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Nuro, a self-driving technology company founded in 2016, is hiring a Senior/Staff Engineer for Machine Learning - Online Mapping in Mountain View, California, with a base pay range of $193,930 to $291,150. The role invol…
Helsing, a defense technology company developing AI-enabled drone software, is hiring a Machine Learning Engineer for Detection and Tracking in Washington, DC, to own the detection and tracking models powering its Altra …
A new set of lecture notes from arXiv:2604.07520v2 provides a framework for performing global statistical fits in high-energy physics using Machine Learning surrogates, including Boosted Decision Trees and active learnin…
A Perspective in Nature Reviews Drug Discovery finds that despite a decade of intense interest, AI in drug discovery has shown disappointingly limited clinically relevant impact, with no evidence yet that AI-discovered d…
A systematic literature review of 212 studies finds that Physics-Informed Machine Learning (PIML) improves predictive performance in Prognostics and Health Management (PHM) across all four classification classes, but the…
A systematic review of 19 machine learning studies on early Chronic Kidney Disease (CKD) prediction found that high data leakage inflates reported accuracy to an average of 95.48%, compared to 80.2% for leakage-free stud…
A new arXiv preprint (2608.28084v1) systematically compares classical and quantum machine learning models for regression on simulated proton-proton collision data from the CERN Open Data portal, finding that classical CN…
Researchers propose ELMZip, a novel framework using Extreme Learning Machines (ELM) and domain decomposition for onboard satellite image compression, which reduces downlink payload by transmitting only compact output wei…
Vulkan 1.4.356 introduces the VK_EXT_shader_ocp_microscaling_types extension, adding support for Open Compute Project Microscaling MX data types (MXFP4, MXFP6, MXFP8, MXINT8) to improve machine learning efficiency. Devel…
Researchers introduced the Coarse-Fine Transport Distance (CFTD), a distribution-based evaluation framework using atom-averaged features from pretrained Machine-Learning Interatomic Potentials (MLIPs) such as MACE, to as…
Claude 3.5 Sonnet is currently the hardest large language model to jailbreak due to its strong attention mechanism and resistance to persona-shift attacks, according to a Machine Learning Forum discussion. The model main…
Synthetic Sciences released OpenScience, an open-source AI workbench for scientific research under the Apache-2.0 license. The platform supports any frontier or open-weight model via API keys, integrates machine learning…
Researchers at arXiv (paper 2609.04300v1) found that Random Forest achieved the highest F1 scores of 0.97 on IEEE-30 and 0.86 on IEEE-14 bus systems for classifying power system contingencies into safe, moderate, or seve…
Researchers developed a hybrid machine learning approach combining EfficientNetB0 feature extraction with Random Forest classification to achieve 94.58% accuracy and sub-millisecond inference for real-time melt pool moni…
Finbold's predictive machine learning algorithm estimates Tesla (NASDAQ: TSLA) stock will rally 3.77% to $343.34 by August 31, 2026, despite being 16.18% down on the monthly chart. The prediction, based on technical anal…
A recent analysis of Chronic Kidney Disease prediction studies found that high-leakage studies reported accuracy rates up to 95.48%, while studies without data leakage achieved around 80.2%, revealing a 15% gap due to me…
A machine-learning surrogate developed for the Modified Implicative Interdependency Model (MIIM) predicts contingency severity and component criticality in cyber-physical power networks, achieving Spearman correlations o…
Researchers introduced The Unwritten Benchmark, a new challenge for multimodal machine learning in abstract perceptual reasoning, and found that while humans achieve over 80% ordered letter accuracy in acousto-kinematic …
A new study from arXiv (2609.04272v1) finds that supervised machine learning models outperform zero-shot large language models (LLMs) in predicting weather-related forced outage risk in distribution grids, achieving high…
A developer set out to test whether machine learning can reliably predict stock prices, documenting common pitfalls like look-ahead bias and the importance of using stationary returns instead of raw prices. After shiftin…