August 24, 2026
Fable Becomes A Better Biology Helper
Anthropic is refining Fable’s biology safeguards to reduce false positives while preventing dangerous dual-use applications.
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Archive →August 24, 2026
50 curated AI news stories from leading AI companies.
August 24, 2026
Nvidia Is Spending $6 Billion to Build a Powerful U.S. Alternative to Chinese AI - WSJ
Nvidia Is Spending $6 Billion to Build a Powerful U.S. Alternative to Chinese AI WSJ
Read original articleAugust 24, 2026
Anthropic’s Playground vs. Open AI’s: The week-old tool beat the six-year incumbent
On August 18, Anthropic replaced its Workbench, the prompt-testing tool in its developer Console, with Playground. Anthropic did more than The post Anthropic’s Playground vs. OpenAI’s: The week-old tool beat the six-year incumbent appeared first on The New Stack.
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August 24, 2026
Enterprise AI Vendor Loyalty Is Fading Fast
The AI market is booming, but vendor loyalty is weakening. Enterprises are spreading bets across Anthropic, OpenAI, and other providers.
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August 24, 2026
Nvidia Reports Earnings on Aug. 26. Here Are 3 Other Artificial Intelligence (AI) Chip Stocks I'll Be Watching Instead. - The Globe and Mail
Nvidia Reports Earnings on Aug. 26. Here Are 3 Other Artificial Intelligence (AI) Chip Stocks I'll Be Watching Instead. The Globe and Mail
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August 24, 2026
Verizon cozies up to Google Cloud even more with Gemini integration - Fierce Network
Verizon cozies up to Google Cloud even more with Gemini integration Fierce Network
Read original articleAugust 24, 2026
After Hugging Face Was Attacked By A.I. Agents, It Embarked on a Crusade - The New York Times
After Hugging Face Was Attacked By A.I. Agents, It Embarked on a Crusade The New York Times
Read original articleAugust 24, 2026
Grok Bot vs. Hermes: Where each draws the security boundary
Put several AI bots to work, and a mistake by one may not stay within its assigned task. For example, The post Grok Bot vs. Hermes: Where each draws the security boundary appeared first on The New Stack.
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August 24, 2026
Prediction: This Artificial Intelligence (AI) Stock Will Go Parabolic After Nvidia Reports Earnings on Aug. 26 - The Globe and Mail
Prediction: This Artificial Intelligence (AI) Stock Will Go Parabolic After Nvidia Reports Earnings on Aug. 26 The Globe and Mail
Read original articleAugust 24, 2026
Run, debug, and scale Databricks workloads from your local IDE
The Databricks workspace is purposefully built for data analysis and data engineering. However...
Read original articleAugust 24, 2026
How Databricks Uses AI to Accelerate Incident Investigation
In our previous blog post, we shared how Databricks uses AI to debug thousands of...
Read original articleAugust 24, 2026
Nvidia senior manager linked to Supermicro scheme smuggling AI servers to China
Nvidia worker indicted after Jensen Huang scolded Supermicro for AI server smuggling.
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August 24, 2026
New AI-powered quick assessments in Migration Center turbocharge modernization
Technology leaders are under mounting pressure to modernize infrastructure, control multi-cloud operational spend, and build data foundations for generative AI. However, the discovery required for that level of transformation can entail weeks of manual spreadsheet analysis, mapping in-house infrastructure, and reconciling siloed, piecemeal cost estimates across disparate teams and sources. To help, we’re announcing AI-powered Quick Assessments in Migration Center, which delivers near-instant total cost of ownership (TCO) modeling and automated service mapping. Compare this to legacy assessment processes, which can stall digital transformation initiatives before they even launch. Manual discovery can delay migration timelines by months, increase engineering overhead, and often miscalculates complex financial models. By replacing manual discovery with AI-assisted automation, IT gains instant, actionable visibility into the TCO and return on investment (ROI) for a given migration initiative. Now, organizations can generate comprehensive migration financial models in minutes rather than months. Teams ingest raw infrastructure data or cloud billing reports and quickly receive an optimized target bill of materials (BOM), service mapping coverage, and projected savings. Decision makers interact with an agentic assistant that explains underlying financial assumptions, recommends technical cost optimizations, and exports ready-to-share executive reports. Inside the AI-powered Migration Center This is made possible with AI-assisted Quick Assessments alongside enhanced Cloud Billing assessment capabilities, both integrated into the new AI-powered Migration Center. AI-assisted Quick Assessment for on-premises workloads Designed for enterprise customers and partners, AI-assisted Quick Assessment automates on-premises infrastructure evaluation to provide rapid financial modeling. Let’s walk through these new capabilities: Instant Compute Engine TCO estimates convert VMware inventory exports (such as RVTools) or aggregated infrastructure inputs into precise Compute Engine cost targets: Migration Center’s Quick TCO Estimator Migration Center’s Quick TCO Estimator results page Advanced architecture modeling supports latest-generation Gen4 compute instances and high-performance Hyperdisk storage pools: Migration Center’s Quick TCO Estimator detailed results page Customizable financial controls allow teams to adjust on-premises baseline cost assumptions to match internal accounting standards: Migration Center’s Quick TCO Estimator detailed results page (continued) Context-aware agentic chat recommends tailored technical cost optimizations aligned with your specific business constraints (such as regional location or compliance needs), clearly explaining the underlying logic and financial assumptions. Migration Center’s agentic chat capabilities Migration Center’s agentic chat capabilities (continued) Migration Center’s agentic chat capabilities (continued) Automated Business Case and Google Sheets export generates ready-to-use reports capturing the complete recommended BOM, TCO comparison, and ROI analysis: Migration Center’s business case The path forward Modernizing your infrastructure starts with fast and accurate data. Migration Center’s Gemini-powered features simplify cloud evaluation, empowering IT decision makers to build defensible business cases generated by machine-learning. Try Migration Center directly in the console today, or take a free migration and modernization assessment to evaluate your workloads and accelerate your strategic cloud journey with Google Cloud.
