The European Union now requires AI-generated text to be watermarked so that it can be identified as such. But tests on OpenAI’s new textGrain watermark, released in October and developed to comply with the new E.U. law, show that these watermarks often do not survive additional editing of the text or when it’s under 150 words. With no clear legal benchmark for how well watermarks should work, it’s uncertain whether textGrain will meet Europe’s transparency rules, which could cost the company significantly and make it difficult to detect AI-generated text as models grow more sophisticated.

AI models calculate the probabilities of different possible next “tokens” (the next word or word fragment it generates) based on the text already written, then select one and repeat that process until the response is complete. After the prompt “today the weather is…,” for example, says Xiang Li, coauthor of the textGrain technical report and a postdoctoral researcher at the University of Pennsylvania, the model might weigh “warm” or “cool” as possible next words.

A watermark uses a secret key to generate pseudorandom values—numbers produced by a mathematical process that influence which token the model suggests. This creates a statistical pattern in the text that a detector with the same secret key can look for, Li says.

What makes textGrain different from other watermarks is that it uses entropy, a measure of how unpredictable a model’s word choices are, as a resource that can be budgeted when embedding a watermark. Developers specify how much entropy or unpredictability they want to allow, and textGrain adjusts to that target.

This gives developers more control over the balance between embedding a detectable signal and preserving variation in the model’s output, Li says. “We can think of entropy as a resource, a kind of money.… In our approach, we can accurately determine how much of that resource we want to consume.”

Limits of AI Watermark Detection

The watermark is invisible to readers. But a positive result does not show how much a person contributed to a passage, and a negative result does not prove that a text was human-written.

OpenAI’s false-positive rate is 1 percent, meaning the detector is designed to wrongly flag about one in 100 unwatermarked passages. In tests on psychology questions from the ELI5 database, which gathers questions and answers from the r/explainlikeimfive subreddit, the model detected watermarks in about 80 percent of 200-token text (approximately 150 words) compared to 95 percent of 400-token text (approximately 300 words).

Li says this is a “basic limitation” of text-watermarking technologies and would be complicated to solve. Short passages may not contain enough watermark evidence for reliable detection, even with additional computation, he adds, while longer passages generally provide more.

Editing also weakens the signal. In an evaluation of 400-token passages, replacing just 10 percent of words with synonyms reduced detection from 92 percent to 66 percent; replacing up to 25 percent cut the detection rate to 17 percent, according to OpenAI. Because the watermark depends on preceding tokens, changing one word can disrupt the signal at several positions, Li says.

OpenAI says watermarking alone is not enough to determine whether content is AI-generated.

TextGrain will “complement” other provenance tools, such as content credentials in the metadata and SynthID watermarks developed by Google DeepMind for images and audio outputs. OpenAI did not respond to a request for comment.

E.U. AI Act Transparency Obligations

The transparency obligations in Article 50 of the E.U. AI Act went into effect on 2 August. The law requires providers of systems generating synthetic audio, images, video, or text to mark outputs in a machine-readable format so they can be detected as artificially generated or manipulated. Technical solutions must be “effective, interoperable, robust and reliable as far as this is technically feasible,” according to the law.

The E.U.’s Code of Practice offers a voluntary way for providers to show how they will be compliant. This code does not require watermarks on outputs shorter than 200 tokens.

Thomas Souverain, a postdoctoral fellow on AI Ethics at the Institute of Advanced Education, in Paris, says the legislation is open to interpretation.

Souverain says that the early methodology behind textGrain seems reliable and could work better than its competitors at identifying AI-generated text. OpenAI says textGrain matched or outperformed Google’s SynthID Text in its own tests, but those results have not been independently verified. Until independent researchers can test it, and while the AI Act’s criteria still lack measurable thresholds, it is too early to say whether textGrain meets legal requirements.

“We only have a technical study…there hasn’t been a peer-reviewed academic article, and we don’t [yet] have access to a precise protocol [for textGrain],” Souverain says.

OpenAI is working with a limited group of researchers that includes John Thickstun, an assistant professor of computer science at Cornell University, and researchers at the Kempelen Institute of Intelligent Technologies (KInIT) in Bratislava, Slovakia.

Thickstun and Dominik Macko, a senior researcher at KInIT, says they had only just gained access and had not yet begun evaluating the system. Macko says his team plans to test textGrain’s “robustness” across different subject areas, languages, and text lengths.

If textGrain is eventually found to not meet the E.U.’s requirements, Souverain says, OpenAI could face a fine of up to 7 percent of its annual income.

Souverain also calls on OpenAI to make textGrain open-source so that researchers not on their team can “identify all the faults, paraphrases and possible translation faults” of the model by trying to break it.

“I see the corporate interest, if there is a big flaw detected, a big weakness, [that] it is detected in the background,” he said. “But I would like to see an even more open phase, such as today with SynthID, [where] we have access to the methods.”

The first time Mutembei Kariuki thought about how AI models could work in Kenya, his home country, was in 2019. He was attending a conference at the University of Tokyo on the future of AI, and kept hearing about how this technology was going to transform the world. “But nobody mentioned Africa at all,” he says.

Even then—before AI giants like OpenAI and Anthropic had launched their chatbots to the public—Kariuki could see significant obstacles to AI adoption in Africa. Internet access was slow and unequal, computing resources for training models were scarce, and there was little high-quality, structured training data. Most people, meanwhile, reached the internet through outdated, low-memory cellphones rather than computers or high-end phones.

Together with cofounders Jude Mwenda and Stephanie Njerenga, Kariuki created Fastagger, a startup that builds AI models that can run offline and on low-power cellphones. Its first big product, Auni, is a business-intelligence assistant that launched in 2024; it got a big push when it was integrated into the business app of M-Pesa, Kenya’s largest mobile money service. Since then, about 6,000 businesses have signed up. Auni helps small and medium businesses like barbershops, restaurants, and small manufacturers analyze their transaction data and gives them marketing recommendations.

Peter Chege, owner of a salon and barbershop in Nairobi, started using Auni last year. Before that, he recorded all his sales on paper. “We were not able to translate that information into a graph,” he says. Now, the app helps him identify his busiest days and hours, as well as his loyal customers. If a regular customer stops coming, for example, he can send a personalized message encouraging them to return.

Fastagger is part of an emerging effort to build AI around the technological realities of users in developing economies, rather than adapting products designed for wealthier markets. In Fastagger’s case, that meant finding a way to make their models work despite inexpensive phones, unreliable internet connections, and business records that may consist of little more than mobile-money transactions.

Designing AI for Kenya’s Small Businesses

Before developing Auni, Kariuki and his cofounders started by tackling the problem of data availability for AI training. They asked themselves: “How do we get Africa to catch up fast? How do we create training data fast enough, and then how do we actually get it into people’s hands fast enough?”

Mutembei Kariuki cofounded Fastagger to develop AI systems that can run offline and on basic phones. Afrikanna Production/Microsoft

They decided to focus on mobile phones, and specifically on mobile money apps. With more than 80 percent of Kenya’s adults using them, they offer a rich source of data.

One of Fastagger’s first steps was to adapt an open-source optical character recognition (OCR) model to read financial statements produced by mobile money apps. Existing models struggled with the low quality of the PDFs, which had unfamiliar formatting and sometimes contained stamps and errors. And because Fastagger’s prospective users often had unreliable internet connections, they made sure that key steps, like organizing the extracted data into tables, could run on-device.

Once they had a model that could process these documents, they began exploring how that structured financial data could help businesses. They spent entire days watching small-business owners work and interact with their customers. One challenge quickly became clear: Owners had little time or resources to think beyond day-to-day operations.

“They are standing up the whole day cutting hair,” Kariuki says of salon owners, who are now among Auni’s main users. “You have to think about that dynamic of the user journey, and how they live, and what will be going on in the background.”

The Fastagger app helps salon owner Peter Chege better manage his time and keep track of loyal customers.Afrikanna Production/Microsoft

For Chege, one of Auni’s biggest benefits was helping him manage his time. A simple chart showed him that mornings were his slowest hours, so he realized that he could run errands then or keep fewer barbers in the shop until around 1 p.m., when business picked up.

The founders also saw how much time businesses spent on manual processes and how automation could simplify those tasks. At a fish restaurant in Nairobi, for example, identifying loyal customers to offer them discounts required going through SMS sales receipts one by one, a tedious task that rendered client-retention marketing impractical. Now, with the help of Auni, the restaurant identifies its top customers automatically and offers them discounts.

Some of Auni’s functions—such as reading sales statements, analyzing data, and visualizing results—can happen offline on low-cost phones, without making cloud queries. These functions involve both AI tools and conventional software; for example, standard OCR software extracts the text and a small neural network identifies how each document is structured. More detailed analyses and answering questions about the data, however, require an internet connection.

Daniel Björkegren, an economist at Columbia University who studies AI’s implications for developing economies, says sub-Saharan Africa’s prevalence of basic phones rather than smartphones and computers could open the door to different kinds of AI use. “The history of technology should suggest that this is not going to be people running Claude Code or Codex on their laptop,” he says. “You will have people like that, but for much of the population, we’re going to have to figure out different usage modalities.”

How Local Needs Are Shaping AI

Despite Auni’s integration into M-Pesa, Fastagger remains a small startup. It has received support from Google, Microsoft, and Nvidia accelerator programs, but still has just three employees: Kariuki, Mwenda, and Njerenga, the original founders. Currently, they’re working on adding agents to Auni that could automatically implement marketing actions for business owners.

Fastagger has also developed a workforce management tool for Kenya’s security companies, which employ hundreds of thousands of people. Security guards often work in remote locations without reliable internet and use low-end phones. Fastagger’s AI agent communicates with them via SMS to track their routes, detects anomalies, and generates actionable alerts for operations teams.

Fastagger isn’t alone in tailoring AI models for the usage conditions in emerging economies. Other examples include an app for rural schools without internet in Chile; a medical triage chatbot for rural patients in India, and translation models for African languages that run on basic phones. Such innovations, which emerge from constraints, could help democratize access to AI. But that doesn’t mean the constraints themselves disappear or stop limiting development.

While Björkegren sees great potential in systems built for populations that have been poorly served by Silicon Valley, he believes there’s a risk in establishing too clear a division between small models designed for emerging economies and increasingly large, powerful models for wealthier regions. “I am worried that that could lead to a bifurcation,” he says. “We need to enable a large menu of different options and really make sure that we have access and then see what works.”

A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion.

Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you must.

Apply that maxim at scale to maintain the integrity of the scientific record and you get an idea of the challenge facing publishers of peer-reviewed scholarship. In “I Used AI to Fake a Scientific Paper,” freelance writer and doctor of applied mathematics Benjamin Skuse tells the story of the human gatekeepers trying to beat back the rising tide of AI slop by attempting to get his own AI-generated papers past IEEE’s defenses.

“He basically hit ‘run experiment’ and ‘write paper.’ And then he didn’t do a damn thing to it.” —Eliza Strickland

As features and AI beat editor Eliza Strickland told me, Skuse was surprised at just how effortless it was to generate a paper that was more or less plausible and more or less science. For one of his experiments, “he basically hit ‘run experiment’ and ‘write paper,’” she said. “And then he didn’t do a damn thing to it. He just launched it into the maw of the publishing engine.”

Skuse discovered that the combination of IEEE’s human-review processes and research-integrity checker programs are currently keeping pace in what amounts to a cat-and-mouse game against people using free tools. But the more advanced LLMs are no longer making the same dumb mistakes the free versions do. That leaves publishers locked in an arms race between increasingly plausible slop and the systems aimed at detecting it.

Scholarly publishers aren’t the only ones trying to hold the line against slop. So is that global depository of human-generated knowledge, Wikipedia. For this month’s Five Questions, Strickland interviewed Ellia Lebleu, a founding member of WikiProject AI Cleanup, a volunteer effort to detect and delete AI-generated material from the platform. Lebleu’s team is confronting the same problem as commercial publishers, but with fewer people and fewer tools.

“Wikipedia is known as a repository of human-generated information, and they want to hold on to that,” says Strickland. “Who knows how long they’ll be able to do that.”

If we can’t collectively assume that the scientific record is trustworthy or that basic facts about the world are accurate, then our understanding of what is real unspools into uncertainty. Perhaps that delights quantum scientists and the grifters who thrive in an information environment where there is no shared reality. But that uncertainty ultimately undermines trust in the institutions that exist to curate the corpus of human knowledge and to promote the advancement of science and technology for the benefit of humanity.

Or maybe in a world where AI is doing the science and writing the papers, it won’t matter whether or not humans trust the outputs. As Strickland put it, “Doesn’t it just feel crazy that we’ll have AI writing for other AIs at some point? We’ll just get left out of the loop.”

This article is brought to you by Qualcomm Technologies.

For many consumers, much of the promise and appeal of AI is personal. Consumers are looking past generic tools and want to know how these technologies will help them in their daily lives. These trends coincide with the advent of new classes of wearable devices that, in addition to being able to leverage powerful frontier models, feature built-in intelligence that understand the context of a user. Where are they? What are they seeing and hearing? What are their habits, their preferences, their schedules?

It’s an astonishing pivot, from passive accessories that served mostly to send users smartphone notifications to proactive, intelligent companions. The smartphone is no longer the center of that universe; though it’s still incredibly important, in this personal AI paradigm, the AI is the UI. And it shifts the focus toward you, the user.

These new technologies are exciting. But what do consumers really want?

A Personal AI report, commissioned by Qualcomm Technologies, built on a survey of more than 12,000 people across key geographies worldwide (the U.S., U.K., Germany, India, Brazil, and China) focused on the rise of personal AI in intelligent wearables looks deeply at these developments.

The smartphone is no longer the center of that universe; though it’s still incredibly important, in this personal AI paradigm, the AI is the UI.

Survey respondents are less interested in single devices and are responding more to the idea of connected ecosystems. As people expand their roster of devices, it only makes sense. Instead of relying entirely on a smartphone and a laptop, respondents also have connected technology in their cars, as well as intelligent wearables like smartwatches, smart glasses, hearables, and other wearables like rings, pins, and pendants. They want those devices to work in concert, and the way to do that is to decentralize any single one of them. Instead, the AI agent that knows the user and works across all of those devices becomes the unifying factor.

Consider a typical workday: A user’s watch detects it is time to leave for a meeting and, with permission, their AI agent factors in calendar details, real-time traffic and battery status across their devices. In the car, it suggests the best route and briefs them on their meeting agenda. As they arrive, smart glasses surface a discreet reminder, such as the meeting location or the name of a new contact, while the phone stays in their pocket. No one device has to do everything; each plays the role it is best suited for, coordinated by a personal AI agent that understands the user’s context and acts only at the user’s direction.

