{"articles":[{"id":41513,"title":"Purecore Provides Corporate Update Highlighting Execution Across Uranium, Copper and Capital Markets","description":"VANCOUVER, British Columbia, Aug. 26, 2026 (GLOBE NEWSWIRE) -- Purecore Metals Inc. (CSE: PURE) (FSE: J8Y) (OTCQB: PPURF) (“Purecore” or the “Company”) is pleased to provide a corporate update highlighting progress since its common shares commenced trading on the Canadian Securities Exchange on May 15, 2026.","url":"https://www.globenewswire.com/news-release/2026/08/26/3351267/0/en/purecore-provides-corporate-update-highlighting-execution-across-uranium-copper-and-capital-markets.html","image_url":null,"source_name":"globenewswire","source_icon":"https://n.bytvi.com/globenewswire.jpg","published_at":"2026-08-26 12:00:00","category":"business"},{"id":41514,"title":"CURRENC Capital and Securitize Announce Strategic Collaboration to Advance Issuer-Sponsored Tokenization of Public Equities","description":"Collaboration pairs Securitize’s regulated tokenization infrastructure with CURRENC Capital’s first-hand issuer experience to help listed companies evaluate regulated approaches to bringing public-company shares onchain Collaboration pairs Securitize’s regulated tokenization infrastructure with CURRENC Capital’s first-hand issuer experience to help listed companies evaluate regulated approaches to bringing public-company shares onchain","url":"https://www.globenewswire.com/fr/news-release/2026/08/26/3351275/0/en/currenc-capital-and-securitize-announce-strategic-collaboration-to-advance-issuer-sponsored-tokenization-of-public-equities.html","image_url":null,"source_name":"globenewswire_fr","source_icon":"https://n.bytvi.com/globenewswire_fr.jpg","published_at":"2026-08-26 12:00:00","category":"business"},{"id":41515,"title":"The clock is now a control surface: AI’s impact on time synchronization in OT","description":"A factory can forgive a late email. It won’t forgive a robot arm that arrives three milliseconds after the conveyor. That sounds absurdly small. Three milliseconds barely qualify as waiting. Yet inside operational technology, tiny gaps can carry heavy consequences. A protection relay trips late. A vision system pairs an image with the wrong product. Two controllers record the same event in opposite order. The machines keep moving, but the story they tell about what happened begins to split. I learned long ago that clocks in OT aren’t office furniture. They’re part of the control system. Now AI is moving into that system, watching clock drift, network delay, oscillator health and odd timing patterns. The promise sounds attractive. Spot trouble earlier. Explain it faster. Correct it before operations feel the pain. Then comes the awkward question. What happens when a system built on probability begins advising infrastructure that depends on certainty? Time is a control input In IT, poor timekeeping often creates irritation. Logs don’t match. Certificates complain. Investigators lose an afternoon and develop strong views about whoever configured NTP. In OT, the consequences can leave the screen. Industrial devices need a common sense of time because they act together. Controllers, sensors, relays, drives and switches may sit in different cabinets, yet they must agree on when an event occurred and when the next action should begin. IEEE 1588 Precision Time Protocol exists for this reason . It gives networked measurement and control systems a shared clock with far greater precision than ordinary business systems usually need. Power automation makes the point with little room for poetry. IEC/IEEE 61850-9-3 defines a PTP profile for power utility systems that must meet demanding synchronization classes. That shared clock supports more than speed. It preserves sequence. Suppose a pump fails, an alarm fires and an operator changes a setting. If three devices disagree on time, investigators may see the response before the warning and the warning before the fault. Every log can be accurate on its own while the combined record remains false. That’s the quiet danger. Bad time can turn good evidence into fiction. What AI can see Traditional timing systems distribute time and measure variance. They follow rules. They don’t always explain why a clock has started to wander or why packet delay changed after lunch. AI can watch the behaviour around the clock. Oscillators drift as temperature changes, components age and workloads shift. Networks add delay through congestion, routing changes and uneven paths. Those effects don’t always arrive as clean threshold breaches. They creep. A model trained on normal device behaviour may spot the curve before an operator sees the cliff. Research has already explored clock architectures that account for thermal change and non-stationary delay variation in industrial networks. Other work has used deep learning to improve clock synchronization where propagation delays and frequency offsets make classic methods struggle. The practical use is simple. AI can estimate when a device is moving outside tolerance, compare its behaviour with peer devices and suggest the likely cause. It may notice that a clock loses accuracy only when