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August 24, 2026
Empowering autonomous agents with advanced security governance
AI agents are the ultimate insiders. We grant them permission to read emails, query databases, and trigger API calls. They don’t just retrieve information, they take action. Agents offer incredible potential for increased productivity and better customer experiences, but they also come with new security concerns. In our new State of AI infrastructure report, 79% of tech leaders cite security, governance, or operations as their most significant challenge to scaling inference. While there’s still a crucial role for traditional security tools, the threat model has fundamentally changed. Autonomous workflows have redefined enterprise risk, so it's crucial that we give agents the access they need without compromising security. The agentic paradoxThe path to success starts with viewing governance as a driver for innovation. To be useful and secure, an agent needs access — and also guardrails. Yet 35% of senior IT decision makers cite insufficient security for multi-system access as a primary issue preventing agentic deployment.Agents expand the surface area that defenders need to protect, and can introduce new threats, including tool poisoning and indirect prompt injection, where an attacker can hijack an agent’s logic through the data it processes. Managing the dynamic permissions that agents need to succeed at their tasks can also be a significant challenge, particularly as legacy security wasn’t designed for today’s automated threats. Securing the chain of thoughtAlong with securing more identity and access issues, it’s important for defenders to secure both the network layer and the model.Security leaders are increasingly shifting their focus from preventing breaches to verifying provenance to guard against misuse, including indirect prompt injection.From an infrastructure perspective, what are your top security concerns related to AI? From blocking to managing We’ve looked at the new security challenges posed by agentic AI. You can’t solve them by simply locking down the system, as that defeats the purpose of autonomous agents. Many organizations are turning to integrated, full-stack cloud platforms to give them greater oversight. 69% of surveyed executives now rate a full-stack platform as a critical requirement, and 80% say data compliance is the primary factor dictating that choice. By adopting frameworks like the Secure AI Framework (SAIF) and moving to a central control plane, purpose-built platforms such as Gemini Enterprise Agent Platform, organizations can manage risk in three main areas: Secure-by-default design: Embedding security directly into the AI development process to proactively guard against threats including prompt injection. Agent governance and oversight: Adopting purpose-built permission and identity management for agents — giving greater control over agent interactions, exposing blind spots and limiting risks tools. Human-in-the-loop control: Enforcing clear rules that automatically flag when an agent requires human approval before moving forward with a critical action. Governance will guide you to success The true value of a modern security foundation is its ability to encourage innovation. By embedding robust governance directly into a unified foundation, organizations can deploy agents with confidence across their most sensitive, business-critical workloads. The leaders of the agentic era are re-architecting their stack to use security as a launchpad — empowering them to innovate securely and scale faster than their competition. Find out more about how enterprise leaders are rethinking security for the agentic era in the State of AI infrastructure report.
