AI News Archive: June 11, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- Inside Hartford HealthCare's strategy to build a safer AI front door for patients
Inside Hartford HealthCare's strategy to build a safer AI front door for patients Healthcare IT News
Score: 60🌐 MovesJun 11, 2026https://www.healthcareitnews.com/news/inside-hartford-healthcares-strategy-build-safer-ai-front-door-patients - ‘Cisco now is delivering that critical infrastructure for the AI era’: Cisco's infrastructure unification push aims to simplify management for the agentic era
‘Cisco now is delivering that critical infrastructure for the AI era’: Cisco's infrastructure unification push aims to simplify management for the agentic era IT Pro
- Google open-sources speedy DiffusionGemma text diffusion model
Google LLC today released DiffusionGemma, a large language model based on an emerging machine learning approach known as text diffusion. The company says the algorithm can generate text four times faster than traditional LLMs. Furthermore, DiffusionGemma does so using less RAM. The model’s memory efficiency enables it to run on high-end consumer graphics cards that […] The post Google open-sources speedy DiffusionGemma text diffusion model appeared first on SiliconANGLE .
Score: 60🤖 ModelsJun 11, 2026https://siliconangle.com/2026/06/10/google-open-sources-speedy-diffusiongemma-text-diffusion-model/ - How to share AI riches
From Donald Trump to Sam Altman, the idea of redistributing them is catching on. Does it make sense?
Score: 60🌐 MovesJun 11, 2026https://www.economist.com/finance-and-economics/2026/06/11/how-to-share-ai-riches - TechForce Robotics Forms Strategic Alliance with Jiun Jiang ‘JJ Enterprise’ to Advance AI Infrastructure, Semiconductor Automation, and Pharmaceutical Robotics
TechForce Robotics Forms Strategic Alliance with Jiun Jiang ‘JJ Enterprise’ to Advance AI Infrastructure, Semiconductor Automation, and Pharmaceutical Robotics Toronto Star
- Reka and Moonvalley Join Forces to Advance Models and Infrastructure for Physical AI
Reka and Moonvalley Join Forces to Advance Models and Infrastructure for Physical AI The Straits Times
- TDK buys U.S. 3D-printing startup to amp up AI ecosystem bet
TDK buys U.S. 3D-printing startup to amp up AI ecosystem bet The Japan Times
Score: 60🌐 MovesJun 11, 2026https://www.japantimes.co.jp/business/2026/06/11/companies/tdk-buys-3d-printing-startup/ - An Interview with Ben Bajarin About Apple, AI, and Compute
An interview with Ben Bajarin about WWDC and the status of the AI compute industry.
Score: 60🌐 MovesJun 11, 2026https://stratechery.com/2026/an-interview-with-ben-bajarin-about-apple-ai-and-compute/ - Autonomous Chemical Plants: How AI and Industrial Automation are driving safer and more efficient operations
By Srinivas Choudhary, Founder & CEO of Alligator Automations The chemical and fertilizer manufacturing industries are embarking on a new age of intelligent manufacturing. As plants become better equipped with […] The post Autonomous Chemical Plants: How AI and Industrial Automation are driving safer and more efficient operations appeared first on Express Computer .
- Microsoft open sources AI evaluation framework for enterprise agents
Microsoft open sources AI evaluation framework for enterprise agents InfoWorld
- Healthcare And Life Sciences: Turning AI Momentum Into Lasting Value
Healthcare and life sciences (HCL) firms are moving fast on AI — much faster than expected. But so are consumers. From domain-specific AI tools to enterprisewide ambient technology integration, accelerating drug discovery, and connecting medical records and health apps for a more personalized experience, the momentum is real. The promise is also real: AI is […]
Score: 60🌐 MovesJun 11, 2026https://www.forrester.com/blogs/healthcare-and-life-sciences-turning-ai-momentum-into-lasting-value/ - AI Agents in Healthcare: 3 Snowflake Summit Takeaways
Explore 3 Snowflake Summit takeaways on AI agents in healthcare, including CoWork, enterprise agentic workflows and Sanofi’s agentic AI transformation.
- What is HRM-Text, a new frontier-level AI model built from scratch for just $1,500?
What is HRM-Text, a new frontier-level AI model built from scratch for just $1,500?