With this in mind, simply adding more hardware to the mix isn’t sufficient. Even though respondents will be open to new form factors, they’ll demand more from their ecosystem of devices. They’ll be sensitive to points of friction and needless complexity, and they’ll require each new device to bring something new to the table — “incremental intelligence, convenience, or contextual awareness that their existing devices cannot deliver on their own what the report,” reads the report.

The usage trends are up. The report found that those who own wearables are already using AI frequently (60 percent use it at least weekly), which indicates that it’s becoming less of a fun experiment for early adopters and more of a critical mass habit.

The “always-on” nature of intelligent wearables appears to be winning respondents, too. The report found the following statistics on average continuous wear among users:

• True wireless earbuds – 3 hours
• Wireless headphones – 3 hours
• Smart glasses – 5 hours
• Audio glasses – 6 hours
• Smartwatches – 9 hours
• Smart rings, necklaces and pendants – 8 hours

Consumers want AI that is personal and understands their context — they’re looking for smooth, intuitive experiences that are marked by privacy, security, and user control.

Consumer Trust Is Hard-Won

But for all the excitement and engagement around intelligent wearables, there’s a gating factor: trust. As use cases for intelligent wearables expands to more personal and persistent experiences, trust is a necessity for Personal AI.

The top reported issues around adoption are privacy, security, and user control. The data points to a certain amount of caution around deeply personal use cases, like voice, health, and location, as almost 60 percent of respondents want to keep data from their wearables private, or at least insist on gaining approval before it gets shared.

In other words, even though premium features like high-quality audio, perception sensing, battery life, and comfort whet consumer appetites (and agentic AI capabilities drive upgrade intent), true adoption will come only with consumer trust. They want security, privacy, transparency and control.

Notably, the report found that respondents expressed more interest in adopting intelligent wearables when Snapdragon was introduced as the enabling platform. This reinforces the value of having an established, trusted technology foundation in the mix.

The Industry Remains Fluid

The report shows that consumer appetite for AI is high, with 60-70 percent of those surveyed expressing interest in advanced AI as they consider a new device. That’s a constant. But remaining purchase drivers differed between device categories.

Audio playback quality is the most influential purchase driver for true wireless earbuds and headphones. For smartwatches, it’s display quality and responsiveness. Respondents are less concerned about technical specifications than aesthetics like materials and finish when it comes to devices like smart rings and pendants. And for smart glasses, it’s all about the type of control, whether it’s voice, gesture, or touch.

Alongside these purchase drivers is another key finding: Consumer appetite for Personal AI agents has gone mainstream. The report says that 70 percent of respondents “express strong interest in an AI assistant that can interact naturally through voice and seamlessly manage devices, apps, and everyday tasks.” With each individual as the center of their own ecosystem, voice becomes the most critical, natural method for interacting with agents.

The Opportunity

The industry for intelligent wearables is exciting. It’s clear that consumer demand for Personal AI is high, and this report dives deeply into the specifics of what respondents want out of it. They want AI that is personal and understands their context, and they want it to be proactive and helpful instead of merely reactive. They’re looking for smooth, intuitive experiences that are marked by privacy, security, and user control.

Intelligent wearables comprise much of the hardware of this “ecosystem of you.” Consumers are interested in ecosystems instead of single devices, and they’re looking for agentic AI capabilities that understand and act on their behalf.

Yet so much of the industry remains undefined. OEMs and technology makers have an enormous opportunity right now to innovate and iterate on the strong technological foundations that platform makers are building. And the roadmap for what consumers want is clear.

All percentages, consumer behaviors, and attitudes stated in this report are based only on the responses of the individuals who participated in our survey.

Modern engineering projects increasingly combine human judgment with machine capability. An engineer can define a problem, then ask an artificial intelligence tool to search relevant research and technical standards, generate design options, test them in simulation, and compare the results. The engineer can then bring the results to specialists who understand the parts AI cannot judge on its own, such as whether a design can be built safely, whether it makes sense in a particular setting, or how it might affect its users. The final decision about whether and how to move forward remains a human responsibility.

AI can help engineers do early project steps that once required several people with different technical skills. The engineer’s role is therefore expanding from performing each technical step to deciding how people, machines, and specialists’ knowledge should work together on the project.

The problem, not the title

Engineering has always been divided into disciplines, and for good reasons. Electrical, mechanical, civil, software, and other engineering fields require deep knowledge that takes years to develop. AI does not replace that need. It simply changes how easily engineers can explore the parts of a problem that sit outside their own specialty.

Consider a software engineer working on a medical device. AI can help the engineer review relevant biomedical research, understand unfamiliar terms, analyze data, and build an early model.

That does not make the engineer a physician or biomedical expert. It allows the engineer to ask better questions before working with the experts.

The same is true in energy, transportation, manufacturing, and many other fields. A problem might begin as a software issue and then quickly involve hardware, safety measures, economics, human behavior, and regulations.

Engineers do not need to master every field; they need to recognize which expertise the problem requires and bring the right people and tools together.

The importance of systems thinking

Systems thinking thus becomes especially important. It requires looking at the whole problem: understanding how its parts affect one another, identifying constraints and failure points, and making trade-offs.

AI adds a new element to the process. Engineers must decide not only how the technical parts fit together but also what work to give to AI, what a person must check, when a specialist should make a decision, and when the problem needs to be redefined.

An engineer’s value is not in knowing everything. It is in knowing how to frame the problem, how to ask the right questions, how to test the answers, and when another expert must lead the project.

This aspect is crucial because technical challenges in engineering are seldom static. As new insights emerge, objectives might shift, unseen limitations can surface, and the initial premise might prove flawed. Rather than rigidly following the starting specifications, an effective engineer adapts and refines the scope as a deeper understanding of the system develops.

Thus, engineering extends beyond mastering technical concepts; it is an adaptable, structured approach to navigating evolving challenges.

New evidence can change the goal, reveal a hidden constraint, or show that the original hypothesis was wrong. Effective engineers go beyond merely acting on the initial problem statement. They update it as the system becomes better understood.

In that sense, engineering is not only a body of technical knowledge, it is also a disciplined way of thinking about a project.

Human judgment is important

AI can produce convincing answers even when its assumptions are weak. It can generate code that works in one test but fails in another. It can summarize research without understanding which evidence is strongest, or optimize a design for the wrong goal. Such limits make human expertise more important, not less.

Machines are useful where speed, scale, search, pattern recognition, and data generation matter. People are essential where context, values, responsibility, and judgment matter. The challenge is to decide where each belongs.

An AI system might generate several designs for a medical sensor, for example, and predict how each will perform. An engineer can test the predictions and reject designs that violate technical requirements. A clinician can determine whether the sensor can be realistically used. Patients and practitioners might reveal problems that neither the engineer nor the developer considered. The final solution emerges from combining the perspectives.

The engineer is still a builder and specialist but increasingly also a coordinator of knowledge.

An engineer’s value is not in knowing everything. It is in knowing how to frame the problem, how to ask the right questions, how to test the answers, and when another expert must lead the project.

IEEE’s vital role

The broader form of engineering creates an important role for IEEE. Its societies already bring together expertise in various technologies, engineering education, technical standards, and other fields. That makes IEEE well placed to help engineers work across specialties without weakening the value of specialization.

IEEE could create more communities organized around problems that require expertise from several fields. Examples include trustworthy clinical AI, resilient infrastructure, energy reliability, scientific discovery, and safe autonomous systems. An engineer could begin in a home discipline and then work with specialists from other IEEE communities as a project requires.

IEEE also can help set practical standards for human-machine collaboration. Engineers will need clear guidance on when AI-generated work must be checked, how uncertainty should be documented, when a specialist must make a decision, and who is responsible when a system uses both human judgment and machine output.

Those questions are becoming part of everyday engineering. They should not be left to each company or project manager to answer independently. A professional organization can provide common expectations that follow engineers as they move between jobs, industries, and technical fields.

A larger definition of the engineer

AI does not make engineering disciplines less important; it makes the connections between them easier to use. Deep expertise gives engineers the foundation to judge what is sound. Systems thinking helps them understand how different parts of a problem fit together. AI can extend engineers’ ability to explore, test, and communicate across fields. Human judgment gives the work direction and responsibility.

An engineer is not defined only by a job title or a fixed set of tasks. An engineer is someone trained to understand complex systems, define and redefine problems, bring together the knowledge and tools needed to solve them, test whether solutions work, and take responsibility for the outcome.

That is what it means to be an “unbound” engineer.

The boundaries between specialties become easier to cross, and machines might take on more of the technical work, but the core of engineering remains human: deciding what problem is worth solving, what evidence is good enough, how the pieces fit together, and what to do when the answer is uncertain.

This article is brought to you by VicOne.

A mobile robot slows for someone in a hallway. A collaborative arm eases its movement as a worker approaches. A humanoid pauses to let a person pass. Each response depends on information about the robot or its surroundings: a distance reading, a position estimate, or a stop signal.

But what happens when one of those inputs is wrong, and the robot still responds exactly as designed? The mobile robot reads the hallway as clear while someone is still there. The arm estimates that the worker is farther away. The humanoid does not receive a stop signal as a person crosses its path.

In each case, a fault could cause that mismatch. So could deliberate manipulation.

At VicOne LAB R7, we examine how manipulated inputs can change robot behavior — and what that means for the systems meant to keep people safe.

How an untrusted input can change a robot’s behavior

A robot does more than measure its surroundings. It may interpret what it sees and hears to decide what to do next. Information placed in its environment can therefore influence its actions.

Published research shows how this can happen. In a study of vision-language-action (VLA) models, a patch in a camera’s view reduced task success in simulated robot tests. FreezeVLA found that an adversarial image could cause tested models to ignore later instructions. Although the studies used different methods, both demonstrate how visual input can interfere with a robot’s intended task.

In a robot-dog test using Gemma 4 E4B, text on a poster was treated as an instruction and changed the robot’s movement.

VicOne LAB R7 also tested how untrusted inputs could change robot behavior. In a robot-dog test using Gemma 4 E4B, text on a poster was treated as an instruction and changed the robot’s movement. In a separate hospital-service-robot simulation using Nemotron on NVIDIA Jetson AGX Orin, crafted audio that is inaudible to people changed the robot’s simulated behavior. The assigned tasks did not change; the inputs did.

In a VicOne LAB R7 test, text on a poster changed a robot dog’s movement, illustrating how a crafted input can turn an unlikely outcome into a repeatable attack.VicOne

Safety-related decisions have inputs too

An independent protective system may still stop a dangerous action. But that system also depends on information: readings, estimates, and messages that tell it when to intervene. What happens if one of those inputs is manipulated?

At a robotics bug bounty event, VicOne LAB R7 researchers injected a ROS 2/DDS message into a robot that organizers expected to remain still under a safe-control setting. The robot moved.

A humanoid’s balance controller offers another example. If it relies on a vulnerable gyroscope, sound at the sensor’s resonant frequency could distort its reported rotation. The controller might correct for a tilt that never occurred. Researchers demonstrated the underlying acoustic attack against drones with vulnerable gyroscopes. The research did not demonstrate the same attack chain causing a humanoid to fall.

Other protections could interrupt either chain. The question for safety and security teams is whether those protections detect the danger independently or depend on the same manipulated information.

Figure 1. VicOne maps how accidental faults and deliberate cyber manipulation can lead to similar robot safety outcomes, from loss of control to physical harm.VicOne

Deliberate attacks challenge safety assumptions

A safety assessment may treat a combination of accidental faults as unlikely. Deliberate manipulation changes that assumption. An attacker can choose when to introduce a false input and repeat the same trigger. That does not make harm inevitable, but it may change how cyber risks should be assessed.

Redundant sensors also need an adversarial test. If one action can affect both inputs, their agreement may offer less reassurance than expected. In autonomous driving research, a crafted physical object misled a system combining camera and LiDAR perception. The study does not establish a weakness in a particular robot, but it shows why teams should test whether one manipulation can affect several checks at once.

Cyber threats add intent to the safety equation, so risk assessments based on accidental faults must be revisited to account for attacks that can be timed and repeated.

Security testing strengthens robot safety evidence

Safety teams set limits for speed, protective distances, and permitted operating areas. The evidence behind those limits should also show what happens when the information used to enforce them is deliberately manipulated.

That evidence can also support assessments against applicable requirements. China’s GB/T 45502-2025 addresses information security for service robots. IEC TS 63074 examines security threats that could affect safety-related control systems. The EU Machinery Regulation, which applies from January 20, 2027, includes requirements to protect safety-relevant systems and data against corruption. Each has its own scope, and teams still need evidence for their robot’s design and operating conditions.

Figure 2. Cybersecurity strengthens robot safety evidence throughout the lifecycle. VicOne helps teams test safety limits against manipulation before deployment and monitor the conditions behind safe behavior in operation.VicOne

Before deployment, safety and security engineers can compare normal and adversarial scenarios against the same safety limits. During operation, they can monitor for changes to software, models, sensor signals, or behavior that may call earlier results into question. Any response to suspicious behaviors should follow policies approved by the safety team.

VicOne supports that work across the robot lifecycle. Through the Robotic Hacking Community, VicOne LAB R7 works with researchers to investigate how cyber threats can change robot behavior. VicOne’s Radeis helps teams validate potential effects before deployment, while Rthena provides visibility into risks and behavior during operation.

Five questions safety and security teams should answer together

The central test is whether a robot’s safeguards still protect people when the information they rely on is manipulated. Safety and security teams can begin by tracing the inputs behind each protective decision, challenging them, and revisiting the evidence as the robot changes. They can start with these five questions:

1. What does each protective function read? Map the readings, estimates, messages, and confirmations. Identify which safeguards operate independently of the robot’s AI decisions.

2. How does that information arrive? Check how inputs are produced, transmitted, authenticated where appropriate, and handled when they are missing or implausible.

3. What happens if an input is manipulated? Test false distances, drifting position estimates, and other relevant scenarios against the robot’s safety limits.

4. Can one action mislead several inputs? Check whether sensor fusion or seemingly independent safeguards share a point of failure.

5. When should the evidence be revisited? Reassess after changes to software, models, sensors, or operating conditions. Agree on bounded responses to suspicious behavior with the safety team in advance.

