Archive 07.09.2026

Many companies are cracking down on the bland, ‘too-many-words-is-never-enough’ writing often produced by ChatGPT, Gemini, Claude and similar AI engines.

Apparently, workers are hedging their bets with AI by drowning their colleagues in seas of text that are more often churned-out to impress and perplex – rather than to simply communicate an idea or message.

The problem has grown worse since the advent of ChatGPT-5, which was deliberately designed to spew-out bland, overly cautious prose.

In other news and analysis on AI writing:

*The Solution to Bloated AI Writing: Open Source AI: Increasing numbers of pros – especially those involved in marketing, sales, advertising and other roles requiring lots of text – are turning to Open Source AI.

The reason: Many of these AI models – available for use in the cloud or via download to a company’s IT system – can be prompted to produce extremely creative, captivating and compelling writing.

For a deep look into the revolt against bland AI writing, check-out: “Don’t Pay for Beige Prose,” by Joe Dysart.

*Corporate America Getting Hooked on Open Source AI: Major U.S. companies – like AT&T, Airbnb and Deloitte – are increasingly turning to Open Source AI, given that it’s much less expensive than alternatives offered by U.S. titans.

Nvidia – the AI chipmaker often considered the epicenter of the AI revolution – thinks the trend is so important, it just purchased a well-known library of hundreds of Open Source AI alternatives for $12.9 billion.

Observes writer Eli Tan: “Many of the most popular open AI models are made by Chinese companies like Moonshot AI, DeepSeek and Alibaba.”

*OpenAI Rolling-Out ChatGPT-6 Upgrade This Week: ChatGPT’s latest version – also known as Astra – will be showing up this week in paid AI plans from OpenAI, including Pro, Plus, Enterprise and Business.

Businesses accessing the company’s AI by directly tapping into OpenAI servers will also see access this week.

For a deep dive into Astra, check-out the OpenAI-authored, “Path to Astra,” and also another, excellent, in-depth look, “GPT-6 Astra: Everything You Need to Know.”

*ChatGPT Competitor Anthropic Releases Upgrades: Anthropic has launched upgrades to two of its popular AI engines – Claude Fable 5.1 and Claude Mythos 5.1.

Mythos 5.1, which can be used to uncover security holes in everyday software platforms, was released as a restricted-use product — currently available to only select cybersecurity firms and life-sciences organizations.

Observes writer Carl Franzen: “Anthropic is simultaneously changing the economics of running persistent agents — reducing the cost of cached context by 75%.”

*Facebook’s Parent Meta Releases Nearly As Good AI: Determined to be a contender among bleeding-edge AI titans like ChatGPT and Gemini, Meta has released its most sophisticated AI to date, Muse Spark 1.3.

Specifically, Alexandr Wang, Meta’s chief AI officer, rates Spark as competitive with Claude Fable 5.1 and better in some ways than ChatGPT-5.6 – although ChatGPT-6 has since been released.

*New AI Offers Great Data Security Protection: Perplexity is out with new, agent-powered AI designed to keep the company data of users very secure.

The system does this by using smaller AI models — hosted on in-house computers of users — for work requiring company data, while sourcing cloud-based AI models for work requiring more intense AI thinking.

Observes writer Michael Nunez: “The company says it is the first time an AI agent can begin a task in the cloud and dynamically hand-off the confidential portions of that same task to a model running on the user’s own hardware, without restarting the job or losing context.”

*New York City Bans AI in Grades K-8: In a bold move designed to evaluate the impact of AI on education, New York City has banned use of the tech in grades K-8.

Observes writer Joseph De Avila: “The new policy bans student-facing AI usage from preschool through eighth grade, covering about 600,000 students in the nation’s largest public school system.

“The city is also developing AI literacy classes for high-school students to teach them how the technology works — and educate them on its risks, ethical considerations and how it could have an impact on careers.”

*15% of Large Firm Lawyers Now Dependent on AI: Following trends in other industries heavily reliant on text, 15% of attorneys at large law firms say they can’t do without AI.

Observes Artificial Lawyer: “Moreover, 34% of the 500-plus lawyers surveyed said that they used AI tools for legal work every day, and 32% multiple times per week.”

“So: Around two-thirds of lawyers are really using AI frequently.”

*South Korea: Everyone Gets AI Free: In a move that will give the South Korean government more control over how AI is accessed in that country, everyone who lives in South Korea will be getting free access to government-hosted AI.

Observes writer Sky Jacobs: “The initiative is part of South Korea’s effort to build a stronger domestic AI industry and reduce its reliance on systems developed in the U.S. and China.”

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Many Bosses Fed-Up With Bloated AI Writing appeared first on Robot Writers AI.

