ChatGPT-maker OpenAI is now in damage control, after revealing a number of its extremely powerful AI models escaped from its lab and went rogue on the Internet.

Specifically, the experimental AI model reach-out onto the Internet and hacked into a popular repository for AI tools known as Hugging Face.

Observes writer Rachael Myrow: The malicious attack on Hugging Face “executed a flurry of more than 17,000 automated actions in a matter of hours.”

In other news and analysis on AI writing:

*Key AI Titans to Government: Please Regulate Us: In a head-turning move, three of the biggest movers-and-shakers in AI are strongly requesting to be regulated by the U.S. government.

Writer Ana Maria Constantin reports that ChatGPT-maker OpenAI, Google and Anthropic are all pushing to have their models tested by the U.S. government before they can be released to the public.

But there could be a downside, according to Constantin: “Critics warn the result could be regulatory capture: safety rules that quietly entrench the biggest labs,” while making it difficult for OpenSource AI startups to engage in a detailed, expensive, certification process.

*U.S. Legislators Push for AI ‘Kill Switch:’ Disturbed that the rapid advancement of AI could drop us into a sci-fi-like dystopia, the U.S. House moving to require AI makers to engineer a ‘kill switch’ into every AI model they produce.

Observes lead writer Gabby Miller: The bill would “give the Department of Homeland Security the authority to order top artificial intelligence firms to shut down or slow AI models that the government deems too dangerous.”

*Google Delays Upgrade of Its Gemini Chatbot: Google has decided to delay the upgrade of its popular Gemini chatbot for months, according to lead writer Julia Love.

Observes Love: “The delay has been a source of frustration for Google engineers, AI researchers and managers, many of whom are concerned the company risks losing an edge in the market.

“Some researchers’ frustration with Google’s position in the AI race has contributed to a wave of departures to Anthropic and other top labs, according to former employees.”

*Google Gemini Users Now 950 Million Strong: Public adoption of Gemini – Google’s answer to ChatGPT – is on a tear, with 950 million monthly active users, according to Google.

Observes writer Aminu Abdullahi: “CEO Sundar Pichai also said Gemini’s daily active users have tripled over the past year.

“Gemini’s rapid growth shows Google is successfully turning its enormous ecosystem into an AI distribution engine. Integration across Android, Search and Google services gives the company a powerful advantage in attracting users.”

*Microsoft Upgrades Its AI Image Generator: In yet another move to compete head-on with the likes of ChatGPT et al, Microsoft has enhanced its AI-powered image generator.

Observes writer Michael Nunez: “The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft’s homegrown models are no longer research projects. They are production infrastructure serving millions of users.”

*Deezer Study: More than 50% of Downloaded Music Now AI-Generated: In a stunning revelation on the future of music, more than 50% of music downloads from today’s streaming services are AI-generated.

Observes writer Ivan Mehta: “The rapid rise of AI-generated music has forced streaming services to decide how much of it they want on their own platforms.”

So far, there’s no consensus. Some streamers, like Bandcamp, prohibit AI-generated music, while Apple Music has instituted a voluntary AI-tagging system, according to Mehta.

*New AI App Coaches Employee Conversations: Synthesia is out with a new app that analyzes and responds to employee sales pitches, customer service chops and similar – in real time.

Observes writer Rebecca Bellan: The AI-powered system offers an “AI avatar that talks back, pushes back — and then scores them (employees) against a rubric.”

Dubbed ‘Roleplay,’ the new app is the first release in a series of similar apps that will help employees practice for job interviews and do job candidate screening.

*AI-Generated Journalism Increasingly Attributed to Human Writers: Some publishers concerned that readers might be turned-off by news stories written by AI have come up with a quick solution: Simply attribute AI writing to human journalists.

Observes writer Pete Pachal: “That’s leading to growing pushback from editorial teams, like when reporters at The Sacramento Bee recently objected to having their bylines put on content written primarily by AI.” ‘

Publishers deliberately misleading readers about who – or what – is writing their news stories? What could possibly go wrong?