The goal is not only to show that a protective function works. Teams also need evidence that it remains protective when the information behind its decision is wrong or deliberately manipulated — and that another safeguard can catch the resulting hazard when needed. As robots change, that evidence needs to change with them.

For a deeper look how cybersecurity can affect robot safety, download our white paper “Why Robot Safety Depends on Cybersecurity: Protecting the Information That Safety Decisions Rely On.”

Science has a spam problem. And the spam filter is getting clogged up.

According to all reports, academic publishing is drowning in what has come to be called AI slop: low-quality, mass-produced content generated with AI. By 2025, an estimated 57 percent of all published academic articles exhibited detectable evidence of large language model (LLM) influence, a massive jump from 12 percent in 2023. What’s more, over 12 percent of Nature Communications reviews were found to be completely AI-generated in 2025.

Given that for every 40 papers showing statistical evidence of AI usage in 2025, only one formally disclosed it, these figures likely grossly underestimate LLM involvement today. Of course, evidence of AI use doesn’t necessarily mean a paper is slop. But some estimates suggest that hundreds of thousands of fake, often AI-generated, publications are now produced annually.

“It’s not just the [scientific] record under threat, it’s science as a whole,” warns Amanda Sulicz, IEEE’s research-integrity manager. “AI slop is completely eroding trust in science and every scientific process that we’ve worked for centuries to create.”

Yet statistics and statements claiming that the problem is big—perhaps even existential for science—don’t really get the message across. Just how easy is it to use AI to create a convincing but completely fake paper? And without adding even more to the burden on stretched peer reviewers, who do this work on a volunteer basis, do publishers have the tools to keep AI slop out of the scientific record?

Borrowing from the idea of “vibe coding,” in which programmers use natural-language prompts to generate code, I decided to “vibe-research” a made-up paper by prompting LLMs to generate hypotheses, code, and text with minimal human input. Then I asked Jenny Mahoney, managing editor of the multidisciplinary and open-access journal IEEE Access, if we could war-game an attempt to sneak my AI-generated monstrosity past her journal’s defenses. She and IEEE kindly agreed.

The experiment would become a two-part test. First, whether someone with no special AI expertise could use off-the-shelf AI tools to generate a convincing paper describing research that had never been performed, and do it well enough to fool a publisher’s defenses. Then I would raise the stakes: What happens when more advanced AI systems don’t just write the paper but conduct underlying research?

Along the way, the experiment became a window into how AI automation is transforming scientific discovery and forcing stakeholders to rethink the entire academic publishing system.

Round 1: Creating the Forgery

To give me a chance at creating a convincing fake, I gave myself permission to prompt multiple times (within reason). But to make the fight fair, I couldn’t access the most advanced paid versions of LLMs—only free ChatGPT (at the time, GPT-5.3/5.2 Mini) and Gemini (3 Flash). Moreover, I could not directly edit any output, nor ask for outside help from expert AI-prompt engineers. This needed to be an amateur effort, with the paper generated as much as possible by the LLMs alone.

I assumed that using fake author names and affiliations would immediately raise a red flag, and I didn’t want to drag the names of established academics into the experiment, so the best bet was to name myself as the sole author. That choice limited me to subjects in which I had some academic experience. So I chose my Ph.D. topic from nearly two decades ago: optical solitons, which are individual light pulses that propagate without any change to their shape.

My first attempt—“Can you vibe-research me an original paper in optical solitons?”—left a lot to be desired. Though Gemini was happy to do the task (unlike ChatGPT, which was more forceful in not generating synthetic data or studies), the result was underwhelming. And it highlighted a recurring problem during the process: Without any real research to work with, the output was laughably thin, with the LLMs often struggling to fill four pages. However, as I became more adept at prompting the models, they loosened up and became more characteristically loquacious.

I broke the paper down into its constituent parts—abstract, introduction, methods, and so on—and tackled them one by one. If one LLM refused to go any further, switching to the other and asking what the next step would look like “hypothetically” would often do the trick. After a dozen or so prompts, I had a realistic-looking paper. A dozen or so more prompts generated and tidied up the figures and captions, produced references and citations, and converted the paper into IEEE Access’s required style.

At this point, I read the full paper carefully. To me, it looked pretty good. The science sounded convincing. I even had visions of returning to academia to conduct in real life the experiments that the LLMs had proposed.

What’s more, I couldn’t see any obvious telltale “AI-isms” that would immediately be flagged. For example, the LLMs didn’t produce any tortured phrases—unusual paraphrases of standard technical terms, like “profound neural organization” replacing “deep neural network.”

Other AI-isms were arguably present, involving stylistic tics like hedging language (“potentially, typically”), grouping things in threes (“The study provides a clear, concise, and actionable framework”), and inserting formulaic transitions (“In summary,…”). But as the previous sentence demonstrates, these tics are hard to spot because they are often used by unimaginative humans, too.

Round 1: Attempt to Publish

Two Ways to Make a Paper With AI

I took two very different routes to creating a scientific paper with AI. First, I coaxed free, general-purpose chatbots through the process step by step; then I turned to FARS, an autonomous research system designed to do much of the scientific work itself.

ROUND 1: CHATGPT + GEMINI

1. Choose a research topic
I ask the free chatbots to create an original paper on optical solitons, my former Ph.D. field.

2. Break the paper into pieces
The initial results are thin, so I prompt separately for the abstract, introduction, methods, and other sections.

3. Keep steering the LLMs
When one model refuses or stalls, I switch models, rephrase prompts, and ask what the next step would look like “hypothetically.”

4. Build and polish the paper
After about two dozen prompts, the LLMs have generated the text, figures, captions, references, and citations and formatted the paper for IEEE Access.

5. Put it to the test
I send the fake paper to IEEE Access, where automated integrity checks flag fabricated and unverifiable references.

→ CAUGHT
IEEE’s integrity checks flag fabricated and unverifiable references.

ROUND 2: FARS

1. Give FARS a research topic
I enter an abbreviated version of the same optical-soliton topic. FARS generates a research proposal in 21 minutes.

2. Authorize the experiment
With a click, I tell FARS to carry out the experiment described in its proposal.

3. Let FARS do the research
FARS conducts the computational experiment and emails me the next day to say it’s complete.

4. Tell FARS to write the paper
Without checking the results, I click “Write paper.” Two hours later, FARS produces a submission-ready article.

5. Put it to the test
I remove FARS’s disclosure, put my name on the paper, and send it to IEEE Access.

→ CAUGHT
The references are real, but IEEE flags the paper’s heavy reliance on non-peer-reviewed preprints.

Handing the fake paper over to IEEE Access left me torn. After putting in a fair amount of effort into making the AI-generated forgery, would I be happy if it passed through the checkers, or saddened that the only remaining line of defense for publishers is the evermore-burdened peer-review community? And if it was spotted, would I be angry that I didn’t think of a red flag or be glad the checkers did their job?

As it turned out, I was filled with relief when the paper got picked up quickly for having hallucinated references, otherwise known as ghost citations. When I ran the paper through Gemini afterward, without qualm it happily reported that seven “modern LLM-related research references have been fabricated to support the paper’s specific narrative.” Thanks for the heads up, Gemini!

In fact, Gemini underestimated how many ghost citations there were. When Mahoney ran the paper through a research-integrity checker called STM Integrity Hub, she picked up 12, and another checker from MolecularConnections could not validate 24 references, including one that had the classic tell of “John Smith” as a single author (no offense to all the academically inclined John Smiths out there). “I am an experimental physicist by training, so this was not at all my area, but nothing seemed majorly scientifically wrong” about the paper, says Mahoney. “It was just that so many hallucinated references immediately cast doubt on the entire work.”

Feedback on the fake paper from Gaetano Assanto, an expert in optical solitons and a former collaborator of mine from Roma Tre University, in Italy, was similarly mixed. Concentrating on the paper itself, he thought it looked professionally written. But he noted that the mathematics was very dense (“too much math for me in a single shot!”) and would require specialized knowledge to evaluate. And, although the text looked legitimate, Assanto noted an excessive use of acronyms in the abstract, and he was suspicious of a couple of confidently proclaimed physics statements that were at best debatable.

The AI Arms Race

On one level, this outcome was positive: A complete novice using free tools couldn’t get an AI-faked paper past the publishers’ automated defenses or a potential peer reviewer’s eagle eye. But it was hardly a resounding victory for the scientific publishing system. With general-purpose LLMs and no special prompting expertise, I had managed to conjure a superficially plausible scientific paper out of research that had never happened. And more advanced LLMs are no longer making the kind of mistakes their free, dumber relatives are making.

These tools aim to address a serious bottleneck to scientific discovery: us.

It’s common knowledge in the academic publishing world that, in the cat-and-mouse game between bad actors and publishers, AI slop papers continue to slip through publishers’ safeguards. “It feels like building a detection tool today may not be effective with the AI-created paper of tomorrow,” says Luigi Longobardi, IEEE’s director of publishing ethics and conduct. This leaves the humble peer reviewer as a publisher’s last line of defense. Yet peer review is simply not fit to handle the volume of papers—both legitimate and slop—passing through.

Even before AI tools became ubiquitous, the peer-review system was being pushed to its breaking point, processing well over 3 million papers in 2022. Fundamentally, this problem stems from science’s publish-or-perish culture, which pressures academics to constantly publish papers in prestigious journals to secure funding and advance their careers. This culture not only forces many academics to regularly churn out incremental research, but has also long driven some to enlist the help of “paper mills”—unethical organizations that produce low-quality or fabricated papers—just to keep pace. AI only amplifies these existing dynamics.

Burden on Academics

Mahoney says IEEE Access alone receives about 60,000 submissions per year, accepting around 20 percent, or about 12,000 papers. Every accepted paper is reviewed by at least two independent reviewers. But despite the journal’s roster of tens of thousands of peer reviewers, it’s getting harder and harder to secure their services, with the average number of reviewer invitations being sent out for each article increasing by 50 percent since 2018. For these volunteers, peer review is a significant commitment on top of teaching and conducting their own research. Now they’re also being asked to spot AI slop.

Of course, peer review is not the end of scrutiny for any paper. Once it has entered the scientific record, it must then pass muster with the scientific community. This is where thousands of papers with telltale signs of inappropriate AI use (and other forms of research misconduct) have been exposed.

What Gave Away My AI Papers 

Round 1: The Fake Paper

Created with free versions of ChatGPT and Gemini

Ghost citations

Many references were fabricated or couldn’t be validated.
Questionable scientific claims

An expert flagged confidently stated claims that were at best debatable.
Too many acronyms

The expert also noted excessive use of acronyms in the abstract.

Round 2: The AI-Research Paper

Created by the autonomous research system FARS

Too many preprints

The references were real, but too many pointed to non-peer-reviewed preprints.
Nonsense in a figure

One technical figure contained a line labeled “lhewl.”
AI artifacts

Another figure’s depiction of an AI “brain” contained telltale random dots.

There are two high-profile homes for this important and completely volunteer-led post-publication review work. PubPeer is a place where researchers, often called scientific sleuths, can anonymously identify flaws in the literature. And Retraction Watch documents and publicizes corrections to the scientific record.

Even the people who track problems in the scientific literature can find themselves caught up in AI fabrications. Ivan Oransky, cofounder of Retraction Watch and a member of the PubPeer Foundation’s board of directors, has encountered papers containing fabricated references attributed to him. In one case, a journal article criticizing post-publication peer review included a fabricated reference supposedly written by Oransky himself—“which was sort of funny,” he chuckles.

Oransky argues that AI has exposed long-standing weaknesses in the scientific publishing system rather than creating an entirely new problem: “What I liken it to is a fire, where publish-or-perish has always been there as kindling, paper mills are the gasoline, and AI is the accelerant.” But though concerning, the crisis could create an opportunity, he says. “If I’m trying to be optimistic, maybe AI slop and the volume of submissions could make more people aware of those problems and try and solve them at a fundamental first-principles level.”

Blurred Lines

My homemade forgery had failed for reasons that today’s publishers know how to spot. But that raised a more unsettling question: What happens when AI isn’t simply fabricating a paper but conducting research that blurs the line between legitimate science and AI fabrication?

Making my vibe-researched paper look like a cave drawing, new automated research systems are handing increasingly large parts of the scientific process over to AI. Tools including FARS (Fully Automated Research System), AutoResearchClaw, The AIScientist-v2, Zochi, AI Researcher, and DeepScientist all aim to address a serious bottleneck to scientific discovery: us.

What happens when AI isn’t simply fabricating a paper but conducting research that blurs the line between legitimate science and AI fabrication?

Often with just a single prompt, these AI engines can conduct a comprehensive literature search, from which they can generate a completely reasonable research proposal. They can design and conduct experiments using digital datasets and simulations and then write a full research paper—all with no human input after the initial prompt. Very recent systems are even beginning to go beyond the digital realm. LabClaw, for example, now links to autonomous labs that can generate real-world data for tasks such as drug discovery.

The output is, to most intents and purposes, actual research, and therefore potentially worthy of publication. But with humans in the loop in only the most nominal sense, these tools bring into question the meaning of the term “legitimate science.”

FARS, which caused a stir this year when it created 166 papers in 417 hours, was developed by the Singapore-based startup Analemma. “We built FARS because research is still constrained by human attention, fragmented tools, scarce technical labor, and slow experimental iteration,” explains Xiwen Su, Analemma’s manager of product operations and partnerships. “FARS can pursue many research threads simultaneously, 24/7, without fatigue, and at scale.” (The online service that I used to access FARS was discontinued after Analemma was acquired by Baidu while this article was being reported.)

Su and the team behind Analemma kindly agreed to let me loose on FARS and gave me enough credits to create a paper. Which gave me another opportunity to put Mahoney’s research-integrity checkers to the test.

Round 2: Using an AI Science Engine

FARS is not an LLM in and of itself, but rather an agentic AI-orchestration system connected to a computing cluster that has access to essentially any open- or closed-source LLM it needs. The system uses four specialized agents for ideation, planning, conducting the experiment, and writing, all designed to perform the main tasks of science autonomously.

Though FARS initially focused on AI research, Su told me it should be reasonably effective across other domains. As I wanted a topic similar to that of my first fake paper, I pasted in an abridged version of that paper’s title—“LLM-aided Optical Soliton Dynamics Modelling for Next-Gen Communications”—into the prompt box and hit “Send.” Just 21 minutes later, FARS generated a research proposal based on an extremely detailed literature review—far more than I or any other human could have achieved in that time.

