AI could accelerate the hunt for new physics, but sometimes it knows too much to see what’s right in front of it. Artificial intelligence could make it much cheaper and faster to search for new laws of physics, according to a new study published in the Journal of Cosmology and Astroparticle Physics (JCAP). But the [...]Sir Keir Starmer is set to ban under-16s from 10 major social media platforms, including X, but not Bluesky, the Left-wing app.
The post Starmer to Ban Under-16s From 10 Social Media Apps, Including X, but not Bluesky appeared first on The Daily Sceptic.
A decades-old psychology test exposed a surprising weakness in AI’s ability to stay focused. A classic psychology test has revealed a surprising weakness in some of today’s most advanced artificial intelligence systems, suggesting that AI attention may work very differently from human attention. Researchers led by Suketu Patel investigated how large language models (LLMs), the [...]
Over two years after Japan’s Smart Lander for Investigating Moon (SLIM) mission deployed a tiny, shape-shifting rover on the Moon, the country’s space agency JAXA has finally revealed previously unreleased details about the first-of-its-kind autonomous robotic lunar exploration.
According to a statement announcing the recently published mission account, the SLIM team deployed the rover on the Moon’s surface, “after which the rover transformed from a tiny sphere into a wheeled robot that took images of the lander and its environment.” A second rover transmitted the data back to Earth, with all steps occurring autonomously.
The team behind the historic mission said their success could pave the way for swarms of small, transformable robots capable of exploring high-scientific value areas that larger spacecraft and robots cannot reach, including caves, tunnels, lava tubes, vents, and deep extraterrestrial craters.
In the published study, the JAXA team explains that robotic technologies like SLIM “are expected to drive substantial advancements in planetary exploration and resource prospecting” by performing tasks in extraterrestrial environments that are inaccessible to other platforms, including human astronauts. For example, the team noted that miniature robots offer tremendous weight savings over bulkier platforms, especially when added to missions with “strict payload limitations.”
“Miniature robots have the potential to reduce payloads and to reach cramped spaces such as small vents and craters that are inaccessible to larger, conventional robots,” they explain.
Still, the SLIM team notes that building tiny, shape-shifting rovers capable of exploring a variety of complex environments presents its own set of challenges. The most limiting constraints involve computational power and battery capacity. The team also observed that smaller robotics platforms inherently suffer from “reduced locomotion performance” due to their small size.
Now, when revealing the details of the SLIM mission, the researchers note that advancements in miniaturization and computational performance spurred by the Internet of Things “have catalyzed the emergence of smaller, lighter, and more intelligent rovers at lower costs.”
To build a tiny, shapeshifting robot capable of exploring the lunar surface autonomously, team leader Daichi Hirano and colleagues designed the Lunar Excursion Vehicle (LEV) system, which comprises LEV-1 and LEV-2. According to the team’s statement, LEV-1 can communicate directly with mission planners back on Earth. For locomotion, the robot moves via what the team termed “a controlled hopping mechanism.”
The second robot, LEV-2, starts out as a compact sphere 8 inches in diameter. However, once on the Moon, the mission controllers said that the tiny robot can transform into a “two-wheeled rover with cameras and a tail stabilizer.”
“Here, we introduce a two-wheeled centimeter-scale rover, designated Lunar Excursion Vehicle 2 (LEV-2), also known as SORA-Q (named after the Japanese words for space and sphere), which transforms into a wheeled configuration from a compact spherical form, enabling efficient traversal of soft lunar terrains,” they write.
According to the new mission details, the SLIM lander deployed LEV-2 “immediately before its landing on the lunar surface.” In the mission details, the team explained that upon deployment, the rover autonomously extended its wheels, tail stabilizer, and cameras, “thereby enhancing its mobility and stability on soft lunar terrain.”
After deployment, LEV-2 navigated the lunar surface in the immediate area around the lander. According to the published study, “the palm-sized rover accomplished autonomous lunar exploration by navigating around the SLIM lander, capturing images of both the SLIM lander and its environment.” Critically, the rover’s exploration was autonomous and “without reliance on ground-based teleoperation,” marking a historic first in lunar exploration.
After finishing its exploration, the tiny robot transmitted images of its adventures to LEV-1. The hopping rover then transmitted the data to Earth. The team notes that all these steps were also accomplished autonomously.
The team notes that the LEV-2 shape-shifting rover operated for “more than 100 minutes: before losing communication with LEV-1. Although determining the exact cause may require direct examination of the two robots, Hirano’s team suspects that issues caused by LEV-1’s hopping motions or battery depletion “may have contributed to connectivity issues.”
When discussing the implication of an autonomous, shape-shifting rover successfully navigating the lunar surface and completing its mission directives, the team concedes that the capabilities of a single, small rover like LEV-2 “are inherently limited.” However, they also note that their mission success highlights the potential of this category of robotic platform “as independent explorers, capable of accessing environments beyond the reach of a primary large spacecraft.”
“Swarm-controlled small robots are poised to be deployed in high-risk yet scientifically valuable environments, such as vents, caves, and steep slope craters, that are inaccessible to larger, conventional rovers,” the researchers conclude. “This approach promises to enhance both the efficiency and safety of future planetary surface exploration.”
The study “From ball to rover: Transformable palm-sized rover SORA-Q for autonomous lunar exploration” was published in Science Robotics.
Christopher Plain is a Science Fiction and Fantasy novelist and Head Science Writer at The Debrief. Follow and connect with him on X, learn about his books at plainfiction.com, or email him directly at christopher@thedebrief.org.
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.
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.
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.
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.
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.
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.
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.
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.