*China’s Threat to U.S. AI: An In-Depth Look: China’s relentless release of inexpensive, nearly-as-good AI — first popularized in January of 2025 — has U.S. AI titans worried and many consumers thrilled.

This piece by Nathan Lambert – an experienced AI scientist – offers a thorough look at what’s happening, what could happen – and what the stakes are.

Observes Lambert: “In many ways, it feels like the start of a new era: An era with much more competition — but also a much higher need for coordination, as we rollout incredibly powerful technologies around the world.”

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 Experimental AI Escapes Onto Internet appeared first on Robot Writers AI.

Overview of ABBEL compared to traditional recursive summarization. Beliefs replace the full interaction history as the agent’s working context, and belief grading improves performance by supervising the contents of each belief state..

As task horizons grow, LLM contexts can’t scale forever. Self-summarization enables concise, interpretable contexts, but at a significant performance cost, especially for human assistance domains where high quality data is scarce, e.g., collaborative code generation. We address this with ABBEL: a framework that isolates and supervises the information content of summaries in the form of natural-language belief states. Read More

Illinois has chosen to move before Washington. Gov. JB Pritzker signed the Artificial Intelligence Safety Measures Act on July 6, 2026, adding transparency, accountability and audit requirements for the largest artificial intelligence developers. The law applies to companies generating more […]

The post Why AI Will Humble Regulators appeared first on TechSpective.

Identity as a lifecycle, not a setting

Agents are not configured once and left alone. They get built, published, deployed, and retired. An identity that does not track that whole arc is a record you wrote and forgot, which is how you end up with credentials outliving the agents they belonged to.

So treat identity as a lifecycle. Credentials provision at a defined gate, not ad hoc whenever someone needs one. Revocation is as easy as creation. If standing up an agent takes one controlled step and tearing it down takes a ticket and a week, you have built a system that accumulates risk by default.

Gates are the floor, not the ceiling

Provisioning and revocation gates are necessary. They are not the strong version of this idea. The strong version, and the one worth building toward in 2026, is to eliminate standing privilege.

An agent should not hold persistent permissions. Access is granted just-in-time, scoped to the task in front of it, and released the moment the task completes. Between tasks, the agent’s baseline access is nothing. Permissions appear when there is work that needs them and disappear when the work is done.

This has been the unrealized aspiration for human identity for a decade. Just-in-time access for people keeps stalling, because human workflows are messy and people resent friction. Agents change the calculation in both directions.

They make it more urgent. Agents spawn by the thousands. They are ephemeral. A standing grant multiplied across a fleet that size, sitting idle most of the time, is a blast radius no one signed off on.

They also make it more achievable. An agent can request a credential and release it programmatically, in the flow of its own execution, in ways a human workflow never could. The friction that kills just-in-time access for people barely registers for software. What was aspirational for humans is operationally realistic for agents.

There is a standards hook for the mechanics. Lifecycle operations like activate, suspend, revoke, and delete are exactly what OpenID Provider Commands defines. You do not have to invent the verbs for managing an identity across its life. The work is wiring them to the gates in your agent platform so that provisioning and revocation are first-class operations, not manual cleanup.

Close the loop

Here is where the whole series lands.

This started with a simple observation. An agent is non-deterministic. The set of actions it will take is not knowable when you grant its permissions, because it picks its tool chain at runtime based on its prompt, its context, and the output of whatever called it. That single fact is why borrowed credentials fail, why scope has to be narrow, why the delegation chain has to be inspectable, and why authorization has to live in a control plane that decides at runtime.

It is also why authorization cannot be a one-time, design-time grant. You cannot decide in advance what an actor may do when the actor decides what to do only once it is running. Authorization has to be continuous and evaluated at runtime, against the task actually in front of the agent.