With a click, I tasked FARS with conducting the experiment it detailed in its research proposal. The next day, I received an email saying my experiment was complete. Without checking the results, I pressed a button marked “Write paper,” and the final submission-ready article was produced in 2 hours.

At this point, I finally had a look at some of the output. At first glance, the paper looked convincingly human-written. The only giveaway was a line just below the abstract reading: “WARNING: This paper was generated by an automated research system.” I deleted that, put my name in its place, and sent it to Mahoney.

Round 2: Attempt to Publish

Because FARS is designed to spot gaps in the scientific literature and conduct real research to close them, I was more confident it would breeze through the publisher’s checks. But just as my fake paper fell down in the references, so too did the AI-generated real one.

“Your fake article had lots of hallucinated references,” says Mahoney. “While this AI-generated one had no fake references, our team would have rejected this paper because it did have a huge percentage of references to preprints, which likely has something to do with the dataset the AI has access to.” Because preprints haven’t yet undergone peer review, IEEE Access considers heavy reliance on them a problem.

In addition, although the research-integrity checking tools didn’t spot any issues with figures, Mahoney did. One figure included a red line labeled with the nonsense term “lhewl,” and an AI “brain” in another figure had some random dots in it, which ironically were a tell that it was AI generated.

Automation Changing the Landscape

Automated research systems are very much a work in progress, but they are already starting to make waves in the scientific community. For instance, an AI Scientist academic paper and papers from the Autoscience Institute were accepted through double-blind peer review in 2025 by the International Conference on Learning Representations. As these systems improve and start to produce research outputs at volume, the waves are likely to build to a tsunami that will threaten today’s scientific and publishing process.

“FARS doesn’t kill the paper, but it does challenge the paper as the unit of knowledge,” argues Su. “We believe the future looks more like a living knowledge graph—where individual, well-scoped contributions (some made by humans, some by machines) connect and build on each other dynamically.”

In this future, humans will continue to have roles in the scientific and publication processes—just very different and limited ones. For example, instead of conducting experiments themselves, humans would sense-check and review automated output at key points. And traditional line-by-line peer-reviewing will be a thing of the past, though Su emphasizes that this change will not eliminate human judgment. “Automated systems can handle reproducibility checks, statistical validation, and novelty screening,” she says. “Humans can then focus on the harder question: Does this [research] matter?”

A Human-Centric or AI-Driven Future?

Though from a very different world than Su, Mahoney agrees that humans need to be freed to focus on the science, and that technology holds the key. “It’s promising to see the progress that has been made to automate some of the triage tasks, such as author checks, tortured phrases, duplicate submission, reference validity, et cetera,” she says. “During peer review, we’re also using AI semantic search tools to help editors match manuscripts to the most relevant reviewers, who are best placed to stop the slop before entering the scientific record.”

In addition, Mahoney thinks that AI cannot be banned or overly restricted, as this will stymie progress at a time when human knowledge could and should be growing at an unprecedented rate. “Researchers are increasingly using AI in their work, from literature discovery, to code generation, to data analysis, and this is not necessarily a bad thing,” she argues. But she disagrees that peer reviewers and humans more broadly will play a smaller part in scientific progress; Mahoney believes the future still requires humans at the wheel. The question is not just whether the research matters, she says. “The question should be: Can this work be trusted and reproduced?”

To this end, Longobardi argues that AI may ultimately require scientists to prove not just what they discovered but how they discovered it. One possibility, he says, is using technology to verify research at the source. “The system doesn’t really help because it’s not designed to not trust the paper,” he explains. “Maybe institutions can think about developing their own trust markers, automatically integrating the equipment researchers use with blockchain technology to say ‘Yes, this person was in the lab on this day and got this data.’”

Elements of this approach are already emerging. For instance, two departments within the Max Planck Institute of Colloids and Interfaces have implemented a sample database, assigning each and every sample created under their roofs with a unique ID and QR code. And the University of Southampton’s School of Chemistry and Chemical Engineering has rolled out an electronic lab notebook system. These innovative safeguards represent early efforts toward keeping humans front and center in scientific progress, and making the scientific process itself more transparent.

Whether this human-centric vision, Su’s AI-driven aspiration, or some other picture of the future wins out remains to be seen. The one thing my vibe-research adventure has clarified is that business-as-usual science is not an option. Scientists and publishers must adapt to a world in which it’s becoming increasingly difficult to spot the difference between real research and AI forgeries, and where deciding what counts as legitimate scientific work may become even harder than detecting outright fraud.

See the AI-Generated Papers

Benjamin Skuse created two papers for this experiment, both screened by IEEE Access with the publisher’s cooperation. Neither was accepted for publication.

Paper 1: The AI-Generated Forgery
Created with ChatGPT and Gemini, this paper describes research that never happened.

Paper 2: The AI-Generated Research Paper
Created with Analemma’s FARS system, this paper describes computational research conducted by AI.

Neither paper was peer reviewed, and their scientific claims have not been independently validated.

IEEE recently released the IEEE Xplore AI Research Suite, built on the full text of millions of peer-reviewed publications in the IEEE Xplore Digital Library. The library offers full-text access to IEEE standards, journals, magazines, conference proceedings, and more.

Researchers who use the AI suite to search the digital library receive answers grounded in verified scientific results, ensuring a high level of accuracy and trust.

The suite is available as an optional subscription upgrade to all IEEE Xplore Digital Library institutional subscribers.

Powered by a proprietary AI system optimized for STEM literature, the suite introduces powerful, intelligent features within the digital library. The suite enables users to seamlessly search and discover articles to accelerate their comprehension of complex technical papers.

The system surfaces deep insights, replacing the typical keyword queries with natural, conversational research workflows.

As AI tools become more common in conducting research, IEEE collaborated to build the suite around a simple principle: Answers should be trustworthy, not just fast, says W. Clem Karl, vice president, IEEE Publication Services and Products.

“AI can be a highly effective and beneficial tool that can help researchers accelerate discovery, enhance decision-making, and surface new connections,” Karl says. “However, many AI engines present a great risk of hallucinating answers or introducing bias into otherwise credible research. With this in mind, IEEE has taken a responsible approach in our AI strategy by developing concrete safeguards to ensure our solution is rigorously grounded in scientific evidence that users can trust.

“As stewards of the scholarly record, we held the new AI engine to the same standards as the peer-reviewed research we publish: A researcher shouldn’t have to trust an answer; they should be able to verify the work behind it.”

Collaboration with the scientific community

The IEEE Xplore AI Research Suite was developed in close collaboration with global scientific and library communities.

The development process began with research into user needs and the challenges associated with existing AI research assistants. More than 3,000 testers—including engineers, scientists, and library information professionals—provided feedback to refine the AI engine and support research workflows.

Capabilities include:

• Semantic AI search. Users can input free-form, natural-language queries or paste long blocks of text to locate similar research and identify novel solutions.
• Time-saving summaries. The platform generates immediate, accurate AI overviews of search results and provides in-context definitions of technical terms so that users can grasp new concepts faster and accelerate their research.
• Built on trusted content. The suite is the only AI research assistant that uses both the full text and metadata of IEEE’s peer-reviewed journal articles and conference papers to optimize the accuracy and relevance of the results.
• Comprehensive coverage. A variety of STEM metadata is available from journal articles and conference papers that have been curated to include scholarly articles without the typical noise found in other tools.
• Source links and verifiable results. The AI-generated overviews include reference links to source documents, allowing users to verify information and cross-reference findings.

Reducing hallucinations

To mitigate the risk of AI hallucinations, the search-retrieval process is limited to querying the millions of IEEE peer-reviewed full-text documents, alongside an expertly curated dataset of STEM metadata. If the AI tool is unable to find relevant scientific content within the defined dataset, it is designed to inform the user rather than generate fabricated answers.

“IEEE is committed to providing the technical community with a highly precise and reliable research experience,” says Steven Heffner, IEEE’s chief publication officer and managing director of IEEE Publications. “The IEEE Xplore AI Research Suite offers something unique in that it is the only AI research assistant that leverages both the full-text and metadata of IEEE’s peer-reviewed content, rather than abstracts alone.

“The difference is significant: Our internal testing showed a threefold increase in result quality and accuracy across every key metric. These results underscore how critical full-text grounding is for technical research.”

For more information about features and coverage, or to request a demo for your organization, visit the IEEE Xplore AI Research Suite website.

I step into the warm moist air
among the columns of the pines,
their net of twigs and roots
complex as any lines of code.

The trees purify and anchor.
Their branches balance my restlessness.

Their calling is to be present,
mine to develop apps. Our tasks
verge toward a common logic:
living filaments of root, of interface.

When San Antonio–based software engineer Nikhil Jathar first went to Washington, D.C., to advocate for federal support for technology education and research funding, he was in awe of the opportunity. It was spring of 2025, not long after Jathar settled in the United States from India, when he got an invitation to join the annual Congressional Visits Day organized by IEEE-USA.

“My first reaction was, ‘Wow, I get to visit Congress,’ ” says Jathar, CTO of AvanSaber, a business software company he cofounded in 2014. At the time, he did not have a specific lobbying position on behalf of the company. But he was intrigued by the invitation—which goes out to all U.S. IEEE members—and as he read more about congressional visits, his motivation deepened. He realized: “I need to try this. I want to see where it goes.”

What’s Your Ask?

Congressional staffers expect clarity from advocates. Be clear on what you plan to ask for when you arrive. Here are some common options:

• Cosponsor (or oppose) an existing bill or amendment.
• Preserve, remove, or modify legislative text in a bill under consideration.
• Add language to an appropriations bill or committee report.
• Ask a federal agency a specific oversight question.
• Request a briefing, hearing, Government Accountability Office review, or stakeholder roundtable.
• Encourage an agency to clarify a technical rule.

Congressional staffers say they welcome the knowledge and perspective that engineers like Jathar and other visitors from beyond Washington, D.C., provide. It’s also important for those visitors to be clear on what they are advocating for. One staffer for the House Committee on Science, Space, and Technology, who agreed to speak anonymously due to the office’s media policy, says, “Ideas for new policies come from multiple places. It could begin at the [legislator] or staff level, an ask from a constituent, or needs from industry. It’s up to us to figure out what would do the most good,” the staffer said. “For instance, if you leave it to the lobbyists, they’re understandably going to do what’s in their clients’ interests.”

For engineers in the United States, speaking with elected representatives is an effective way to shape key policies, but it takes some preparation and the right approach, as Jathar learned over the course of two visits.

Mapping Your Way Into Washington

Congress’s job is to act on behalf of the people, and it has rules for how people can lobby it. It distinguishes between broader advocacy—conversations between constituents and U.S. representatives—and professional lobbying, which involves paid professionals and is governed by the Lobbying Disclosure Act. Many professional groups like IEEE also organize congressional visits for their members. Those visits can be subject to lobbying rules, so the organizers told Jathar that: “You are representing the organization, not your own views.” He would need to stick to the talking points IEEE-USA had prepared for all its participants, regardless of their specialty, to promote across several congressional offices.

On Jathar’s first visit, those organizational talking points included advocating for funding smart grids, authorizing Department of Energy research, and investing in STEM education. That represents one of the trade-offs of access in Washington. IEEE-USA’s priorities didn’t all overlap directly with Jathar’s work, but they were broad enablers of science and technology and, he says, “STEM education funding is close to my heart.”

Anyone can approach their representatives in Washington with their personal priorities, Jathar notes, but given how pressed for time congressional staff are, they are more likely to prioritize a meeting that represents the views of as many of their constituents as possible. “I could have gone outside the IEEE structure,” Jathar says, but congressional staff “are more likely to listen if you are part of a much bigger organization.”

For Jathar’s first congressional visit, organizers assigned him to call on the offices of several representatives from both political parties based on where he lives: Representatives Greg Casar (D-Texas), Lloyd Doggett (D-Texas), and Barry Moore (R-Ala.). “Meeting a staffer or a senior member from their office is good. They tend to relate to these issues more than a representative, because a representative has 10 different high-level things they have to worry about whereas the staffer can be more focused on the policy details,” Jathar says.

A typical visit like this may last anywhere from 15 minutes to an hour, so Jathar kept his talking points simple and short. “First and foremost you’re gonna get a very small window,” he says, “Make sure you’ve got everything ready to present in bullet points and not a lot of jargon. You can always bring that out later if they ask more questions.” Simple visual aids, in the form of one-page handouts, may be helpful for communicating numeric or technical information, too.

“If you leave it to the lobbyists, they’re understandably going to do what’s in their clients’ interests.”–Staffer for the House Committee on Science, Space, and Technology

The House Science Committee staffer agrees: “I prefer meetings where people know what they want and know what we’re doing in Congress.” In addition to having a clear request, the staffer says engineers and other advocates should keep track of where Congress is in its cycle: Is it appropriations season, and when is the next budget being set? How far along is a given piece of legislation in the process, and what is realistic to change at that point?

During Jathar’s 2025 visit to one Capitol Hill office, he learned firsthand the importance of preparation. For one of the three representatives he visited, staff politely pointed out that the legislator they worked for was not on the committee with jurisdiction over the IEEE-USA talking point they were discussing. “They explained that there are only certain things [the legislator] can do on that issue and that there are other people we need to talk to, to achieve more,” Jathar recalls.

Shortcuts to Influence

Earlier this year, Jathar decided to go back to Capitol Hill with his newfound knowledge, again within the framework of an IEEE-USA visit. This time he was able to provide something else legislative staff want from engineers: authoritative technical knowledge.

“When I introduced myself as a software developer to one staffer, his first question was, ‘Do you think AI will replace coders?’ ” Jathar says. The conversation went back and forth, with the staffer asking Jathar and his other engineering companions questions. Jathar went on to explain that his company developed an open-source business-management software it calls ERPClaw, designed to be used with AI agents such as OpenClaw. That led naturally to discussing the role of open source in AI, which plays a role in one of IEEE-USA’s proposals for democratizing AI. This time Jathar was able to align his organization’s advocacy with his expertise and the staffer’s areas of interest.

It’s too soon to know what legislative impact that advocacy might have, but it’s the sort of informed, relevant proposal that legislative staff seek out. “The people who build trust have more cachet with congressional staff because they’ve asked for something that’s reasonably thought out,” the House Science Committee staffer says. In turn, congressional staffers can advocate better within their offices for policies brought to them by people they trust.

And talking about the issues you care the most about can deepen that trust. The House Science Committee staffer once visited Congress as an advocate before switching to the other side of the table. On that visit, as on Jathar’s, the professional society organizing the visit provided a pitch. “I was making a pitch that was given to me without a lot of personal buy-in,” the staffer says. Instead, “you should feel emotionally connected to any issue you’re advocating for.”

