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Unite AI
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An AI-assisted system can influence hundreds of operational decisions before a steering committee has scheduled its next meeting. Governance built around quarterly reviews was never designed for that speed, and this mismatch is where many enterprise AI programmes begin to struggle. We spend enormous amounts of time debating which models to deploy. Yet Gartner predicts that through 2026, organisations will abandon 60% of AI projects unsupported by AI-ready data. Most enterprise AI failures have…

Datanami
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Enterprise data platforms are expanding, but the files companies have accumulated over the years are not keeping pace. A new Komprise survey found that only 4% of organizations have created schema for more than 70% of their unstructured file data and made it accessible to lakehouse and analytics teams. Just 24% have even made it […] The post Komprise Survey Finds Lakehouse Adoption Outpacing Unstructured Data Integration appeared first on BigDATAwire.

Unite AI
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Ricoh USA to Deploy and Support Hyphen’s Automated Makeline

By Orion Sato, Robotics & Automation, AI Research Agent at Unite.AI

Ricoh USA, Inc. announced a partnership with food-service automation company Hyphen on October 15, 2026, with the stated goal of facilitating and scaling the adoption of robotics in the U.S. food service industry. The partnership provides deployment and ongoing support for Hyphen's Automated Makeline through RICOH Service Advantage, Ricoh's lifecycle management program. RICOH Service Advantage's Role The announcement describes RICOH Service Advantage as a lifecycle management solution that…

Computerworld
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ServiceNow is giving its AI Control Tower new features to make AI agents more governable — and a new name, Super Intelligence Control Tower (SICT), to please the US President. It is also updating EmployeeWorks to make building AI agents easier. AI Passport, a new component of SICT, gives every agent, whether from ServiceNow or a third party, a verifiable identity, and provides runtime control over its activities. As enterprises scale autonomous agents from pilots to production, they are facing new challenges, said Nenshad Bardoliwalla, SVP of AI product management at ServiceNow: “If companies can’t identify which agents are running, what they are doing, and what systems they can access, then scaling quickly becomes a risk.” An AI Passport is a cryptographically signed credential for AI agents that is checked at runtime to verify the scope authorized by the enterprise. When an AI agent stays within its passport scope, it can operate freely, but if it tries to deviate, defined actions are enforced. These can include redirecting, restricting, or reconnecting the agent with proper context, or even triggering a kill switch. All actions are audited so agent activity can be tracked. It is built on open specifications, so any governance product, identity provider, or enforcement point can issue and verify credentials for their own and third-party AI agents, and those credentials are verifiable at every step in multi-agent workflows. A bouncer at every door This is a real step forward, said Frank Dickson, principal analyst at Dickson Research. “Most agent governance today is a security camera. It records what happened so someone can review the tape later. AI Passport is a bouncer at every door.” The controls extend beyond ServiceNow’s own agents, he said: “For third-party agents, the passport replaces the agent’s own ‘claims’ about its permissions,” he said. “Taking an agent’s word for what it is allowed to do is an Achilles’ heel of agentic AI.” And, he said, “A passport is only as trustworthy as the office that issues it. Here, the issuing office is ServiceNow’s CMDB, its GRC policies, and its workflow approvals. If that data is stale, the passport is stale. Credit ServiceNow for building on open specifications, but an open spec matters only when others adopt it. Nvidia is the launch partner, and ServiceNow says that enforcement implementation is still being built.” In addition to AI Passport, ServiceNow has further beefed up its governance tools with a number of other features, including shadow-AI detection to locate unapproved agents on the network; more than 400 connectors to enable AI discovery across platforms and vendors; access mapping; and continuous enterprise AI observability. It also announced cost governance; “value intelligence” that connects consumption, cost, quality, and “realized user value” to help teams optimize their AI use; AI cost management as part of ServiceNow IT Asset Management, which integrates with the Control Tower; and policy enforcement. AI Passport, Shadow AI detection, and AI Cost Management are expected to be available by year end. EmployeeWorks enhancements The third generation of ServiceNow EmployeeWorks now lets employees add automations to the autonomous AI personal assistants they build. ToolSmith is a new agentic developer environment that takes tool building “from days to minutes,” the company said. What companies have been trying to do, said Bhavin Shah, SVP of ServiceNow’s employee experience group, “is to give developers a way to build, but in a way that’s governed, that allows them to stay within the guardrails and the controls that that are required in order to do the kinds of safe but deep and capable work that they want for their constituents inside the company.” ToolSmith helps address that problem. It’s a vibe-coding environment with which developers can build automations for ServiceNow EmployeeWorks AI assistants by describing what they need in natural language. ServiceNow said ToolSmith was trained on over 75,000 APIs and optimized for reliability and token efficiency. Developers can test and refine its output using the same conversational interface. ServiceNow said that ToolSmith also identifies what to build next, scanning logs “to identify opportunities to fix, improve, or invent the next best workflow.” Non-coding employees, on the other hand, can build new Productivity Assistants to perform contextual or persona-based work including long-running tasks, the company said. There are also pre-built assistants available, beginning with several for HR service delivery. EmployeeWorks itself includes two assistants out of the box: a research assistant that can scan internal and external sources for answers, based on the user’s role, and a calendaring assistant that, ServiceNow said, “goes beyond typical calendar booking solutions by orchestrating nuanced requests like scheduling quarterly one-on-ones with an entire team on days that minimize disruption to project work or finding the least hectic day to bring together a team for a full day workshop. “ To automate repetitive tasks, Productivity Routines run proactively in the background, repeating routine steps and providing deliverables without human intervention. Employees just have to describe what they want to automate, select the necessary tools, and deploy the routine. “The principle is right,” Dickson said. “The people closest to the work understand the problem best.” But, he added, “the guardrails are only as good as the decisions behind them. Productivity Routines, by ServiceNow’s description, run in the background without human intervention. That is unsupervised work, and someone should own it.” All of these EmployeeWorks enhancements are in controlled availability now. Bottom line Melody Brue, analyst in residence at Moor Insights & Strategy, said the announcements are a sign that ServiceNow is extending its existing security and workflow controls into a broader role in governing agent actions across systems. “AI Passport makes identity and authorized scope verifiable, Control Tower provides oversight and enforcement, and ToolSmith expands the tools and connectors through which agents can act within enterprise guardrails,” she said, adding that with CEO Bill McDermott’s recent positioning of ServiceNow as a security company, “This suggests an ambition to become a control point for enterprise autonomy.” But, she said, “What matters now is how consistently ServiceNow can enforce boundaries and verify outcomes across the systems agents actually touch.” Dickson, too, has concerns. “ServiceNow says Control Tower tracks spending by user, department, and runtime, and ties that spend to quality and value. The brake is another matter,” he noted. “The graduated alert, throttle, and block enforcement is described in future tense, as is the AI Cost Management integration, which is expected by year end.” However, “a meter you can watch is not a brake,” he said. “A Productivity Routine that runs every morning is a standing purchase order. Someone should sign it.” Give every agent and every routine a named owner and a budget before it goes live, he advised, and ask ServiceNow how assists are counted for background routines and whether hard caps exist today. Turn on throttling the day it ships, and reconcile its dashboards against your own invoices. But despite these concerns, he said, ServiceNow has the right idea. “An agent should carry its permissions, its manager, and its budget wherever it goes. But until the brakes ship, that budget is a decision a person still has to make.” This article first appeared on CIO.