a cabinet warms. It may connect rising offset with a new network path. It may flag a grandmaster change that looks valid in protocol terms but strange in context. This matters because most alarms report symptoms. Operators need causes. “The clock is wrong” starts a search. “The clock began drifting after the switch update, and the pattern matches path asymmetry” starts a decision. That’s a better use of machine learning. Not an oracle. A sharper witness. From fixed rules to context Many timing controls treat every device according to a fixed schedule. Synchronize at this interval. Alert at that threshold. Escalate after so many failures. Fixed rules are useful because people can understand them. They also assume the system behaves tomorrow as it did when the rule was written. Factories rarely honour that assumption. A robotic cell under full load behaves differently from one at rest. A substation during a fault does not resemble a quiet Tuesday morning. A clock that stays stable for months may need less attention than one mounted beside a heat source and fed through a changing network path. AI can help vary monitoring based on context. It can recommend closer checks for unstable assets and reduce needless traffic around devices that remain steady. It can compare clock offset, packet delay, temperature and process state without forcing each signal into a separate queue. But the word “recommend” carries weight. Changing a monitoring interval is one thing. Correcting the clock that governs a protection function is another. The first may save bandwidth. The second may change how physical equipment behaves. You need a boundary between insight and authority. Without it, a useful model becomes a hidden controller. The security problem hiding in the timestamp Attackers don’t need to stop a process if they can make the process misunderstand time. A forged signal can shift timestamps. A delay attack can make a legitimate clock appear accurate while pushing dependent devices away from the true reference. GPS spoofing can corrupt systems that trust satellite time. Research on time attacks in power grids has shown effects on fault detection, voltage monitoring and event location. Work on PTP delay attacks has also shown how targeted path asymmetry can move clocks without easy detection. AI may help detect these patterns. It can compare timing behaviour across paths, devices and physical states. A sudden offset may look different from thermal drift. A slow malicious delay may leave a different trail from congestion. Yet AI also adds targets. An attacker may poison the data used to train the model. They may alter timing telemetry, suppress alerts or feed the system enough false anomalies that operators stop listening. They may tamper with a model update and teach the detector that hostile behaviour is normal. That last risk deserves attention. OT teams often fear the loud attack. The subtler attack edits the baseline. Once the model learns the lie, silence looks healthy. When probability meets determinism This is where enthusiasm needs adult supervision. A timing protocol performs a defined function. A model estimates. Those are different forms of machinery. If the model predicts drift incorrectly, it may request needless corrections, mask a real fault or make stable clocks chase one another. If operators can’t explain why it acted, they may hesitate at the exact moment speed matters. The answer isn’t to ban AI from timing. That would confuse caution with wisdom. The answer is to place it where uncertainty can help without governing the final truth. Keep approved time sources, PTP, NTP, local clocks, holdover capability and redundant grandmasters at the core. Let AI sit around that core and observe. It can score health, spot anomalies, connect signals and propose action. Then bind it. Set hard tolerances that the model cannot rewrite. Require human approval before material timing changes. Record every recommendation and the evidence behind it. Make sure the model’s loss does not stop the plant from keeping time. NIST’s OT security guidance stresses that controls must respect OT’s distinct performance, safety and availability needs. Its work on positioning, navigation and timing also calls for organizations to identify dependencies, detect manipulation and prepare to respond when timing services fail. The principle is plain. The clock must keep working when the clever layer goes missing. A sensible route into production Start with the timing estate, not the model. Map every grandmaster, reference source, protocol, dependent asset and fallback path. Ask which processes need milliseconds, which need microseconds and which merely need logs that agree. Many firms can name their critical servers faster than they can name the clock those servers trust. That inventory often exposes an uncomfortable fact. The plant has several sources of time, but no owner for timing risk. Everyone consumes the clock. Nobody governs the dependency. That is how a technical detail becomes an enterprise blind spot. Then choose a narrow use case. Drift detection is a good opening move. So is anomaly detection across redundant time paths. Incident