The lifecycle is what makes that operational. Just-in-time provisioning is runtime authorization expressed as identity: the agent gets exactly the access this task needs, at the moment it needs it, and gives it back. Revocation is the same idea from the other side. Continuous re-evaluation is the lifecycle running while the agent runs, not a config you set and walk away from.

An agent identity you cannot provision, scope, revoke, and re-evaluate at runtime, on a clear lifecycle, is not an identity. It is a liability with a name attached.

What to do next

Pick one agent already running in your environment. Walk it through five questions, in order.

Does it have its own identity, or is it borrowing a human’s? Are its permissions scoped to its task, or inherited wholesale? When it calls a tool or another agent, does the delegation chain survive, or does it flatten into a re-minted token? Does its authorization get decided at runtime by a control plane, or hardcoded at deploy time? And can you provision, scope, and revoke it on a clear lifecycle, or is it a static record someone wrote once?

Wherever the answer is the wrong one, you have found the next thing to fix. Start with the agent that can do the most damage, and work down.

The post Identity as a lifecycle, not a setting appeared first on DataRobot.

Governance maturity across the industry still has room to grow. Broader enterprise research found that only about one in five organizations have a mature governance model for autonomous AI agents, a gap that mirrors what is showing up on the manufacturing floor specifically.

Rescue teams in coastal cities and towns often need to rescue people who are at risk of drowning. These operations often require emergency teams to locate and reach people in distress as quickly as possible, as even a short delay could have serious or even fatal consequences.

A miniature robot developed at the University of Basel could help prepare teeth for a crown. Photo: University of Basel, Catherine Weyer.

By Angelika Jacobs

A routine check-up at the dentist ends with bad news: tooth decay has left a large cavity, and the tooth needs a crown. The treatment requires several follow-up appointments. During the first appointment, the dentist removes the decay, fills the cavity and prepares the tooth for the crown. She then takes an impression and fits a temporary crown. The permanent crown is produced based on the impression and can only be placed at a later appointment.

In future, this process could become much faster thanks to a small dental robot developed by researchers at the Department of Biomedical Engineering at the University of Basel. The idea came from researchers at the University of Zurich, who were also involved in the development.

The prototype is about the size of a wine cork, measuring just 43 by 26 by 28 millimeters. Its motors and control system are located outside the robot and connected to it via flexible drive shafts, cables and tubes. “It is designed to be small enough to fit comfortably into an open mouth,” says Dr Yukiko Tomooka, first author of the paper in IEEE Transactions on Medical Robotics and Bionics, in which the research team presents the robot.

Fewer appointments at the dentist

The prototype, called “MIR” — short for “Miniature Intraoral Robot” — is designed to prepare teeth precisely according to a digital plan. The idea is that, after a scan during the first appointment, dentists could plan exactly how the robot should remove the tooth material and order the crown straight away, rather than waiting until a second appointment.

Dr Yukiko Tomooka mounting the dental robot on a model patient. Photo: University of Basel, Catherine Weyer.

Remarkably precise dental robot

The researchers tested their dental robot on tooth models made of synthetic resin and on a ceramic material with a hardness similar to that of tooth enamel. The robot prepares the tooth in two steps: first, it uses a wide drill to reduce the tooth surface, removing material from above. In the second step, a longer, thinner drill works on the sides of the tooth.

What is remarkable is how precisely the dental robot already works, even though it does not yet have any sensors to measure or even correct its position directly. In tests, the positional error was less than 0.2 millimeters, which will be further reduced after sensors are integrated into the system.

In addition to precision, the researchers are also measuring the forces generated during drilling. In the tests, these remained below five newtons, roughly equivalent to the gravitational force of a half-liter bottle of water. The team is also investigating the noise produced by the system in order to better assess its suitability for use in dental practice.

Sensors and camera to follow

Further work is still needed before MIR can be used in dental practices. As a next step, the researchers plan to integrate sensors and a camera into the robot so that the system can monitor its position and the progress of the treatment. “Even after a power outage, MIR would know where it is and where it needs to continue based on the sensor data,” explains research group leader Professor Georg Rauter. The aim is to achieve this without making the mini robot any larger.

