastructure. Full article: https://davidohnstad.net/why-enterprise-ai-projects-...
To have an efficient artificial intelligence (AI) governance in the world that is characterised by increased auto-automation, it is vital to navigate the emergence of explainable AI (XAI). Since AI systems are used for making important decisions in fields as diverse as the financial industry or healthcare, their comple...
📣 PROMO READY #447 [Quora] — E8 Intelligence live AI trading ecosystem I've spent years watching retail traders get sold 'AI signals' that are really just repackaged moving averages. So when I came across E8 Intelligence, I was sceptical. But then I saw the live board — it's not a backtest. It's a real-time, 24/7 engi...
Data quality observability: Portable multi-cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract The engineering problem behind data quality observability is dece...
Artificial intelligence (AI) is transforming customer-brand relationships by acting as an intermediary between consumers and businesses. Trust in AI is critical in determining whether AI intermediaries strengthen or erode brand equity as firms increasingly rely on AI-mediated interactions. Although earlier studies emph...
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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