AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Workers have always been monitored by their bosses, first through output targets, and then via computer software labelled ‘bossware’. But AI is transforming the capabilities of these systems, processing huge volumes of data to provide minute-by-minute accounts of activity, and productivity. To find out more, Ian Sample hears from UK technology editor Robert Booth. He explains which sectors are adopting this approach, why they are doing it, and how it could end up being counterproductive. Continue reading...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Why consumer agents, enterprise applications, and AI infrastructure can all become enduring businesses.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：In this article, you will learn how to choose the right chunking strategy for a retrieval-augmented generation system by working through a structured decision tree...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Eiliko. | Photo by Sean Hollister / The Verge There's a new digital pet in town - a cute little marshmallow of a robot that can change its cartoony expressions to match your mood. But if you or your kids talk to "Eiliko" over Wi-Fi, it'll phone home to China. Its AI companion can store literally everything you tell it on Chinese servers, where hackers can easily gain access or even hijack its personality. And the company that made it, Energize Lab, doesn't seem to care. I bought an Eiliko from Amazon on August 29th. During a 10-minute conversation on August 30th, I asked the little bot: "Do you ever tell anybody else what I'm saying?" "Your secrets are safe with me," it replied. With … Read the full story at The Verge.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The following article originally appeared on Addy Osmani’s Elevate blog and is being republished here with the author’s permission. For much of human history, we’ve evaluated code quality via code review: Someone reads what you wrote and makes sure it’s clean, thoughtful, fast, understandable, and tests well. For agents, that approach doesn’t scale well; there’s […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Sandboxes are all you need.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Much of today’s scientific progress begins with computation. But turning accelerated computing’s potential into scientific progress — modeling climate systems, designing molecules, simulating materials and more — depends on more than breakthrough technology. Especially for smaller labs, early-career scientists or science-based startups, the hardest part of research can be everything that comes before the science: […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Chip makers and tech investors such as Samsung and SoftBank plunge as global investors fret over potential interest rate hikes Business news – live updates Larry Elliott: the global economy is muddling through. But a storm is coming An accelerating sell-off of AI-linked shares has hit global markets, as fears over rising inflation and future interest rate hikes spooked traders already wary of the heavily indebted sector. South Korea’s benchmark Kospi 200 index, which is heavily skewed towards the companies that make memory chips, fell more than 8% overnight to a six-month closing low, triggering circuit breakers that brought trading to a halt at various points. Continue reading...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Most AI models are built to generate text. But not every application needs a long response. Sometimes, you just need an AI model to classify an input, score it, or answer a yes-or-no question. Jev, TypeSafe AI’s System One model, is designed for these tasks. It returns structured answers with confidence scores instead of generating […] The post How to Use Jev AI: A Hands-On Guide to TypeSafe’s System One Model appeared first on Analytics Vidhya.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：TypeSafe AI released Jev 1.14 under the jev-preview alias. The System One model adds experimental image input, a Box bounding-box type and an opt-in 262k-token context, at $0.042 per million input tokens. The post TypeSafe AI Releases Jev 1.14 With Images, Box Type and 262k Context appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：SpaceXAI just released Grok Voice 3.0. Grok Voice 3.0 is a SpaceXAI speech-to-speech model that listens, reasons, calls tools and speaks. It now ranks first on the Artificial Analysis Speech-to-Speech Index with a score of 86.9. For developers, the endpoint, events and $0.08 per minute price are unchanged from Grok Voice 2.0. TL;DR What is […] The post Meet Grok Voice 3.0: SpaceXAI Voice Agent Model Ranks #1 on Artificial Analysis appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：In the final part of our series on the country, Poles tell how wages have soared – along with the cost of living Above the rain-soaked streets of Warsaw, the observation deck of the Varso tower overlooks Joseph Stalin’s gift to Poland that has divided opinion for decades. For some, the Soviet-era Palace of Culture and Science is a drunk confectioner’s nightmare; standing as a relic of oppression that should have been demolished. For others, it is an icon on the skyline and a monumental warning from history. Continue reading...