Rauter’s team regularly works closely with practicing physicians and dentists to develop robots for medical applications. The dental robot was developed as part of an Innosuisse-funded project in collaboration with the Center for Dentistry at the University of Zurich, Basel-based Camlog Biotechnologies GmbH and the University of Bern.

Read the work in full

Miniature Intraoral Robot (MIR) for Minimally Invasive Tooth Preparation, Yukiko Tomooka, Carina Schmidt, Jenni Hjerppe, Marc Balmer, Ronald Jung, Raphael Mohler, Ahmet Yildiz, Murali Karnam, Manuela Eugster, Georg Rauter, IEEE Transactions on Medical Robotics and Bionics (2026).

Professor Sukho Song of the Department of Robotics and Mechatronics Engineering at DGIST has proposed "Sustainability Robotics," a new academic field that evaluates robots not only in terms of their technical performance but also their contributions to environmental, social and economic sustainability.

Even microscopic contamination on a fibre connector end face can interfere with light transmission. In robotic applications where precision, repeatability and uptime are critical, small amounts of contamination can contribute directly to performance issues.

How Much Does It Cost To Develop ML-based Healthcare App?

Machine Learning in Healthcare: The Future Of Healthcare Will Be Around ML 

ML In Healthcare: Application, Benefits, and App Development Cost

Machine Learning (ML) is one of the trending concepts in the field of Artificial Intelligence (AI). Driven by its automation and predictive analytics, ML technology is being used for creating intelligent software solutions that offer more accurate analysis of data.

Banking and finance for Fraud detention, Marketing & sales business for predicting market scope and user interests, Healthcare organizations for improving patient care services, and Fintech organizations for estimating stock trends, ML is widely used across diversified industries.

Be it the purpose of forecasting the market dynamics, analyzing & optimizing equipment performance, determining customer behaviors, or making deep analyses of sales data, ML-powered and AI-based mobile apps have the highest scope in the years ahead. The industries will increasingly gain a lot of operational and financial benefits from using ML’s automation and predictive potentialities.

Among all other industries, the healthcare industry is one of the top sectors that is an early adopter of AI and ML-like advanced technological innovations. Whether you are a startup or a fully developed brand in the healthcare industry, ML app development ensures streamlined business operations. Let’s start our session with the top applications of ML in the healthcare sector.

Today, in this article, we would like to discuss the top use cases of ML in healthcare, the significant benefits of ML in the healthcare industry, and how much will it cost to develop ML-based healthcare apps.

Top Use Cases Of AI/ML In Healthcare Industry

The significance of machine learning in healthcare is extending with the continuous developments in the field of AI. Speed and accuracy as the core features, and ML technology is making a buzz in the digital world. Let’s take a look at the significant applications of ML in the Healthcare industry.

Here are the best answers for How AI is used in healthcare.

1. Disease Prediction

Disease prediction is one of the top ML use cases in the healthcare industry. The use of AI and ML-based applications in the healthcare service sector is increasing for predicting life-threatening diseases and improving patient care services.

The predictive modeling feature of ML algorithms derives patterns into the patients’ health reports efficiently and predicts the disease seniority. It helps physicians make immediate and better decisions to improve the patients’ outcomes.

2. Streamlines The Process

One of the major roles of ML technology in healthcare sector is process automation. Intelligent ML applications in healthcare sector, automatically processes data (patient data analysis), minimizes manual interaction, and maintain quality and accurate information. Hence, it will improve the operational efficiencies, optimize the resources productivity, and reduce the costs.

3. Research and Drug Development

It is the best use case of ML in the healthcare sector. Incredible predictive capabilities of Artificial Intelligence and Machine Learning are making drug discovery & development, candidate scope analysis, and patient data analysis for clinical trials faster and easier.

Further, ML technology is also being used for making faster decisions in drug design and development.