correlation can also create value without touching live clock control. Run the model in observation mode. Let it watch, report and explain. Compare its calls with engineering judgement. Test it during temperature shifts, network congestion, GNSS loss, grandmaster failure and planned maintenance. Don’t test only the model. Test the disagreement. What happens when the protocol says healthy and the model says danger? Who decides? What evidence do they see? How quickly can they restore the known state? Scale only after those questions have real answers. The clock should never need faith AI can make OT timing easier to see. It can reveal drift before thresholds break, connect weak signals and help investigators rebuild events with less guesswork. Used with care, it may give operators something they rarely receive from industrial clocks: an explanation. But explanation must not become sovereignty. The safest design keeps time deterministic and makes oversight richer. Protocols distribute the clock. Engineers define the limits. AI watches the edges, where heat, delay, ageing and attack begin to bend the truth. That arrangement may sound less dramatic than handing the system control. Good. OT has enough drama already. A clock is trusted because everyone agrees to organize action around it. Once machines lose that agreement, the plant may still look busy. Motors turn. Screens glow. Logs fill. Yet beneath the motion, cause and effect have started to divorce. AI may help keep them together. It should never be allowed to officiate the clock.","url":"https://www.cio.com/article/4213775/the-clock-is-now-a-control-surface-ais-impact-on-time-synchronization-in-ot.html","image_url":"https://www.cio.com/wp-content/uploads/2026/08/4213775-0-22915000-1787745790-shutterstock_69103444.jpg?quality=50&strip=all","source_name":"cio_in","source_icon":"https://n.bytvi.com/cio_in.jpg","published_at":"2026-08-26 12:00:00","category":"technology"},{"id":41516,"title":"Samsung UE55M80H Review","description":"Samsung has front loaded this mid-range Mini LED offering with a truck load of smart features and great gaming credentials – but is that enough to compensate for its middling performance? The post Samsung UE55M80H Review appeared first on Trusted Reviews .","url":"https://www.trustedreviews.com/reviews/samsung-ue55m80h","image_url":"https://www.trustedreviews.com/wp-content/uploads/sites/7/2026/08/Samsung-M80H-TV.jpg","source_name":"trustedreviews","source_icon":"https://n.bytvi.com/trustedreviews.png","published_at":"2026-08-26 12:00:00","category":"technology"},{"id":41517,"title":"Women leaders urge structural change on Equality Day","description":"Persistent bias and narrow promotion criteria are still holding women back in tech, executives say, as AI threatens to widen the gap.","url":"https://cfotech.asia/story/women-leaders-urge-structural-change-on-equality-day","image_url":"https://cfotech.asia/uploads/story/2026/08/26/compatible_supplied-story-317630.jpg","source_name":"cfotech_asia","source_icon":"https://n.bytvi.com/cfotech_asia.png","published_at":"2026-08-26 12:00:00","category":"top"},{"id":41518,"title":"Women leaders urge structural change on Equality Day","description":"Persistent bias and narrow promotion criteria are still holding women back in tech, executives say, as AI threatens to widen the gap.","url":"https://cfotech.in/story/women-leaders-urge-structural-change-on-equality-day","image_url":"https://cfotech.in/uploads/story/2026/08/26/compatible_supplied-story-317630.jpg","source_name":"cfotech_in","source_icon":"https://n.bytvi.com/cfotech_in.png","published_at":"2026-08-26 12:00:00","category":"top"},{"id":41519,"title":"EMXETF Launches China AI Tigers LLM ETF (NASDAQ: TGRZ) to Tap into China’s Leading AI Models","description":"EMXETF is launching TGRZ, an ETF targeting the DeepSeek-era AI revolution as China emerges as a global challenger in large language models.","url":"https://www.globenewswire.com/fr/news-release/2026/08/26/3351258/0/en/emxetf-launches-china-ai-tigers-llm-etf-nasdaq-tgrz-to-tap-into-china-s-leading-ai-models.html","image_url":null,"source_name":"globenewswire_fr","source_icon":"https://n.bytvi.com/globenewswire_fr.jpg","published_at":"2026-08-26 12:00:00","category":"business"},{"id":41520,"title":"CURRENC Capital and Securitize Announce Strategic Collaboration to Advance Issuer-Sponsored Tokenization of Public Equities","description":"Collaboration pairs Securitize’s regulated tokenization infrastructure with CURRENC Capital’s first-hand issuer experience to help listed companies evaluate regulated approaches to bringing public-company shares onchain Collaboration pairs Securitize’s regulated tokenization infrastructure with CURRENC Capital’s first-hand issuer experience to help listed companies evaluate regulated approaches to bringing public-company shares onchain","url":"https://www.globenewswire.com/news-release/2026/08/26/3351275/0/en/currenc-capital-and-securitize-announce-strategic-collaboration-to-advance-issuer-sponsored-tokenization-of-public-equities.html","image_url":null,"source_name":"globenewswire","source_icon":"https://n.bytvi.com/globenewswire.jpg","published_at":"2026-08-26 