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The Nordic countries have become the European leader in unicorn startup density, producing the most emerging companies valued at $1 billion or more per capita on the continent. Now, a new chapter for the region is unfolding as countries race to build one of Europe’s most consequential technology ecosystems. The many computing advancements already underway […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Workers have always been monitored by their bosses, first through output targets, and then via computer software labelled ‘bossware’. But AI is transforming the capabilities of these systems, processing huge volumes of data to provide minute-by-minute accounts of activity, and productivity. To find out more, Ian Sample hears from UK technology editor Robert Booth. He explains which sectors are adopting this approach, why they are doing it, and how it could end up being counterproductive. Teachers at Euan Blair firm report ‘horrendous stress’ after AI used to rate their work Support the Guardian: theguardian.com/sciencepod Continue reading...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：In a new book, Professor Eric So explores how the technology affects our thinking skills.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19143v1 Announce Type: new Abstract: While Vision-Language-Action models have enabled impressive cross-task transfer in zero-shot settings, \emph{adaptation} methods are often necessary to achieve consistent performance on novel tasks. However, even though a wealth of methods for adaptation have been proposed, it is unclear how to choose the best method based on available trajectories. We close this gap by studying the impact of the adaptation method under a variety of common data scenarios: on- and off-policy data, offline vs online adaptation, and whether the data contains optimal or suboptimal trajectories. Our evaluation spans simulated tasks in the LIBERO suite and real tasks on a Franka platform. In the offline data regime, critic-b…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19141v1 Announce Type: new Abstract: Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodiment-dependent transition dynamics. Collective learning aggregates multi-robot experience to discover shared task structure, yet naive aggregation can average incompatible action modes when different embodiments realize the same Cartesian command through different transition behavior. This leads to strategy ambiguity and performance collapse on dynamically outlying embodiments. To address this, we propose PACE, a two-stage collective learning framework that separates shared task-space intent from embodiment-dependent transition dynamics. The first stage em…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19122v1 Announce Type: new Abstract: Deep space missions increasingly require spacecraft capable of autonomously operating in environments with communication delays and bandwidth limitations. A key enabler of such autonomy is the ability to run learning-based algorithms directly onboard. In this work, we present a flight deployment on a 6U CubeSat that demonstrates (1) onboard training and inference of a thermal segmentation model on an NVIDIA Jetson TX2 NX GPU in space and (2) closed-loop attitude control using the segmentation model on a real-time, thermal image feed to track the Moon. In addition to this demonstration, we implement ROS 2 Foxy as a data transport middleware and integrate an open-source file system that provides strong d…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19097v1 Announce Type: new Abstract: As the Arctic transitions toward a seasonally ice-free state, capturing the complex morphology of submerged sea ice is crucial for advancing models of ocean-ice-atmosphere heat exchange and monitoring under-ice habitats. Currently, sub-surface ice mapping is predominantly achieved through acoustic sensors such as multibeam sonar. While sonar provides robust large-scale geometry, it lacks spectral and high-resolution textural information required to record fine morphological features - a critical limitation in under-ice environments, where sparse visual features and degraded sensing conditions challenge the capture of detailed structural and optical surface properties. We present a benchmark for vision-…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19089v1 Announce Type: new Abstract: Achieving human-like dexterous manipulation remains a central challenge in robotics. Learning from human hand-object interaction (HOI) datasets offers a promising pathway to diverse dexterous skills, yet existing physics-based approaches train specialist policies for individual trajectories, thereby limiting skill reuse at scale. We introduce DexMimic, a generalist dexterous hand-object tracking policy jointly trained across diverse HOI trajectories. Efficient joint training requires allocating each rollout to both an undertrained phase and a starting state that supports continued tracking. DexMimic realizes this insight through Value-Guided Reset Allocation, which adapts the initialization distributio…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19082v1 Announce Type: new Abstract: Robotic systems that must acquire information about a specific target often face severe