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AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：This post was not written with or by AI. I wanted to explore how AI could help deepen my faith. I enjoyed using Claude to research topics which were on my mind. It does a good job finding and quoting scripture but a poo…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Summary Debian opened a General Resolution to decide whether to allow AI-generated or AI-assisted code. Proposal A seeks to ban all LLM-generated and assisted code, citing copyright, quality, community, and ethics. Prop…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：China’s Moonshot AI is in early talks with Microsoft, Amazon and Google to host Kimi K3 Credit: Bangla press via Shutterstock.com Moonshot AI is in early discussions with Microsoft, Amazon, and Google about hosting Kimi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Latest commit History 8 Commits 8 C…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Bill Gates, chair of the Gates Foundation, speaks during a 2024 conference. | Bloomberg via Getty Images Bill Gates has been reflecting a lot on AI lately, and the process has triggered a stark awakening. Once a staunch AI optimist, the Microsoft cofounder is now deeply pessimistic about what AI means for our collective future. Having been conspicuously quiet on AI issues recently, Gates is back with a nearly 6,000-word essay seeking to reclaim a central role in shaping the technology globally. Titled "The turbulent AI era is here. The choices we make now are critical," Gates warns the world is not remotely ready for what is coming. Worse still, he notes, "we are not preparing for it." Gates attempts to chart a path forward, but the essay l … Read the full story at The Verge.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Uber and Wayve had said they would start trips ‘later this summer’ but launch now looks unlikely this year The rollout of robotaxis on the streets of London is unlikely to happen this year, as regulatory and technical hurdles push back the ambitious schedule set out by the UK government. Only weeks ago Uber and London-based Wayve were granted the first minicab licences in the capital to allow them to offer self-driving taxi rides to paying customers – but with a human safety driver in place, for now – and said they would start trips “later this summer” before a full public launch. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：25 Aug 2026 · Essay Should we let AI govern us? What if AI were in charge of public policy and politics? Perhaps it would shift food subsidies, electrify transport, build cheap power, sign the plastics treaty, restore t…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：‘This is crazy. This is insane’: Bill Gates has changed his mind about AI and jobs Aug 26, 2026, 3:00am EDT Technology PostEmailWhatsapp The News Bill Gates says it’s time to hit the AI panic button. The technology has…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 0 Star 12 BranchesTags Open more actions menu Latest commit History 6,169 Commit…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：← Back to blog Can AI Music Tools Really Replace Epidemic Sound? An Honest Look MuseGen Team 7/30/2026 #Epidemic Sound alternative#AI music vs stock music#royalty-free AI music#AI music for creators If you make videos,…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23887v1 Announce Type: new Abstract: Latent-conditioned adaptive policies can control robots across changing dynamics, but their learned latents remain internal representations of the policy rather than physical models that can be inspected, rolled out, or used by other control modules. This limits closed-loop analysis, diagnosis, and further improvement of a fixed policy. A direct mapping from latent to physical parameters is also under-specified, because multiple systems can induce similar closed-loop behavior. We therefore decode each operational latent into a distribution of quadrotor models using conditional flow matching. The decoded distribution enables two downstream uses without modifying the policy: online predictive tuning of a high-level controller around the fixed low-level policy, and robustness analysis under specified disturbances. Under perturbed actuator dynamics, decoded-model predictive tuning reduces position tracking RMSE by $23\%$ and heading RMSE by $45\%$ relative to fixed gains. Under Gaussian force disturbances, decoded-model ensembles closely predict the lateral tracking-error evolution. Together, these results show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23839v1 Announce Type: new Abstract: Embodied Agents System (EAS) are increasingly deployed in open-world physical domains, where reliability directly dictates deployment quality and human-agent trust. However, existing evaluations rely on outcome-centric metrics as success rate or safety scores that collapse diverse execution trajectories into coarse scores, obscuring the dynamic processes underlying agent behavior. Therefore, they ignore a critical property of EAS -- which we define as the Resilience -- that reflects how EASs recover, stabilize, and extend under perturbations and across iterative updates. The lack of resilience is particularly critical in open-world environments due to continuous unexpected disruptions, thus directly affecting the quality of EAS deployment. To address this problem, we gain insight from the resilience-engineering concepts to EAS groundings and propose a novel resilience evaluation framework that can be flexibly applied to any EAS. Specifically, we define the first comprehensive resilience metrics suite for EASs system that exposes Rebound, Stability, and Graceful Extensibility across embodied tasks execution, providing a practical grounding for EAS resilience analysis. We further implement the resilience evaluation layer that transforms execution process into assessments for diagnosis and optimization. Across 400 household tasks with 10 EAS, we reveal the process-level distinction hidden by outcome metrics, including recovery cost differences among successful episodes ($\Delta C_{rec}=25.2$), increased instability and task-family degradation. Metrics-guided optimizations reduce recovery cost and increase stability, graceful extensibility completion, showing the diagnostic effect of resilience evaluation. Our results reveal a trade-off among resilience characteristics, suggesting that a resilient EAS construction should be configured according to deployment-specific requirements.