Policy

Can you trust an AI model to do what you intended? This is a central question both for those deploying AI systems and for those seeking to evaluate their capabilities. In deployment, a model that pursues a goal through…

My lab develops low-cost and easy-to-use tools for identifying fake medicines, but we’re always on the lookout for other types of fakes that we can go after. For example, the cosmetics industry has a massive problem wit…

Health & Bioscience

How AI Watermark Mandates Could Unmask Journalists Who Never Touched AI | Techdirt How AI Watermark Mandates Could Unmask Journalists Who Never Touched AI (Mis)Uses of Technology from the everything-is-a-tradeoffs dept…

AI for kids・Askie Safe AI for children Your child's AI helper and study buddy for bedtime stories, school help, and creative AI stories for kids. Safe AI chat designed for children ages 4-15 with parental controls. AI c…

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Discover Connery: open-source plugin infrastructure for LLM apps. Secure integrations, personalization, and human-in-the-loop control for AI agents.

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The EU AI Act: A Strategic Roadmap for CIOs and CTOs August 26, 2026 · 1,572 words Every CIO and CTO must read this, Not because the EU AI Act is another compliance checkbox to file away with GDPR, but because it is abo…

The rise of generative and agentic AI has dramatically changed the software-building process - and the skills required.

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…

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…

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…

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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.

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...

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…

‘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…

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← 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,…

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.

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.

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.

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.

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An insurer settles a claim months after the loss. An asset manager marks someone...

“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…

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…

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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.

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.

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.

Anthropic is merging Claude chat and Cowork memory, raising privacy questions about what AI remembers and if it's worth the tradeoff.

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.

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…

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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...

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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…

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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.

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.

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…