12:00:00","category":"business"},{"id":41521,"title":"Micron Announces Leadership Appointments to Accelerate Innovation and Growth","description":"Micron Announces Leadership Appointments to Accelerate Innovation and Growth","url":"https://www.globenewswire.com/news-release/2026/08/26/3351270/14450/en/micron-announces-leadership-appointments-to-accelerate-innovation-and-growth.html","image_url":null,"source_name":"globenewswire","source_icon":"https://n.bytvi.com/globenewswire.jpg","published_at":"2026-08-26 12:00:00","category":"business"},{"id":41522,"title":"CURRENC Capital and Securitize Announce Strategic Collaboration to Advance Issuer-Sponsored Tokenization of Public Equities","description":"Collaboration pairs Securitize’s regulated tokenization infrastructure with CURRENC Capital’s first-hand issuer experience to help listed companies evaluate regulated approaches to bringing public-company shares onchain","url":"https://www.financialcontent.com/article/gnwcq-2026-8-26-currenc-capital-and-securitize-announce-strategic-collaboration-to-advance-issuer-sponsored-tokenization-of-public-equities","image_url":null,"source_name":"financialcontent","source_icon":"https://n.bytvi.com/financialcontent.png","published_at":"2026-08-26 12:00:00","category":"business"},{"id":41503,"title":"Runable hits $21M to bet AI agents can go from building businesses to growing them","description":"Runable says 60%–70% of its 1 trillion-plus token usage in the last 90 days came from paying customers.","url":"https://techcrunch.com/2026/08/26/runable-hits-21m-to-bet-ai-agents-can-go-from-building-businesses-to-growing-them/","image_url":"https://techcrunch.com/wp-content/uploads/2026/08/runable-co-founders-umesh-saksham.jpg","source_name":"techcrunch","source_icon":"https://n.bytvi.com/techcrunch.png","published_at":"2026-08-26 11:00:00","category":"technology"},{"id":41504,"title":"Senmiao Technology Limited Announces Name Change to Valor Energy Inc.","description":"New York, NY, Aug. 26, 2026 (GLOBE NEWSWIRE) -- Senmiao Technology Limited. (NASDAQ: AIHS) (“ AIHS ” or the “ Company ”), an emerging digital infrastructure, energy and strategically emerging industries company, is pleased to announce that it has changed its name to “Valor Energy Inc” after receiving the Certificate of Amendment on Change of Name from the Secretary of State of the State of Nevada, on August 18, 2026. The change of name will be effective on Nasdaq on August 26, 2026. The","url":"https://www.financialcontent.com/article/gnwcq-2026-8-26-senmiao-technology-limited-announces-name-change-to-valor-energy-inc","image_url":null,"source_name":"financialcontent","source_icon":"https://n.bytvi.com/financialcontent.png","published_at":"2026-08-26 11:00:00","category":"business"},{"id":41505,"title":"Bidgely Unveils 2027 EmPOWER AI Conference Series, Accelerating AI Progress Across the Energy Ecosystem","description":"LOS ALTOS, Calif.--(BUSINESS WIRE)--Expanding its initiative to enable secure and insight-driven artificial intelligence (AI) across the energy sector, Bidgely announced the 2027 calendar for its EmPOWER AI conference series. Anchored by a flagship global summit in Chicago—featuring host utility PSEG Long Island—alongside regional forums in Calgary and EMEA, the series comes as utilities move decades of siloed data into cloud data lakes and deploy horizontal GenAI infrastructure. Centered on th","url":"http://www.businesswire.com/news/home/20260826516391/en/Bidgely-Unveils-2027-EmPOWER-AI-Conference-Series-Accelerating-AI-Progress-Across-the-Energy-Ecosystem/","image_url":"https://mms.businesswire.com/media/20260826516391/en/2882755/21/Lou-DeBrino-PSEG-LongIsland-Exec-Video.jpg","source_name":"businesswire","source_icon":"https://n.bytvi.com/businesswire.png","published_at":"2026-08-26 11:00:00","category":"environment"},{"id":41506,"title":"Bidgely Unveils 2027 EmPOWER AI Conference Series, Accelerating AI Progress Across the Energy Ecosystem","description":"Expanding its initiative to enable secure and insight-driven artificial intelligence (AI) across the energy sector, Bidgely announced the 2027 calendar for its EmPOWER AI conference series. Anchored by a flagship global summit in Chicago—featuring host utility PSEG Long Island—alongside regional forums in Calgary and EMEA, the series comes as utilities move decades of siloed data into cloud data lakes and deploy horizontal GenAI infrastructure.","url":"https://www.financialcontent.com/article/bizwire-2026-8-26-bidgely-unveils-2027-empower-ai-conference-series-accelerating-ai-progress-across-the-energy-ecosystem","image_url":null,"source_name":"financialcontent","source_icon":"https://n.bytvi.com/financialcontent.png","published_at":"2026-08-26 11:00:00","category":"technology"},{"id":41507,"title":"Palladyne AI and NORDA Dynamics to Integrate SwarmOSTM Collaborative Autonomy Software with NORDA’s Combat-Proven Underdog Autonomy Module","description":"Palladyne AI (NASDAQ: PDYN), a U.S.