All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
Сильного продукта уже недостаточно, чтобы выйти на крупного корпоративного клиента в США. Стартапу необходимо доказать спрос, показать измеримый результат, выйти на людей, принимающих решения, и заслужить доверие на новом рынке. Об этом говорили участники сессии How can AI…
Германия впервые выпустила модель. С результатом почти 97% в математике. Всего 78 млрд параметров, из которых на каждый токен активируются только 3,46 млрд. Открытые веса под лицензией Apache 2.0. Модель обучена для немецкого и английского языков. По собственным оценкам Aleph…
Искусственный интеллект будет мониторить строительные площадки в Казахстане
В эти дни в Астане проходит международный технологический форум AI & Digital Bridge 2026. Форум стал площадкой не только для обсуждения перспектив развития искусственного интеллекта и цифровых технологий в Казахстане, но и для заключения практических договоренностей между…
🌐 AI больше не AI? Искусственный интеллект предлагают называть «суперинтеллектом» Илон Маск поддержал идею отказаться от термина «искусственный интеллект» — AI — в пользу «суперинтеллекта», или SI. Более того, предприниматель заявил, что его компания SpaceXAI будет…
Корреспондент Digital Business узнал, как AI агенты выявляют мошенников, зачем моделируют новые способы атаки на банки и можно ли с помощью ИИ сэкономить $10 млн в год на урегулировании страховых случаев.
🏆 Кто стал победителем AI & Digital Bridge Awards 2026? 3 октября в рамках форума AI & Digital Bridge 2026 в Астане прошла церемония ежегодной премии, на которой отметили лучшие проекты и компании в сфере искусственного интеллекта, цифровых технологий и трансформации. Награды…
В рамках международного форума AI & Digital Bridge 2026 состоялась выставка проектов программы AI-Sana. На выставке были представлены лучшие проекты в сфере искусственного интеллекта, разработанные студентами 20 казахстанских вузов. На открытии выставки с приветственным словом…
Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Traditional approaches to cognitive modeling are interpretable and benefit from human expertise, but lack flexibility and…
Flow matching models excel at generative modeling, and many downstream applications require their samples to satisfy prescribed constraints, such as observed measurements and physical laws. However, existing constrained samplers often face a trade-off: \textit{enforcing…
Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed at scale with LLMs, provide a useful signal about how companies disclose their response to AI? We test this by applying a…
Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condition subsequent decisions. In long-horizon tool use, final-outcome rewards provide weak credit assignment over long interaction…
Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, applied broadly across prompts. We provide a controlled geometric analysis of this global-unsafety assumption and reveal a…
In Recirculating Aquaculture Systems (RAS), precision feeding is critical for minimizing costs and improving fish welfare. However, existing methods lack cognitive alignment between fish behaviors and management knowledge, impeding translation into executable, interpretable…
Text-to-image generation enables users to explore several images generated from the same prompt. For these generated images to be useful, each one must reflect the user's preferences, measured by a learned reward, and differ visually from the others to maintain diversity.…
Recent years have seen the employment of a plethora of machine learning (ML) models in high-stakes domains, but they remain largely opaque to the practitioners who act on their predictions. While post-hoc explanation methods offer a lens into this model behavior, wielding them…
Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all $n\times m$ point pairs in every Sinkhorn iteration. We present \textbf{FlashSinkhorn~2} (FS2), a solver for squared-Euclidean cost on…
Large language models spend the same amount of computation on every token they generate, regardless of how difficult each token is to produce. Methods such as speculative decoding and model routing are built on the premise that much of this computation is unnecessary, yet the…
LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreement on acceptance rates or occupational aggregates. We introduce O*NET-BENCH, an audit suite derived from an existing survey of…
World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruction. However, existing WAMs struggle with long-horizon prediction, as generating dense video…
Natural Visibility Graph (NVG) based analysis characterizes network traffic through topological descriptors reflecting different structural properties. However, not all descriptors contribute equally to cyber-attack classification, and extracting a large metric set can increase…
Using a frontier model like Claude Opus as a meta-agent to generate terminal tasks and verifiers for RL training is increasingly common. Yet a runnable Docker image and executable test suite do not guarantee a faithful end-to-end pipeline for terminal agent training. We present…
The operation of dynamically polarized targets in nuclear physics experiments relies on continuous tuning of the microwave frequency to compensate for radiation damage and evolving material properties, a task that is traditionally performed through manual trial-and-error by…
Reinforcement learning for large language models typically maximizes expected return, adding up the probabilities of all successful trajectories. However, the classical sum formulation can only report how often the model policy succeeds, not which solution actually worked, and…
In the UK, public healthcare staff report turning to machine translation (MT) - predominantly Google Translate (GT) - to communicate with patients across language barriers. Though intended to support their duty of care, potentially uninformed reliance on MT in such contexts…
Balancing electricity demand and supply is increasingly difficult due to the inherent intermittency of renewable power generation and the stochastic power consumption. Grid operators require fine-grained, decision-relevant insights into household energy consumption to manage…
Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that…
World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Language Model (LM) to serve as a WM has emerged as a dominant paradigm. However, despite the widespread…
ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This coupling makes action authorization and completion control difficult to enforce independently, allowing errors to propagate and…
As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate oversight and supervision of these systems has become increasingly urgent. One promising approach is AI debate, which seeks to…