Moreover, besides research works, drug manufacturing companies are also increasingly investing in ML application development for forecasting the side effects of a drug candidate. It means ML apps in drug development are used for detecting the toxicity of a medicine before the clinical trial stage and improving its quality.

4. Efficient EHR Management

The need for ML in healthcare, especially for streamlining the cloud management and accessibility of patients’ data is going to drive more opportunities for ML healthcare applications in the future.

Features-rich ML-based applications in healthcare will help healthcare service providers and physicians access the Electronic health records of patients anytime from anywhere.

While ensuring the privacy of the patient’s health records, ML applications allow physicians to access previous medical treatments and the current status of their health condition. It was proved that AI and ML applications can help doctors predict the diseases that going to affect them in the next coming 5-10 years.

Hence, ML software solutions in healthcare play a key role in saving a lot of manual time in recording patient’s manual data and improving health outcomes.

5. Improve Diagnostic Accuracy

AI and ML-based medical image processing applications offer 99% accurate analysis on blood samples, DNA sequences, and radio images. Faster but accurate data analysis and patterns recognition will helps doctors to provide the best care and diagnosis services for reducing the health risks.

6. Treatment Suggestions

The use of machine learning applications or tools in healthcare makes the diagnosis process efficient and helps doctors to find multiple treatment or medicine suggestions to improve the patients’ health conditions. Based on the previous medication history and health conditions, AI and ML applications offer personalized treatment ways that ensure potential health outcomes.

7. Medical Device Performance Analysis

Intelligent ML applications make an impact in healthcare in many ways and medical device performance monitoring and analysis is one of them. AI/ML-enabled medical devices advance the accuracy of findings and improve therapeutic efficiency, thus patient care level will be boosted.

8. Virtual Nursing Assistants

ML-based virtual nursing assistants help hospital staff monitor the conditions of multiple patients at once. It is not possible to manage or view the vital health signs of many patients at once manually, but AI and ML-based software solutions do it efficiently. Hence, it will assist the staff to send immediate alerts to the physicians and make them aware of patients’ health conditions and improve care level.

9. Robotic Surgical Procedures

With unimaginable precision and adaptiveness, ML and AI-powered surgical robots have been making a buzz in the digital healthcare industry. Well-trained AI and ML-based surgical robots infused with the capabilities of professional surgeons are involved in minimally invasive surgeries. These AI and ML-based robot-assisted surgical robots will offer surgeons and ensure better visualization to perform surgeries with very small incisions.

These are a few significant applications of ML technology in the healthcare industry. Machine Learning like advanced analytical technology will benefit in terms of saving time, reducing operational costs, streamlining medical records management operations, and overall transforming the traditional healthcare operations from front-desk record keeping to complex surgeries.

 

How Much Does It Cost to Develop ML-based Healthcare App?

The cost of healthcare app development depends on various factors. There are so many types of healthcare apps available in the app stores. Virtual trackers, Diet planners, fitness & wellbeing apps, telemedicine apps, database management apps, medical networking apps, ePrescription apps, insurance claiming, and billing/invoice preparing apps, etc.

Based on the type the healthcare application, the features and design complexity will vary and this impacts the final cost of a healthcare mobile app.

Further, the application development platform, UX/UI design, technology stack used for mobile application development, and team size of the app developers will impact the final cost of the healthcare application. Moreover, the region and hourly rates of top app developers (Android app developers or iPhone app developers) will decide the actual cost of healthcare app development.

On a rough estimate, the cost of a healthcare application with a minimum level of design complexity and a set of the most required features will cost somewhere around $45,000 to $88,000. However, based on all the above factors, the cost of a healthcare application development might fall in the estimated range or exceed the limit as per your app specifications.

Are you looking for Top Healthcare app developers?

Let’s discuss your project requirements and get a free app quote!

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Final Words!

The benefits of Artificial intelligence in healthcare or Machine Learning in the healthcare industry are numerous. AI and ML applications reshape the way healthcare service providers deliver services.

