Барлық жарияланған талдаулар, егеменді стек зерттеулері, экожүйе жаңалықтары және ЖИ модельдерінің шолулары.
🔄 Anthropic обновляет Claude: три главные новости этой недели Собрали последние новости Anthropic — от новых функций Claude до позиции компании по регулированию искусственного интеллекта 👇 💳 Claude сможет подключаться к банковским счетам Anthropic тестирует новую функцию…
Бұл Anthropic бір мезгілде: «Ей, біз алдымен жоспарымыз жоқ, оны қалай басқаруға болатын ағымдағы жоспарымыз жоқ, бүкіл адамзатты жою мүмкіндігі бар нәрсені салып жатырмыз, және айтпақшы, біз бірнеше аптадан кейін IPO-ға шығамыз». Мысалы, сіз маған осы логиканы түсінуге көмектесе аласыз ба?…
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 🎉.
GPT-6 Astra практически без ограничений???.
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…
Өзбекстан 1,7 миллион доллар инвестиция салып, DigitalBusiness.kz зауыттары үшін AI жүйесін жасады
Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in policy learning. Existing methods typically assume a \emph{universal utility} function shared across a population and treat…
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…
KV-cache reuse can reduce inference cost in retrieval-augmented generation and agentic systems, but cached contexts may become stale when retrieved knowledge, working memory, or user state is edited. Under causal self-attention, even a local edit can affect downstream KV states.…
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…
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…
High-resolution land use and land cover (LULC) products derived from Sentinel-2 imagery are widely used for environmental monitoring and land management, yet their performance can vary across regions with complex ecological gradients and heterogeneous surface conditions. In…
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…
Fairness-aware model compression requires selecting methods and configurations that balance accuracy, fairness, and deployment cost. These decisions become more difficult when compression methods are composed or the user's requirements change. In this paper, we propose…
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…
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…
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…
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…
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…
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.…
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…
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…
A large language model (LLM) agent can follow more graph paths without acquiring more independent evidence. GraphEcho tests whether agents mistake these repeated encounters for additional corroboration. The benchmark varies path counts and evidential origins while holding…
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…