Score: 60🤖 ModelsJun 11, 2026https://indianexpress.com/article/technology/artificial-intelligence/what-is-hrm-text-low-cost-ai-model-10734501/ - Pine Labs launches AI-powered payments protocol for UPI transactions
Pine Labs launches AI-powered payments protocol for UPI transactions YourStory.com
Score: 60🌐 MovesJun 11, 2026https://yourstory.com/2026/06/pine-labs-ai-powered-payments-protocol-upi-transactions - L’IA s’invite déjà dans la campagne présidentielle
Gabriel Attal a profité de l’arrivée à l’Assemblée d’une proposition de loi sur le secteur de l’intelligence artificielle pour se faire le relais des mastodontes de la tech. Anthony Lattier discute dans Playbook Paris de l’irruption de l’IA dans la campagne présidentielle avec Océane Herrero, journaliste au service Tech de POLITICO.
- Four in five UK boards discussing which decisions should be led by AI
Business experts worry governance processes are failing to keep pace with the technology
- AFL-CIO skeptical of government AI stake
“We think that our plan forward is actually a more responsible way to use technology to benefit the people,” AFL-CIO President Liz Shuler said.
Score: 60🌐 MovesJun 11, 2026https://www.semafor.com/article/06/11/2026/afl-cio-skeptical-of-government-ai-stake - Travala Launches World’s First End-to-end Agentic AI Travel Protocol
Travala Launches World’s First End-to-end Agentic AI Travel Protocol markets.businessinsider.com
- 5 charts from Goldman Sachs show how AI mania compares to 2000 and 2021
5 charts from Goldman Sachs show how AI mania compares to 2000 and 2021 Business Insider
Score: 60🌐 MovesJun 11, 2026https://www.businessinsider.com/ai-bubble-how-it-compares-to-dot-com-2000-crash-2026-6 - China leads US in everyday AI apps but firms are overvalued, experts say
China holds an edge over the US when it comes to getting artificial intelligence applications into the hands of everyday users, according to tech executives and investors, though they warn that Chinese AI firms are looking increasingly overvalued. China still lagged in computing power but was only “100 days behind” the US in frontier AI model capabilities, according to Chi Zhang, general manager of finance industry at the Alibaba Cloud Intelligence Group, speaking on Thursday at the 2026 HKEX...
- The Rise of the 'Botsitters'
The Rise of the 'Botsitters' Business Insider
Score: 60🌐 MovesJun 11, 2026https://www.businessinsider.com/botsitting-ai-hidden-human-labor-at-work-2026-6 - Inside Cisco’s Makeover: Targeting AI With Deeper Stack Integration
Cisco has gotten serious about embracing agentic infrastructure operations to provide better ease-of-use and integration of its products.
- Microsoft C.E.O. Satya Nadella Says ‘Everyone Is a Stakeholder’ in A.I.
At The New York Times’s Hard Fork Live event, Mr. Nadella addressed the backlash against artificial intelligence and President Trump’s comments about Americans sharing in the wealth of A.I. companies
Score: 60🌐 MovesJun 11, 2026https://www.nytimes.com/2026/06/10/technology/microsoft-satya-nadella-artificial-intelligence.html - SLB strikes deal with Venezuela's PDVSA to modernise oil sector with AI push
SLB strikes deal with Venezuela's PDVSA to modernise oil sector with AI push Reuters
- Google DeepMind’s TacticAI can predict football plays 8 seconds before they happen. Palmeiras is the first to use it.
Google DeepMind built an AI that can predict football plays before they happen. TacticAI uses geometric deep learning to model player movement, forecast dynamics up to eight seconds into the future, and recommend tactical adjustments, all from broadcast-style visual data. Brazilian club Palmeiras is the first to use it for live open-play analysis. The system […] This story continues at The Next Web
Score: 60🌐 MovesJun 11, 2026https://thenextweb.com/news/google-deepmind-tacticai-football-palmeiras-predict-plays - The hidden cost of enterprise AI: 6.4 hours a week babysitting bots
While AI is proliferating across the workplace, it is introducing a new productivity paradox: While the technology makes work feel faster, it actually pushes more burden onto employees to provide context, perform quality checks, then rinse and repeat across numerous disparate tools. This, according to a new survey of 6,000 full-time digital workers by Glean’s Work AI Institute, results in two emerging behaviors: “botsitting,” all the unrecognized work that goes into making AI actually usable; and “botshitting,” shipping AI-generated work that is unverified, not that well understood, or perhaps not even trustworthy. The survey report was co-authored by experts from Work AI Institute, Emory University, Stanford University, UC Berkeley, UC Santa Barbara, UNC Charlotte, University College London, and University of Notre Dame. “It’s definitely in many ways a vicious cycle that feeds itself,” said Rebecca Hinds , head of Glean’s research center the Work AI Institute, a research collaborative of AI experts. Enterprises need to begin understanding and addressing the “massive, massive human labor that’s at the core of this.” Workers are using AI more, getting more frustrated There’s no doubt that AI is quickly becoming a central teammate in the workplace. Glean’s Work AI Institute found that 87% of digital workers are using AI: It is already automating more than a quarter of their work and saving about 11 hours a week. Still, only 13% say the use of AI has significantly improved their company’s performance, and their time savings are being