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August 24, 2026
Self-Driving Cars Could Someday Take Requests
This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. The idea of letting a machine do the driving for you may put a lot of people off autonomous vehicles. But research could make it possible to backseat-drive an autonomous vehicle just as you might with a human driver.Self-driving cars carefully balance a host of parameters to ensure a smooth ride, including things like speed, acceleration, and the smoothness of turns. But human driving preferences can often vary depending on how much of a rush they’re in, whether they’re feeling carsick, or how busy the traffic is.These cars have a software component called the motion planner, which is responsible for choosing a safe and efficient path through traffic. The motion planner is normally tuned by engineers before the vehicles hit the road so that there’s little scope for passengers to adjust a vehicle’s driving style on the fly. But now researchers at the Delft University of Technology (TU Delft) in the Netherlands have developed a system that uses a large language model (LLM) to translate natural-language user requests such as “I am running late, go fast” into adjustments to a self-driving control system. The researchers posted their preprint on arXiv and are presenting the work at the IEEE Intelligent Transportation Systems Conference in September.LLMs Personalize Autonomous DrivingThe system doesn’t give users direct control over the vehicle’s driving decisions; it simply tunes the parameters of a safety-aware motion-planning algorithm, which helps to keep the vehicle’s behavior within safe bounds. And the system keeps the human in the loop by describing how it’s going to alter its behavior in nontechnical language, and by asking the passenger to confirm before making changes. When the system was tested in simulation, the researchers found it adjusted the speed and smoothness of driving in line with natural-language instructions.“The motion-planning problem is not only about reaching a place while avoiding collisions, it’s also how you do it,” says lead author Diego Martinez-Baselga, a postdoctoral researcher at TU Delft. “The motivation here is trying to make the way the autonomous car drives adaptable by end users easily, just by talking to the car.”Previous research has investigated the potential of using LLMs and video-language models (VLMs) to direct decision-making for self-driving vehicles, but the researchers deliberately targeted driving style instead. Using LLMs and VLMs to directly control vehicles faces several challenges, says Martinez-Baselga. These include relatively slow response times, which can make these models unsuitable for the fast-paced decision-making required in driving, and the fact that they can’t provide concrete performance guarantees in the way a deterministic motion planner can.Instead, the researchers used an LLM’s language and reasoning capabilities to translate fuzzy human preferences into something a vehicle’s motion planner can use. The system relies on a model predictive-path integral controller previously developed by the researchers, which identifies multiple paths the vehicle could take to reach its goal and then judges them on various criteria, including speed, steering angle, and collision probability. It then finds an optimal path that is a combination of the trajectories that scored best on those judging criteria.The team combined this with OpenAI’s GPT-4o-mini model to parse passengers’ natural-language suggestions and use them to tune how the controller chooses its path. The model is given the users’ prompt and a natural-language description of the scenario the vehicle is operating in. The description was handwritten by the researchers for the purposes of the study, but it could ultimately be provided directly by a car’s perception system, says Martinez-Baselga.The model doesn’t directly tweak the settings of the controller; it uses the prompt to rate the relative importance of the judging criteria the controller uses to assess trajectories. This rating is then used to adjust each criteria up or down either side of a safe baseline set by the researchers. So, if a user says they are feeling dizzy, the LLM will dial up parameters that encourage smooth steering and gentle acceleration to make the vehicle favor more sedate travel.Prior to making any changes, however, the model first presents the user with a natural-language description of the adjustments it plans to implement. The user can then sign off on the plan or make further suggestions. The system is also interactive, so the user can request further adjustments if the vehicle’s behavior doesn’t match expectations or the user‘s preferences change.Martinez-Baselga says this human-in-the-loop system allows the passenger to catch instances when the model misinterprets prompts. But it also helps deal with the inherent subjectivity of suggestions like “go faster” or the possibility that models don’t accurately describe changes they plan to make. In that case the passenger can simply follow up with additional prompts “as you would do if you were in a taxi or with a friend that is driving,” says Martinez-Baselga.The researchers tested the system in the popular self-driving simulator nuPlan in scenarios that involved merging onto a busy highway. Across eight different prompts, the system changed the controller’s parameters in ways matching user intent, with requests for a more comfortable ride dialing up smoothness and those indicating urgency leading to higher speeds.This isn’t the first time LLMs have been used to tune a self-driving car’s motion planner. Nicolas Baumann, a Ph.D. student at ETH Zurich in Switzerland, published research last year in which an LLM tweaked the parameters of a model racing-car controller, allowing the user to alter driving style but also give more concrete instructions like “reverse the car” or “maintain a specific speed.”The strength of the approach, says Baumann, is that separating the LLM from the main controller means that even if the model hallucinates, it can’t do anything dangerous. “You get the possibility of language interaction, but you can guarantee that it is going to be within the constraints of this classical controller, so you can bake in safety,” he says. However, setting these constraints requires considerable engineering work, he adds.And if you want provable safety, you need to go a step further, says Matthias Althoff, a professor of cyberphysical systems at the Technical University of Munich. His group built a system that gets an LLM to suggest driving decisions, but then uses a mathematical process to check them against traffic rules and predictions about the behavior of other road users. This makes it possible to verify their safety before committing to them, something the Delft paper doesn’t provide. “As with any LLM, it is not guaranteed that the result is correct,” says Althoff. “For that reason, we safeguard the decisions of the LLM in our works.”
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August 24, 2026
Attorney General Marshall Launches Investigation Into Open AI and Sam Altman for Massive Artificial Intelligence Data Breach - Alabama Attorney General's Office (.gov)
Attorney General Marshall Launches Investigation Into OpenAI and Sam Altman for Massive Artificial Intelligence Data Breach Alabama Attorney General's Office (.gov)
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August 24, 2026
Giga-Scale AI and the Ethernet Evolution: How Spectrum-X Ethernet Rewrites the Rules
The massive growth of generative AI has fundamentally altered data center design. As distributed model training scales to span hundreds of thousands of GPUs,...
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August 24, 2026
NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt
AI agents have expanded inference from single-turn interactions into multi-step workflows that reason, invoke tools, coordinate subagents, and carry growing...
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August 24, 2026
Maximizing AI Factory Performance per Watt with NVIDIA DSX Max LPS
AI factories are power-constrained industrial systems. The question is no longer how many GPUs fit in a data center, but how much AI output each available...
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August 24, 2026
NVIDIA Blue Field-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories
Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users,...