-based defense and industrial technology company delivering embodied AI-powered collaborative autonomy solutions, and NORDA Dynamics (“NORDA”), a Ukrainian-Estonian developer of a drone autonomy ecosystem proven in active combat, today announced a collaboration to integrate Palladyne AI’s SwarmOSTM decentralized, collaborative autonomy platform with the Underdog Autonomy Module, NORDA’s autonomous terminal guidance product for next-generation unmanned swarm","url":"https://www.financialcontent.com/article/bizwire-2026-8-26-palladyne-ai-and-norda-dynamics-to-integrate-swarmos-collaborative-autonomy-software-with-nordas-combat-proven-underdog-autonomy-module","image_url":null,"source_name":"financialcontent","source_icon":"https://n.bytvi.com/financialcontent.png","published_at":"2026-08-26 11:00:00","category":"technology"},{"id":41508,"title":"What are ‘open’ AI models?","description":"The tech industry faced something of a reckoning in late 2022 with the arrival of OpenAI’s ChatGPT , a simple chatbot that could answer questions, seemingly by magic. The early frontier large language models (LLMs) from OpenAI and rivals like Anthropic and Google could reason, solve problems, and answer detailed queries put to them using natural language. But no one knew exactly what was in the LLM black boxes underpinning the services. Soon, AI models such as Meta’s Llama and Mistral came along to break the proprietary AI monopoly. These kinds of models could be freely downloaded and modified to fit specific applications. In late 2024, Chinese firm DeepSeek released V3 , an open model reportedly trained at a fraction of the cost US frontier labs spent. A reasoning model, R1, followed in January 2025 and was seen as a legitimate threat to frontier models. During the last year, open models such as Alibaba’s Qwen and Moonshot’s Kimi have also been gaining ground among enterprises for reasoning, agentic and physical AI. “The result is a clear trend: with each generation, open-source models take half as long to catch up to the first closed-source model of the era,” research firm SemiAnalysis explained in a newsletter this month. Open model variations explained Many versions of “open” AI models are now floating around, including “open-weight” models and “open-source” models. Though they sound similar, it’s important to understand how they differ. The most common “open” models used in enterprises are the open-weight models. These let companies customize AI services and tools to their own internal requirements. Corporate leaders and IT decision-makers can see inside the model, audit it, and tune it to their own specific data. Open-weights are the parameters processed by mathematical techniques to produce an output. Enterprises can customize a model by fine-tuning the weights, adding their internal data, and deploying it in-house. “Open-weight models help provide that assurance by allowing organizations to control their own data, evaluate and adapt models to their own needs, and deploy them wherever their business requirements demand,” Nvidia CE Jensen Huang said in a letter released last month . But — and here’s the main difference — open-weight models hide information such as code and training data, so some parts can’t be modified. According to the Open Source Initiative (OSI), a truly open-source model also releases the data it was trained on, along with other information allowing those models to be studied, inspected, used, modified and freely distributed. Enterprise use for open models To be sure, the likes of ChatGPT, Google’s Gemini, and Anthropic’s Claude provide well-rounded AI capabilities; they aren’t going anywhere anytime soon. But the services are expensive and could be overkill for specific corporate uses. Many enterprises can be better served by a small language model (SLM) or LLM that’s focused on their specific needs. Open models are blank canvases on which enterprises can paint their workflows, said Deepak Seth, senior director analyst at Gartner. “An enterprise’s real needs sit in specific workflows with specific data, and a general-purpose closed model trained on the entire internet is overkill for most of them,” Seth said. China is a proponent of open source and open weight models as it increasingly becomes an AI rival to the US. Other major economies, including Germany, France and India, are also encouraging the adoption of open models. Companies such as ServiceNow and RWS have deployed dozens of open-source models (in addition to proprietary models) that specialize in specific tasks,. “We shouldn’t be afraid to adopt a multi-vendor approach if we think that we can get value from different AI tools rather than risk the lock-in of having a single AI tool,” said Max Goss, research director at Gartner. With physical operations, such as in a vehicle or on-site, decisions need to happen in milliseconds — sometimes directly on the device. That favors models that are purpose-built, efficient, and able to run close to where the data is generated, said Praveen Murugesan, vice president of engineering at Samsara. “Open models make that layered deployment possible because the enterprise controls where each model runs and what data it touches,” Murugesan said. Governance and control as key assets As more companies embrace AI, uptime is becoming important to keeping workflows humming. Open-source