eaten up by the same technology that is producing them. Employees lose about one-third of their work week (6.4 hours) botsitting: feeding AI context, supervising outputs, debugging errors, cleaning up AI-generated work , and switching between AI tools. “We’re seeing high, high rates of multiple tool usage, and often those tools aren’t connected,” said Hinds. In terms of context-feeding , large language models (LLMs) are trained on the vast corpus of the internet, but not always on enterprise-specific data. Thus, employees often have to provide additional information around their company’s products, customers, services, or other details. “They’re often feeling frustrated when the tools don’t understand enough about day to day work to be useful,” said Hinds. Also, because employees are using multiple tools, they often have to repeat the same prompt over and over. “It’s exhausting for workers to not only do this, but to have the work be unrecognized, often unrewarded and unacknowledged within the organization,” she said. Similarly, workers are having to catch outputs that might look polished and finished on the surface, but could be wrong, incomplete, or missing important context. Debugging is the biggest driver of exhaustion, because it is often conducted by people who didn’t necessarily contribute to the initial output, Hinds noted, so they first have to dig up background information. However, “not all botsitting is bad,” Hinds emphasized. “Certainly, we want workers to have some level of ownership and oversight.” But when it is unnecessary, it can lead to botshitting, where users ship AI-generated work they haven’t verified because they’re overwhelmed or time-constrained. Sixty-nine percent of users admit to doing so, and 41% say they sometimes deliver work they could not explain if asked. Another 28% blame AI for mistakes they themselves caused. “Botshitting is offloading your critical human thinking, judgment, and understanding,” Hinds explained. “You’re offloading that work that absolutely needs to remain with the human.” Workers using multiple AI agents are significantly more likely to do this, she added, because agents are so scalable, and can spiral out of control if they don’t have the right controls or permissions built around them, causing overwhelmed users to give up on their verification efforts. “You don’t often see the negative impacts until 3, 4, 5, steps down the line,” said Hinds. “Then it requires all of this cleanup work, detective work, to understand where did the agent go wrong.” Using AI … but not too much Interestingly, more than half of the workers surveyed said they get more day-to-day help from AI than they get from their managers, and consider it easier to collaborate with than humans. Still, they seem to be facing a Goldilocks problem when it comes to sharing their use of AI. Among self-identified high AI achievers, 54% are using unapproved tools or using approved tools in noncompliant ways, and 36% are hiding how much AI helps them. As Hinds explained, depending on the context and the level of psychological safety an organization has provided, it can be “differentially beneficial or harmful” to show you’re using AI, and, on the flip side, to conceal that you’re using it too much, because that might make you less valuable, or perceived as less valuable, she said. It’s a complicated balance, because, she noted, “there’s massive pressure in so many organizations to demonstrate AI fluency, to demonstrate you’re a power user.” What successful organizations are doing differently In fact, the report said, “The companies pulling ahead are doing something different. They aren’t spending a greater share of their AI time using AI. They’re spending a greater share on the work around it: setting context, defining what ‘good’ looks like, building judgment, and deciding what should never have been handed to a model in the first place.” The most transformative organizations are addressing AI challenges proactively: Providing training and support, treating AI as an opportunity to redesign work, and formally rewarding AI skills. In addition, it noted, the hardest skill to build is knowing when not to use AI. It is “not just clicks of the tool, not just tokens used , but real skills, real learning,” said Hinds. In addition to investing in workers, these organizations are clearly stating AI strategy and clarifying the “why” behind it. Governance should also be “living and breathing,” with companies continuously re-evaluating policies. And it needs to happen at every level, top execs included, said Hinds: “It’s being able to see the executives use the technology, sharing both the success stories and the failures.” Successful companies are also actively using metrics anchored in existing key performance indicators (KPIs). They are measuring quality, efficiency, and employee engagement in different ways, and putting data in the hands of employees so they can assess their own adoption and success. “It’s less about surveillance and more about feedback in terms of how we work collectively,” said Hinds. What’s “fascinating but perhaps not surprising,” she said, is that workers are increasingly using AI itself as a teacher, and prefer it over other learning channels. This speaks to the importance of low-code, no-code tools, with low learning curves and organizational context, that are embedded directly into workflows. “It is starkly different from what we’ve seen with previous technologies,” she said.