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August 24, 2026
Solving Agentic AI Fleet Challenges with NVIDIA Vera CPU
AI factories are interconnected systems where fleet economics depend on how efficiently the entire stack converts power and capital into completed agent tasks....
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August 24, 2026
How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin
NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72, the...
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August 24, 2026
Open AI is building AI agents for everything. Will everyone use them?
Inside the frontier lab’s push to bring AI agents from software engineers to the masses.
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August 24, 2026
Google Halo To Bring Agentic Capability To Android Phones
Google’s Halo and Spark aim to enable hands-free, remotely running AI agents monitored through Android devices.
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August 24, 2026
Anatomy of an Autonomous Attack: 5 Alarming A.I. Capabilities - The New York Times
Anatomy of an Autonomous Attack: 5 Alarming A.I. Capabilities The New York Times
Read original articleAugust 24, 2026
What It Takes to Be an Adaptable Engineer
The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice? Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver, an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this. “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it,” says Brunhaver, who received a National Science Foundation award in 2020 to study how to foster greater workplace adaptability among young engineers. For this ongoing project, she has interviewed engineering managers, early career employees, and undergraduates about their experiences. Part of the problem, she says, is that every employer has its own idea of what to be adaptable means. Generally, Brunhaver defines adaptability as “the ability to recognize that a change or uncertainty is occurring, and then respond effectively to that change.” But the skill is context-dependent. In software engineering, that might mean responding to turnover in the tools you use on a daily basis, while aerospace or biomedical engineers may need to keep track of changing procedures and regulations. “Managers are all saying adaptability is important,” Brunhaver says, “but defining it in different ways.” At the same time, engineers are all contending with changes beyond these industry-specific expectations. Jobs in the technology, media, and telecom sectors are experiencing the fastest pace of skill turnover, according to a June 2026 report on the effects of AI from the professional services network PwC. And the World Economic Forum’s most recent Future of Jobs Report, published in 2025, found that employers across all sectors expect 39 percent of workers’ core skills to change by 2030. This uncertainty can be uncomfortable. But with the right mind-set and support from leadership, adaptability can help keep you afloat. How to Cultivate AdaptabilityThe AI transition is a big shift—but not an unprecedented one, says Jenna Butler, a research scientist at Microsoft who studies developer well-being and productivity. During this type of paradigm shift, there is often a “chaos period” when a new normal is being established, Butler says. In AI’s case, it challenges the understanding of what a computer can do. “I think we’re still in this in-between, difficult period that we’ve seen before, but [it] is maybe moving faster than it has historically.” Software engineers—in one of the fields most affected by AI—are now facing a significant increase in code review. “If you ask 20 developers, you get 23 different ways of working with it. Everyone is trying to sort it out,” says Butler, who describes this period as “the uncomfortable middle.” “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it.”– Samantha Brunhaver, Arizona State UniversityBrunhaver says one way educators can help prepare students before they enter the workforce is by offering a diversity of real-world experiences, such as internships, team-based projects, community service, and leadership roles. Each of these teach students to adapt to different challenges, easing their transition from school to work. It’s also important to encourage reflection, Brunhaver adds, noting that metacognition helps individuals use the skill more effectively. “In order to adapt, you have to think that you have agency and the ability to get through a situation.” Ultimately, it comes down to three steps: Perceive a need to adapt, evaluate your options, and act. For those already in the workforce, that action may mean taking the time to learn new tools and ways of working. Software engineering, for instance, may soon rely more on prompting models and managing agents than coding line by line. “I think people who went into software because they like solving problems are going to have a lot of fun, and people who just enjoy the art of writing code are not,” Butler says. The More Things Change… Although the tools engineers use on a daily basis are evolving, the core responsibilities of the job are more stable than they may seem, says Andy Hunt, a software developer who coauthored The Pragmatic Programmer (Addison-Wesley Professional) in 1999. The book outlines practical coding principles, and has been taught in many computer science classrooms. When Hunt was working on the 20th anniversary edition of the book, he was surprised by how much of the advice still applies. And now, seven years later, he maintains that belief.“The fundamental part of the job is problem solving and communication, and that’s always going to be there,” he says. Hunt emphasizes the importance of developing systems thinking over particular tools. To him, identifying as a Java programmer, for instance, is “like a carpenter saying, ‘I’m a hammer user,’ or ‘I specialize in cordless drills.’ ” He acknowledges that today’s hiring process, in which companies often filter résumés for certain languages or years of experience, makes it harder to embrace a more expansive way of relating to your job. Employers, he says, should recognize that “the tech’s not the hard part, and it never has been. Understanding information theory, understanding systems thinking, understanding what constraints you’re up to—that’s still the hard part.” With this type of misalignment between employers and employees, AI is also intensifying an old source of tension: How can engineers slow down enough to adapt and learn new tools when the pressure to become more productive keeps mounting? Who’s Responsible for Enabling Change? Young engineers need to embrace change. However, educators and employers also play a role in building a successful workforce. From the educator’s perspective, Brunhaver says “we need to be more explicit about what [adaptability] means and why it’s important.” Managers, meanwhile, should invest in their employees’ professional development.Microsoft research scientist Butler often encourages leadership to set aside intentional time for continuous learning for their engineers—even just an hour a week—without any expectation that they will produce code or progress in their daily work. “I realize that’s difficult,” says Butler. “I would encourage people to do it on their own, but I would really encourage organizations and leaders to do it, because you’re not going to get this sudden change in your people if they don’t have time and space to learn how to work differently.” This also means providing enough instruction, Butler adds. When developers aren’t given enough guidance on adopting something new, while being pressured to increase productivity, they risk doubling down on the tools they already know and burning out. “I do imagine the next number of years could be challenging,” Butler says. Engineers will have to adapt to find their place in an evolving workforce—but they also have a say in shaping that future. “Being adaptable sort of implies that you’re going to change based on what’s happening around you, and I would really like people to realize the change that’s happening is somewhat up to us,” she says. All individuals have a choice in how they use AI, for instance, and which models they use. “We need to be adaptable and go with the flow to a degree, but we also need to be directing that flow. The future with AI is absolutely not predetermined.”This article appears in the September 2026 print issue as “The Adaptable Engineer.”