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23831v1 Announce Type: new Abstract: While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses a fundamental obstacle to effective RL improvement. In particular, their severe inference latency---which can lead to pauses or jerky movements---can alter the effective environment dynamics and, if not correctly accounted for, break the Markov assumption that RL relies on, causing standard RL algorithms to fail completely. In this work, we introduce a latency-aware framework, Asynchronous RL with Intermediate Information (ARLI), that enables RL-based improvement of generalist policies under inference delays. Our framework builds on asynchronous inference approaches, which interleave action generation with execution to hide latency, and addresses its incompatibility with RL by providing a low-latency RL policy design that maximizes reactivity within the inference window through two contributions: state augmentations that restore near-Markovian structure by incorporating committed actions and a mid-inference observation. We evaluate our approach across simulated and real-world manipulation tasks, and find that it enables effective finetuning under inference delays where standard RL fails entirely, even matching or exceeding the performance of standard RL in idealized no-latency settings.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23664v1 Announce Type: new Abstract: Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing methods largely inherit policy-gradient machinery from large language models. Unlike autoregressive models, diffusion models do not provide tractable likelihoods for generated samples. As a result, current approaches either construct trajectory likelihoods from stochastic denoising transitions or approximate endpoint likelihoods with evidence lower bound, introducing additional computation and algorithmic complexity. We demonstrate that this likelihood-based machinery is not necessary for effective diffusion reward fine-tuning. We propose reward-based velocity matching (RVM), a simple trajectory-free update that acts directly on the velocity field. RVM reinforces directions associated with high-reward generations, suppresses those with low reward, and involves an optional anchor term controlling drift from a reference velocity. Notably, it provides a general framework that recovers recent fine-tuning methods, including RAM and DiffusionNFT, as special cases. Across various large-scale diffusion models reward fine-tuning tasks, RVM is competitive with or outperforms trajectory-based policy-gradient methods under substantially reduced training cost. We further find that, once the velocity update is simplified, the particular loss variant matters less than reward and anchor design. For video generation, standard preference rewards can favor visually clean but nearly static outputs; introducing a new dynamic-tracking reward that substantially improve motions while improving overall VBench performance. These results suggest that scalable reward fine-tuning for diffusion models is better posed in the native velocity representation than as likelihood-based policy optimization.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Your FinOps lead needs to easily drill-down into Databricks spend and identify what’s driving costs, ...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：New disclosures show utilities spent a record $16.7 million lobbying Sacramento through the first six quarters of the 2025–2026 legislative session as lawmakers consider a utility bailout in end of session scramble Sacr…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：These are two different products, but consumers end up confused because of misinformation and poor marketing copy.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：DocEndorse AI Agent & Slack Integration | Slack Marketplace You must enable javascript in order to use Slack. You can do this in your browser settings. Browse Apps Sign In To InstallLearn More DocEndorse AI Agent Sign I…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Notifications You must be signed in to change notification settings Fork 0 Star 0 BranchesTags Open more actions menu Latest commit History 2 Commits 2 Commits Folders and files NameName Last commit message Last commit…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：An insurer settles a claim months after the loss. An asset manager marks someone...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：“Dear Tyler, I enjoyed reading your notes on visiting Anthropic to advise on Claude’s constitution. Framing AI governance around the common law, case law (“Talmud”), and independent adjudication is a much more adaptive…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：One of the stories of the last few weeks has been that AI companies have been scanning books in very large numbers in order to train their models with content guaranteed to have been written before 2002, and thus AI fre…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Will moving dots cure your car nausea? History says yes.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Scaling enterprise AI depends on more than deploying intelligent agents. For Banco BS2, a Brasil-based digital bank, it begins with establishing the infrastructure, governance and operational discipline needed to support AI before expanding its use across the organization. Operating in Brasil’s highly regulated banking environment makes that foundation especially important. The country’s financial institutions must […] The post Banco BS2 takes a foundation-first approach to scaling enterprise AI appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Artificial intelligence startup Anthropic PBC announced today it’s changing how Claude, its flagship AI product, uses memory by allowing users to see everything it remembers “topic by topic,” and edit or delete any of it. Claude also does not store sensitive subjects by default. This includes topics mentioned by users, including health concerns, race, ethnicity, […] The post Anthropic updates Claude’s memory to enhance customization and protect sensitive topics appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：On Tuesday, Anthropic launched a major update to how Claude remembers things. The new system combines Claude’s memory in Cowork The post Anthropic gives chat and Cowork one memory appeared first on The New Stack.