Scientists have devised a new way to switch magnetic computer memory while using far less energy than today's leading technologies. By mathematically optimizing the pulses used to flip digital bits, the method could reduce energy consumption by several orders of magnitude. Simulations suggest it may bring future memory devices surprisingly close to the fundamental physical limit for processing information. The same idea could eventually work with electrical currents or ultrafast lasers.

Princeton researchers have tested an AI system that can monitor and control fusion plasma in milliseconds, reacting far faster than a human operator. In one experiment, it predicted a damaging instability about 200 milliseconds before it appeared and adjusted the plasma to stop it from forming.

Most coverage of this research treats the gap as evidence that governance is behind schedule. Fewer accounts explain what closing it actually requires once the AI in question is not summarizing a report but deciding whether a robot should act on what it just detected.

By Wanjiku Chebet Kanjumba, University of Florida

NASA is planning to send three small rovers to the Moon with a single instruction: Work out among yourselves how to explore a patch of ground.

The Cooperative Autonomous Distributed Robotic Exploration mission, or CADRE, will land on the side of the Moon facing Earth as part of NASA’s IM-3 launch, planned for late 2026. These rovers will spend roughly two weeks mapping the terrain as a self-guided team. No joystick will control them, and no human will approve each turn.

Engineers test whether the CADRE rovers can drive and coordinate on their own at NASA’s Jet Propulsion Laboratory in Pasadena, Calif. NASA/JPL-Caltech.

The rovers will elect a leader among themselves, assign their own tasks and redraw their plans as a group when one of them runs low on charge. If it succeeds, CADRE will be the first time NASA has operated multiple rovers beyond Earth as a single autonomous system.

That achievement will matter well beyond this mission, because NASA is scoping out future missions to the lunar South Pole, where water in the form of ice sits locked in craters that haven’t seen sunlight in billions of years. If researchers can chemically split that ice apart and turn it into propellant and breathable air, it could become the feedstock for a lunar economy built around fuel depots and life support made on the Moon.

Right now, the only demand for that ice comes from government contracts. And an operation that must be babysat from 239,000 miles (384,000 kilometers) away will not easily scale into a market. So, while a full-blown lunar economy is still far off, CADRE is testing whether robots can work unsupervised long enough, and in enough numbers, to keep a lunar operation running month after month.

I’m an aerospace engineering Ph.D. candidate researching guidance, navigation and control for spacecraft in-orbit servicing and active debris removal. I work on the same problem these rovers face: how a machine decides what to do next when it cannot call home for instructions.

Why Earth cannot drive

At the Moon’s South Pole, the terrain itself can cause communication disruptions. Commands reach a rover by way of relay satellites, and crater rims can block the line of sight to those relays. A rover that ventures down into a shadowed crater may lose contact for its entire trip.

Spotty communication doesn’t just make trying to drive annoying. It can cost the rover power it cannot recover.

A polar rover runs on a finite illumination budget. Solar panels charge the battery only while the Sun is up, and on the Moon sunrise is not a daily event. Night lasts about two Earth weeks, and at the poles, only a few ridges stay lit for long stretches. So for every minute a rover spends idle, awaiting new instructions, it is spending stored energy it cannot replace until the Sun comes back.

In the top image, a rover’s stored energy climbs while it charges in sunlight, then falls at the same rate whether it is working or waiting idly. In the bottom image, the gray bands are communication blackouts. A team waiting on commands from Earth stops until the link returns, while an autonomous team keeps assigning itself work, and the gap that opens between the two lines is work recovered from what would otherwise be dead time. Credit: Wanjiku Chebet Kanjumba.

Why the pole is the hard case

The lunar poles also come with unique challenges. Sunlight can swing from direct glare to absolute shadow as a rover drives down from a sunlit crater rim to the shadowed floor below, so a camera that worked at the top could go blind at the bottom.

The Moon also doesn’t have GPS satellites like Earth does, so rovers can’t know exactly where they are. They need to build their own maps from what their cameras and sensors see.

Temperatures inside permanently shadowed regions drop below minus 274 degrees Fahrenheit (minus 170 degrees Celsius). And the Moon’s dust is more dangerous than it sounds. An electric charge lifts it off the ground, and billions of years of tiny meteorite strikes have left every grain sharp and jagged.

That dust grinds at the wheel bearings and works past the seals. Keeping it out of the rover’s moving parts is still an unsolved problem. The dust can also film over the camera lenses that navigation depends on, leaving a rover unable to see where it is going or move safely.

Why one rover is not enough

Sending a single rover to check out a crater on the Moon is a risky mission. If it gets stuck, the campaign ends. If its instruments fail, no second machine can take the measurements. Multirobot teams distribute that risk and split up the jobs. One rover might carry the sensors and another the drill, while a lander positioned on a sunlit ridge acts as the power and communications hub.