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August 24, 2026
Autonomous AI Outgrows Agents As Blitzy, XBOW And Google Deliver
Autonomous AI. The world's top hacker is no longer human. Blitzy, XBOW, Google and OpenAI are racing past AI agents to autonomous systems.
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August 24, 2026
Hugging Face reportedly in talks to be acquired for $13 B
Hugging Face has reportedly been fielding acquisition offers that would value the company at around $13B. But with the founders' feeling of responsibility to community, doubts arise as to whether a sale will happen.
Read original articleAugust 24, 2026
Thomson Reuters trained its own AI model. Then it kept using Anthropic’s anyway.
Thomson Reuters has developed its own AI model for legal, tax and compliance work, trained on the company’s proprietary professional The post Thomson Reuters trained its own AI model. Then it kept using Anthropic’s anyway. appeared first on The New Stack.
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August 24, 2026
Thomson Reuters bets $40 M on owning its AI instead of renting from Open AI or Anthropic
Thomson Reuters is launching "Thomson," its own language model built on Alibaba's Qwen, at a cost of about $40 million over two years. But the benchmarks only show top marks when the model can tap into the company's own content, like Westlaw. CTO Joel Hron makes the point that what matters isn't intelligence itself, but knowing which intelligence you need to own. The article Thomson Reuters bets $40M on owning its AI instead of renting from OpenAI or Anthropic appeared first on The Decoder.
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August 24, 2026
Google brings Antigravity under Gemini Enterprise to provide granular spend controls - Info World
Google brings Antigravity under Gemini Enterprise to provide granular spend controls InfoWorld
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August 24, 2026
Hugging Face Considers $13 Billion Sale of Its AI Platform - PYMNTS.com
Hugging Face Considers $13 Billion Sale of Its AI Platform PYMNTS.com
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August 24, 2026
Advancing price-performance for developers with GPT‑5.6 in Kiro
GPT‑5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance.
Read original articleAugust 24, 2026
Chat GPT Plugin Can Now Read And Send Your i Messages, But Should It?
OpenAI's new ChatGPT plugin can read, draft and send Apple Messages on Mac. The bigger question is whether that's a good idea.
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August 24, 2026
Some of Russia’s A.I. Drones Are Powered by Nvidia Microcomputers, Ukrainian Officials Say - The New York Times
Some of Russia’s A.I. Drones Are Powered by Nvidia Microcomputers, Ukrainian Officials Say The New York Times
Read original articleAugust 24, 2026
AI chatbots regularly link pregnant users to anti-abortion websites without disclosure
When asked about unplanned pregnancies, AI chatbots regularly link to anti-abortion groups without disclosing their stance. In an AlgorithmWatch investigation of 270 responses from ChatGPT, Gemini, Grok, and Claude, the anti-abortion organization Profemina appeared in 17 percent of answers. In Germany, the chatbots also sent users to Caritas for mandatory pre-abortion counseling, even though Caritas doesn't issue the legally required certificate. The article AI chatbots regularly link pregnant users to anti-abortion websites without disclosure appeared first on The Decoder.
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August 24, 2026
Hugging Face Reportedly Wants to Be Acquired for About $13 Billion - Gizmodo
Hugging Face Reportedly Wants to Be Acquired for About $13 Billion Gizmodo
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August 24, 2026
Nvidia in talks to invest in Perplexity at $30 billion-plus valuation
Nvidia is negotiating an investment in Perplexity at a valuation above $30 billion, more than 50 percent higher than its last funding round, The Information reports. Perplexity's annualized revenue has tripled to over $750 million. Much of Nvidia's investment money tends to flow back as revenue when portfolio companies buy its chips. The article Nvidia in talks to invest in Perplexity at $30 billion-plus valuation appeared first on The Decoder.
