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Artificial Intelligence

Jeff Bezos Wants to Build an ‘Artificial General Engineer’Cade Metz | The New York Times ($)

“‘All societal wealth is driven by invention,’ [Bezos] said in an interview with The New York Times. ‘Six thousand years ago, somebody invented the plow, and we all got wealthier. Then, much later, somebody invented the steam engine, and we all got wealthier.’ …’What Prometheus seeks to do,’ he added, ‘is to offer a set of tools that dramatically accelerates that invention loop.'”

Computing

Why Orbital Data Centers Are Harder Than Silicon Valley ThinksAndrew Cavalier | IEEE Spectrum

“Proponents tout the many wonders of computing in space: abundant solar energy, free cooling, and freedom from Earth-based disturbances like earthquakes, floods, and protesters. But a sober look at the physics of space-based computing paints a much more nuanced picture.”

Biotechnology

Longevity Startup Doses First Human in Bid to Reverse Age-Related Sight LossIsabella Ward | Wired ($)

“It is the first-ever cellular-rejuvenation therapy using this technology to receive FDA clearance to enter human clinical trials, and hence the first chance to test whether the technology can ‘ameliorate human disease,’ according to Life Biosciences cofounder David Sinclair, who is also a professor of genetics at Harvard Medical School.”

Future

AI Absolutism Is Breaking Our Brains. The Apocalyptic Future We’re Being Sold Isn’t InevitableSamantha Oltman | The Guardian

“Contradictory as they may be, all these arguments and anxieties fit neatly into the overarching message of the people building this technology: AI’s dominance is inevitable. Get on board or you will be left behind. …[But] the version of AI that we’re being sold doesn’t have to be the version we buy. Nor does it need to be the story we believe in.”

Energy

Commonwealth Fusion Makes the Physics Case for Its 400 MW ReactorJohn Timmer | Ars Technica

“According to our best models, developed using real-world data from multiple tokamaks, ARC should be able to regularly trigger fusion reactions that release more energy than we put into them. But there’s ‘working’ from a physics perspective, and ‘working’ from a market perspective. …the finances are going to be the hardest risk to retire and may require having ARC operate for decades before we have a definitive answer.”

Artificial Intelligence

Google DeepMind Is Worried About What Happens When Millions of Agents Start to InteractWill Douglas Heaven | MIT Technology Review ($)

“According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other agents creates a whole new class of risk.”

Future

Meta Deletes Face-Recognition System From Its Smart Glasses App After Wired ReportDhruv Mehrotra | Wired ($)

“One day after Wired revealed that Meta had quietly embedded an unreleased face-recognition system into an app installed on more than 50 million phones, the company removed it, according to a Wired analysis of the latest version’s code. …The version published the day of Wired’s report included several code libraries explicitly named for face recognition. Friday’s release includes none of them.”

Space

A Falcon 9 Booster Turns 5 Years Old—and Just Set a Remarkable Reuse RecordEric Berger | Ars Technica

“Since [SpaceX’s] Booster 1067 made its debut in June 2021, [ULA] has flown its workhorse Atlas V rocket a total of 22 times and the Vulcan rocket four times, and the Delta IV Heavy vehicle made its final three flights. So in the time that this single Falcon 9 first stage has flown and landed 35 times, its competitor company has made 29 total launches. Put another way, this rocket has put more mass into orbit than more than two dozen expendable rockets over half a decade of effort.”

Artificial Intelligence

Why Apple’s Slow-And-Steady AI Bet Is Starting to Look Pretty SmartLucas Ropek | TechCrunch

“In short, Apple is spending less, making more, and now launched a suite of AI features that—for many iPhone users—will feel indistinguishable from the other AI applications already available to them through the App Store. If that doesn’t exactly count as ‘winning the AI race,’ it may be the smartest way to run it.”

Future

Who Will Actually Thrive in the Hybrid AI-Human Work ForceStaff | The New York Times ($)

“The transformation that’s coming is going to take place in the world as it is familiar to us today, and every single day will feel familiar. And there’ll be tiny, tiny changes along the margin. There’ll be tiny bits of automation along the margins. And 10, 15, 20 years later, we’ll look back and we’ll say, My god, everything is different. But you’ll never notice it happening. That’s the way it always goes.”