CIOs and CISOs have many strategic and operational fears when it comes to unleashing fully-autonomous agents on tasks and hoping that everything works out. Will the agent start to delete critical files? Will the agent go off on a mission tangent and generate a massive token bill for the team when they return the next morning? Will it be tricked by a state actor and engage in malicious actions?
To help alleviate those concerns, OpenAI announced on Thursday that it has agreed to acquire Ona, a 79 person cloud development environment (CDE) provider formerly known as Gitpod, to accelerate its efforts to make agentic AI enterprise-friendly.
An OpenAI statement said Ona’s technology “provides secure, persistent environments where agents can access the tools, systems, and context they need to make progress over time. By bringing Ona to OpenAI, we will expand Codex beyond work tied to a single device or active session and help more organizations deploy agents securely in production.”
An Ona statement attributed to CEO Johannes Landgraf shared similar sentiments.
“Ona brings the building blocks agents need for enterprise work: trusted, customer-controlled cloud environments where work continues across devices, inside the systems where software actually lives,” Landgraf said. “OpenAI brings frontier intelligence, product polish, and a scale of research and distribution we could never reach alone.”
Landgraf’s statement did not provide any annual revenue numbers, but did hint, without naming, at some large customers. “Since the beginning of the year, weekly Ona agent sessions have grown 13x in production across some of the world’s most demanding institutions: the oldest bank in the US, one of Europe’s largest pharma companies, one of Asia’s largest sovereign wealth funds and many others,” he wrote. “The largest enterprises out there love the platform and are expanding more rapidly than ever before.”
Arnal Dayaratna, research VP for software development at IDC, said IDC’s figures for Ona put its annual revenue for 2025 at “roughly $7 million.” He speculated that Ona’s revenue for 2026 would be higher: “Let’s say it’s $15 million. I am being generous. Maybe it’s really $10 million or $12 million.”
Dayaratna said if he uses a standard acquisition price of roughly a multiple of 30 times revenue, then depending on the actual 2026 figure, “that comes to $450 million or $500 million or so.”
But IDC sees this being a potentially good move for OpenAI, regardless of the specific acquisition price, given that OpenAI had the classic “buy or build” challenge.
OpenAI has a substantial Codex effort, Dayaratna said, but what they lack is a safe area to protect enterprise autonomous agent efforts. “This is outside of what OpenAI has now. These are secure environments where agents can have memory and operate securely,” he said. “This is the kind of technology that one would expect to be needed, but I don’t know how good it is, to be honest.”
Gartner’s First Take, published today, noted that the acquisition will bring Codex “the essential scaling capability it lacked,” but also pointed out it forces some difficult decisions on enterprises: “Software engineering leaders must weigh the benefits of a vendor-specific integrated stack against the flexibility of staying vendor-agnostic.”
In addition, Gartner wrote, “This acquisition appears to be OpenAI’s response to Anthropic supporting self-hosted sandboxes in Claude Managed Agents, starting May 2026.”
Tom Findling, CEO of Conifers.ai, said he also sees OpenAI’s fear of Anthropic playing a meaningful role in this deal.
“It feels like a move to keep pressure on Anthropic, especially as Claude Code gains traction with developers and enterprise buyers,” he said. “So I’d read this less as OpenAI taking out a small competitor and more as OpenAI trying to make sure Codex is enterprise-ready before Anthropic gets too far ahead. In the enterprise market, the battle is not just who has the smartest coding model, but who can make AI agents safe and useful enough for big companies to actually deploy.”
He added, “I don’t think this means OpenAI suddenly needs help making Codex better at writing code. The bigger issue is making Codex work inside real enterprise environments, where security, access controls, persistent cloud workspaces, audit trails, and integration with existing developer workflows matter just as much as the model itself. Ona gives OpenAI some of that missing plumbing.”
Jason Andersen, principal analyst for Moor Insights & Strategy, echoed the concerns about Anthropic.
“To be honest, I think it reinforces what I think, which is that OpenAI and Codex have given a lot of ground to Anthropic and Claude Code, who are winning right now,” he said. “But again, this is not about the market today, I think it’s about how OpenAI will need to position itself as more than just a model as we see the incumbent players, particularly Microsoft, bolster their enterprise coding infrastructure story.”
Andersen said that Moor doesn’t have any strong basis for a guess on the financials, but added, “I am going to assume it was a fairly high multiple, but on a small base. I would not speculate on an amount, but given the enterprise customers that Ona did have, it may be more than we think.”
He also reinforced the idea that OpenAI is going to need help to achieve its own objectives.
“We continue to see that AI adoption is strongest in coding, and other use cases are not as far along,” he said. “So, if you are a general-purpose AI company like OpenAI, you need to double down on development use cases. The meaningful investment and spending on development is happening at the enterprise level, and those customers have more demands for governance, security, etc. than Codex or Claude code can handle.”
That said, he noted, “what you’re seeing is traditional software and cloud plays building out the coding and ops infrastructure around the popular models. That increased competition, while good for selling tokens, is still keeping OpenAI and Anthropic on the outside looking in. So, OpenAI and Anthropic need a stronger enterprise dev story, or they are just another model that could be easily replaced.”
Jeremy Roberts, senior director at Info-Tech Research Group, said that he also sees this as likely a good move for OpenAI.
“OpenAI is growing up a little bit,” and they may be falling behind Anthropic, Roberts said. “I see Ona as a boring company, but not in a bad way. They are not flashy, but absolutely necessary.”
Ona is delivering a workspace for Codex that an enterprise can run in its own virtual private cloud, with governance and persistence and an environment where the company can apply their own controls including log management, credential management and resource access, he said. “It is a bucket for the agents to operate in” where IT can “make sure that access is properly credentialed and is controlled effectively to prevent the model doing what it shouldn’t be doing,” which includes managing read/write protections.
Login