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August 24, 2026
Anthropic Researcher Says Claude Helped Build a Complex Structure on S⁶, Taking Aim At The Unsolved Hopf Problem
A mathematician working at Anthropic has posted what could become one of the most consequential AI-assisted mathematics results yet: a proposed construction of... The post Anthropic Researcher Says Claude Helped Build a Complex Structure on S⁶, Taking Aim At The Unsolved Hopf Problem appeared first on OfficeChai.
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August 24, 2026
Best GPU Neoclouds 2026: Core Weave, Nebius, Lambda, Crusoe, and Groq Ranked by Published Pricing and Contracted Power
The five largest GPU neoclouds now run on very different models. CoreWeave and Nebius report to the SEC; Lambda and Crusoe are private and heading toward IPOs; Groq rebuilt itself as an inference cloud after licensing its LPU technology to NVIDIA. This comparison checks each provider's live rate card, Q2 2026 financials, active and contracted gigawatts, anchor contracts, and SemiAnalysis ClusterMAX tier. Nebius posts the lowest H100 rate and the only published B300 price, Lambda has the cheapest B200, Crusoe is the only one with AMD on its card, and CoreWeave commands a 10–15% premium as the sole Platinum-rated provider. Figures verified August 21, 2026. The post Best GPU Neoclouds 2026: CoreWeave, Nebius, Lambda, Crusoe, and Groq Ranked by Published Pricing and Contracted Power appeared first on MarkTechPost.
Read original articleAugust 24, 2026
Bankruptcy Prediction via Hybrid Resampling and Stacking Ensemble Techniques with Explainable Artificial Intelligence (XAI)-Driven Analysis
arXiv:2608.20343v1 Announce Type: new Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ensembles, and explainable artificial intelligence to improve minority-class detection in severely imbalanced financial data. Using the Taiwanese Bankruptcy Prediction dataset from the UCI Machine Learning Repository, five feature-selection algorithms were first applied, and a consensus retention rule reduced the input space to 23 robust variables. The balanced training data were then generated using SVM-SMOTE, SMOTE-Tomek, and SMOTE-ENN. Five ensemble machine learning classifiers, namely gradient boosting, extreme gradient boosting, histogram-based gradient boosting, LightGBM, and AdaBoost, were compared with five deep learning models, including RNN, LSTM, GRU, DNN, and MLP. In addition, hybrid stacking ensembles combined the five machine learning classifiers as base learners with each deep learning model as a meta-learner. Model performance was assessed using accuracy, recall, specificity, G-mean, and ROC-AUC, while SHAP was used to explain feature contributions. The results show that resampling strategy materially shaped model behavior. SVM-SMOTE and SMOTE-Tomek favored accuracy and specificity, whereas SMOTE-ENN delivered stronger minority-class detection. Among standalone models, the GRU with SMOTE-ENN achieved the best overall predictive balance, with recall of 0.8627, G-mean of 0.8517, and ROC-AUC of 0.9431. Among stacking ensembles, SMOTE-ENN with (GB+XGB+HGB+LGBM+AB)+LSTM provided the strongest compromise between sensitivity and specificity. SHAP analysis identified leverage, profitability, solvency, and operational efficiency indicators as the most influential predictors of bankruptcy risk. These findings support more reliable and interpretable early warning systems for financially distressed firms.
Read original articleAugust 24, 2026
BF1: A Causal Dyadic Sparse-Attention Retrofit for Efficient Long-Context Transformers
arXiv:2608.20427v1 Announce Type: new Abstract: Dense causal attention remains expensive at long context even when implemented with highly optimized exact kernels. We study BF1, a deterministic block-aligned dyadic sparse-attention route that combines a small exact local neighborhood, a global first block, and logarithmically spaced historical blocks. The route is related to prior log-sparse and dilated attention patterns; our contribution is a correctness-gated pretrained-model retrofit, a matched topology-control study, and a systems characterization that connects per-layer sparsity to whole-model latency. For fixed block width, every converted layer uses O(n log n) selected token interactions and has O(log n) graph communication depth. On an NVIDIA RTX PRO 6000 Blackwell GPU, an optimized BF16 implementation crosses dense attention between 2K and 4K tokens and reaches a 10.91x per-layer prefill speedup at 32K. Retrofitting eight of 28 Qwen3-0.6B attention layers lowers warm whole-model time to first token by 7.7%, 11.3%, and 15.3% at 8K, 16K, and 32K, respectively, while the remaining dense layers keep the complete model asymptotically quadratic. Under a matched 1,000-step, 16.384M-token adaptation protocol, BF1 ranks first across three training seeds: mean report perplexity is 1.68639 versus 1.69154 for a matched static-random nonlocal graph, 1.69258 for dense continued training, and 1.81505 for equal-budget local sliding. At seed 1234, the packed-report paired interval places Dense-CT 0.3169-0.4055% above BF1 and static-random graph 17 0.2441-0.3642% above BF1. These results establish BF1 as a reproducible sparse operator and selective retrofit primitive with real long-context systems value. This paper evaluates numerical correctness, selected-interaction scaling, kernel performance, partial-model inference, and matched next-token language modeling.