The post This Week’s Awesome Tech Stories From Around the Web (Through June 13) appeared first on SingularityHub.

Vědecký tým úspěšně vyvrátil jihokorejskou teorii o zpomalování vesmíru • Jihokorejští experti totiž udělali zásadní metodické chyby v analýze dat • Temná energie tak nadále prokazatelně pohání zrychlené rozpínání vesmíru

Vláda USA nařídila společnosti Anthropic pozastavit přístup k modelům Fable 5 a Mythos 5 pro všechny cizince, včetně zaměstnanců Anthropicu.

Splunk has released security updates to address a critical security flaw in Splunk Enterprise that could be exploited to conduct unauthenticated file operations and even remote code execution. The vulnerability, tracked as CVE-2026-20253, is rated 9.8 on the CVSS scoring system. "In Splunk Enterprise versions below 10.2.4 and 10.0.7, an unauthenticated user could create or truncate arbitraryRavie Lakshmananhttp://www.blogger.com/profile/[email protected]

Kolegové ze slovenského Živé.sk zveřejnili rozsáhlou investigativu, pokračování (a pravděpodobně také epilog) ambiciózního čipového projektu Tachyum. Původní článek najdete zde, níže je s kosmetickými úpravami v češtině. Čipový startup Tachyum čelí návrhu na nucenou likvidaci, který na přelomu ...

The US government has ordered Anthropic to block all foreign nationals from accessing Fable 5 and Mythos 5, forcing the company to suspend both models worldwide. Anthropic is complying but disputes the basis, calling the cited jailbreak narrow and the capability widely available elsewhere. [...]

Vědci našli obří hlubinné pohřebiště velryb staré miliony let • Extrémní tlak a minerály dokonale zakonzervovaly jejich kosti • Gigantické ložisko neustále roste a sytí neznámé formy života

Doba, kdy byly digitální kompakty nejrozšířenějším typem fotoaparátu a jejich pořizovací cena byla podobná, jako je tomu dnes u mobilu střední třídy, je již dávno pryč. Na letošní rok však připadá jedno zajímavé jubileum: 30 let od uvedení prvního digitálního kompaktu Canon PowerShot.

Americká vláda nařídila firmě Anthropic, aby znepřístupnila dva své nejvýkonnější modely Fable 5 a Mythos 5 každému, kdo nemá americké občanství. Odkazuje se při tom na směrnici o kontrole exportu, kterou opírá o pravomoci v oblasti národní bezpečnosti. Nařízení samo o sobě nepožaduje kompletní ...

Vyšetřování tragické havárie indického Boeingu 787 nabírá zpoždění • Detailní analýzu motorů a řídicích jednotek provádějí experti v USA • Záznamy odhalily záhadné vypnutí přívodu paliva těsně po startu

Anthropic said on Friday it will "abruptly disable" its most advanced artificial intelligence (AI) models, Claude Fable 5 and Mythos 5, for all users after the U.S. government ordered it to suspend access to the models for foreign nationals, whether inside or outside the U.S., citing national security concerns. The AI company said it received an order at 5:21 p.m. ET, instructing it to suspendRavie Lakshmananhttp://www.blogger.com/profile/[email protected]

Společnost Murena představila (YouTube) novou verzi 4.0 mobilního operačního systému /e/OS (Wikipedie) založeného na Androidu a LineageOS bez aplikací a služeb od Googlu.