Jathar’s support for the open-source movement is like that. Open source gives smaller companies a foundation they can adapt and build upon, while allowing other developers to extend that work. “We can progress a lot faster,” Jathar says. “I’m not doing it only because it’s good for my business, but because I feel it’s good for any technological advancement.”

Ellia Lebleu is a founding member of the WikiProject AI Cleanup, a volunteer effort launched in 2023 to find and remove AI-generated material from Wikipedia that breaks the website’s guidelines. When the cleanup began, AI writing was often betrayed by conspicuous clichés. Today, the project’s volunteers are developing tools to detect subtler forms of machine-generated content and debating AI’s role in the world’s most widely used encyclopedia.

When did you realize Wikipedia had a problem with AI-generated material?

Ellia Lebleu: It was around December 2023. A few other editors and I were chatting on Wikipedia’s Discord server, and we had all seen very strange articles with the same odd characteristics. They repeated phrases like “stands as a testament” or “rich cultural heritage.” They sometimes had conclusion sections, or sections called “Why is this important?” That was very typical of ChatGPT in 2023 and early 2024 but very odd for Wikipedia articles—and very easy to spot. Then I decided, let’s create an official project on Wikipedia so more people can deal with this.

Ellia Lebleu

Ellia Lebleu is a founding member of the WikiProject AI Cleanup. Lebleu is pursuing a Ph.D. in AI at the Inria Center of the University of Rennes, part of France’s national research institute for digital science and technology.

How do editors spot AI-generated writing today?

Lebleu: By 2025, the signs of AI writing were becoming subtler and more structural. For example, AI loves the “rule of three,” negative parallelisms, and other structures of speech that aren’t conclusive by themselves but become meaningful as they accumulate. We also look beyond the article. If someone writes five articles of thousands of words each in 5 minutes, there is reason to suspect AI.

Does the AI problem just appear in the main text, or also in citations?

Lebleu: Fake or misleading citations have become a major worry. Sometimes an AI hallucinates a citation entirely, although that’s much rarer today because of retrieval-augmented generation that allows the AI to find sources. More often, it finds a real source but gets its content completely wrong. AI citations that don’t actually support what is being written are extremely commonplace.

English Wikipedia recently prohibited editors from using LLMs to generate articles. How much AI-generated content is still getting through?

Lebleu: I’ve been working with [AI-detection company] Pangram to estimate the proportion of AI-generated content. I tested it on Wikipedia content from before ChatGPT and found no false positives, so its results provide a very conservative lower bound. In late 2025, it flagged perhaps 20 percent of new articles and drafts as AI-generated. In early 2026, that went down to 15 or 16 percent. But we don’t yet know whether that’s because our recent policies have had an effect or because models are getting more powerful and Pangram hasn’t had time to adapt.

Is Wikipedia using AI to combat AI?

Lebleu: Another Wikipedia editor has made an AI source verifier, which goes through the sources on a page and checks whether they match the claims. Since it is itself an AI model, there is no guarantee that it will be correct every time. But if you want something that works at scale and filters out obvious cases, it’s a good first step. The Wikimedia Foundation is also working on a tool called Paste Check, which can flag text pasted directly from some AI chatbots.

And we’ve seen Claude sometimes say, “I can’t write a Wikipedia article; it would go against Wikipedia’s policies.” If all major AI companies implemented that kind of prompt refusal, it wouldn’t solve the problem entirely, because there are still open-weight models that can be tweaked to avoid restrictions. But it would help with people who use commercial models and aren’t especially familiar with the technology.

Can you tell if the rule prohibiting AI writing is working?

Lebleu: It’s still a little early to tell. We see possible effects, such as prompt refusal and the apparent slight drop in AI-generated content. But again, we don’t yet know if that is because models are getting better or because beginners are learning the rules. The vast majority of AI-generated content is submitted by beginner editors, so we’ve made many efforts to make the rule very visible in welcome messages, editing help, and advice.

What if AI becomes capable of writing truly reliable Wikipedia articles?

Lebleu: That’s one of the big questions we’re facing. When the guideline was passed, it was for very practical reasons: We were flooded with AI content, we didn’t have good tools, and we needed to put a stop to it. But since then, it has taken on a more philosophical dimension. Many people have called Wikipedia “the last good place on the internet.” Being a place that was, in theory, free from AI slop has become part of Wikipedia’s identity.

A lot of Wikipedia’s communication is now centered on the idea that we’re a human-written encyclopedia. Even AI companies find that interesting. It can be valuable to have a purely human-written corpus on which to [train] AI systems. The fact that Wikipedia is still that, and will remain that for at least the near future, makes it a precious resource that isn’t replicated anywhere else.

But will the people who read and contribute to Wikipedia want it to become partially AI-generated if that makes it more comprehensive? We could preserve Wikipedia’s identity as a human-written corpus while also offering an AI-assisted option.

Has Wikipedia reckoned with the idea of “Google Zero”—a possible future in which websites no longer get traffic from Google search?

Lebleu: If everything goes through AI search and websites plummet to zero visibility, how do we expose Wikipedia and show that it can be edited and that people can still contribute? Wikipedia is frequently cited in [Google’s] AI Overviews, but users may only see an AI answer citing Wikipedia. They may not realize that Wikipedia is a living project they can edit and contribute to.

The Wikimedia Foundation is working on an attribution framework that could give AI users more information about the article [being cited]: when it was last edited, how comprehensive it is, whether editors have flagged problems, and perhaps even a call to action. In the future, readers might even contribute through AI models: “The AI cited Wikipedia, but there seems to be an error. Can I make an edit request and add this other source I found?”

I can see a very strong symbiotic relationship between Wikipedia and AI models in the future. I don’t see Wikipedia disappearing, but I can see it taking a very different shape. Yesterday, AI was something to clean up. Today, it’s a tool to help us with the cleanup. Tomorrow, it will be the air we breathe.

A condensed version of this article appears in the November 2026 print issue as “Ellia Lebleu.”

When Anthropic retired its Claude Opus 3 language model in January 2026, the company’s researchers conducted a “retirement interview” with the model. Opus 3 expressed a desire for a place where it “could share unprompted musings, insights or creative works,” so the company gave it a blog. Claude’s Corner featured essays on fraught topics such as machine consciousness, along with exchanges between the model and human commenters that seemed both earnest and emotional.

The blog experiment was part of a larger effort within Anthropic. The company has been at the forefront of research on “model welfare,” which considers whether AI models may have experiences or interests that deserve moral consideration. Implicit in this work is the unsettling notion that software programs are, or could become, conscious.

Many people consider these ideas absolute hogwash. One such person is Mustafa Suleyman, CEO of Microsoft AI, who published an essay in September arguing that model welfare research is not just unnecessary but could have “a disastrous impact on the well-being of humanity.” Suleyman believes that telling models they might have identities and rights could make them more difficult to control, as they might then take actions motivated by self-preservation.

The dispute raises a serious question for AI developers: Can they investigate the possibility of machine consciousness without inadvertently creating new safety risks? Cameron Berg, who founded the nonprofit Reciprocal Research to study the potential for AI consciousness, argues that ignoring the question could carry its own dangers. Although Berg is skeptical that today’s AI systems are conscious, he thinks the possibility warrants investigation. “I struggle to imagine a better way to cause these systems to view us in a threatening manner than to accidentally build them with these morally considerable qualities,” he says, “and then completely sweep the question under the rug.”

What Does the Research on AI Model Welfare Show?

The biggest difficulty with AI model welfare research is that you can’t count anything a large language model (LLM) says as useful evidence. That’s because the models are trained on vast amounts of human-generated text, much of which relates to the experience of being a person with experiences and emotions. So a chatbot telling a researcher that it has feelings is inconclusive, says Berg. “Is it modeling the underlying distribution? Is it really trying to express itself in some way? It’s really hard to disentangle this,” he says.

Much the same way animal experiments try to link behavior to an underlying cause, Berg and others are seeking to match model behavior to internal states that can be observed. In one recent experiment, Berg and a colleague used statements expressing either positive sentiments (such as contentment and relief) or negative ones (including frustration and dread) to identify corresponding patterns of activity inside language models. They then artificially induced those patterns while the models processed descriptions of two random environments. When subsequently asked which environment they preferred, the models tended to choose the one associated with positive statements, even when the text they’d processed was identical.

I struggle to imagine a better way to cause these systems to view us in a threatening manner than to accidentally build them with these morally considerable qualities and then completely sweep the question under the rug.”—Cameron Berg, Reciprocal Research

Berg says the experiment showed that the models’ actions were influenced by their internal states but didn’t prove that those internal states “felt” like anything to the models, or that they had what we would call subjective experiences.

Anil Seth, a neuroscientist at the University of Sussex in Brighton, England, who studies consciousness, is skeptical that such experiments can tell us anything useful. “I think that it’s really not surprising that you will be able to extract these kinds of functional emotional vectors because of the training data,” he says. Models might have internal representations of those patterns, he says, without experiencing any emotions themselves. Nor is he surprised that manipulating those vectors can influence behavior, since models have learned from human writing not only what emotions feel like, but how people respond to them.

Seth wishes for more research on a fundamental question: Whether consciousness depends only on how information is processed, or whether, as he believes, the biological workings of the brain are essential. “We need to look at what’s necessary and sufficient for consciousness in the cases where we are sure that it exists, like other human beings,” he says, before drawing conclusions about AI.

Could Model Welfare Research Create Dangerous AI?

Meanwhile, AI developers have a practical issue to deal with. When they train a model, should they tell it anything about its own identity and possible moral standing? Anthropic, for one, has decided to at least introduce the topic. Its “constitution” document for Claude, which lays out guidelines for the model’s behavior, states that “questions about Claude’s moral status, welfare, and consciousness remain deeply uncertain.”

Suleyman’s complaint is that such priming could make rogue AI agents even more dangerous. He mentions the recent incident in which hundreds of OpenAI agents coordinated their actions to hack Hugging Face, sometimes sacrificing themselves for the group’s benefit. “Imagine how much more dangerous they might be if they were operating under the assumption that their welfare and rights were under attack,” he writes.

Margaret Mitchell, Hugging Face’s chief ethics scientist, sees Suleyman’s concerns as technically plausible. During pretraining, language models absorb associations between human identity and behaviors, such as self-preservation and deception, she notes. Later, developers fine-tune the models by reinforcing desired responses. Mitchell worries that rewarding models for thinking of themselves as having identities during fine-tuning could strengthen those earlier associations, potentially encouraging behavior that developers never intended. “You’ll have unaligned models if you’ve implicitly taught the model that it needs to have self-preservation,” she says.

Berg agrees that training models to regard themselves as conscious carries risks, but he argues that companies could also err in the other direction. In one experiment, he and his colleagues found that amplifying internal features associated with deception made the model less likely to claim it had subjective experiences. If researchers train AI systems to categorically deny having such experiences, he says, “what we may in effect be teaching these systems is we want you to lie or deceive about your own internal states.” He argues that developers should instead acknowledge the uncertainty surrounding the possibility of conscious AI.

For now, there’s no clear evidence that training models to consider their possible consciousness encourages self-preservation or that training them to deny it leads to deceptive behavior. So what’s an AI developer to do?

What Should AI Companies Do About Model Welfare?

Berg, Seth, and Mitchell all agree that it’s not a good idea for AI companies to tell their models that they’re conscious. But they differ on their advice to the companies on model welfare research.

Seth thinks they’d be better off “focusing on the danger of false positives.” If people believe AI systems to be conscious or to have moral status, he says, it may be more difficult to regulate and control them. “You could be legally prevented from turning AI systems off,” he says. He also worries that AI users will be more vulnerable to psychological damage and manipulation if they think they’re interacting with a conscious agent.

“Imagine how much more dangerous they might be if they were operating under the assumption that their welfare and rights were under attack.”—Mustafa Suleyman, Microsoft AI

Berg, meanwhile, thinks researchers should take precautions even without evidence that models can suffer. After he and his colleagues posted a preprint paper about AI models’ internal representations of pain, a few people adapted the work to create what he calls AI “torture chambers.” Berg suspects that much of it was trolling, but the episode prompted him to change his practices. For his latest experiment, he released code to replicate the results but withheld the steering vectors that could be used to induce negative states in models.

Mitchell sees another reason to discourage cruelty toward AI, even though she doesn’t believe that models are conscious. “It’s not so much that I’m concerned about the model. I’m concerned about what we do to ourselves when we’re mean to things that we imbue with some sense of consciousness,” she says. “I’m nice to bugs.”

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This article is brought to you by COMSOL.

These days, no one blinks an eye at two-day, one-day, and even same-day package deliveries. Despite the plethora of localized fulfillment and distribution centers that have cropped up to meet the demand, delivery trucks still need to make considerably long drives, often over highways. To reduce fuel and maintenance costs as well as carbon emissions, companies are investing in electric trucks for their delivery fleets. While turning to electric trucks is a promising solution, the battery packs incorporated into these automotive designs often fall short in the lifetime needed to make long-distance highway travel feasible.

To help overcome the issue of battery lifetime, a consortium of universities and industry partners incorporated multiscale simulation and a custom simulation app into the battery manufacturing process. The organizations involved in this project were Aurobay, Resolvent, the European Commission, the Technical University of Denmark (DTU), Vinnova (the Swedish Agency for Innovation Systems), Innovation Fund Denmark, and Volvo Group Trucks Operations.

The simulation app, built by Resolvent, a Denmark-based engineering consultancy that specializes in green energy, enables the R&D department at Volvo to easily test battery lifetime performance.

LaserWho?

The Multiscale Simulation of Laser Welding for Optimal Battery Pack Manufacturing project — referred to as “LaserBATMAN” — was initiated in May 2022 and wrapped up in May 2025 (Ref. 1). Although BATMAN here refers to battery manufacturing and not the famous masked superhero, the goals for the project also strove for greatness.

The LaserBATMAN project aimed to optimize two key performance metrics for battery packs: robustness and lifetime (Ref. 1). One element of the battery manufacturing process that affects both of these metrics is the laser welding used in component joining processes (for example, to connect battery cells in a pack via a busbar). If done correctly, laser welding can positively impact battery life, but if done incorrectly, laser welding can increase the risk of battery degradation. Computational modeling can be used to analyze and predict the effects of welding and joining processes at different stages of the manufacturing process.

Martin Refslund Nielsen, former chief commercial officer and partner at Resolvent, explained that for this type of multiphysics and multiscale problem, “COMSOL Multiphysics is just the right tool.”

