Machine learning news — deep learning, reinforcement learning, neural architecture search, diffusion models, and new ML frameworks and libraries.
A new article explores solving neural ordinary differential equations (ODEs) from first principles, providing a foundational approach to understanding this class of models. The piece aims to demystify neural ODEs by deri…
Stanford researchers have developed a new approach to scaling laws that could fundamentally change how AI models are trained, potentially leading to more efficient and powerful systems. The work challenges traditional as…
A new tutorial demonstrates a step-by-step approach to forecasting daily river discharge for the Mississippi River at St. Louis using open data from USGS and Copernicus ERA5, emphasizing methodology and reproducibility f…
A team of CV engineers deploying a pedestrian detector on a Cortex-A53 board enabled Winograd convolution for a speedup but lost 2.1 mAP due to fp16 overflow in the F(4,3) transform, with small objects dropping 7 points.…
Software developers in 2026 face major challenges including keeping pace with rapidly evolving AI tools, managing technical debt, addressing cybersecurity threats, and balancing speed with quality. Burnout and remote col…
A developer from Lekhai.app has published a guide on running AI models locally on a Mac, highlighting benefits such as privacy, speed, cost savings, and offline access. The guide covers requirements including Apple Silic…
Researchers introduced cuTile Rust, a tile-based system for safe, idiomatic GPU kernel authoring in Rust that extends Rust's ownership discipline to GPU kernels. On the NVIDIA B200 GPU, cuTile Rust achieved 7 TB/s for el…
Kolon Industries signed an AI research agreement with the University of Seoul to collaborate on AI-integrated R&D for advanced materials, aiming to accelerate AI transformation in materials development. The partnership i…
Vaisala has signed an agreement to acquire Atmo, Inc., a San Francisco-based AI weather company, for a fixed purchase price of USD 70 million plus up to USD 60 million in earn-outs. The deal, expected to close by end of …
Researchers at Google DeepMind instrumented DiffusionGemma 26B, a masked discrete-diffusion mixture-of-experts model, and found its decoding is neither parallel nor sequential but follows a partial left-to-right commit b…
ML engineer Vicki Boykis's blog post 'Running Local Models is Good Now' hit the top of Hacker News, signaling that local AI models have crossed from hobbyist experiment to genuine developer workflow in 2026. The shift is…
A new report reveals that while 80% of security operations center (SOC) practitioners use AI or machine learning tools daily, only a third have integrated them into structured workflows with governance and validation, hi…
Researchers introduced Looped World Models (LoopWM), the first looped architectures for world modeling that iteratively refine latent states through a parameter-shared transformer block, achieving up to 100x parameter ef…
A 124M-parameter GPT-2 model trained from scratch on OpenWebText data using a custom deep learning library achieved a validation loss of 2.764 nats and a perplexity of 15.87 after 56,000 steps (27.5B tokens). The model, …
A developer has been refining KMDS, a framework for building repeatable and auditable machine learning systems, with recent updates focusing on analytical governance. New features include a Feature Advisor service that r…
A new arXiv preprint (2606.18164) by Niklas Forner and coauthors analyzes how transformers learn chain-of-thought state tracking in a solvable setting, training a simplified one-block transformer on permutation compositi…
A guide explains the Naive Bayes algorithm, a simple yet effective machine learning method widely used for classification tasks, highlighting its practical applications and enduring relevance in AI.
A beginner's guide on Towards AI explains ensemble learning methods bagging and boosting, covering decision trees, Random Forest, and XGBoost with analogies, examples, and code.
Researchers introduced a geosteering framework that combines particle filtering with reinforcement learning to optimize well trajectory decisions under geological uncertainty. The framework evaluates three decision-makin…
Researchers propose negative token filtering, a method enabling stable single-rollout training for critic-free reinforcement learning in large language models, outperforming group-based techniques on agentic tasks.