Read original articleAugust 24, 2026
Metag: A dataset to build agentic meta-reviewing capabilities
arXiv:2608.20488v1 Announce Type: new Abstract: AI tools increasingly support tasks across the scientific research cycle, from experiment design and manuscript preparation to peer review. At the same time, the continuing growth in conference submissions has increased the burden on meta-reviewers, who must synthesize reviewer feedback, author rebuttals, and manuscript revisions. To address this concern, this paper introduces Metag, a dataset to accelerate the development of meta-reviewing agents, specifically to identify changes made to scientific articles during the review-rebuttal process. Each instance contains a reviewer concern, the author's proposed resolution, and the manuscript diffs implementing the stated change. Metag is collected by obtaining manuscript versions from before the review deadline and after acceptance, computing differences between the two documents, and asking human annotators to align these differences with action items from OpenReview discussions. The resulting dataset consists of 349 high-quality action items tied to paper differences and will enable building methods to empower meta reviewers to quickly identify whether authors have addressed reviewer statements and where in the paper those changes have been made, resulting in additional transparency and traceability throughout peer review. The dataset is publicly available at https://github.com/microsoft/Metag-dataset.
Read original articleAugust 24, 2026
Learning Exact NVIDIA SASS Encoders with $\mathbb{F}_2$ Linear Algebra
arXiv:2608.20532v1 Announce Type: new Abstract: NVIDIA provides a SASS disassembler but no public SASS assembler for recent data-center GPUs, limiting controlled machine-code rewriting. We present F2Asm, which learns exact 128-bit SASS encoders from paired disassembly and original CUBIN instruction words. To our knowledge, F2Asm is the first system to learn SASS instruction encoders as vector-valued affine maps over F2 and the first open-source NVIDIA SASS assembler to support Rubin SM107. F2Asm uses Gaussian elimination over F2 to incrementally build a compact basis, detect inconsistencies, and reject inputs outside the learned span. F2Asm separates target-specific control bits, relocation rules, and CUBIN metadata from its learning algorithm. We train encoders for Hopper SM90/SM90a, Blackwell SM100, and Rubin SM107 using 3,225 CUBINs from pinned NVIDIA and third-party production libraries, CUDA 13.3 packages, and CUDA 13.4 Developer Preview archives. In round-trip tests, F2Asm reassembles the disassembled SASS for each CUBIN, and all compared executable text sections match the originals exactly.
Read original articleAugust 24, 2026
Meta-clustering of milk mid-infrared spectra identifies dairy cow groups associated with negative energy balance in early lactation
arXiv:2608.20653v1 Announce Type: new Abstract: Clustering methods have been used to identify distinct groups of milk samples, cows, or herds. Fourier-transform infrared (FTIR) spectroscopy, particularly mid-infrared (MIR) spectroscopy, has been applied to individual cow milk samples to predict various milk traits. Applying clustering directly to MIR spectral data may reveal latent groups of cows associated with milk traits or health disorders and can help prevent these conditions or monitor at-risk animals. This study aimed to identify groups of individual dairy cows in early lactation directly from milk MIR spectra and to analyze their associations with milk traits. Using a dataset of 407,632 individual milk MIR records from 3,408 commercial farms, we combined (i) spectral filtering that selects informative wavenumbers, (ii) two dimensionality-reduction methods: principal component analysis (PCA) and an autoencoder, and (iii) two clustering algorithms: k-means and spectral clustering to yield eight different clustering approaches. We regrouped the assigned clusters into meta-clusters that encompassed the most similar ones identified by the eight approaches. Our results revealed five distinct meta-clusters of early-lactation individual dairy cows significantly associated with milk traits. Despite substantial differences, the eight approaches converged on the same five meta-clusters, and the classic, computationally efficient PCA-based k-means approach using the full spectrum recaptured clusters identified by more sophisticated, computationally intensive approaches. The five meta-clusters were strongly associated with DIM and appeared to reflect a gradient of negative energy balance (NEB) severity: severe, moderate, and possibly mild, while the remaining two likely represented cows recovering from NEB, one with rapid restoration of energy balance and one in early recovery.
Read original articleAugust 24, 2026
Prime Agent Orchestrator: Memory-Primed Agent Spawning for Personal AI Infrastructure
arXiv:2608.20342v1 Announce Type: new Abstract: Large language model (LLM) coding agents start each session with an empty context window, discarding accumulated knowledge from prior work. We present (PAO), a system that spawns new instances of Claude Code -- Anthropic's terminal-based coding agent -- pre-loaded with relevant memories compiled from the user's existing personal databases. At spawn time, PAO queries two independently-operated memory backends in parallel (a PostgreSQL entity-observation database and a Cloudflare Worker semantic search index), fuses results using backend-specific retrieval strategies, and delivers the compiled briefing via filesystem injection that exploits the host agent's configuration auto-read behavior. PAO manages the full agent lifecycle including trust pre-seeding, readiness polling with error detection, and adaptive terminal text injection. We report on four months of regular deployment (December 2025 through March 2026) as an experience report, documenting three generations of context delivery mechanisms, the failure modes that motivated each redesign, and the engineering tradeoffs of bridging heterogeneous memory systems rather than building a unified one.