Score: 59🌐 MovesJun 11, 2026https://www.cio.com/article/4183804/the-hidden-cost-of-enterprise-ai-6-4-hours-a-week-babysitting-bots.html - Walmart's head of growth says AI is rewriting the rules for its fast-growing ads business
Walmart's head of growth says AI is rewriting the rules for its fast-growing ads business Business Insider
Score: 59🌐 MovesJun 11, 2026https://www.businessinsider.com/walmart-growth-chief-says-ai-is-rewriting-ad-strategy-2026-6 - Everyone hates frontier AI labs, says Palantir boss
'Enterprises are fed up,' says Alex Karp, because LLM makers 'want to tokenmax' instead of understanding enterprise needs
Score: 59🌐 MovesJun 11, 2026https://www.theregister.com/ai-and-ml/2026/06/11/everyone-hates-frontier-ai-labs-says-palantir-boss/5254516 - Meta Stock Slumps Further As Bulls And Bears Debate Tech Giant's AI 'Evolution'
Meta stock has slumped amid a fierce bull-bear debate about its plan to spend more than $125 billion on its AI push this year. The post Meta Stock Slumps Further As Bulls And Bears Debate Tech Giant's AI 'Evolution' appeared first on Investor's Business Daily .
Score: 59🌐 MovesJun 11, 2026https://www.investors.com/news/technology/meta-stock-ai-bull-bear-debate/ - AI robot cleaners leave the lab for China’s living rooms
AI robot cleaners leave the lab for China’s living rooms The Straits Times
Score: 59🌐 MovesJun 11, 2026https://www.straitstimes.com/asia/east-asia/ai-robot-cleaners-leave-the-lab-for-chinas-living-rooms?ref - ‘It is insanity’: SF luxury condo owners fume over aggravating neighbors. (They’re Waymos)
Robotaxis are clogging garage entrances, getting stuck in tight quarters, and generally being a nuisance, say residents of the Soma Grand building.
- AI will create more jobs than it disrupts, says Microsoft India chief
Artificial Intelligence is poised to create more opportunities than it disrupts, and Indian engineers must shift their focus from job security fears of collaborating with the technology, a senior Microsoft India executive has said. Rajiv Kumar, Managing Director and President of Microsoft India Development Center (IDC), in a blog post on Thursday, said the rapid evolution of technology is shrinking the lifespan of technical skills, making continuous learning and adaptability crucial for the workforce. Citing the World Economic Forum's Future of Jobs Report 2025, which surveyed over 1,000 employers across 22 industry clusters and 55 economies, Kumar noted that 39 per cent of core job skills are expected to change by 2030. In India specifically, an estimated 63 per cent of the workforce will need significant upskilling or reskilling by the same year. "Virtually every major technology wave in history has ultimately created more opportunities than it destroyed... The real question is n
- China tests AI robot cleaners in homes
China is testing AI robot cleaners in Beijing homes, where machines assist human cleaners in early trials focused on data collection and domestic tasks.
Score: 58🌐 MovesJun 11, 2026http://www.euronews.com/video/2026/06/11/china-tests-ai-robot-cleaners-in-homes - Meta’s Subscription Push Exposes Its Weak Hand in AI
Charging users is the latest idea to expand beyond ads—a pickle Meta isn’t likely to get out of soon.
