Machine learning news — deep learning, reinforcement learning, neural architecture search, diffusion models, and new ML frameworks and libraries.
Druva has deployed AI-based ransomware detection and identity-analysis tools to counter attackers who use AI to test more paths, shift tactics faster, and hide malicious behavior inside legitimate activity, the backup an…
A June 9, 2026 arXiv paper finds that LLM-driven ML research agents reproduce and discover high-performance models even when constrained to extremely short prompts or one-bit feedback, across 8 datasets spanning tabular …
Researchers are pursuing in-context robot learning with vision-language model (VLM) agents to let robots adapt to unfamiliar environments, according to a report on the work. The approach aims to address the limitation th…
Affirm unveiled a transformer-based machine learning model for real-time, transaction-level underwriting at its Investor Forum, following roughly 18 months of development, and said the model outperformed its existing mai…
A developer's technical explainer outlines four ways deep generative models make complex data distributions learnable: autoregressive models factorize a joint distribution into conditionals, variational autoencoders intr…
MIT Technology Review's newest 35 Innovators Under 35 list includes nine individuals in the climate and energy category, spanning energy, agriculture, and critical materials. Among them, Jae-Won Chung built software to m…
Hamilton Ulmer built a DuckDB extension that uses TypeSafeAI's Jev to classify rows in any CSV, Parquet file, or DuckDB table, processing roughly 1,000 rows in about 10 seconds. Ulmer said the approach is faster than usi…
A developer outlined the fundamental differences between supervised and unsupervised machine learning, explaining that supervised models train on labeled data for classification and regression tasks while unsupervised mo…
Speculative decoding, an inference optimization introduced independently by Chen et al. at DeepMind and Leviathan et al. at Google in 2023, cuts LLM decoding latency without changing output quality by having a small draf…
A developer's practical guide outlines the full end-to-end machine learning workflow, arguing that beginners should treat ML as a lifecycle rather than a simple dataset-to-model-to-prediction pipeline. The writeup walks …
A paper posted to arXiv on 7 Feb 2026 by Francesco Cagnetta claims the first theory that quantitatively predicts data-limited neural scaling exponents for modern LLMs trained on natural language. The theory isolates two …
Researchers identified a systematic failure mode in Proximal Policy Optimization (PPO) critics used for reinforcement learning of large language models, which they call Value Flattening, in which state values estimated f…
AGBO partner Chris Brearton and chief scientific officer Dominic Hughes will deliver a keynote titled "Technology and Storytelling" at TIFFCOM on Oct. 29, moderated by Variety Asia editor Naman Ramachandran, focused on h…
A developer documented a step-by-step process for installing Kubeflow on Amazon EKS, using eksctl to provision the cluster, VPC, subnets, and a managed node group in a single command. The guide notes that Kubeflow's cont…
A developer published a technical explainer tracing the evolution of attention mechanisms in transformer models, from the original Multi-Head Attention (MHA) introduced in the 'Attention Is All You Need' paper to newer v…
The University at Buffalo has developed AudioSight, a smartphone-based platform that uses pupillometry, near-infrared hardware, and cloud-based AI to objectively screen for hearing disorders including hidden hearing loss…
The University at Buffalo has developed SYNAPSE, a multimodal auditory assessment platform that synchronizes EEG and pupil-response measurements to objectively screen for tinnitus, hyperacusis, misophonia, and other soun…
Google Research introduced Retrieve-for-Train (R4T), a search framework that trains a fan-out language model with reinforcement learning once, then uses that model to synthesize training data for a 53.9M-parameter diffus…
A proposed zeroth-order paradigm for LLM preference alignment addresses likelihood displacement, a problem that motivates alternative ways to extract information from preference pairs with small likelihood margin. The ap…
A method called SpectralShift extends the effective context window of Gated DeltaNet, a linear attention architecture used in place of softmax attention for long-context modeling, by reparameterizing the model's spectral…