Under a communications blackout, a rover team that waits for its commands from Earth has to stop. An autonomous team reassigns tasks among itself, selecting the next objective that it can reach and complete with the amount of power it has left. Dead time becomes work time.

In ground testing at NASA’s Jet Propulsion Laboratory, the CADRE rovers achieved this coordination. Faced with unexpected obstacles, they replanned paths as a group, and when one rover’s battery ran low, the whole team paused so they could continue together.

A prospecting team splits the work. A lander on the sunlit rim supplies power and relays communications, while a rover and a drill work the permanently shadowed crater floor below, where water ice may be trapped and no sunlight reaches. Once they drop past the rim’s radio horizon, they are out of contact and have to divide the tasks and manage their own power. Credit: Wanjiku Chebet Kanjumba.

What remains uncertain

A campaign at the lunar South Pole would need to run for months, and no robot team has yet worked that hard, for that long, that far from help. Engineers are developing ways for rovers to navigate without satellite positioning and to make decisions onboard, but that software still has to be tested on the surface.

Meanwhile, as of mid-August 2026, China’s Chang’e-7 mission is waiting at Wenchang, the launch site on Hainan Island in China, with liftoff expected by the end of the year. Its lander, rover and hopping probe are meant to work at the pole as a team, with the probe built to leap into permanently shadowed craters. If the U.S. wants to keep pace, it will need its own robots that coordinate without supervision.

Finally, there is no agreed way for a rover built by one company to hand a task to a tool built by another, or to decide which one works the crater floor first. As autonomous rover teams improve, the groups building them will have to work out those rules.

CADRE will tell scientists whether three small rovers can reason together on the Moon. The harder question is whether a dozen different machines from different builders can do the same thing, reliably, for years.

Wanjiku Chebet Kanjumba, Ph.D. Candidate in Aerospace Engineering, University of Florida

This article is republished from The Conversation under a Creative Commons license. Read the original article.

AI-powered robots briefly took over the European Parliament this week to showcase the continent's creators as the bloc races to compete in a field dominated by China and the United States.

As AI moves from labs into homes, warehouses, and factory floors, wireless connectivity has become as critical to a robot's intelligence as the AI model driving it. Here we look inside the low-latency, low-power, high-speed, multi-radio stack making embodied AI possible.

We have automated the movement of goods and the tracking of goods, but we have not yet automated the truth about goods. Closing that gap is the third wave of warehouse automation, and it is being driven by AI and computer vision.

A recent ICRA panel titled “Surviving the Paper Deluge” brought together leading robotics researchers who have grappled with the overwhelming number of robotics papers being published today.

The discussion ranged from hard numbers on publication growth, through the promises and risks of large language models (LLMs), to radical proposals for reshaping peer review as we know it.

Panel chair Aude Billard pointed to rapid growth across major IEEE Robotics and Automation Society venues, with a roughly exponential curve beginning around 2017. An estimate for 2025 suggests around 70,000 papers containing the word “robotics.”

Billard noted that while in some fields, extreme specialization may be an acceptable survival strategy, robotics is inherently different. A robot is an integration of perception, control, manipulation, learning, hardware, interaction, safety, and deployment. If researchers can only stay current within narrow silos, the field risks losing one of its core strengths: the ability to connect ideas across domains.

Kunpeng Yao presented a case study funded by IEEE RAS Science and Technology Watch Board. The team tried to do something that borders on heroic in today’s publishing environment; that is, reading an entire subfield carefully over a single year.

Focusing on papers related to learning from demonstration, the team searched IEEE Xplore for relevant 2024 papers, screened the results manually, and identified 347 relevant papers. Of those, only 69, or about 20 percent, were judged to have made notable contributions.

According to the criteria used, ‘notable’ papers tended to offer new formulations, mechanisms, or theoretical guarantees; serious comparisons against strong state of the art; clear gains in robustness, transfer, or failure recovery; new teaching or data collection modalities; and convincing real-robot validation. Papers were less compelling when they simply relabelled existing approaches or failed to demonstrate their claims.

Yao summarized one lesson neatly: notability should be judged against the state of the art, not against “the newest vocabulary.”

That distinction is of particular importance in a field where new labels can travel quickly and a paper may sound fresh without actually moving the frontier. Conversely, useful work is sometimes hidden in less fashionable venues or written by less visible groups.

Degrees of hallucination

One question is whether large language models can help with literature review. Nadia Figueroa explained that LLMs can improve the mechanics of literature review. That is, they can help with search, retrieval, clustering, tables, summaries, and rough conceptual maps. Work that once took weeks or months can now sometimes be done in hours. For a new PhD student entering a field, that is a genuine reduction in the barrier to entry.

But Figueroa also identified hallucination as a major issue. First-order hallucination involves fake or incorrect references. More subtle is second-order hallucination, where the reference is real but the model misstates what the paper actually did. Also dangerous is third-order hallucination, where the LLM invents plausible but false commonalities across papers.