Score: 58🌐 MovesJun 11, 2026https://www.wsj.com/tech/ai/metas-subscription-push-exposes-its-weak-hand-in-ai-2fad4680?mod=rss_Technology - Nvidia hires veteran lobbyist Bruce Andrews to head government affairs
Nvidia hires veteran lobbyist Bruce Andrews to head government affairs Reuters
- Microsoft’s open-source SkillOpt automatically upgrades AI agent skills without touching model weights
Agent skills have become an important part of real-world AI applications, providing a mechanism — a set of instructions saved in a folder of text-based markdown (.md) files, usually — for models to adapt to specific enterprise use cases and complex workflows. However, optimizing these skills is a slow process and faulty process, as they cannot be trained in the same way as the parameters of the underlying AI model. Instead, users typically must update them manually by retyping the instructions in each file, playing a "guessing game" as to what changes might improve agentic AI performance and reduce errors. SkillOpt , a new, open source ( MIT Licensed ) framework developed by Microsoft, does one better: it introduces an optimizer designed for agent skills, turning the agent's skill .md document as a trainable object that evolves based on performance feedback. It uses deep-learning-style optimization to make it possible for the AI to systematically explore modifications to the document and find the best combination of instructions. Most importantly, it accomplishes this procedural adaptation without making changes to the underlying model's weights. On various industry benchmarks, SkillOpt outperforms existing baselines, significantly boosting accuracy for models like GPT-5.5 and Qwen. The result is a set of compact, transferable skill artifacts that allow AI agents to adapt to new domains effortlessly. The challenge of optimizing agent skills Agent skills package procedural knowledge into natural-language specifications, including domain heuristics, tool-use policies, output constraints, and known failure modes. These skills provide an external interface for agents to adapt to complex enterprise workflows. In practice, agent skills are stored as text documents and inserted into the agent's context before execution. One of the key benefits of skills is that they customize the behavior of the underlying model without changing its weights. However, the skill document itself needs to be tweaked and optimized to get the best performance out of the agent. While deep learning relies on strict mathematical controls for stability, human prompt engineering often relies on trial and error. When attempting to automatically update a skill document based on feedback, the lack of mathematical discipline makes text highly volatile. Yifan Yang, Senior Research SDE at Microsoft Research Asia, told VentureBeat that the problem is not making changes, but ensuring those changes are mathematically sound. "The breaking point isn't whether a team can change a skill, it's that they can't guarantee the change is an improvement," Yang said. "Three failure modes recur: no step-size control, so skills drift; no validation, so a fix that reads as reasonable gets written in and can quietly regress performance; and no negative memory, so the same failed edit keeps coming back." To illustrate how easily performance can drop when edits aren't mathematically validated, Yang noted that "an ungated rewrite pushed GPT-5.5 on SpreadsheetBench from 41.8 down to 41.1." According to Yang, these failure modes are amplified in multi-step workflows "because that's where frontier models are weakest zero-shot. Not on reasoning, but on procedural discipline: format, self-verification, tool policy." Before SkillOpt, agent skills were primarily hand-crafted, generated in a single shot, or evolved through loosely controlled self-revision pipelines that could not reliably improve under feedback. Prompt optimization methods like TextGrad and GEPA treat language artifacts as optimizable objects and use trajectory feedback to evolve prompts, but they focus on single-prompt configurations rather than generating persistent, reusable skill artifacts. Meanwhile, skill evolution and discovery methods like EvoSkill and Trace2Skill convert agent execution experiences into trajectory lessons to refine skill folders, build domain-specific libraries, or perform evolutionary search. None of them apply deep-learning-style controls, such as learning rates, validation gates, and momentum, which are necessary to continuously train a single, compact skill document. Importing mathematical discipline to text SkillOpt optimizes a text document through an iterative propose-and-test loop that separates the model executing the tasks from the model optimizing the skill. The process unfolds in several steps: SkillOpt starts with an initial skill document and a frozen target model (or harness), where the target model runs a batch of tasks to generate execution trajectories that act as the evidence for the current step. An offline optimizer model analyzes these trajectories, separating successes from failures into minibatches. Looking at a minibatch helps the model identify systematic procedural errors rather than one-off anomalies. Based on these patterns, the optimizer proposes structural add, delete, or replace edits to the skill document. The proposed edits are reviewed to filter out duplicates or contradictions, and the optimizer then ranks these candidate edits by their expected utility. Rather than applying all proposed changes, SkillOpt clips the list to a maximum edit budget for that step, generating a candidate skill. The candidate skill is evaluated on a held-out validation set using the target model. If the candidate improves the validation score, it is accepted and becomes the new current skill. If it fails, the edits are rejected and sent to a rejected-edit buffer, providing negative feedback so the optimizer knows not to repeat that mistake. SkillOpt directly addresses the problem of treating text as a trainable object by importing mathematical concepts from deep learning. The creators note that “the deep-learning analogy is operational rather than decorative,” helping the framework avoid the instability issues associated with other optimization techniques. The edit budget acts as a learning rate. By limiting how many edits can be applied at once, the skill version is prevented from moving too far from its previous state, preserving continuity while allowing