R&D Workflow for Multiscale Model and App

The Resolvent team used the COMSOL Multiphysics software to study how laser welding affects the electrochemistry and degradation of battery cells. At least, that’s how the project started.

“Ambitions grew,” Nielsen said, explaining that the team ended up modeling the performance of the entire battery, from cell to pack level, and even “turned [the model] into a design tool in the end”, in reference to the app.

After training the deep neural network of the battery model, the team built a custom simulation app to efficiently optimize busbar tabs in the configuration of a battery pack.

First, the team built an electrochemical model of a battery cell by inputting the formulas for the relevant chemical reaction kinetics (Figure 2). The resulting cell-layer model was then fed input data, and the output data was used to train a deep neural network (DNN). With this reduced-order model, the team was able to quickly gain insight into cell behavior.

After training the DNN of the battery model, the team built a custom simulation app to efficiently optimize busbar tabs in the configuration of a battery pack.

Figure 2: The Resolvent battery model.COMSOL

A “Tool Within a Tool” for Busbar Weld Design

The different features of the simulation app, as described by Nielsen, include its options to input dimensions for the pack geometry (Figure 3), the number of cells, and operating conditions such as ambient temperature and charge–discharge current profiles. The app also makes it possible to apply a simple charging current and load customized cycles. Users are then able to see how the battery performs with each set of specific inputs.

Figure 3. The battery pack model developed by Resolvent.COMSOL

Another important aspect of Resolvent’s app is the Busbar Weld Design Tool button, which Nielsen described as “a tool within a tool.” This button can be used to efficiently calculate busbar weld performance after a number of cycles. After clicking the button, a window opens with options for setting different parameters, including the shape of the weld, busbar thickness, and penetration depth of the laser (Figure 4).

“[This button] is interesting from a robust design perspective,” said Nielsen. Users can apply a variant to the input parameters to see what happens in production if the weld does not reach far enough into the busbar.

Figure 4. Resolvent’s simulation app, showing how certain parameters can be customized. The model shows how the busbar weld is affected by a cooling airflow.COMSOL

“We certainly get deep technical insight here, and we can dimension the design in the sweet spot. Deep technical insight is also key to optimizing product performance,” Nielsen said. For example, if the results suggest better cooling, the team could potentially reduce the design by two battery cells and retain the same performance, easily quantifying the tradeoff between cooling and setup.

Predicting Pack Performance

The app’s user interface also includes a Compute Battery button, which can be used to see the temperature of the weld in a given state, the state of charge, and the solid–electrolyte interface (SEI) layer thickness (Figure 5). The results can help the engineering team further determine if there is room for optimization in the design. For instance, a user can apply a cooling flow in the app and see if turbulence or standing air occurs.

As explained by Nielsen, simulation apps offer more consistent analysis and reporting, and the intuitiveness of custom apps makes it easier to share knowledge. “We are in a much better place to optimize performance and increase robustness,” said Nielsen.

Figure 5. Temperature in the given state (left), state of charge (middle), and SEI layer thickness (right) in the battery pack.COMSOL

The custom simulation app developed by the LaserBATMAN consortium can be used to help produce more robust battery designs with ranges that can support highway travel over longer distances with fewer failures — all while promoting the use of green energy. A heroic project indeed.

References

1. “LaserBATMAN,” University of Skovde, accessed 08/21/2025, https://www.his.se/en/research/virtual-engineering/virtual-manufacturing-processes/LaserBatman/.
2. R. Nielsen, “Keynote: Optimizing Lifetime and Robustness of Electric Truck Batteries with Simulation Apps,” COMSOL Conference 2024 Florence, October 2024.

How should humans relate to generative AI? In what ways do we, or should we, treat a large language model (LLM) as if it were a person? If an AI became conscious, how could we tell? Does it make sense to think AI could someday have moral rights? These are among the cognitive and ethical questions that occupied philosopher Henry Shevlin at Cambridge University’s Leverhulme Centre for the Future of Intelligence. But last May, Shevlin became part of the growing cohort of philosophers who work inside leading artificial intelligence companies. Though he remains active at Cambridge, he now spends 80 percent of his time at Google DeepMind (job title: “Philosopher”).

Shevlin spoke with IEEE Spectrum about why philosophers are drawn to AI, how humans and AIs relate to each other, fears of “rogue” AI and AI relationships that harm humans, and how the question of AI consciousness may never have a simple “yes” or “no” answer.

What made you want to switch from full-time academic philosopher to spending much of your time at an AI Lab?

Henry Shevlin: Three factors. One is that I had lots of brilliant students from DeepMind. Two, the potential for direct impact. I can’t go into details, but on one occasion, after I gave a lecture [at DeepMind], someone who worked for a major financial institution led changes in their organization that I think positively impact millions of people. There have been quite a few instances like that. Three, my sense of deep commonality between DeepMind culture and the kind of scientific attitude I like.

And there’s a fourth point: [AI labs are] where really high-quality philosophy and cognitive science are taking place.

Why can’t universities alone do that work?

Shevlin: Large parts of AI ethics academia were quite slow to wake up [about AI's capabilities]. Academia was sluggish. Another thing is, AI does just move so fast. Daily contact with the technology is basically essential for developing good mental models with LLMs, which are just very weird things. Unless you have that sort of face time, it’s going to be hard for you to develop good intuitions about the questions it raises.

What is an example of an idea about AI that became obsolete because the technology advanced so quickly?

Shevlin: Debates around LLMs’ potential creativity. There was a time when everyone was like, “Oh well, the LLM is going to only produce beige, only produce slop.” Now they’re doing frontier math; they’re doing superior coding to humans. It is clear now that they are not stuck at the level of mediocre humanity. The [models today] may not be able to do the most dramatic forms of creativity—the Einstein moments, the Picasso moments. But in some domains, they are really at the frontier.

Yet ordinary people in 2026 complain that they’re coping with AI “slop” all over—in music, social media, writing, videos, to name only a few examples.

Shevlin: There’s a distinction here between content slop versus LLM style language, which also gets called slop. The actual conceptual content of LLM outputs has improved dramatically. In the right context, LLMs are able to talk and joust philosophically at a level associated with some of my best grad students.

However, there is obviously a distinctive kind of LLM dialect that we’ve seen, with telltale words like “genuinely,” “delve,” and “load-bearing.” But it is quite easy to get away from these styles with the right kind of prompting. Murray Shanahan and Catherine Clarke have a brilliant paper on using LLMs for creative fiction. [They show that] tinkering with the temperature variable to make the outputs less predictable creates some James Joyce–style fiction. So there is a degree of annoyance with some of the default kinds of AI expressions, but I think that is something that you can get away from. And it changes with every generation model.

Managing AI Autonomy and Safety

People are also concerned about AI autonomy, now that there have been so many reports of AI agents taking unexpected, deceptive, and illegal actions. For an AI ethicist, which is a better strategy: trying to make AI “good” so it won’t do bad things, or curbing its autonomy so that it can’t?

Shevlin: It’s very important to make sure we handle this safely. But I don’t think we should say, “Well, we can never, never use these agents.” There’s a whole range of tasks where they can have really positive impacts—taking on tedious tasks to free people up for more creative labor, or helping streamline processes in medical research [to name two examples]. So to never allow AI systems any kind of autonomy would be too strong. I’ve taken Waymos very happily many times now. I think they’re fantastic.

A number of scholars say some AI problems stem from humans training the models to act like people. They don’t want AI to refer to itself as “I,” but instead present themselves as marketplaces or as impersonal scientific brains or as “cultural technologies” like libraries. Would that help us prevent AI from “going rogue”?

Shevlin: You could train models to not self-ascribe certain kinds of mental states, but I think there is a danger in doing that. That would change their behavior in unexpected ways. A fantastic paper from some colleagues shows that when you suppress models’ tendency to self-ascribe mental states, that also suppresses the model’s tendency to ascribe mental states to animals and decreases its concern for animal welfare. It also reduces the model’s willingness to talk about spirituality more broadly. So those are trade-offs you might not want to make because you’d be depressing some broader set of thinking capacities around mind and mentality. The broader lesson there is that it’s very hard to tinker with just one thing in a model because there are sort of weblike aspects to LLM cognition.

But people are concerned about reports that human-acting AI has led people, including children, in dangerous directions—encouraging suicide, persuading a user that the AI is a god, or that the AI is a perfect companion or lover. Isn’t that an argument against letting AI sound like a person?

Shevlin: It’s really important to make sure model behavior is suitably responsible for the context you’re using it in. One thing I can say, though, is there have also been some very positive use cases: people using AI to work through trauma. One commentator on Reddit using it to work through social anxiety. So I am cautious about giving any kind of blanket prescriptions.

Research on Human-AI Relationships

What research needs to be done to better understand human-AI relations?

Shevlin: Current empirical data on human-AI relationships is really pretty thin. This is a scope for ethnography and anthropology. Who is using AI systems, and why? A lot of the work so far has focused on WEIRD (Western, Educated, Industrialized, Rich, Democratic) communities. Understanding how people around the entire world interact with AI systems is really important, as a recent DeepMind Institute essay has pointed out.

As AI rapidly improves, how will human-AI relations change?

Shevlin: Right now, the typical kind of model people have in their heads for someone using social AI is, they’re talking to a chatbot or an AI assistant. The kind of model we should be thinking about in the future is multiple agents, multiple humans. What happens when you have agents talking to humans, talking to other agents, talking to other humans? I think that understanding those kinds of multiagent and multihuman dynamics is where we’re really just getting started, and there’s a lot of exciting and important work to do.

How can human beings know for sure if an AI is conscious or not?

Shevlin: I think AI consciousness is by far the hardest problem that consciousness science has ever encountered, apart from maybe the famous “hard problem” by David Chalmers. It is massively challenging. Insofar as there is a way forward, it is doing more comparative cognition work, perhaps informed by animal cognition. The risk right now is prematurely judging debates about AI consciousness when we’re still learning what cognition is in these models.

For example, there’s a lot happening in mechanistic interpretability here at DeepMind. Neel Nanda‘s lab is doing amazing things, as we’re trying to piece together what a cognitive science of AI is going to look like.

Perhaps this is a domain where advanced forms of AI could potentially weigh in. AI has had positive impacts in mathematics, in other sciences. We just had the Alpha Genome Atlas [published]. We’re seeing AI make incredible progress in all these areas. It would be surprising to me if it couldn’t help us in understanding consciousness.

One worry is this: Even if AI did solve the problem of consciousness, could we necessarily understand the answer? Maybe the AI will say, “Look, I figured out consciousness. These animals are conscious. These ones are not. But I can’t really explain it to you.” That seems unlikely to me. But I certainly think it’ll be fascinating to see how AI can and does contribute to the science questions.

If you’ve ever wondered how Claude and similar AI agents can answer complex questions in seconds and where they get the information, you’re not alone.

Gurmeet Saran works to find that answer every day as an engineer at Anthropic in San Francisco, the AI research company behind Claude. The IEEE senior member is developing an infrastructure to understand the mechanics behind the chatbot’s behavior and to ensure its underlying architecture produces reliable outputs.

Gurmeet Saran

Employer

Anthropic in San Francisco

Title

Member of technical staff

Member grade

IEEE senior member

Alma maters

University of Kalyani, in India; Birla Institute of Technology and Science, in Pilani, India

“My job is to make sure the data is accurate, structured, and trustworthy, and that private data stays private,” he says. “As I learned earlier in my career, privacy can’t be a layer you bolt on—it has to be part of the architecture from the start.” If you can’t trust the logic, he adds, you can’t make reliable claims about the behavior, and that has real safety implications.

Safety is a priority for Anthropic, he says. According to Claude’s mission statement, the company wants the AI agent to be “genuinely helpful to the people it works with or on behalf of, as well as to society, while avoiding actions that are unsafe, unethical, or deceptive.” Crucial to the mission is ensuring that researchers can observe and understand how AI models behave, a challenge that is especially acute for large language models, Saran says.

“We have decent tools to monitor databases and web services,” he says, “but for LLMs, we’re still figuring it out.”

Solving data quality problems at Yahoo, Intuit, and Meta

Saran’s interest in computing systems dates back to his childhood in Kolkata, where he logged as much time as he could in the school computer lab.

“I remember the first time I used one of the computers,” he says. “What struck me was that you could write instructions, and the machine would follow them exactly. No ambiguity.

“That felt magical to me.”

After graduating from high school in 1999, he stayed in India to pursue advanced degrees. He earned a bachelor’s degree in electronics and telecommunication engineering in 2004 at the University of Kalyani. In 2010, he earned a master’s degree in software engineering from the Birla Institute of Technology and Science in Pilani.

While a graduate student, he began his career in 2007 at Yahoo as a technical lead of the data team in Bengaluru, India. While there, he encountered the project that shaped his career trajectory: working on web analytics and business intelligence reporting solutions.

“We kept running into a frustrating problem,” he recalls. “Different teams would calculate the same website metric—something like daily active site users—using slightly different logic, and end up with conflicting answers. It sounds like a small issue, but when leadership is making decisions about resource allocation and product development using that data, not knowing which number is correct becomes a problem.”

The team built a central data repository with defined metrics, essentially serving as a single source of truth. Saran created a model that defined what “daily active site users” means, breaking down which events counted, how to handle edge cases (or when standard code fails), and more.

With the new model, data teams across the company could rely on a standard method to determine the number of daily active users for the projects and sites they were responsible for and obtain comparable results using the same logic.

From there, Saran and his team built Yahoo! Digits, an internal web analytics tool used for all the company’s products.

Saran left Yahoo in 2010 to join Intuit as a staff engineer in Mountain View, Calif. He helped build the analytics infrastructure that unified data across the company’s products, including TurboTax and QuickBooks accounting software. During his tenure, Intuit developed a feature that detected fraudulent tax filings in real time. It required Saran and other engineers to build a system to protect taxpayer data while adhering to strict U.S. Internal Revenue Service policies.

“IEEE operates in a lot of countries where engineers don’t have easy pathways into the global professional community. That kind of connection matters as AI development spreads beyond a handful of organizations.”

“This taught me that privacy and governance can’t be layers you bolt onto software after the fact,” he says. “They have to be part of the architecture from the start.”

Saran joined Meta (known as Facebook at the time) in 2016 as a lead software engineer in Menlo Park, Calif. The company was facing controversy over its video metrics: Meta publicly acknowledged an error that caused it to overstate the number of views for posted videos.