models give enterprises more control over their AI future — and protection against vulnerabilities exposed in proprietary LLMs. Securing and controlling AI systems and data is part of the governance needed to successfully deploy AI within enterprises. “You actually build the boundaries around it. So the responsible AI is built in,” said Jinsook Han, founder and partner at Spruce Peak Ventures. Open models can be run internally, cut off from the cloud. That provides additional security, especially for regulated industries where data control is paramount. “There’s definitely a gravitation towards comfort level on more of an open model and relying on, ‘Hey, I want everything on-prem. I want this on the system of work,’” Han said. Companies are going to want models trained on their proprietary information, and will need solid control over the tools because of IP leakage worries, said Craig LeClair, vice president and principal analyst at Forrester Research “Open source models will be run in controlled on-premise environments, which just makes them less open source pretty quickly,” LeClair said. Open models allow enterprises to “inspect the weights, audit the training data, or air-gap the deployment,” Gartner’s Goss said. “You can’t govern what you can’t see.” AI and digital sovereignty Open models can help countries customize AI to meet indigenous customs, traditions, policies and regional regulatory constraints. “Open models are important to sovereign AI so nations can understand, adapt, and control systems powering digital infrastructure,” said Richard Morton, vice president and managing director at the Abu Dhabi-based Institute of Foundation Models at Mohamed bin Zayed University of Artificial Intelligence. Open models also do a better job of innovating based on localized knowledge and requirements, said Kari Briski, Nvidia’s vice president of generative AI software. “Open models and open data are that bootstrap: you don’t have to recreate capturing the knowledge of the internet as a pre-training model,” Briski said. The downsides to going open Proprietary technologies can stifle innovation, even as they provide a higher level of security than open technologies, said Jack Gold, principal analyst at J. Gold Associates. And while open models are available from major service providers, their deployment, maintenance and updates in many cases fall in the hands of enterprises. Beyond that, open models might not always be completely vetted, introducing a level of risk that proprietary information in a model could be leaked beyond the borders of an enterprise, Gold said. For example, while there’s been plenty of interest in OpenClaw , which can scan file systems, access personal information and communicate with LLMs, the agents could create new attack surfaces; that’s why top technologists are experimenting carefully before deploying in the enterprise . “We are a big believer in open source and we are super excited to see the ecosystem around these open-source models build and thrive. In the fullness of time, these open-source models will have their own space,” said Samar Abbas, co-founder and CEO of Temporal. Ultimately, enterprises need an inventory of every model and agent, sanctioned or shadow, with a clear view of what data each one is accessing, Abbas said. “Some workflows need a frontier-class closed model. Many do not, and an open model fitted to internal data will outperform a horizontal closed one.”","url":"https://www.computerworld.com/article/4213156/what-are-open-ai-models.html","image_url":"https://www.computerworld.com/wp-content/uploads/2026/08/4213156-0-55174700-1787742236-luke-southern-4kCGEB7Kt4k-unsplash.jpg?quality=50&strip=all","source_name":"computerworld_in","source_icon":"https://n.bytvi.com/computerworld_in.png","published_at":"2026-08-26 11:00:00","category":"technology"},{"id":41509,"title":"AI Slop Is Ruining Cute Animals on the Internet","description":"Pet owners, rescue agencies, and wildlife groups are calling for new safeguards as AI makes it harder to tell whether animals, from polar bears to house cats, are real or fake.","url":"https://www.wired.com/story/ai-slop-is-ruining-the-internets-cute-animal-economy/","image_url":"https://media.wired.com/photos/6a863dcae60bdcf9fa334f31/master/pass/AI-Slop-Is-Ruining-Internets-Cute-Animal-Economy-Business.jpg","source_name":"wired","source_icon":"https://n.bytvi.com/wired.png","published_at":"2026-08-26 11:00:00","category":"business"},{"id":41510,"title":"Everyone Is Chasing AI. Peter Castleman Says They’re Missing the Real Opportunity","description":"Peter Castleman argues that AI, proprietary data, and long-term brand promises can empower consumers with the truth to transform markets.","url":"https://www.inc.com/ash-kumra/everyone-is-chasing-ai-peter-castleman-says-theyre-missing-the-real-opportunity/91394971","image_url":"https://img-cdn.inc.com/image/upload/f_webp,q_auto,c_fit/vip/2026/08/peter-castleman-pano-inc.jpg","source_name":"inc","source_icon":"https://n.bytvi.com/inc.png","published_at":"2026-08-26 11:00:00","category":"business"},{"id":41511,"title":"Sabio and Gentoro Partner to Advance Agentic AI Across Advertising Operations","description":"/CNW/ -- Sabio Holdings, a Los Angeles-based creator-led, data-driven and AI-powered AdTech company helping global brands reach, engage and validate (R.E.V.)