new procedures to be acquired. Just like checking validation loss in deep learning, the strict held-out examples ensure that plausible-sounding text edits are only kept if they mathematically improve the agent's actual performance on the validation split. At the end of an epoch, SkillOpt performs a slow update by comparing tasks under the previous and current epoch's skills. This acts like a momentum term, carrying durable, long-horizon procedural lessons forward while isolating them from the fast, step-level edits. SkillOpt in action To evaluate the technique in practice, researchers tested SkillOpt across different models, ranging from large-scale frontier models like GPT-5.5 to smaller closed and open models including GPT-5.4-mini and Qwen3.5-4B. They also deployed the skills within different execution harnesses, using plain chat as well as complex coding harnesses like the Codex CLI and Claude Code. The evaluation spanned diverse industry benchmarks including single-round question-answering, multi-round code generation involving tool use, and multimodal document reasoning. SkillOpt was measured against multiple baselines ranging from a default no-skill setting to human-written skills and one-shot LLM-generated skills. It was also compared against advanced prompt-optimization and skill-evolution methods, specifically Trace2Skill, TextGrad, GEPA, and EvoSkill. SkillOpt dominated across the board, proving highly effective on all 52 evaluated combinations of model, benchmark, and harness. It was particularly effective with frontier models, delivering an average absolute improvement of +23.5 points against the no-skill baseline on GPT-5.5. Furthermore, SkillOpt outperformed a hypothetical oracle baseline that cherry-picks the best competing method for every problem. Small target models saw immense relative gains, proving that a compact text file can supply procedural knowledge that small models lack in their weights. For example, GPT-5.4-nano nearly doubled its score on multimodal document QA and tripled its score on embodied interaction and sequential decision-making. These academic benchmarks map to critical enterprise pain points. Zero-shot models often hallucinate formatting or fail to use tools properly in multi-step scenarios. Yang explained that the biggest performance leaps occurred in operations that enterprises historically struggle to automate reliably. "Document data extraction... exact figures out of contracts, invoices, and forms — AP automation, claims, compliance," Yang said. "What improves is reliability: precise formatting, self-verification, auditable outputs. And the gains come from learning procedure, not memorizing answers." For enterprise practitioners, the true value of SkillOpt lies in its portability, efficiency, and compatibility with existing infrastructure. Experiments confirm that the framework is harness-agnostic. In addition to basic chat, the same optimization loop was successfully integrated into tool-backed execution environments like the Codex CLI and Claude Code with significant gains on industry benchmarks. Developers can train a skill using one execution loop and deploy it in another. For example, a spreadsheet skill trained entirely inside the Codex loop was moved directly into Claude Code and drove a +59.7 point gain over Claude Code's native baseline without any further changes. SkillOpt artifacts also transfer cleanly across model scales. A skill optimized for GPT-5.4 was deployed onto the smaller GPT-5.4-mini and GPT-5.4-nano models with positive gains, proving that the learned procedures encode reusable workflows rather than just exploiting quirks of a specific model's architecture. Finally, the framework is highly efficient regarding token usage and context window real estate. Across all benchmarks, the final deployed skills never exceeded 2,000 tokens, with a median length of roughly 920 tokens. This results in highly readable, auditable artifacts that a human practitioner can review and manage in minutes. Implementation strategies and the enterprise 'catch' For enterprise tech leaders, adopting a new framework requires understanding the overhead and limitations. While the research paper notes that training tokens can reach up to 210 million for academic benchmarks, the reality for day-to-day enterprise use cases is much lighter. The high token counts in testing were largely due to re-scoring massive held-out test sets. "The real upfront work is the verifier and a representative held-out split. The optimizer is light; the evaluation harness is where the engineering goes," Yang said. He added that for everyday use, "in community frameworks like GBrain, where SkillOpt updates run on Claude Sonnet, training a skill for a single task averages just $1–5." This optimization cost is a one-time fee that amortizes completely at deployment. However, the framework requires specific conditions to work effectively, namely a few dozen representative examples and a scorable feedback signal. Teams should avoid applying SkillOpt to open-ended or subjective tasks. "With no clean automatic scorer you have to design a human- or model-based evaluator and watch its stability," Yang said. SkillOpt also integrates smoothly with existing orchestration stacks, removing a major adoption hurdle. For instance, developers already using pipeline compilers can run both systems harmoniously. "DSPy is a different, complementary layer," Yang said. "It compiles declarative LM pipelines and optimizes program structure; SkillOpt optimizes the external skill state a frozen agent loads. You can run them together." Looking ahead, open-source developers are already scheduling SkillOpt to run periodically over their agents' past trajectories, creating a small ecosystem of self-optimizing code-agent plugins. This continuous feedback loop represents a significant shift in how AI systems adapt. "The valuable version of self-improvement is an agent autonomously discovering knowledge to improve its own behavior and the user experience, under verification and audit," Yang said. "Skills are the fastest, cheapest, most reversible first step, and the same mindset points toward agents eventually optimizing themselves, all the way down to their own weights."
- Meta earmarks $115M for workforce academy to support data center construction
The program will provide free skilled-trades training, industry credentials and guaranteed jobs as the company expands its AI infrastructure footprint.