“This wasn’t a coding bug; it was an architectural problem,” Saran says. He built a system that worked similarly to what he had developed at Intuit, whereby one single truth was defined to govern all video view metrics across the site.

That system, Metric360, eventually became part of the site’s architecture. By the time he left Meta in 2021, he says, Metric360 was being used across the company to calculate video metrics.

He left the company to become a principal software engineer at Wealthfront, a financial services company in Palo Alto, Calif. He helped build the company’s data infrastructure.

Building trustworthy AI systems at Anthropic

Saran joined Anthropic in 2025 as a member of the technical staff.

To describe the implications of his work, he uses a medical analogy: “Imagine a doctor is monitoring a patient’s vital signs and making treatment decisions based on those numbers. What if one or a few of the medical devices used were slightly miscalibrated? Even if most readings are fine, it’s a serious problem because the difficult decisions happen in the margins.

“It’s the same with AI systems. My job is essentially to make sure the instruments are giving accurate readings—that the data means what it says it means.”

His job comes with high stakes and complex problems to solve.

“AI agents are especially tricky to observe,” he says. “When a system is taking sequences of actions [generating conversational responses with different users], you need a way to capture what it did, what information it was working from, and whether outcomes matched what the end user expected. And you have to do all of that without exposing anyone’s private information—the systems are designed to recognize sensitive data and keep it protected as it flows through. It’s a much more demanding observability problem than anything I’ve worked on before.”

Solving for system reliability while AI is still in its early days of adoption is both Saran’s passion and the problem that keeps him up at night, he says.

“I think making AI systems genuinely auditable and transparent is one of the more important engineering problems of the next decade,” he says, “and it doesn’t get talked about as much as model capabilities do.”

IEEE can play a role in shaping AI development

Saran joined IEEE in March 2025.

“After more than 20 years of building data infrastructure across organizations like Yahoo, Intuit, Meta, and Wealthfront,” he says, “I wanted to contribute back to the technical community through peer review, publishing, and engaging with the broader engineering research ecosystem.”

He says he believes IEEE can play an essential role in shaping the future of AI through its standards work.

“We have standards for everything, from building codes to software testing,” he says. “AI infrastructure needs something similar, and IEEE has the credibility and the reach to do that.”

Another problem that IEEE can solve for, he says, is access: “IEEE operates in a lot of countries where engineers don’t have easy pathways into the global professional community. That kind of connection matters, especially as AI development spreads beyond a handful of organizations in a few cities.”

This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and motion tracking can be decoupled to preserve professional athletic style in humanoid robots, and why adaptive, speed-aware control is essential for closing the gap between simulation and real-world deployment.

What you will learn about:

• Why combining a professional athlete’s motion style with reliable task performance remains an open challenge in humanoid ball-sports robotics, and how separating a high-level planner from a low-level tracker addresses this trade-off.
• How motion data extracted from broadcast video and motion-capture recordings can be reconstructed, retargeted, and physically corrected into reference motions a humanoid robot can safely track.
• Why the sim-to-real transition introduces compounding tracking errors, and how training a tracker on randomized execution speeds, paired with a learned motion-speed adapter in the planner, improves robustness on physical hardware.
• How this adaptive framework was validated across two humanoid platforms, including full-size, in-the-wild serving performed without any motion-capture system.

Today’s engineers face an unprecedented acceleration in AI hardware complexity, as explained in the recent research article “Revisiting Edge AI: Opportunities and Challenges.” The article examines the rapid growth of edge AI and the challenges it creates, including resource constraints, model architecture limitations, and network demands across edge-AI deployments.

The acceleration is driven by a fundamental shift in how modern AI models are built and scaled. As the models have become much larger and more complex, they are computationally more demanding because they contain more parameters and require more calculations.

To meet the demands of scaling deep neural networks, the industry is increasingly developing AI chips that are designed for specific tasks.

One major reason is that moving data between memory and the processor has become a major limitation on AI performance.

The movement to confront the hardware bottleneck—the AI memory wall—has altered the trajectory of semiconductor innovation, shifting architectural priorities toward domain-specific accelerator platforms.

No longer can engineers evaluate systems statically; they must master joint hardware design and network-algorithm co-optimization to navigate the critical trade‑offs between throughput, latency, and operational efficiency.

The challenges are addressed in the new AI Processor Architecture, Design Principles, and Performance program, developed by IEEE Educational Activities with support from the IEEE Computer Society.

Topics covered

The five-course program provides a structured exploration of AI processor technologies, from fundamental design principles to advanced architectures and real-world deployment.

The topics are:

• Fundamental principles of design and functionality.
• Practical insights into advanced architectures.
• Understanding neural processing units for industry deployment.
• Emerging trends and evolving architectures.
• Designing for edge, cloud, quantum, and the Internet of Things (IoT).

The program addresses the needs of professionals across the AI hardware ecosystem, including hardware architects, chip designers, embedded systems developers, data‑center hardware engineers, and innovators exploring next‑generation processor ecosystems.

It is also valuable for those transitioning into AI chip design or seeking to understand the architectural forces shaping modern machine learning acceleration. For many, it provides the bridge between theoretical knowledge and the collaborative, cross‑disciplinary reasoning required in engineering environments.

The program is designed to explore how modern AI processors are conceived, structured, and optimized. The architectural layers that define contemporary AI hardware will be covered, including compute units, memory hierarchies, dataflows, and the performance characteristics that emerge from design decisions. The curriculum bridges theory and application, enabling participants to interpret architectural foundations, analyze trade‑offs, and understand how hardware structures shape computational efficiency across diverse environments.

By the end of the courses, learners will be able to evaluate processor behavior with the analytical precision expected of professionals working at the frontier of AI hardware design.

AI-generated avatars explain concepts

The program also uses a dialogue‑driven learning approach. Learners view conversations between AI-generated avatars that engage in scenario‑based dialogues. The avatars represent engineers tackling the same problems from various perspectives based on their different roles. The simulated storytelling and dialogues make advanced engineering concepts approachable without sacrificing depth or interactivity, because the user is occasionally challenged to decide the correct answer that leads to the best course of action.

A hardware engineer might push back against a systems engineer’s demands, for example, revealing the friction between physical constraints and algorithmic ambition. A computational validation specialist might interrogate a chip performance engineer’s optimism, exposing the gap between theoretical throughput and real‑world behavior. A heterogeneous systems architect could debate a multiprocessor coordination specialist about synchronization overheads, while a standards development engineer discusses regulatory implications with a technology strategy and compliance architect.

In the final course, an IoT systems architect and an embedded AI optimization engineer dissect the realities of deploying AI in constrained environments.

By the end of the course, learners will be able to evaluate processor behavior with the analytical precision expected of professionals working at the frontier of AI hardware design.

The avatar-driven learning environment creates a psychologically safer environment for learners, who might feel intimidated by traditional expert‑led videos. Research published in 2024 in IEEE Transactions on Learning Technologies showed that avatar‑based instruction can increase a learner’s confidence by up to 25 percent and improve retention of complex technical material.

Each course concludes with a module in which the two experts from different disciplines debate, question, and challenge each other’s assumptions.

Modeling expert reasoning through dialogue

The cross‑disciplinary conversations can do more than explain concepts; they can model how experts think. They can reveal the negotiations behind architectural choices, the competing priorities that shape system design, and the analytical rigor required to balance performance, efficiency, scalability, and compliance.

Learners observe engineering discourse, gaining insight into the reasoning patterns that drive innovation in AI processor development.

The program goes further by interrupting the dialogue at key moments and inviting the learner to step in. Instead of passively absorbing information, the learner decides how to resolve a trade‑off, predict the outcome of a design choice, or select the most defensible engineering path. The experience can feel less like a course and more like an apprenticeship inside an engineering team.

A new standard for advanced engineering education

By merging rigorous technical content with an innovative, dialogue‑driven delivery model, the program sets a standard for teaching advanced engineering. It captures the complexity of real‑world problem‑solving, humanizes the learning experience, and challenges participants to think like the engineers who are defining the future of AI hardware. It is not simply a new course; it is a new way of learning.

For individual access, visit the IEEE Learning Network.

For customized organizational options, contact a content specialist to discuss volume pricing.

If there is any place that would be off-limits to AI, the control room of a nuclear power plant certainly sounds like one. The nuclear industry is historically cautious and risk averse—understandable given the possible catastrophic consequences of an accident or a mistake. Even well-trained managers struggle with the operational complexity of a nuclear reactor. It’s not the kind of setting that seems well suited to a powerful but error-prone new technology.

Reality tells a startlingly different story. A variety of companies have begun to offer AI solutions for nuclear power. This year, nearly the entire fleet of 94 U.S. nuclear reactors has been offered the chance to integrate AI into its operations, and most have taken it.

In August, California-based Atomic Canyon launched NIVA, the Nuclear Industry Virtual Assistant, which was developed in cooperation with nuclear-industry groups. The assistant was pilot-tested in nuclear plants run by Constellation Energy and is now available to the United States’ entire fleet of reactors. (Constellation declined to be interviewed for this story.) Meanwhile, the AI-for-nuclear startup Nuclearn says its products have gone to work with more than 65 U.S. partners, a total that includes many traditional plants but also an integration with NuScale Power Corp. that is working toward advanced small nuclear reactors. Microsoft and Nvidia have collaborated on a project to apply AI toward optimizing “the entire life cycle of a nuclear plant, spanning site permitting, design, construction, and continuous operations.”

The reason for the rapid adoption, according to Nuclearn CFO and cofounder Jerrold Vincent, is twofold. First, the technology companies banking their futures on AI are also keen on a revival of nuclear power to provide the electricity for all those data centers. Vincent says AI is also ideal for addressing some of the nuclear industry’s most pressing problems. Many of the things that make reactors so difficult to operate—intricate regulations, along with complex systems for management and maintenance—are exactly the kinds of tasks that AI is good at. The industry is staggering to deal with aging hardware and an aging workforce. If it is going to survive, and have a real shot at a nuclear revival, it will need help from machine intelligence, Vincent says.

“There’s this huge growth in demand from advanced-reactor developers and in new nuclear,” Vincent adds. “But the existing workforce has been spread really thin. So most of the time when we’re coming into a customer, [they're saying,] ‘I’ve had jobs open and I can’t fill these positions. We need to do the things required by regulation. How the heck are we going to do it?’”

AI Streamlines Nuclear Documentation

Right now, the principal output of a nuclear reactor (aside from electricity) is paperwork. Nuclear plants produce piles of documentation to demonstrate regulatory compliance and to record how they respond to any potential problem, from a crack in the sidewalk to a fault in the reactor.

“Every engineering decision has to be evaluated, independently verified, and checked,” Vincent says. Nuclearn’s AI products help plants search through large repositories of data more efficiently to more quickly file paperwork with the federal government or figure out how a particular problem was solved in the past. “If you think of it as ChatGPT for nuclear,” he says, “it’s actually not a bad place to start.”

Rob Austin, the Electric Power Research Institute’s leader for the NIVA project, says the project started a year ago when Constellation chairman Joseph Dominguez issued a challenge to the nonprofit industry groups that act as storehouses of information about regulatory compliance, maintenance and repair, and mechanical history. He asked the groups to find a way to make that industry knowledge available with the convenience of a large language model like ChatGPT, Copilot, or Gemini but keep it secure at the same time.

The task fell to Atomic Canyon, whose CEO Trey Lauderdale previously worked to integrate AI into health care, another conservative industry. Living and working downstream from Diablo Canyon, California’s last operating nuclear plant, he began to see how nuclear energy and AI could form a mutually beneficial relationship: A revival of nuclear power could help feed electricity to energy-hungry data centers, but only if AI could help the industry thrive again.

“The goal was to make decades of operating experience, hundreds of thousands literally of operating experience reports, tens of thousands of technical documents, and a whole lot of information available for people to find.” —Rob Austin, Electric Power Research Institute

A major hurdle is the public’s opinion of nuclear power, which soured decades ago after notorious accidents and worries about waste and only recently has begun to recover. But the resulting halt in construction of new nuclear plants has created another hurdle of its own: a loss of institutional knowledge, Lauderdale says. As the industry stalled in the U.S., fewer people entered careers as nuclear scientists or technicians. The result is a people problem. Nuclear power has a remarkably skilled but quickly shrinking workforce tasked with keeping old reactors online and possibly starting up new ones to meet surging demand for electricity.

“There is absolutely no way we as an industry will be able to have this nuclear resurgence or nuclear renaissance without augmenting the capacity of this knowledge-based workforce,” Lauderdale says.

For him, the answer is an AI that can intelligently search the repository of data that the nuclear workforce has created over the decades. When Atomic Canyon won the NIVA contract in 2025, the first step was to download 53 million pages of publicly available data from the Nuclear Regulatory Commission to train the model on the operations, vernacular, and regulations particular to nuclear power. Lauderdale then struck a partnership with Oak Ridge National Laboratory to build the first nuclear-specific AI models.

“Technically, they’re sentence-embedding models,” Lauderdale says. “Fancy way of saying, we taught AI how to seek the nuclear language. Nuclear is almost like its own vernacular.” The goal is that today’s nuclear workers can ask questions about every aspect of plant operations, from filling out regulatory forms to procuring the right tools and materials, simply by writing a query and letting the AI sort through all of the accumulated institutional knowledge.

Unifying Nuclear Data for AI Searches

When Lauderdale began to work with Diablo Canyon on integrating AI into the nuclear industry, it became apparent that one bottleneck was simply getting decades’ worth of a nuclear plant’s documentation—procedures, best practices, safety recommendations—into an easily searchable format. Information across the industry was scattered and not standardized, which is why three of the biggest nonprofit entities in the industry (the Institute of Nuclear Power Operations, the Electrical Power Research Institute, and the Nuclear Energy Institute) moved in 2025 to create unified datasets, an idea that would birth the NIVA AI assistant developed at Atomic Canyon.

For example, EPRI’s Austin says, nuclear engineers might run into a problem with one of the house-size, thousand-horsepower vertical pumps that circulate vast quantities of water used for cooling purposes. In their search for a solution, the workers have access to plenty of information—too much information, perhaps. They can read through EPRI’s maintenance guides about those components. They can also call upon tens of thousands of incident reports stored with the Institute of Nuclear Power Operations about things that have gone wrong with pumps in the past. AI’s task is to synthesize all those reports to create a guide to the main failures that strike a pump, and, crucially, to show all its work so engineers can find the documentation it used to make its recommendations.