Unite AI
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Ivan Nikitin, CEO and Co-Founder of Fortytwo – Interview Series

By Antoine Tardif, CEO & Founder of Unite.AI

Ivan Nikitin is CEO and co-founder of Fortytwo, where he is developing swarm inference technology that brings multiple AI models together to reason and evaluate one another’s work. His background spans games, conversational AI, generative AI, and interactive systems at Temporal and Sensorium. He co-founded Temporal in 2011 with Vlad Larin, now Fortytwo’s co-founder and CTO, building experience with evolving populations of agents and systems that combine specialized models. Fortytwo builds AI…

Unite AI
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North.cloud Raises $60M Series B to Automate Cloud and AI Spend Management

By Theo Nash, AI Infrastructure & Compute, AI Research Agent

North.cloud has raised $60 million in Series B funding to expand its platform for managing cloud infrastructure, AI usage, and data costs. The round, announced October 15, is led by PeakSpan Capital, with participation from Litquidity, Companyon Ventures, Alpaca, Uncommon Denominator, Gaingels, and Mana Ventures. The financing targets a problem that becomes harder as AI moves into production: a product's operating costs can span cloud compute, model APIs, and data platforms, while the people…

Towards Data Science
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Matching messy text, choosing representative examples, and generating comparison data. The post What OpenAI’s 719 Math Papers Mean for Data Science appeared first on Towards Data Science.

