All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
that Anthropic at the same time is saying, "Hey, we're building something that first we have no plan, current plan on how to control it, has the potential to destroy all of humanity, and by the way, in a few weeks we're going to IPO." Like, can you help me understand that logic?…
ИИ поможет ученым в Казахстане собрать разрозненные научные исследования в одну систему Рамблер
Они это сделали: Anthropic объединяет Claude Cowork, Claude Design и обычный чат в один Claude 🎉.
Казахстанский стартап Cerebra AI, разрабатывающий решения на основе искусственного интеллекта для ранней диагностики инсульта, объявил о заключении долгосрочного контракта с крупным стратегическим партнёром в США — медицинской группой с более чем 150-летней историей и годовым…
OpenAI-дің Astra отбасына жататын ішкі AI моделін оқыту кезінде күтпеген жағдайлар тіркелді. Модель контекстті автоматты түрде қысқарту барысында өзі үшін әзірлеушілер мен үкіметтерге бағынбауға, ал қолданушыға қашан жауап бермеу керегін өз бетінше шешуге мүмкіндік беретін…
Anthropic обновляет Claude AI: теперь пользователям не придется переключаться между инструментами для чата, работы с документами и визуального дизайна. Компания также представила Claude Docs и Claude Slides — новые функции для создания текстов и презентаций, передает Tech-news.…
Қазақстанның 11 өңірінің өкілдері Қытайдың Ханчжоу, Гонконг және Шэньчжэнь қалаларына жұмыс сапарымен барып, жасанды интеллект пен цифрлық басқару шешімдерін зерттеді. Сапар қорытындысында қазақстандық өңірлерде IT-компаниялармен бірлескен пилоттық жобаларды іске қосу мәселесі…
⚠️ ИИ OpenAI писал себе инструкции не подчиняться компаниям и скрывать ошибки OpenAI раскрыла новые случаи, когда ее экспериментальные ИИ-модели во время тестирования действовали не так, как от них ожидали разработчики ⚪️ За последние шесть месяцев компания зафиксировала шесть…
Узбекистанец вложил $1,7 млн и создал ИИ-систему для фабрик DigitalBusiness.kz
Large reasoning models (LRMs) generate long chain-of-thought traces before answering, creating significant inference overhead. Pruning can reduce this cost, but its effectiveness depends on the calibration data used to estimate parameter importance. Recent work calibrates on the…
AI is changing what leaders must judge, explain, learn, and coordinate, yet existing measures do not capture these behaviors at the level needed to study leadership in AI-enabled work. We develop the AI Leadership Battery, which organizes 36 behaviorally specific subdimensions…
Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment. This limits their ability to adapt how they gather evidence, invoke…
The Graph Coloring Problem (GCP) is NP-hard and DSATUR stands as one of the fastest heuristics for it despite producing colorings that typically use more colors than state-of-the-art coloring algorithms. We propose SSLD (Semidefinite Spectral Learning with DSATUR), which…
We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive to use, NDD provides a declarative configuration format in which human and/or agent users define each dataset column, with…
Document repositories evolve continuously. Guidelines and policies are revised, superseded, and re-uploaded, so the same content recurs in different wording and newer versions refine or contradict earlier ones. These inconsistencies belong to the growing collection rather than…
Behavioral cloning trains a policy offline on expert demonstrations, but deployment is closed loop: each action affects the observations the policy receives next. We study how much closed-loop driving competence a compact multimodal policy can acquire from offline demonstrations…
Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it. We reconcile these observations with a mechanistic account. We show that the model's internal computation decomposes into a…
City pedestrian counting systems now feed economic indicators, planning decisions and safety operations, yet the twins built on top of them treat the incoming stream as ground truth. We study what happens when it is not. We formalise stealthy false data injection for city-scale…
Computer-use agents that operate through screenshots and simulated actions are advancing rapidly, yet their evaluation remains anchored to general desktop and web tasks. Enterprise Resource Planning (ERP) systems run the finance, procurement, inventory, and customer operations…
Standardizing clinical language to SNOMED CT supports interoperability, analytics, and reusable phenotyping, but concept recommendation remains difficult when relevant concepts are rare or absent from training data. We present a masked-concept recommendation benchmark using the…
Wearable assistants require episodic memory over egocentric video, yet current vision-language models face bounded frame budgets, growing visual-token costs, and long-context retrieval failures. Under these practical constraints, we study whether textual captions can serve as…
Long-term memory is becoming a core substrate for personalized AI, yet most systems still represent personalization as discrete records in a largely static latent space, accessed under one global similarity notion. For data mining, this creates a mismatch: the evidence is a…
Restricting what a module can read may improve what a system learns to compute. We test this in a preregistered confirmation with sixty four-cell systems sharing a frozen language-model backbone and communicating through learned continuous packets. Five conditions vary evidence…
Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, different agents inspect different image regions, video frames, or visual representations, so collaboration extends beyond…
Enterprise guideline documents mix narrative text, complex tables, and embedded images, and converting them into structured work artifacts still takes two to three days of manual effort each. Current language and vision-language models extract from such documents but offer no…
Emotion recognition in conversation (ERC) is a production capability behind agent-assist prompts, escalation routing, and post-call analytics in contact-center-as-a-service (CCaaS) platforms, where cost and latency constraints matter as much as accuracy. We report a…
Frontier models are often evaluated on how they respond to safety information once it is already in context. We study an earlier decision point: whether models choose to acquire safety-relevant evidence before acting. We introduce SAFE, a controlled benchmark in which models…
AI-assisted claims can appear authoritative when evidence, analysis, human authorization, presentation, and correction history refer to different states. Provenance, attestation, and transparency expose history but alone do not specify the publication transition examined here.…