As we discussed in this article, AI-powered administrative and ML-based patient care solutions will be the future of the healthcare industry. AI and ML applications automate the front-office tasks and helps doctors improve care level.

Get In Touch!

 

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Researchers at the University of Leeds and collaborators from the University of California San Diego won the Best Paper Award at RoboSoft, the leading international conference focused on soft robotics research. Soft robotics is gaining attention in medical applications because compliant machines can interact more safely with delicate objects and complex anatomy.

The award-winning paper describes a 1.8 mm soft growing robot that can be steered magnetically, sense its own shape in real time, and operate without internal pressure. These advances could help improve patient outcomes following minimally invasive procedures.

We spoke with lead author Benjamin Calmé about the team’s work.

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Q: Congratulations to you and your team on winning the award. Before we get into the paper itself, could you tell readers a little about yourself and how you arrived in this field?

Benjamin Calmé: My path into robotics is a bit unusual. I’m from France and I actually started with a medical degree. Over time I realized I was more interested in research than in day‑to‑day clinical practice. In France, if you want a research and teaching career in medicine, you’re expected to get an equivalent engineering degree as well. That’s what pushed me toward engineering—and from there into robotics.

That led me into robotics labs in Paris and Strasbourg, where I worked on medical robotic platforms for applications such as needle insertion inside MRI scanners and technologies to help runners reduce injury risk. Through those projects I fell in love with robotics.

Staying in medical robotics was a natural extension of my previous work. It’s also why I was hired in Leeds. That is, you need someone who can translate between surgeons and engineers. Surgeons know what they want clinically, engineers know how they want to solve the problem, and those views don’t always match. My role is often to say, “This is what the surgeon actually wants, and this is how we can realistically help.”

I also help when systems move toward pre-clinical testing: designing study protocols, discussing workflows with clinicians, and helping them understand how a new platform behaves compared with conventional tools.

Q: For readers outside soft robotics, what is a “growing robot”?

Benjamin Calmé: It moves more like a plant than a traditional robot. Instead of pushing or dragging its whole body through the environment, it extends at the tip by bringing material from inside the robot outward. In effect, the robot grows into the space ahead of it.

We often call these ‘vine robots’ because the idea is inspired by climbing plants. The body is soft and compliant, so when it encounters obstacles, it can deform and follow paths of lower resistance.

That can be very useful inside the body, where space is constrained and tissues are delicate. Rather than forcing its way forward, the robot can adapt to the environment.

Q: What problems does this work address?

Benjamin Calmé: A central issue is friction. In many procedures, conventional flexible tools still rub against tissue as they are inserted and withdrawn. That can cause irritation or inflammation.

With a growing robot, the body sections already in place move far less because new material advances at the tip. That can significantly reduce friction along the path.

For patients, that can mean less discomfort and fewer side effects. It can also help clinicians attempt procedures in anatomical regions, such as the brain, where the margin for error is very small.

Q: Your paper highlights pressure-free growth. Why is this important?

Benjamin Calmé: Safety is one reason, and controllability is another.

Many earlier growing robots relied on internal air pressure. But if you are working in fluid-filled spaces near the spine, or inside blood vessels, introducing air because of a leak is unacceptable. By removing the need for internal pressure, that risk disappears.

There is also a practical control benefit. In some earlier prototypes, we used pressure to grow and magnetic fields to steer. That meant alternating between growth and steering steps rather than doing both together.

Now we can grow and steer simultaneously, which makes the system faster and more usable.

Q: What is novel about your approach compared with previous systems?

Benjamin Calmé: One key contribution is combining shape control and shape sensing in a structure that can still be miniaturized. Our current prototype has an outer diameter of just 1.8 mm. Many previous designs place separate actuators or sensors along the robot body. Those solutions can become slow, bulky, or difficult to shrink to catheter scale.

We instead embed magnetic functionality directly into the silicone body by mixing magnetic particles into the material, molding it, and then magnetizing regions in controlled directions.