NanoClaw, a secure agent framework, has partnered with supply chain platform JFrog to allow AI agents to fetch resources from JFrog's reviewed registries. Gavriel Cohen, creator of NanoClaw and co-founder of NanoCo AI, announced the tie-up on Thursday evening in San Francisco at a JFrog event that concluded with a World Cup watch party. Cohen explained that one of the features of Claw agents – OpenClaw and variations like NanoClaw – is that they can improve themselves by fetching tools and resources that they don't have. That works fine, he explained, when there's a manual approval process for accessing known local data. But it's not ideal for npm packages, even when the agent involved is sandboxed and isolated as it is in NanoClaw. Malicious code within a container may still be able to take harmful actions, even if the scope of potential activity is constrained. Developers, Cohen said, may not be familiar with a given package and it can take time to thoroughly assess whether a package is legitimate and uncompromised. "So we teamed up with JFrog and we integrated NanoClaw with JFrog's registries," said Cohen. The arrangement provides a way to reduce the agent's exposure to untrusted content. When the agent downloads new tools and libraries, the software comes from a vetted source. Cohen also announced the availability of what he called an agent factory, his company's homegrown system used to handle pull requests (PRs) using NanoClaw agents. The agent factory, he explained, is an attempt to triage pull requests, which have surged thanks to AI coding agents. "It's very easy now to point a coding agent at a repo and say, 'open a pull request for this repo,'" he explained. "And it's very difficult as a maintainer to tell the difference between a high quality contribution from somebody who's really using the open source project versus someone who's just trying to build up the reputation [using automated methods]. So to help us tackle this, we built an agent factory that helps us review every single contribution to NanoClaw." The agent factory is referred to as the PR Factory in the actual pull request. It's built with NanoClaw and hosted on exe.dev, a service that provides VMs with persistent storage. "When a PR opens, the factory spins up a dedicated worker agent for it, posts a thread to Slack, and the worker triages the change, reviews the diff, and proposes a test plan," Cohen explains in the documentation. "Nothing consequential happens on its own: merges, test runs, and credentialed GitHub actions each surface as an approval card in the thread, and only fire when a human clicks approve." Cohen acknowledged that some developers will think it's madness to process unsanitized PRs that could contain prompt injections or unsafe code. And he asked the assembled audience of developers how many had seen the phrase on the projected slide: "Never, ever, ever do this." Anyone who has spent time using and configuring AI agents in a development context has seen something of the sort in configuration files like Claude.md, which gets loaded as instructions to the underlying agent and model. "If you see something like this in the Claude.md file and the agent instructions say, 'Important: Never run drop database production,' it tells you two things. You know that that agent has deleted a production database before. And you know that it can actually still do it again. That's why the instruction is there." This elicited a knowing laugh from the audience. Cohen went on to say that the agent will do it again because instructions are not a way of enforcing security or safety. "Instructions help steer an agent AI towards valuable output, but it's not a safety mechanism," he said. "The only way to reliably prevent an agent from taking undesired action is not allowing it to take that action, not giving it the ability to take the action." That is the purpose of NanoClaw. ®

Ucelený přehled článků, zpráviček a diskusí za minulých 7 dní.

Pětatřicet kilo těžký humanoidní robot Pemba vyrobený čínskou společností Unitree Robotics stanul na vrcholu ekvádorské sopky Chimborazo. V plánu má výstupy i na další hory včetně Mount Everestu. Vše bez kyslíkového přístroje.

Pozoruhodný systém komunikace mezi ptačími rodiči a jejich nevylíhlými potomky slouží jako účinná ochrana nové generace před ničivými vedry.

Černé díry jsou sice ikonické objekty astrofyziky, ale se všemi svými absurdními nekonečny jsou v mnoha ohledech přítěží. Dvojice fyziků z Frankfurtu nabízí jako alternativu gravastary, které obsahují kromě běžné i temnou hmotu. Zhroucení do černé díry v nich zarazí jejich vlastní malý Velký třesk a vznik minivesmmíru, jehož rozpínání stabilizuje gravastar.

Extremely powerful large language models (LLMs) still operate as though they’re typing on a keyboard, processing workloads in a simple left-to-right fashion. But in locally-run, single-user scenarios, this sequential processing can leave graphics processing units (GPUs) and tensor processing units (TPUs) underutilized.

Google is betting that DiffusionGemma can get around this bottleneck. The new experimental open model generates text “exceptionally fast,” creating entire blocks of text simultaneously through diffusion techniques rather than through token-by-token processing. The company says this technique results in 4x faster inference compared to auto-regressive models that rely on sequential processing.

It can also save users money. Technology analyst Carmi Levy noted that existing pay-per-token monetization models “penalize the use of less than optimally efficient AI solutions.”

