Full-text search across 12422 articles. Combine with topic and date filters; results sorted by relevance.
ClearML, an open-source MLOps platform, can be deployed as a self-hosted alternative to Azure Machine Learning, offering experiment tracking, pipelines, and model serving on any infrastructure. A developer's guide detail…
Mistral AI Audio Lead Pavan Muddireddy said enterprise speech recognition deployments still face severe fragmentation in noisy, multi-speaker environments despite benchmark claims that the problem is solved, in a Machine…
A second-year Computer Engineering student built Aigris, an experimental on-device AI cybersecurity agent for Android that detects phishing, malicious links, and social engineering attacks while preserving user privacy. …
Researchers introduced a practical Dataset Usage Inference (DUI) framework that estimates what fraction of a dataset was used to train a machine learning model without requiring shadow models or held-out data. The method…
A new interactive timeline titled 'Years of AI History' chronicles 83 years of artificial intelligence milestones from 1943 to 2026, tracing the field from the first mathematical neuron to modern models. The timeline hig…
Google's Android 17 QPR2 Beta 4 adds a dedicated 'Data logging' menu for Private Compute Core within Privacy Dashboard, letting users monitor and manage outbound data from the secure sandbox that powers machine learning …
Jeff Dean, Google's chief scientist and a key figure in the development of TensorFlow and other AI technologies, announced he is leaving Google to co-found Discovery Loop, a Public Benefit Corporation aimed at automating…
Rowan Stewart, Sr. Director of Product for AI Safety & Platform at Transcend, leads product initiatives to help organizations discover, govern, and safely use data in AI systems, drawing on over a decade of experience in…
Towards AI launched Towards AI Deployment, an enterprise division staffed by AI engineers who began as community members and students. Co-founders Louis-François Bouchard and Denis P. detailed the transition from teachin…
Explainable AI (XAI) is essential for deploying machine learning in high-stakes domains like healthcare, finance, and autonomous vehicles, where opaque models can lead to liability, non-compliance, or safety risks. In he…
A new benchmarking framework for mutual information estimation reveals that no single estimator works best across all scenarios, challenging the conventional wisdom of one-size-fits-all solutions. The framework uses a co…
Princeton University researchers will present 12 papers at the 43rd International Conference on Machine Learning (ICML 2026), held July 6-11 in Seoul, South Korea, including four oral presentations and eight spotlight po…
At the VibeKode Munich conference, two parallel tracks revealed a stark divide: one focused on traditional machine learning, the other on AI-driven development with agents and autonomous pipelines. A CTO of a 70-person c…
Scott Hanselman, VP of Developer Community at Microsoft, starred Edit Mind, a video knowledge base project that uses local machine learning models to enable natural language search of videos.
Researchers introduced MEA, a reward-driven multi-agent framework that generates natural-language explanations of machine learning model behavior, reporting faithfulness gains of +28% on tabular data, +21% on text, and +…
Apple researchers published a paper in October 2026 titled "Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems," reporting a week-long exploratory study in which participants used one of two Wiz…
TypeSafe AI, a two-year-old stealth startup founded by ex-OpenAI researcher Diogo Almeida and backed by a $40M DCVC-led seed round, launched Jev, a decision-only model that returns typed Choice, Score and Noul answers wi…
A practitioner at the consultancy iaastha reports that roughly nine in ten promising machine learning projects never reach durable production use, arguing the failure lies in the pipeline rather than the model. The firm …
A new arXiv paper (arXiv:2609.35856v1) proposes using generative large language models with weak supervision to classify Economic Policy Uncertainty (EPU) in news articles, replacing keyword-based methods that produce hi…
A systematic study by Iuri Macocco, Pau Rodríguez Lopez, Arno Blaas, Luca Zappella, Marco Baroni and Xavier Suau Cuadros, published in Transactions on Machine Learning Research (TMLR) in September 2026, found that effici…