Score: 58🌐 MovesJun 11, 2026https://www.constructiondive.com/news/meta-workforce-academy-data-center-construction/822698/ - Tesla’s Robotaxi Falls Short With Long Waits and Stalled Rides
When Elon Musk kicked off Tesla Inc.‘s quarterly earnings call in July 2025, robotaxis were top of mind. The carmaker had just launched its automated ride-hailing service several weeks earlier in Austin, to glowing early reviews from the hand-picked crop …
Score: 58🌐 MovesJun 11, 2026https://www.insurancejournal.com/news/southcentral/2026/06/11/873480.htm - Map Shows Where Texas Facing Greatest Data Center Demands
Map Shows Where Texas Facing Greatest Data Center Demands Newsweek
Score: 58🌐 MovesJun 11, 2026https://www.newsweek.com/map-where-texas-greatest-data-center-demands-12058983 - Apple's Craig Federighi: Siri Won't Be Your AI Girlfriend
Apple software engineering chief Craig Federighi and marketing chief Greg Joswiak sat down for an interview with Mostly Human after during WWDC, discussing the iOS 27 Siri changes , Apple's take on AI, new child safety protections, and more. Apple set out to deliver an AI utility, not an AI companion. When asked whether users could create an AI boyfriend or girlfriend with the new Siri , Federighi said absolutely not. Siri is meant to help, and Apple didn't want to focus on engagement like other AI companies. From Federighi: Quite the opposite, because as you may know, if you use many of the existing chatbots, they're really focused on engagement to a large degree. And sycophancy, right? They kind of want to pull you in. They might encourage you to reveal things about yourself, and then use that as a basis to establish a connection. We view it quite the opposite. I mean, the way that we have designed Siri, Siri really wants to say 'Listen, that's not what I'm here for, right? I'm here to help you. I can help you get things done. I can help you learn about the world.' But if you try to engage Siri as a romantic partner, Siri's not up for that. Siri's 100 percent not into that. Joswiak said Apple didn't want to do AI for AI's sake, and the company wanted AI to blend in with existing iPhone features. We like when technology disappears, right? You just focus on what you want to do, or you focus on the content. And it's the same thing with AI. [...] We don't do AI for AI's sake. 'Hey, look at us, we're doing AI.' It's how does AI make everything better? And that makes our products better, our features better. He went on to say that he doesn't want iPhone users to have to be "prompt experts" to use AI. "We want to meet them where they're at," said Joswiak. "Have the products and features become better, and this is just a really helpful technology in making those features and products better." Federighi wanted to make it clear that Apple's approach to AI is privacy forward. I think it's a challenging thing for a lot of people to understand the distinction between what your iPhone knows and what, say, Apple as a company knows. Your iPhone is yours, right? Your data is yours and it stays on your phone and your control and Siri is using it for you. Apple doesn't get to know any of this stuff, and that is very different than I think most players in the space, and I think super important. The full interview covers other topics like child safety, AI and jobs, iOS 27 features, Apple's 50th anniversary, the future of AI, scammers, and much more. Tags: Craig Federighi , Greg Joswiak , Siri This article, " Apple's Craig Federighi: Siri Won't Be Your AI Girlfriend " first appeared on MacRumors.com Discuss this article in our forums
- STARCHIUM Showcases ‘ArchiPilot’ to Delegation of EU Ambassadors, Presenting Blueprint for European Expansion
STARCHIUM Showcases ‘ArchiPilot’ to Delegation of EU Ambassadors, Presenting Blueprint for European Expansion azcentral.com and The Arizona Republic
- Why AI that works in the lab often fails in production — and what actually fixes it
Presented by Capital One Enterprises aren’t struggling to experiment with AI; they’re struggling to make it work in the real world. Moving from promising prototypes to reliable, production-scale systems is where most efforts stall. In my role within Capital One’s AI Foundations organization, I’ve seen firsthand that successful AI implementation isn’t just about adopting the latest models or tools. It requires a disciplined R&D approach that connects foundational research to real-world systems, and holds ideas accountable as they move from concept to production. That’s harder than it sounds. AI capabilities are evolving quickly, but enterprise environments can be complex, fragmented, and risk-minded. The question isn’t just what’s possible, but what actually works — for a specific workflow, user, or decision — with today’s technology and constraints. What follows reflects how organizations can turn AI ambition into production reality through a more deliberate approach to research, evaluation, and deployment. Bridging foundational and applied research Delivering impactful AI requires closing the gap between cutting-edge research and practical, real-world use cases. When research exists in an academic vacuum, untethered from operational reality, models that may perform well in an offline environment often fall short when faced with real-world latency requirements and the complexity of live production data. Without a tight feedback loop, it’s easy to lose sight of what actually moves the needle for the end user. Our AI teams are intentionally designed to span the spectrum from foundational