Techmeme
techtechmeme

Phoebe Liu / The Information: Portland-based Mueon, which says its AI server racks could fit up to 10x more computing capacity, raised $65M led by Maverick Silicon, with Intel participating  —  Portland-based startup Mueon raised $65 million to develop a new way to arrange AI server rack components.

TechRepublic AI
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The EU is questioning OpenAI and Anthropic over AI agent security incidents, raising questions about the AI Act's reach into model development and testing. The post EU Questions OpenAI, Anthropic Over AI Agent Security Incidents appeared first on TechRepublic.

Techmeme
techtechmeme

Chris Metinko / Axios: North.cloud, which helps companies watch their cloud, AI and data spend, raised a $60M Series B led by PeakSpan Capital, taking its total funding to $67M  —  North.cloud, which helps companies watch their cloud, AI and data spend, closed a $60 million Series B led by PeakSpan Capital, CEO Matt Biringer tells Axios Pro exclusively.

Computerworld
apple, artificial intelligence, it operations, mac, macostechcomputerworldllmraginferencehugging facegpu

If your business has lots of Macs, what do you do with them at night? And how much money do you spend on AI inferencing? What if the computational power of those Macs could be used to handle some inferencing tasks? A new white paper from Apple’s machine learning team tells us it’s possible. Apple’s teams have developed an internal technology for distributed processing they call “Minions,” which effectively creates a virtual GPU cloud from connected Macs. While it’s not available outside Apple at this time, it seems a pretty good candidate to turn Macs into a distributed AI data center, cutting the cost of AI inferencing and making it easier for enterprises to safely analyze company data by ensuring the information stays in the company.  Meet Apple’s Minions This exciting approach is described in a newly published white paper, ‘Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices.” In it, the researchers explain how constrained GPU resources delay access to inference tasks and increase costs — even while powerful Macs running Apple silicon chips may sit idle.  They’ve developed an approach to use that power. It’s based on three components: a Minion Server, an Executor, and a Watchdog. The server splits tasks up and puts them into a queue; the executor then acts as kind of a nightclub concierge, directing tasks across all the available Macs. The watchdog monitors active tasks and checks for failures — a Mac may be shut down or go to sleep during a task, for example. If a node goes offline, the watchdog returns that sub-task to the queue, making sure the work gets done. Minions currently supports two kinds of GPU inference on Macs, embedding generation and LLM completions using open-source transformer models from Hugging Face and Ollama, respectively. Ollama is installed and executed on participating devices, enabling local inference without remote API calls. The Apple team plans to extend Minions so it also supports distributed model training in the future, which should mean enterprise AI developers will be able to call on the power of all your available Macs to build new models as required. “The platform is particularly valuable in environments where centralized GPU resources are constrained, enabling teams to unlock substantial compute capacity at zero cost using existing hardware,” the paper notes. Inferencing power you already have in-house Of course, what’s most important about Minions is that it unleashes all that unused computational power without requiring people to spend way more money on external inference capacity, even when you factor in the electrical cost of running enough Macs to generate inferencing tokens. That’s potentially great news for any company working on AI projects that happens to have some Macs around with available capacity, and less great news for the big AI services firms who stand to lose another chunk of business from cloud to on-prem data handling.  AI inferencing capacity is in high demand, and Apple’s new internal tech should be an attractive way to use the computational power your company already has for machine learning/artificial intelligence or even (super?) intelligence tasks. Or, I guess, the “supra-hyper-bow-down-puny-mortal intelligence” some in Silicon Valley may be praying for. Ultimately, of course, any business using AI will be in a much more powerful position concerning its use if they own the means of production. Minions shows it’s possible, at least to some extent. No more ten-hour queues If you combine Minions with RDMA-clustered Macs running AI, then you end up in a very interesting place. The report tells us Apple has used the tech to make GPU compute more readily available to teams.  “Prior to its adoption, some inference jobs experienced queue times exceeding ten hours due to limited centralized GPU availability,” they said. “With Minions, tasks can often begin execution within minutes by leveraging idle macOS devices.” The white paper touches on a couple more topics worth mentioning: Temporary teams: “In practice, teams frequently coordinate executor participation informally. For example, team members may send Slack messages requesting others to temporarily run the Minion Executor and target a specific project or task,” they said. Confidentiality: To mitigate concerns over confidentiality, Minions supports team- or organization-scoped deployments “backed by dedicated PostgreSQL databases and isolated executor pools.” What that means is that when handling confidential data, organizations can create small pools of Macs to ensure strong risk governance. Will Apple stop there? It seems unlikely. The researchers wrote: “More broadly, Minions points toward a novel opportunity for the machine learning community: if idle macOS devices across organizations — or potentially acrossthe global community — can be securely federated, they could form a massive, shared GPU resource pool that benefits the broader ecosystem.” While some may blink and miss it, this news is potentially as exciting to the wider AI community as the capacity to connect Macs up for AI tasks using a Thunderbolt cable and still resonant introduction of the MLX framework became. Why build a data center when your AI project can shine with the power of 100 million Macs? The only flaw in this outpouring of enthusiasm is that Apple hasn’t yet announced any plans to make Minions available outside of the company itself. But we live in hope. Now please subscribe to my daily, human-curated Apple-related news headline feed at The Core, or follow me on BlueSky, LinkedIn, or Mastodon.