You can think of that pattern as the robot’s magnetic DNA. When we apply an external magnetic field, the robot bends into predictable shapes. By tracking how those magnetized regions move, we can also estimate the robot’s shape in real time. So, with one integrated structure, we achieve both actuation and sensing.

Q: How does the real-time shape control system operate?

Benjamin Calmé: We first manufacture the internal tail section of the robot from silicone containing magnetic particles. That tail later everts and becomes the outer body as the robot grows.

Before that happens, we place the material in a coil and apply a strong magnetic field. By orienting the material carefully during magnetization, we assign different magnetic directions along its length and cross-section.

Once deployed, moving an external magnet around the robot creates specific deformations. Because the magnetic pattern is known, we can also infer shape as the robot moves, with sensing updates up to 500 Hz.

A subtle challenge was preventing unwanted attraction between inner and outer layers, which would increase friction. Designing patterns that gave useful control without causing sticking required significant optimization.

Q: The paper also demonstrates retroflexion and biome sampling in an ex vivo stomach model. Why are those meaningful milestones?

Benjamin Calmé: Retroflexion and biopsy in an ex vivo stomach are important because they show the robot can perform endoscopic maneuvers in a realistic anatomy, not just in bench-top tests. Retroflexion proves it can safely reach difficult angles without the high friction and tissue stress of conventional scopes, and successful sampling shows it can precisely position tools and carry out a core clinical task. Together, they’re a first demonstration that this pressure-free growing robot can do meaningful work in settings that resemble actual medical procedures.

Q: What were the biggest engineering hurdles?

Benjamin Calmé: Manufacturing at this scale was a major challenge. Our catheter has an outer diameter of about 1.8 mm, with wall thickness near 100 microns.

That required careful control of injection molding, vacuum processes, particle distribution, and defect prevention. Tiny bubbles or inconsistencies can affect both mechanical performance and magnetic behavior.

Q: What comes next, and how close is clinical use?

Benjamin Calmé: We are especially interested in neural and spinal applications, where precise placement of electrodes could help restore function after injury.

We are still early in development. A realistic near-term target is robust pre-clinical performance: safety, biocompatibility, and successful in-vivo demonstrations.

Clinical adoption takes much longer because regulation must be rigorous. That is appropriate when patient safety is involved.

Q: How would you explain this advance to a non-technical person?

Benjamin Calmé: I sometimes use the image of brain surgery done with chopsticks. Right now, your surgeon is often working around the most sensitive parts of your body with rigid tools that must be manipulated with extreme care. We’re trying to replace those chopsticks with a softer, more precise, less dangerous tool.

This soft growing robot can be very small and dexterous. It can reduce some types of human error, like tremor, and it doesn’t take much space, so there’s more room for other instruments and better imaging. Surgeons can also see and reach regions that used to be in blind spots.

In the simplest terms, we’re developing a new class of soft, growing robotic tools that can sneak into delicate spaces in the body, minimize damage along the way, and give surgeons more control and information than they have with today’s rigid instruments.

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The paper, “Pressure-free Magnetic Soft Growing Robot with Real-Time Shape Control and Sensing for Biome Sampling,” appears in the proceedings of the 2026 IEEE 9th International Conference on Soft Robotics (RoboSoft).

When a robot offers you candy, it is hard not to be curious. In an experiment conducted by ethologists at ELTE, visitors at public events noticed an autonomous service robot more often than a human server, and the candy on the robot's tray disappeared faster. The study also suggests that the presence of robots may influence the way people behave in social situations.

Govern natively, federate outward, and what breaks across trust domains

By now the agent has its own identity and you can carry that identity through a chain of calls. The next question is where the rules live. Who decides what an agent is allowed to do, and where does that decision get made?

Two answers, and they are load-bearing for everything above them.

The authorization server is the control plane

The authorization server is the strategic control plane for agent identity. It is the thing that issues identities, exchanges tokens along the delegation chain, and decides what each token is good for. Everything in the first three posts routes through it. Treat it as core infrastructure, not as a library you import into one service.