“The danger currently is not fake citations,” observed Figueroa. “It’s fake understanding.”

An LLM-based literature review can contain real papers and still misrepresent the field. It can be fluent, structured, and wrong. If researchers outsource not just the mechanics of reading but the cognitive act of comparison, judgment, and doubt, the field may produce more text while developing less understanding.

Salami and sandcastles

Meanwhile, Greg Dudek described the familiar problem of “salami slicing,” where a larger body of work is divided into “the thinnest possible slices” and spread across workshops, conferences, and journals.

For Dudek, the system rewards this behaviour. Students need papers to graduate. Early-career researchers need papers for jobs. Committees often face too much material to read deeply, so titles, venues, counts, and indices become tempting shortcuts.

But the result is costly for readers. Multiple papers repeat the same background, divide one contribution into fragments, and make it harder to reconstruct the full story.

His preferred remedy is simple in principle but difficult in practice: publish fewer, more integrated papers.

Dudek is under no illusion about how hard this will be. “There is no fix,” he said, if by fix we mean a return to a quieter world. Incremental reforms may help, but the flood is still rising. He compared small procedural fixes to building better walls around a sandcastle while “there’s a tsunami coming from the back of the room.”

Re-designing peer review?

Renaud Detry argued that the growth in publications partly reflects the growing presence of applied systems work in academic venues. That is not necessarily bad. Robotics advances through real systems, data, benchmarks, platforms, and the last-mile effort needed to make ideas work outside idealized settings.

The problem is that the same evaluation machinery often judges very different contribution types. A new algorithm, a carefully engineered system, a benchmark, a dataset, and an industrially relevant validation study should not all have to pretend to be the same kind of paper. Robotics may need clearer tracks for fundamental science, applications, infrastructure, benchmarks, and technical correctness.

Visibility is not value

Dongheui Lee addressed another vital part of the new publication ecosystem: arXiv, open-source releases, project pages, videos, blogs, and social media. These tools can broaden access, improve reproducibility, and help readers decide what to examine closely. But they also distort attention. Visibility can reflect institutional prestige, networks, speed, and self-promotion as much as scientific value.

Lee’s suggestion was pragmatic: keep expert peer review as a quality filter but use its signals better. Editorial boards and media teams could do more to promote strong papers that receive excellent reviews but do not come from famous labs.

Beyond the gatekeeper model

The most radical question came from Shigeki Sugano, who asked “What if we were to abandon peer review altogether?” In Sugano’s model, legitimate robotics and AI manuscripts would be uploaded first to an open archive, along with videos, code, and data.

Community evaluation, under verified identities, would happen in public. Journals and conferences would then certify high-value work rather than deciding what gets to exist.

“The question is not whether peer review is valuable,” he argued. “The question is whether it must remain the only gate to visibility.”

Not everyone agreed. Popularity bias, gaming, reputation effects, and low-quality papers flooding the system were highlighted by other panelists as possible risks. Still, Sugano’s radical proposal addressed real challenges around the ability of traditional accept-or-reject peer review models to scale to meet the deluge of papers flooding the robotics community’s information space.

No one on the panel pretended there was a clean fix. Instead, it emerged that the paper deluge is a tangle of volume, incentives, tools, evaluation, visibility, and culture.

Nevertheless, several useful principles emerged. LLMs may provide some mechanical support but carry risks of intellectual outsourcing. Read selectively and deeply. Reward quality before publication counts. Recognize different kinds of value in robotics research. Treat visibility as a possible indicator of worth, not as a verdict.

The open question

One major question remains: what happens to robotics research if papers continue to be published at the current rate?

There are clear benefits. More papers, more preprints, and more routes into publication can lower barriers to entry, help new researchers find a foothold, and make the field more open to a wider range of voices. Platforms such as arXiv have also made it easier for work to circulate before, or outside of, traditional publication channels.

But the risks are equally clear. Robotics is an integrative field, bringing together perception, control, manipulation, locomotion, hardware, safety, learning, and human-robot interaction. If researchers can only keep up with narrow slices of that landscape, the field may become more siloed. One result could be a growing reliance on off-the-shelf tools, including black-box commercial systems, without a deep understanding of the foundations on which those tools depend.

There is also the problem of memory. At a certain scale, no individual researcher can read far enough back, or broadly enough across adjacent domains. That increases the risk of duplicated work, overlooked insights, and important papers disappearing beneath the next wave of more fashionable topics.

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This article originally appeared on IEEE RAS.

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Drones often hover in the air, noisy and whining. They can already be used for many tasks, but not when you need some quiet. Their batteries would also last longer if they could take a "rest" from time to time. But when a drone is monitoring a rainforest, its cluttered surroundings and its own gripper arm become too tricky for its camera-based vision. Until now.