Read original articleAugust 24, 2026
State Sight: Benchmarking Latent Spatial-State Reconstruction in Vision-Language Models
arXiv:2608.20414v1 Announce Type: new Abstract: Vision-language models are increasingly used for multimodal question answering, yet their ability to reconstruct latent spatial structure from a single image remains difficult to isolate. Broad benchmarks often combine perception, optical character recognition, domain knowledge, linguistic priors, and reasoning in the same evaluation. We introduce StateSight, a procedurally generated benchmark for cube-net opposite-face reasoning, occluded cube-tower counting, and 4-neighbor connected-component counting. Each task family contains 300 single-image prompts with deterministic oracle labels and exact-match scoring. OpenAI GPT-5.5, using the API model identifier gpt-5.5, achieved 59.3%, 33.3%, and 28.3% accuracy across the three tasks, while Claude Sonnet 5 achieved 53.3%, 18.7%, and 7.3%. All final direct runs had zero format errors. A 30-participant human baseline on 60 items exceeded both models on every task, with mean accuracies of 80.8%, 68.8%, and 64.3%. Visible-derivation analysis identified recurring errors in image-state reconstruction and reasoning procedure. We also introduce StateSight-Steps, a companion dataset of 900 interleaved image-text examples and 3,600 deterministic intermediate visual states. The results show that format-valid responses can mask failures to recover the spatial structure required for verifiable visual inference.
Read original articleAugust 24, 2026
SIREN (Luring LLMs onto the Rocks): PAIR-Driven Preference Manipulation in Web-RAG Recommenders
arXiv:2607.21951v2 Announce Type: replace Abstract: This paper investigates the adversarial manipulation of the ranked recommendations produced by web-augmented large language models (LLMs). When an LLM answers a recommendation query by retrieving and reading live webpages, it acts as a recommender, and each retrieved page becomes a potential attack surface. Prior work has examined fabricated products, retrieval poisoning, and rank promotion. However, these studies do not compare how different edits to an already retrieved page change the model's final ranking while the surrounding source set remains unchanged. To address this gap, we propose SIREN, an automated attacker--judge method that adapts the PAIR jailbreaking loop to competitive rank manipulation, with the goal of moving a chosen entity to rank~1 in an LLM-generated recommendation. SIREN retrieves and captures webpages using Anthropic's web tools, then iteratively edits a retrieved source using an interpretable taxonomy of 23 content-poisoning techniques. The custom-RAG replay platform keeps the same sources in the same order, so changes in the model's ranking can be linked to changes in the supplied content rather than to differences in retrieval. Across two production Claude models, SIREN reaches rank~1 in 62 of 124 technique trials nested within eight query--model contexts. The payloads that reached rank~1 were then tested in fresh sessions, where they reproduced the result with a mean success rate of 0.805. Across the evaluated settings, declarative ranking claims and seeded lists were generally more effective than directive-form injections, although the strength of this difference depended on the target model. To the best of our knowledge, this is among the first controlled studies of competitive rank manipulation in production LLMs where the supplied source context is kept fixed.
Read original articleAugust 24, 2026
Bolo: Verified Model Hub for Next-Generation AI Databases
arXiv:2608.20525v1 Announce Type: new Abstract: Verified, ready-to-use inference pipelines are a cornerstone of future AI databases. They allow multi-modal databases to incorporate specialized language, vision, and tabular models that can deliver both high accuracy and efficiency. Unfortunately, existing model platforms such as Hugging Face fall short of this goal. While they host millions of model repositories, many contain only raw weights without runnable pipelines. Even well-documented models often fail due to missing dependencies, unsupported model classes, or incorrect task assignments. Moreover, different models fail for different reasons, with no uniform solution. Constructing a large-scale, verified model hub is nearly impossible with human effort alone. We argue that AI agents can achieve this at scale. We present \system, a model platform that hosts verified, ready-to-use inference pipelines, powered by a multi-stage agentic system for model remediation. For models that fail under standard usage, the agent inspects errors and repairs broken pipelines (Type~I). For models outside the scope of existing interfaces, it synthesizes pipelines from scratch using model metadata and documentation (Type~II \& III). To prevent incorrect pipelines from entering the database, the agent applies multi-stage verification---checking not only program structure but also semantic model behavior, ensuring pipelines produce meaningful outputs rather than merely executing without error. In preliminary experiments, \system achieves 97.27\% and 86.08\% runnable coverage for Type~II and Type~III models, respectively, demonstrating that agentic synthesis with targeted verification can transform large collections of unusable model weights into a verified database of ready-to-use inference pipelines. The preliminary database is open-sourced at \textcolor{blue}{https://bolobao.ai/}.
Read original articleAugust 24, 2026
Sam Altman voices fears that control of AI could be centered in too few hands
OpenAI Group PBC Chief Executive Sam Altman is worried that artificial intelligence technology will end up being controlled by just a handful of companies or people in future, resulting in nobody else having any say about how it impacts society. Speaking in an interview with the podcaster David Senra today, Altman (pictured) warned against a […] The post Sam Altman voices fears that control of AI could be centered in too few hands appeared first on SiliconANGLE.
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