Naponta frissítve1649 ellenőrzött történetek
Source-checked AI coverage of product launches, policy shifts, safety research, and industry moves, explained in plain English by a nonprofit education team.
Every story links to the strongest available evidence: original sources when available, otherwise clearly attributed reporting.
Mi történt, miért számít, és mire kell figyelni – nincs zsargonadó.
Ha a jel gyenge, akkor nem teszünk közzé semmit, csak feltöltjük a hírfolyamot.
Ellenőrzött perspektívák egyre növekvő száma azoknak az embereknek, akiknek meg kell érteniük az AI-t anélkül, hogy felhajtást kergetnének.
Stanford HAI’s 2026 AI Index says national AI strategies are spreading, while data-localization rules, state-backed computing capacity and public investment remain uneven across regions.
A Together AI analysis of 904 DeepSWE rollouts reports that OpenAI's GPT-5.6 Sol leads on first-attempt coding accuracy while the open-weight GLM-5.3 leads once retries are allowed, at roughly half the cost per attempt.
An arXiv preprint proposes an AI generative model and watermarking method for more reliable multivariate time-series data after editing. Authors report tests across finance, energy and neuroimaging benchmarks, but the abstract gives no numerical results or evidence of deployment.
Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly domain-specific terminology, for which open datasets and specialized models have been virtually non-existent.
Large language models (LLMs) and agentic AI systems are increasingly being explored for domain-specific maintenance and prognostics tasks, raising the question of whether they can effectively support prognostics and health management (PHM).
A new arXiv preprint introduces NepOOC, a 1,090-pair Nepali-English benchmark for detecting misleading captions attached to authentic images. On this dataset, a text-only mBERT model matched the best tested multimodal system, while image-only models performed near chance.
A new arXiv preprint tests financial named-entity recognition across filings, news and social media, finding that some confidence measures remain useful under domain shift while others deteriorate sharply.
A study explores whether conversational AI can shift how majority-group members categorize and relate to Latine immigrants.
A new arXiv preprint reports that injecting a new subject every few hundred tokens can increase language-model outputs’ judged surprise and connection. The effect did not produce integrated documents or improve the best result in an online bin-packing test, exposing limits in how long generations are evaluated.
A preliminary arXiv study finds that responses from language models have become less diverse across open-ended creativity tasks, raising questions about homogenization in human-AI creative work.
An arXiv preprint describes a method that trains transformer language models to work with sparse attention and reports frequent gains over models trained with exact attention.
Google Cloud presents CodeMender as an AI security agent that scans codebases, tests whether vulnerabilities can be exploited, and generates patches for developer review. The source does not describe it as a formal-verification framework or provide independent performance evidence.
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