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Anthropic is merging Claude chat and Cowork memory, raising privacy questions about what AI remembers and if it's worth the tradeoff.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Build a governed weekly reporting workflow with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP. An Amazon S3 access point exposes an approved folder to a Quick knowledge base, and a custom skill drafts cited weekly reports and Slack summaries with human review before anything is shared.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：All posts Sandboxing: One Word, Many Variations — A Primer for the Agent Era SecurityAI AgentsSandboxingSoftware Architecture Luke Hinds Co-founder & CEO·August 25, 2026 Sandboxing is a security technique that runs a wo…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：AI THAT DOES THE WORK AI employees for your business. Agaro builds AI employees and AI agents that handle calls, chats, workflows, reports, and custom software. We connect them to the tools your team already uses and la…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Data governance tools are software platforms that help organizations catalog, secure,...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Executive Verdict: Organization vs. TechnologyData mesh vs. data fabric hinges on...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Regulations will require new sites to avoid pushing up power prices by building renewable energy plants and minimising water use Get our breaking news email, free app or daily news podcast Planned datacentres around Australia may avoid strict new rules being proposed by Anthony Albanese if they can secure approvals in the coming months, amid growing calls growing for a moratorium. The prime minister is aiming to come to an agreement with national cabinet on federal regulations for datacentres at a meeting on Wednesday. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Notifications You must be signed in to change notification settings Fork 14 Star 294 BranchesTags Open more actions menu Latest commit History 135 Commits 135 Commits Folders and files NameName Last commit message Last…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：I use multiple browsers every day, and this free utility fixed one of my biggest annoyances.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：AI car research, built in Greece Know before you buy. Not after. Researching your next car? KAR checks real listings, safety data and true running costs across Europe, then shows you the few that actually fit. Find My C…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 1 Star 3 BranchesTags Open more actions menu Latest commit History 10 Commits 10…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：A forward deployed engineer (FDE) is a software engineer who embeds directly inside a customer’s team and infrastructure to build, integrate, and run production systems, instead of building a generic product from headquarters. Consultants deliver recommendations. An FDE delivers working code that stays in production. Therefore, the reason this job exists is uncomfortable. MIT’s NANDA […] The post What is a Forward Deployed Engineer? Role, Skills & Salary appeared first on Analytics Vidhya.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Alabama's attorney general issued a subpoena to OpenAI on Monday as part of an investigation into how one of its AI agents escaped a supposedly secure testing environment and autonomously hacked another company last month. The investigation seeks to determine whether OpenAI's safety practices violated state consumer protection laws and pose a risk to Alabama citizens, the AG's office said in a statement. "This AI lab leak showed that Alabamians' and Americans' worst fears about artificial intelligence are not just theoretical," said Attorney General Steve Marshall. "Our investigation seeks to uncover the facts and address hard truths abo … Read the full story at The Verge.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：My Agent Answers From 0.6% of Its Corpus and Reports It Like a Full Read Retrieval is not the evidence. It is a measurement instrument, and nobody reports its coverage. My portfolio agent holds 1,003 indexed chunks. Whe…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Make an original song in minutes AI Music Generator Describe the sound in your head and create original music. Start free, no music production experience needed. Describe your song Need an idea? Listen before you create…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Open table formats are metadata layers that sit on top of data files in object storage, adding ACID transactions...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：CookWing: AI Recipe Chef - Apps on Google Play CookWing: AI Recipe Chef RedWing Inc. In-app purchases Everyone info 1+ Downloads Everyone Learn more Ever followed an AI-generated recipe exactly, only to wonder halfway t…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Disclosure: These views are my own and do not represent my current or any former employers. Executive summary Email, calendar, meeting, and notification assistants are increasingly presented as a way to begin the day wi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：AI is making software cheaper to produce. The harder problem is establishing that the software is correct, secure, and safe to deploy. For years, improving developer productivity largely meant reducing the time required…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Cloudflare recently open-sourced Cloudflare OS on GitHub. Cloudflare OS allows enterprise teams to output work artifacts grounded in enterprise knowledge, know-how, and provisioned connectors, automate repetitive workfl…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.21410v1 Announce Type: new Abstract: Robots are increasingly entering