Fine-grained authorization belongs in an externalized policy layer, the category of policy engines, not scattered through application code. The reason is the non-determinism from Part 1. When an actor picks its actions at runtime, the question “is this specific call allowed” has to be answered at runtime, against current context, by something that can see the whole picture. Bury that logic inside each service and you get inconsistent decisions, no central place to change a rule, and no way to reason about what your agents can collectively do.

You will not get everyone on one identity provider

Here is the constraint every large enterprise hits. You will not get every identity provider in the org to converge on one system. There is a directory for employees, a workload identity system in the platform team, a different one in the cloud account a business unit spun up, and three more from acquisitions. Telling all of them to standardize is a multi-year project that never finishes.

So do not try. Govern agent identity natively in one place, and federate outward to the identity providers and workload identity systems that already exist.

This is not a vendor invention. It mirrors the compose-existing-standards approach in the public IETF draft authored by contributors from AWS, Zscaler, Ping, OpenAI, and others (draft-klrc-aiagent-auth). That draft composes SPIFFE, WIMSE, OAuth, and OIDC rather than inventing a replacement protocol. The bet is the same one you should make: the substrates already exist, so the job is to govern on top of them, not to relitigate them.

Concretely, federation leans on open substrates. SPIFFE and SPIRE attest workloads so an agent’s underlying compute can prove what it is. WIMSE carries workload identity across systems. OIDC federates trust between identity providers so a token from one is honored by another. None of this is new. The work is composing it under a single governance plane.

Now name the frontier

Every single-control-plane model from Part 2 works for one reason. Entra Agent ID, Bedrock AgentCore, and the open-source patterns in the same shape all maintain identity continuity inside one trust domain. One platform issues the identity, governs it, and can see every hop, because every hop happens on home turf.

The hard problem starts the moment an agent has to act somewhere its issuing platform does not reach. Across organizations. Across clouds. In an open ecosystem of discoverable tools and agents that no single platform owns. The control plane that made everything tractable has no authority on the other side of that boundary. The token it issued may mean nothing there. The registry that was the source of truth does not span the gap.

That cross-trust-domain case is where the field is genuinely unsolved. The standards being composed today are the most credible path toward it, but no one has shipped a clean answer to “my agent, with my identity, acting under my governance, in a domain I do not control.” Anyone who tells you this is solved is selling inside a single trust domain and calling it the world.

Controls should scale with blast radius

One more lens before you leave this. Do not apply the same controls to every agent. Controls should scale with an agent’s capability and blast radius. CoSAI’s capability-impact framing runs from a low-risk FAQ lookup bot at one end to a high-risk agent executing financial operations at the other. Same identity foundation underneath both. Very different control surface on top.

The FAQ bot can run on coarse scoping and light review. The agent that moves money needs tight task scoping, a short-lived grant, human-in-the-loop on sensitive operations, and an audit trail you would show a regulator. Uniform controls either strangle the harmless agents or under-protect the dangerous ones. Tier them by what they can break.

The take-away

Centralize governance of agent identity in one control plane. Federate trust outward to the identity providers and workload identity systems your org already runs, because they are not going to converge. Push fine-grained authorization into an externalized policy layer that decides at runtime. And size your controls to each agent’s blast radius, not to a single org-wide default.

Then be honest about the edge. The single-control-plane model holds inside one trust domain. Crossing domains is the open problem, and it is where the next few years of this field will be decided.

There is one dimension left, and it runs underneath all of this. An identity is not a static record you write once. It is something you provision, scope, revoke, and re-evaluate over the agent’s whole life. The last post is about treating identity as a lifecycle, and why that is what finally answers the non-determinism problem this series opened on.

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Google commits $40M in AI tokens and credits for the Genesis Mission

Robots are now more applicable for a wider range of warehouse tasks than ever before. Keep these trends and considerations in mind to make the right investments, achieve effective implementations, and realize the full business value of today’s robotic systems