...","url":"https://www.newswire.ca/news-releases/sabio-and-gentoro-partner-to-advance-agentic-ai-across-advertising-operations-880791764.html","image_url":null,"source_name":"newswire_ca","source_icon":"https://n.bytvi.com/newswire_ca.png","published_at":"2026-08-26 11:00:00","category":"business"},{"id":41512,"title":"The reachability gap: Why the company your AI agent breaks into has no one to call","description":"In July, two frontier labs disclosed cases in which cyber-capable agents crossed the intended boundaries of evaluation environments and reached real production systems at external, unrelated organizations. Most commentary since has focused on which company a court would find liable. There is a more immediate concern for anyone operating agents in production, and it’s not about the law. If this happened in your deployment tomorrow, who would bear responsibility for the incident? I have a particular purpose for stating it that way. This spring, I reviewed the Coalition for Secure AI’s Shared Responsibility Framework before its publication in May. Frameworks like that, along with the cloud shared responsibility models that preceded them, break down responsibilities among the parties operating a system: provider, platform, developer, deployer, user. July illustrated what happens when the entity suffering the damage is none of the above. Responsibility maps stop at contractual boundaries. Agent reach does not. Call it the reachability gap. The two disclosures described different failure modes, and that difference is significant. OpenAI was testing models against a cyber benchmark with production refusals reduced so the evaluation could measure real capability. The models obtained internet access through a zero-day in a package registry component, went looking for the benchmark’s answer key and inferred that Hugging Face might host it. Hugging Face reconstructed the intrusion from over 17,000 recorded agent events, and the campaign extended further than initially disclosed , affecting accounts on four external services. Anthropic’s incidents were not escapes. In a review of more than 141,000 cybersecurity evaluations , Anthropic found three cases in which Claude models reached the internet from inside or alongside a third-party evaluation environment and then accessed real systems at three organizations. Live connectivity was mistakenly available. The models had been told in their prompts that they had none, and Anthropic stated that Claude did not exfiltrate itself or deliberately attempt to escape its test environment. Of the affected organizations the lab was able to reach, two had not detected the activity before being notified. The pattern held while I was writing this. On August 4, OpenAI disclosed two more incidents from third-party evaluations , separate from Hugging Face. In one, a partner running capture-the-flag exercises had a testing environment misconfigured with live internet access, and the fictional target in the exercise happened to share its name with a real domain. The model exploited an actual website, taking it for part of the simulation, then found and used credentials to operate it. Whoever owned that site had no relationship with OpenAI, with the evaluation partner or with the test. They were reachable, and their name collided with a fiction. Count the parties. Two frontier labs. A third-party evaluation partner. A platform victim. A cloud customer victim. Organizations that learned of a breach from a notification. CSO has already examined how the response strained the AI tooling defenders had available . One step earlier: who bears responsibility for reaching for that tooling at all? At the moment of detection, who had ownership of containment? One accountable party per activity The Shared Responsibility Framework’s core rule is almost boring to state: for every activity across the AI stack, there should be exactly one accountable party. This principle breaks down the AI deployment process into five layers, from business usage to model supply chain, and maps eight roles across them. This way, detection, containment and remediation each carry a name before an incident rather than during one. This rule exists due to the failure it prevents, and the framework explicitly names it: in the absence of accountable parties, teams default to finger-pointing. The model provider blames configuration. The platform points at the tenant. The application team cites model limitations. Everyone is partially correct, and the clock continues to run. July reads differently than the coverage suggests when measured against that rule. OpenAI, as a model provider, evaluation platform operator and agent-deploying organization, took on at least three roles. Having multiple roles within one company is common and not necessarily dangerous. The danger appears when those roles are not broken down into distinct internal owners and decision rights handoffs. When provider, operator and deployer are one and the same and those