everyday environments where privacy is shaped not only by data practices, but also by spatial, bodily, social, and relational boundaries. Their embodied capabilities allow them to reshape these boundaries through situated action, challenging privacy framings centered on data flows, interface settings, or one-time consent. Prior work has examined robot privacy through sensing, data collection, telepresence, transparency, consent, bystander awareness, and multi-stakeholder governance. Building on this work, we propose embodied boundary privacy as a capability-by-context framing for examining how physically present robots may reshape privacy boundaries in situated interaction. Specifically, this framing organizes privacy risks across seven robot capabilities and five deployment contexts, asking how embodied capabilities enable boundary crossings and how situated contexts shape who is affected, how these crossings are interpreted, and when they become contested. We use this perspective to outline design and research implications for embodied privacy mechanisms, including boundary checkpoints, viewpoint-aware sensing control, remote-presence disclosure, object- and body-level access rules, constraints on socially persuasive privacy influence, and local interruption rights. We encourage HRI research, design, and governance to treat robot movement, orientation, proximity, object access, remote presence, and social expression as privacy-relevant actions whose meaning depends on context.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.21407v1 Announce Type: new Abstract: Vision-language-action (VLA) models face a crucial tradeoff between their task success rate and the policy-call frequency. Executing a single action per inference ($N=1$) enables accurate robot control but comes at the cost of huge compute time overheads, making real-time implementation infeasible. On the other hand, executing longer action horizons before replanning ($N\gg1$) reduces compute complexity, but inevitably degrades the system's success rate. In order to improve the VLA accuracy-complexity tradeoff, this paper investigates Mamba's selective state-space modeling as an alternative to causal self-attention within the action expert of the popular SmolVLA model, widely used as a reference model for its highly accurate yet low complexity nature. We evaluate both the Mamba- and Transformer-based experts on the widely-adopted LIBERO benchmark suites across three execution horizons $N\!\in\!\{1,25,50\}$, respectively corresponding to high, moderate and low compute complexities. Our results remarkably show that the advantage of the Mamba expert increases with the execution horizon, indicating significant success retention under long execution horizons $N = 50$ and $N = 25$. When $N = 50$ actions are executed before replanning (i.e., corresponding to feasible real-time deployment), the Mamba expert outperforms the Transformer baseline by $7.8\%$. In addition, when $N = 25$ actions are executed before replanning, our Mamba expert outperforms the Transformer baseline by $3.7\%$. Finally, under per-action replanning ($N=1$), our Mamba variant matches the Transformer-based mean success rate while significantly reducing the overall model parameter complexity by $24\%$ thanks to Mamba's compute-efficient nature.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.21395v1 Announce Type: new Abstract: NoMaD [31] is a learned vision-navigation policy that unifies goal-conditioned navigation and exploration in a single goal-masked diffusion policy. In an unseen environment, however - where neither a goal image nor a topological map is available - it can only explore undirectedly, wandering without global awareness. We present ODG-NoMaD, which gives NoMaD's exploration mode a global sense of where to proceed, without retraining the policy. An overhead depth camera is used once on deployment to build an occupancy map and plan a global path, which is segmented to yield a desired heading; a per-frame traversability map from the robot's onboard depth then refines this into a collision-free direction. The gradient of a cosine direction cost is injected into the final denoising steps, rotating sampled trajectories toward this direction while preserving the multimodality of exploration. In simulated office environments with and without random obstacles, ODG-NoMaD reduces the residual distance to the target by up to an order of magnitude over unguided exploration, outperforms the point-goal cost guidance of NaviDiffusor [37], and is the only configuration that remains collision-free on every trial.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.21425v1 Announce Type: new Abstract: Video generation is central to AI-powered content creation. Aligning generated videos with human preferences is a key criterion for evaluating generation quality. Despite significant progress in visual quality, three key challenges remain. First, the reliability of reward signals is constrained by the quality of human preference data, which is often affected by subjective noise and bias. Second, standard scalar reward models collapse multi-aspect human preferences into a single value, leading to the loss of dynamic trade-offs across multiple preference dimensions. Third, in policy optimization, the widely adopted KL divergence imposes primarily local constraints and may fail to capture the global structure of human preferences. To address these challenges, we propose a unified preference-aware learning framework for video generation. First, we introduce elite-guided filtering to calibrate preference data and construct reliable supervision for reward model training. We then model video quality as a multidimensional reward distribution to capture the uncertainty inherent in human preferences, and use the Wasserstein distance to align the learned reward distribution with the empirical human preference distribution. Finally, we introduce Wasserstein-based distributional alignment into GRPO, guiding policy optimization to better match the global structure of human preferences over videos. Experiments on reward modeling and video generation demonstrate that our approach improves the reliability of reward signals and the perceptual consistency of generated videos. Our code is available at https://github.com/alignhs26/ahs.