research to highly applied problem-solving, addressing these friction points before they stall a project. This integrated model brings research and application together under one umbrella, creating space to explore underlying technology while staying grounded in actual business and associate needs. When foundational research and applied development are connected by design, you can accelerate learning, avoid dead ends, and account for real-world constraints early on. At Capital One, this approach has helped us to tackle challenges that are core to financial services, including improving fraud detection, enhancing digital user experiences, and improving customer-first technologies leveraging proprietary AI solutions. For example, our research into combining multi-agent architectures goes beyond simple LLM reasoning; it aims to enable specialized AI agents to coordinate across distinct tasks, such as researching customer context and preparing documentation simultaneously. This research supported the launch of Chat Concierge , a car-buying solution that mimics human reasoning to not simply provide information, but take action on customers’ behalf based on their requests. We’re also breaking ground in delivering state-of-the-art solutions in agent servicing, AI personalization, and more. By keeping research tethered to the use case, we can accelerate state-of-the-art breakthroughs that actually scale in the real world. Moving AI from concept to production Not every AI idea should go straight to production. Rigorous evaluation from proof of concept to pilot to production is essential to determining what’s truly worth scaling, but only if those stages are treated as honest hurdles. Some considerations include: A proof of concept must be functional, not just theoretical. It shouldn’t be a “here’s what we could do” slide deck. It must be a machine actually doing something measurable. Even at this stage, you need an objective signal that the work is worth continuing. A negative pilot result isn’t a failure. If pilots always “succeed” by definition, then they aren’t functioning as decision points—they’re just a slow-motion commitment to production. A pilot should expand scope and realism, providing valuable data on whether a solution actually helps a human do real work. Production is a team sport. Solving the core model or algorithmic problem is only part of the job. Moving to production requires a cross-functional reality involving software engineering, science, product and design, technical program management, operations, and other disciplines across an enterprise. The technical breakthrough is necessary, but it’s not the end of the work. Throughout this journey, measurement is an important input. At Capital One, the ultimate ROI is a happy customer so we focus on a number of key AI performance indicators like accuracy,latency,, and more to ensure we’re meeting the moment for our customers. If you can’t tell whether you’re improving, then you won’t. Prioritizing accuracy over opticsis what enables continuous improvement and progress. Enabling continuous learning and responsible innovation Sustainable AI innovation depends as much on culture as it does on technology. Because research involves exploring the unknown, uncertainty is normal. A healthy culture recognizes that reality and creates space for informed risk-taking, paired with accountability. Organizations must encourage course-correction. If acknowledging “this isn’t working” is treated as a disaster, teams will learn to hide problems rather than solve them. But if teams are encouraged to evaluate honestly, pivot when needed, and learn from false-starts, then the organization can move faster and safer at the same time. That means treating pilots as real decision points — stopping, reshaping, or narrowing efforts based on what the data shows, rather than pushing them forward by default. At Capital One, we enable teams to try ambitious things, learn quickly, and build an ecosystem that works to ensure AI is useful, reliable, and safe. Final thoughts Building impactful AI isn’t about chasing every new breakthrough. It’s about thoughtfully guiding ideas from research to reality through evaluation, collaboration, and a culture that embraces learning. As AI continues to evolve, leaders should invest not only in tools, but also in R&D processes and cultural foundations that allow innovation to scale responsibly. When you bridge research and application, prioritize continuous evaluation and measurement, and foster environments where teams can learn and adapt, you give AI its best chance to deliver lasting impact, at enterprise scale, in the real world. Liz Boschee us VP, AI Foundations at Capital One. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com .
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- PIMCO’s Richard Clarida: AI Now a Major Economic Driver
Richard Clarida, Managing Director and Global Economic Adviser at PIMCO and Former Vice Chair of the Federal Reserve Board of Governors, discussed the significant impact of artificial intelligence (AI) on the economy and markets over the next five years. He emphasized AI as a potential disinflationary force due to increased productivity and possible wage compression, while also noting the financing risks associated with AI investments. He speaks with Romaine Bostick & Katie Greifeld on "The Close." (Source: Bloomberg)
Score: 55🌐 MovesJun 11, 2026https://www.bloomberg.com/news/videos/2026-06-11/pimco-s-richard-clarida-ai-now-a-major-economic-driver-video