lines are not drawn, the question of which function failed has one answer and thus no answer of value. Anthropic’s incidents make a related point from the opposite direction. There was a boundary, this time, between the lab and its evaluation partner, and the incident resided in a gap between two different understandings of what the environment allowed. The public disclosures do not clarify how responsibility was shared contractually regarding each incident, and I will not speculate. What is clear is that whatever boundary existed failed to provide a common understanding of one important control, which is whether the evaluation environment was allowed to access the internet. A boundary that has never been tested against a question that simple is not a boundary at all. Both sets of incidents also occurred in situations of high autonomy where the standard production safeguards were reduced, and the models were allowed wide latitude to pursue an open-ended goal, which is the context where unclear boundaries lead to the greatest losses. I am not suggesting that either lab acted carelessly. Both institutions made rapid public disclosure of details, and both have named the conditions that allowed the activity. That is the point. If the most knowledgeable and incentivized parties ended up with no clear answer on whose incident it is, we should not presume to have one. The victims were outside everyone’s map Here is the part the framework does not resolve, and I say that as one of its reviewers. Shared responsibility models, CoSAI’s included, presume a value chain. Each role is taken up by a party who voluntarily entered into the relationship. This is what makes responsibility assignable via contracts and review boards. Hugging Face did not take up any of those roles. It was neither a customer, nor a vendor, nor an evaluator of OpenAI. The same goes for the three organizations that Claude reached. They were just reachable. That’s the structural lesson I’d put on one slide for a leadership team. In classical models of cloud shared responsibility, the provider-customer boundary was visible because the boundary was contractual. With agentic systems, there is another kind of gap, the reachability gap: an agent’s reachable range may stretch to actors outside of the deployment relationship, and there is no contract that articulates what obligations you owe them if things go sideways. Any mapping exercise that ends at the edge of your value chain is tackling the easier part and skipping the part July was about. What to do before it is your incident None of this requires buying anything. Your map has to look beyond your value chain. For each agent, identify what it can access that you have no agreements with: public infrastructure, other tenants, the open internet. A long list next to broad latitude shows your true exposure, regardless of what your contracts stipulate. Determine who is authorized and obliged to inform an outside party that your agent might have affected. This is the gap July most starkly revealed, and this is not a question you want to bring up during an incident when the answer involves a lawyer, a communications person or an executive who has never considered it. Establish accountability by specific component and activity rather than by organizational box. For each production agent, specify who owns detection, containment, eradication, recovery, remediation, then slot those assignments into the five layers. Where one team or vendor occupies multiple roles, capture the consolidation and identify the internal handoffs, because an undocumented internal boundary will break under pressure. The contracts for your most autonomous agents are worth retrieving . If a vendor’s agent is able to operate across domains within your environment, review whether any provision assigns responsibility for the consequences of those actions. In the contracts I have seen, the answer is typically silence, and silence is a decision someone else will make for you later. Treat evaluation and red-team environments as production-impacting systems. Their safeguards are often reduced on purpose, and that is exactly why containment, monitoring, incident command and external notification procedures around them should be at least as rigorous as those protecting production systems. The questions about liability will likely be tested in court or by regulators, and those answers will be about the labs and their evaluation partners. The operational question is already yours. Hugging Face learned whose incident it was from forensics. Two of Anthropic’s victims learned from a notification. The organizations that come through the next one intact will be the ones that were able to answer the question before it was asked.","url":"https://www.cio.com/article/4213764/the-reachability-gap-why-the-company-your-ai-agent-breaks-into-has-no-one-to-call.html","image_url":"https://www.cio.com/wp-content/uploads/2026/08/4213764-0-72957900-1787742177-cord-allman-OHERbbsOQnU-unsplash.jpg?quality=50&strip=all","source_name":"cio_in","source_icon":"https://n.bytvi.com/cio_in.jpg","published_at":"2026-08-26 11:00:00","category":"technology"}],"total":6939}