But DiffusionGemma “could herald a new generation of task-defined, efficient solutions that can enable expanded compute capacity without draining the operations budget,” he said.

A contrast to left-to-right processing

Built on Google’s Gemma 4 family and its Gemini Diffusion research, DiffusionGemma is a 26B mixture-of-experts (MoE) model designed to maximize text output generation.

It essentially shifts how models use hardware, giving processors a larger hunk of work each cycle so it can draft full 256-token paragraphs in sequence. This allows the model to generate text up to 4x faster on GPUs, Google claims. It activates only 3.8B parameters during inference, and, when quantized, can fit within 18GB VRAM on high-end consumer GPUs like Nvidia RTX 5090.

“It upgrades your model inference from a single, sequential typewriter to a massive printing press that stamps the entire block of text simultaneously,” Google research scientists Brendan O’Donoghue and Sebastian Flennerhag wrote in a blog post.

AI image generators begin with pure, random ‘visual noise’ and iteratively refine that into a finalized picture (what’s known as ‘diffusion’); DiffusionGemma applies this same process to text. It does not generate tokens in order, but begins with a “canvas of random placeholder tokens” that it processes in multiple passes, identifying the context tokens it feels are most relevant and using those to refine the rest.

The model has the ability to self-correct, using confidence scoring to re-evaluate tokens in the next pass. “The model iteratively refines its own output, allowing it to evaluate the entire text block at once to fix mistakes in real-time,” O’Donoghue and Flennerhag explained.

DiffusionGemma also has bidirectional attention, they wrote. “Generating 256 tokens in parallel with each forward pass allows every token to attend to all others.” This can be particularly helpful in domains that are non-linear in nature, such as mathematical graphs, code infilling, and in-line editing, they said.

DiffusionGemma is optimized across Nvidia’s hardware stack, making it compatible with consumer setups as well as with high-performance enterprise systems like Hopper and Blackwell.

Because it is released under the Apache 2.0 license, developers can freely use, modify, distribute, and commercialize the software using their preferred tools. It can be run on GPUs or in the cloud through Google Cloud Model Garden or Nvidia NIM, and is available on Hugging Face, GitHub, and vLLM, with support for the open-source library llama.cpp coming soon.

Key use cases

The model is particularly useful in local workflows that are “speed critical,” such as generation of non-linear text structures, and unlocks what Google calls “new patterns of model behavior” like multimodal understanding and generating and rendering code in near real-time.

Levy explained, “DiffusionGemma is particularly well suited for interactive coding and editing where its efficiency allows rapid processing and iterations,” noting that its ability to fit within 18GB of VRAM and its deployability on commonly available local GPUs can potentially benefit customer service-related workloads that lean heavily on real-time interaction and local processing.

“DiffusionGemma also incorporates a thinking mode that is especially adept at problem solving,” he said. For instance, the model was fine-tuned to play Sudoku, a typically challenging task for autoregressive models because each token depends on future tokens. This “rather handily” illustrates the model’s capability to solve more complex problems, Levy noted.

Limitations

Google freely admits that DiffusionGemma is geared to specific workflows, and there are “key trade-offs.”

The model is engineered for small batch size inferencing and low-latency, high-speed generation low-to-medium batch sizes on a “single capable accelerator.”

In high-QPS cloud serving environments, (where infrastructure is designed to handle tens or hundreds of thousands of requests per second with ultra-low latency), DiffusionGemma’s parallel coding “offers diminishing returns,” and can even result in higher serving costs, Google conceded. In addition, its overall output quality is lower than that of standard Gemma 4, which is built for apps demanding maximum quality.

However, Levy noted that while DiffusionGemma “can be less precise than other models in certain workloads,” subsequent refinement cycles could overcome this limitation.

While Google isn’t sharing runtime costs, it’s clear that this is an efficiency play, he added. “When deployed across the kinds of workloads that would optimally benefit from its architecture, DiffusionGemma seems to have the potential to reduce processing overhead and related costs,” he said.

This article originally appeared on InfoWorld.

Maine has taken its public data breach reporting portal offline after fraudulent breach disclosures were published on the state's website, prompting a review of procedures to prevent abuse in the future. [...]