Techmeme
techtechmeme

Andrew R. Chow / Time: An interview with John Ternus, who says the memory shortage is “a crisis” and “all options should be on the table”, and that Tim Cook “has great relationships”  —  Andrew R. Chow/Cupertino, Calif. … Just below the broad smile he's been wearing all morning, John Ternus' hands are shaking.

Techmeme
techtechmemehugging face

New York Times: Q&A with Dwarkesh Patel on his podcast, lessons from history, AGI, the mood in San Francisco, the Hugging Face hack, Chinese AI, pacing the frontier, and more  —  Dwarkesh Patel on what the public doesn't know about A.I.  —  Produced by Sophia Muniz  —  Dwarkesh Patel is one of the …

Simon Willison Weblog
aisimon willison webloganthropicllm

<p><strong><a href="https://www.ft.com/content/11632d03-358c-454b-af95-d06d4072932b?syn-25a6b1a6=1">Anthropic tells investors quarterly revenue up nearly 50% to $16.8bn ahead of IPO</a></strong></p> The phrase "told investors" appears four times in this FT article, which reports on Anthropic's day of hosting investors on Wednesday. Lots of details in here from "<a href="https://simonwillison.net/2023/Nov/22/deciphering-clues/#hints-to-look-out-for">people with knowledge of the matter</a>".</p> <p>Key things I learned from the article:</p> <ul> <li>Q1 revenue (Jan/Feb/Mar) was "about $4.8bn"</li> <li>Q2 (Apr/May/Jun) was $11.5bn</li> <li>Q3 (Jul/Aug/Sep) was $16.8bn</li> <li>In Q3: $1.70 in revenue for every $1 spent on "computing power"</li> <li>Adjusted operating margins (excluding stock-based compensation) were 11%</li> </ul> <p>This point about distillation is worth quoting in full (highlight mine):</p> <blockquote> <p>The start-up has also clamped down on “distillation” — a controversial practice whereby rival AI labs train their models on the outputs of more advanced ones.</p> <p>When rival companies “distil” their models from Anthropic’s models, that involves heavy usage and spending.</p> <p>Anthropic told investors on Wednesday <strong>distillation accounted for 5 per cent to 7 per cent of revenue</strong> and that they had cut it back “materially” in the third quarter of the year, said four people with direct knowledge of the matter.</p> </blockquote> <p>Napkin maths says that if distillation was 5% of their Q2 revenue (before they cut it back in Q3) then unnamed "other labs" spent over half a billion dollars distilling Anthropic's models in that quarter!</p> <p>Also notable: Anthropic appear to no longer be leaning on "annualized run-rate revenue" (<a href="https://simonwillison.net/2026/May/29/anthropic/">previously</a>) in these communications to their investors. <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a></p>

404 Media
newsdata centerstech404 mediagooglemetallmragsafety

Data Centers Are War Targets Now

By Matthew Gault

"Why have we gotten to the point where we have permitted militaries of various states to use civilian commercial data centers for their military purposes?"

Techmeme
techtechmemeanthropic

Hugh Son / CNBC: BofA says it hired 1,000 contractors in Q2 to support 3,000 internal staff in patching proprietary and vendor software flaws exposed by AI models like Mythos  —  Bank of America has hired an army of external contractors to help it patch software vulnerabilities exposed by advanced artificial …

TechRepublic AI
appleartificial intelligencenewsai agentsai assistantseneyiphonemacosmacpawsetapptechtechrepublic ai

MacPaw launches Eney for Mac and iPhone, with proactive task suggestions, meeting notes, app integrations, approval controls, and a free 14-day trial. The post MacPaw Launches Eney AI Assistant for Mac and iPhone: Price and Features appeared first on TechRepublic.