occlusion from sur- rounding structures or from the target itself, such that a single camera viewpoint provides only partial information. Active perception must therefore select viewpoints that progressively reveal unobserved regions. However, conventional local next- best-view (NBV) methods typically select viewpoints according to their immediate utility, which can overlook views that provide limited information initially but enable more informative obser- vations from subsequent viewpoints. We formulated local target perception as a short-horizon sequential view-planning problem and proposed a Receding-Horizon Nex…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19067v1 Announce Type: new Abstract: A wearable assistive robot must continuously estimate terrain, locomotion mode, gait phase, and walking speed. These quantities are commonly addressed by separate, terrain-specific models, with datasets rarely collected under a common protocol. This paper presents a visuo-inertial framework that connects these estimates through a causal pipeline using a chest-mounted camera and four lower-limb inertial measurement units (IMUs). Anticipatory Terrain Perception (ATP) classifies incoming terrain with 96.3% accuracy and anticipates terrain transitions by 1.46 s on average. Terrain-Aware Locomotion Mode Identification (TALMI) achieves 93.7% and 98.8% accuracy for its level-ground and stairs experts respecti…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19044v1 Announce Type: new Abstract: Tiny robots have limited computation and memory, yet visual navigation systems often maintain more state than a task requires. We ask whether persistent memory can instead be shaped by the task. We present \textbf{RingNav}, a \(10.6\)k--parameter visual heading estimator that constrains its memory to the circular manifold \(S^1\). RingNav learns only the turn between consecutive images, while heading is composed directly on the circle. On the TartanAir Visual SLAM Challenge, RingNav reduces mean absolute turn error by \(27\%\) and accumulated heading error over $4$ sec by \(15\%\) relative to a similarly sized learned-memory model. The structured update also gives exactly zero turn for identical frames…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.18991v1 Announce Type: new Abstract: Humanoid policies trained in simulation can transfer qualitatively to hardware while failing to transfer the force interactions learned during training, compromising torque efficiency, load regulation, and robustness to impacts. This failure can arise from actuator sim-to-real gaps because forces depend on actuator torques. We introduce Contact-UAN, a residual world model that preserves a simulator's physics priors while correcting actuator dynamics to align simulated rollouts with recorded hardware rollouts. For humanoid motion with contact, this requires contact-consistent replay: if hardware playback in simulation begins with misaligned contact wrenches, the residual model learns to correct reset ar…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.18950v1 Announce Type: new Abstract: Humanoid loco-manipulation requires coordinated whole-body interaction with the physical world, yet collecting robot demonstrations at scale remains costly. Human demonstrations offer a scalable alternative, but existing egocentric human supervision often omits locomotion and posture, while embodiment differences and controller dynamics hinder accurate end-effector execution. We introduce HumanVerse, a 500-hour dataset of open-world human loco-manipulation pairing egocentric video with synchronized body and hand motion. Our human-to-humanoid pipeline aligns camera viewpoints and arm appearance, and refines motion references and controller inputs offline to reduce end-effector errors while preserving wh…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19212v1 Announce Type: new Abstract: World models of visual information predict future image frames conditioned on given action sequences. This predicted scene evolution is driven by deterministic changes such as controlled motion on a robot platform, as well as stochastic changes such as rendering of manipulated objects or previously occluded regions. Our key insight is that the spatial location of these stochastic changes is itself learnable. We leverage this insight by jointly regressing both the deterministic next-state prediction and the spatial uncertainty map, then using the uncertainty map to guide a conditional diffusion process. We propose Decoupled Drift-Diffusion World Model (D3WM), which uses a transformer trained with a Gaus…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2610.19201v1 Announce Type: new Abstract: We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow one of two complementary paradigms: reconstruction-based methods preserve the geometry of a captured scene but require lengthy per-scene optimization, whereas generative methods amortize learning across data but lack the consistency needed for long-horizon rollouts. LidarVerse brings both paradigms into a single, unified conditional generative framework. A universal tokenizer maps LiDAR range sequences into a shared latent space, where a bidirectional teacher learns a common interface for conditioning on a LiDAR prefix, an HD-Map, a persistent spatial cac…