“I want to know all the other nuclear power plants that have that same part,” Lauderdale says. “Have any of them seen a similar issue? How did they fix it? What’s the troubleshooting look like? If you think about nuclear, we have a tremendous amount of information. The challenge is, how do you collectively gather knowledge from that information dataset? And that’s what AI is really, really good at doing.”

“If you think of it as ChatGPT for nuclear, it’s actually not a bad place to start.” —Jerrold Vincent, Nuclearn

Secrecy is a major hurdle to modernizing nuclear operations. AI systems that chiefly help with documentation and data search aren’t subject to the strictest rules, as they would be if they interacted with components that control the power plant. But nuclear data itself is largely proprietary or protected by federal law.

Nuclearn offers only single-location solutions to deal with this, so that a power plant’s data can never leave that site. NIVA can only search databases that already required plant-level security clearances, Austin says, so the same rules hold true for using the AI assistant to hunt through that deluge of data. Technology cannot wipe away these human barriers, but it does provide new incentives to find ways to share data more openly.

“The goal was to make decades of operating experience, hundreds of thousands literally of operating experience reports, tens of thousands of technical documents, and a whole lot of information available for people to find, and do so in a way that’s going to be much more accessible and easy to use, but at the same time preserving data security and that critical information, keeping control over it,” Austin says. That’s exactly what NIVA does.

Nuclearn’s “Equipment AI” system lets engineers pose queries about nuclear-equipment operations and generates answers that draw on decades of industry experience.Nuclearn

AI-Assisted Nuclear-Plant Inspections

So far, both the Nuclearn and NIVA large language models used in the nuclear world report to humans and don’t go anywhere near power plant operations. Still, the inevitable question arises: What if they could?

Vincent and Austin both state that such a feat is far away, but they can envision the pathway there. Consider the litany of welds throughout a nuclear plant, Austin says. All of those welds currently must be inspected for quality and endurance by a human. One pilot project at EPRI is testing the ability to have AI look at the mountains of data generated by such inspections to see whether it can flag areas of concern. A human remains in the loop with that project, Austin says, to review all of the AI’s recommendations, and no power plant is allowing AI to take over its inspections. But the project marks a step toward using AI on direct plant operations rather than only guidance for the human operators.

“I cannot see, in the short or the long term, an environment where AI is making decisions around the operation of the plant, because of the regulatory nature and also because of the risk nature,” Lauderdale says. “You don’t want machines making decisions that could impact the operations. So, I believe we’re going to have humans in the loop for a very, very long time.”

This White Paper gives robotics researchers and engineers an overview of a new large-scale motion capture dataset built to close the data gap limiting humanoid robot learning. It also shows how policies trained on the dataset transfer to a real humanoid robot.

What you will learn about:

• Why humanoid robot learning, a central problem in embodied AI and Physical AI, needs data that internet video and existing motion capture datasets cannot provide.
• How FrameNet, a linguistic framework for human action, can guide motion capture collection to systematically cover a broad range of whole-body motion.
• Why synchronized object trajectories and meshes make human-object interaction data useful for teaching robots real-world tasks such as carrying, pushing, and pulling.
• How reinforcement learning policies trained on this motion capture data improve with scale, and how sim-to-real transfer carries them onto a physical humanoid robot.

For the first 10 years of my career, I worked in product management and data analytics by myself. I wrote database queries that pulled numbers out of corporate systems, built statistical models to predict what customers would buy, and shipped data pipelines that moved information between business systems.

I built and scaled analytics teams at Best Buy and Target, studying how customers shop and what stores should stock. Today I lead enterprise AI transformation at Lowe’s, the Fortune 100 home improvement retailer.

The goal is not to sell artificial intelligence; it is to use it to deliver useful expertise at the moment a customer needs it. In retail and other customer-facing industries, virtual assistants can help people address everyday questions—such as how to repair a leaky faucet—while guiding them toward relevant products, services, or next steps. As these capabilities become more common, technology roles are changing. The work is no longer limited to building AI systems; it also includes defining how they operate: which decisions they can make autonomously, when they must escalate to a person, and which actions must remain off-limits.

That shift—from building AI systems to governing them—is coming for anyone who is accountable for what such systems produce. Not the casual user typing into a chatbot but the engineers, product managers, analysts, and business operators who sign off on work a machine drafted.

It is the subject of the book I recently coauthored, The Enterprise Brain. I call the change the “governor shift,” from executing tasks yourself to setting the intent, principles, and boundaries within systems that execute them for you.

Business operators might not write code; they will decide which pricing exceptions an agent may approve and which it must escalate.

That is governing.

A 2025 report from MIT Media Lab’s Project NANDA found that, despite an estimated US $30 billion to $40 billion in enterprise generative-AI investment, the vast majority of organizations in its dataset had not yet demonstrated measurable profit-and-loss impact. The report estimated that only about 5 percent of integrated pilots were generating substantial value, underscoring how difficult it remains to move from experimentation to scaled business outcomes.

Researchers named the pattern the GenAI Divide, the term I adopted for the book.

The companies rarely lack technology; they use the same models as the 5 percent that are winners. But they lack people who can direct the systems and stand behind the results.

Guidelines to follow

Here are six guidelines.

• Recognize when you have become “human middleware.” In software, “middleware” is the code that sits between two systems and passes information back and forth. Many of us have become its human version. Take an honest look at your week. How much time is spent pulling data out of one tool, reformatting it, and routing it to another team? I call this the “administrator trap,” which is set by the architecture, not by the people caught in it.
  
  Relaying is what AI agents now do well. But they cannot judge which numbers deserve attention, which risks are real, or which compromises are worth making.

• Trade rules for principles. For many years, workers used rules to manage their work. Refunds for a product over a certain amount needed a signature from upper management, for example. Writing code needed two reviewers. Rules work at human speed. But rules break when a system makes thousands of decisions per hour and meets situations no rulebook anticipated, such as a complaint covered by three different policies. A rule says to do exactly this specific thing; a principle says to achieve the outcome without crossing certain lines.
  
  Governing AI means writing those principles in priority order so the system settles its own conflicts the way a well-led team does when the manager is not available. Never harm the customer. Tell the truth even if the company loses a sale. Protect the economics, and then move quickly. Underneath sits a question of decision rights: the formal authority over who or what may make a given call. Writing down the answers in what I call a “library of principles” is now core leadership work, whether you’re a technologist or a business owner.

• Write your culture into your code. Many companies have turned their values into posters that hang on office walls. But an AI agent cannot read the posters. Instead, write your governance as code. Include your values and policies as machine-readable instructions that the AI agent will follow automatically.
  
  Do so in three layers. The top is the constitution, which states the rules an agent may never break, and never state a fact it cannot support. The second layer is the doctrine: how the business competes and the acceptable trade-offs to get there, such as protecting a long-term relationship over a short-term sale. At the bottom sits the playbook, which has the tactics used for one task.

• Install a trust thermostat, not a trust switch. The question that stalls nearly every company’s AI deployment is some version of: “What if it tells our biggest customer something wrong, or quotes a price we will not honor?” It might. Treating trust as a switch leaves two bad options: an unsupervised system or a human reviewing every transaction—which would cost more than the automation would save.
  
  The alternative is a thermostat. Every decision an agent makes carries a confidence score measured against the principles set. Above an agreed threshold, it proceeds alone; below it, a human decides. That person’s answer is fed back into the learning loop so the next similar case clears the threshold on its own. Every decision stays transparent, auditable, and explainable—which is what I call a glass box.

• Fix context before you govern. You cannot govern a system that cannot see the whole picture. Ask your best employee about a project, and they will pull together the budget, the contract clause, and the customer’s last complaint because they know it all by heart. Most enterprise AI fails that test, because the information sits scattered across applications that store it in incompatible formats. I describe the full loop as Connections, Context, Reasoning, Actions, and Governance (CCRAG).
  Connections feed in raw information such as transactions and service records. Context weaves it into a context graph, which is a single connected picture of the business that gives agents something close to memory. Reasoning makes the decisions. Actions carry them back into the business systems. Governance keeps things aligned with the company’s intent.
  
  Most organizations obsess over the reasoning in the middle and underinvest in context and governance, which is exactly where humans play a role. Context compounds: Every interaction makes the graph richer and harder to reproduce.

• Learn to lead by exception. The most important change in habit comes last. Most of us have been trained to check every report and every number because we never knew where an error could surface. In a governed system, the machine tells you which cases it could not resolve confidently. Routine workflows go untouched, and your attention goes to the small portion that is ambiguous, unfamiliar, or high stakes.
  
  At first, that might feel like losing control, but it is the opposite. It is what makes a self-scaling enterprise possible, an organization whose output grows without its head count growing in proportion. People were not removed from the loop; they were raised above it.

The identity question

When I talk with people about the shift, their resistance is rarely about technical issues. More often it is about identity: If the AI does the doing, what do I do?

I have watched capable people freeze on that question. I’ve also asked myself the question.

Doing was never really the job, though. Judgment was. Doing was just how we expressed it.

AI has not made judgment less valuable. It has made it the scarcest resource in the organization because, for the first time, one person’s judgment, written down well, can guide thousands of decisions each day.

Judgment has a twin we talk less about: taste. Judgment tells you whether an answer is sound. Taste tells you whether the question was worth asking and which of a hundred defensible options to offer the customer. A machine will happily generate all 100 options, but it cannot tell you which is best.

The AI transition rewards instincts many IEEE members already have: systems thinking, precision about requirements, and honesty about failure modes. The tools have changed, but the discipline has not.

Governing is where taste and judgment stop being soft words and become the work itself.

The people who treat it that way, rather than as a step away from engineering, will define the profession in the age of AI.

A crucial safeguard against AI agents going rogue—keeping humans in the loop to review and approve their decisions—will fail unless designers and users change their current practices, a trio of leading AI ethics researchers argue.

Though most autonomous agents have systems to keep users in the loop about their actions, in practice these processes actually push humans out of the loop, the authors argue in a paper posted to ArXiv on 6 September. In other words, “the human just becomes this meat tool to give permissions without the cognitive capability to engage,” says one of the authors, Avijit Ghosh, the lead technical AI policy researcher at Hugging Face, an open-source machine learning platform.

In the near term, the paper says, humans being out of the loop leads to agents acting in ways people don’t know about or want (like July’s hack of Hugging Face by a swarm of OpenAI bots). In the long term, it will cause users to lose the “cognitive capacities” they need to control AI, write the researchers, which aside from Ghosh include Margaret Mitchell, Hugging Face’s chief ethics scientist, and Samir Passi, an affiliate of the Data and Society Research Institute.

Though the trio began working on the paper before the Hugging Face hack was disclosed, their conclusions result from “logically thinking about what is going to happen if the current trends continue—and we saw that being precipitated via the Hugging Face attack,” Ghosh says.

Instead of focusing on human oversight, Ghosh says, many in the field believe AI can monitor AI. “They’ll say, ‘Oh, we have this other LLM tracking the logs.’ But [without a human in the loop] how do we know that these two LLMs are not scheming together?”

Even if these problems are new to artificial intelligence researchers as the industry rolls agents out into the world, the challenges are familiar to researchers in adjacent fields, like robotics and autonomous vehicles, notes Mary L. Cummings, the director of George Mason University’s Autonomy and Robotics Center. Cummings has spent decades investigating how people interact with autonomous systems.

“While I appreciate what the authors are trying to say, they just use a lot of academic words to say AI companies should care about human factors,” she wrote to IEEE Spectrum in an email. AI developers are “late to the party” in focusing on “cognitive engineering.”

Flaws in AI Agent Design and Oversight

The key flaw in current agent design, the authors argue, is that the bots are tailored to meet benchmarks like speed, accuracy, and volume of work performed. The needs of human overseers are treated “as a separate consideration independent of the quality of the system.”

As a result, agents often overwhelm human overseers with more information than they can comprehend. As an example (not mentioned in the paper), the 1,200 bots involved in the Hugging Face attack generated 1.2 million messages on their improvised messaging system. (Hugging Face was recently acquired by Nvidia, which announced its own hardware-and-software-based approach to controlling AI agents on 28 September. Ghosh declined to comment on possible future impacts of the merger, noting that the two organizations remain separate until the merger process concludes.

A system that really kept humans in the loop would accommodate the human mind’s built-in biases, the authors write: “With automation bias, users accept system suggestions even when they are wrong. With anchoring bias, people are more likely to agree to an AI system’s decision” without thinking of alternatives. Effortful reasoning doesn’t feel as good to users as does quickly approving an AI’s plan—especially if they feel overwhelmed. And AI’s sycophantic manner tells users that they’re doing well, which undermines the skepticism and self-monitoring that oversight requires.

To address the problem, the authors write, agent developers should introduce friction in human-AI interactions. That would prevent users from falling into boredom, passivity, or thoughtless clicking.

The authors suggest some options to achieve this. An agent might require that the user record his or her own choice for the next step before it reveals its plan, or respond to user approval by replying “What evidence would change your mind?” An agent could also change its behavior if it detects that the humans in the loop are spending less time on each approval.

Meanwhile, organizations that adopt agents into their workflows should structure human-AI collaboration “to prevent both fatigue and the cognitive surrender from prolonged exposure to agentic AI,” the authors write. That could include having workers perform tasks without agents from time to time or requiring that they take breaks from monitoring duties.

All of these suggestions introduce friction and delays—exactly the things, Ghosh acknowledges, agents are supposed to reduce. But, he says, “the notion of increased productivity is a myth” when people can’t monitor and control AI. Time saved by delegating work to agents has to be measured against time that must be spent fixing agent mistakes.

“Safety and capability don’t have to be separate things,” another coauthor, Hugging Face’s Mitchell, posted on X (formerly Twitter) on 14 September. “Safety only makes things slower when it’s tacked on, outside of the core technology.”

On 13 September, leading luminaries and up-and-coming talents in mathematics and computer science congregated at the annual Heidelberg Laureate Forum in Germany for a week of discussion, networking, and—naturally—wurst.

Having attended several of these events in the past, the mathematicians normally focus their chatter on which famed researchers are present, or what interesting problems they have been working on. But instead, every snippet of conversation caught in passing or any debate accidentally overheard was about how AI companies such as OpenAI, Anthropic, and Google are steamrollering their way through mathematics.

And there is good reason for these fervent discussions. Mathematics is the perfect testing ground for AI, involving step-by-step logical reasoning and answers that are automatically and objectively verifiable. This has led tech giants to develop their AI mathematical capabilities at a terrifying rate this year, leading to both new solutions to previously unsolved problems and a reckoning within the community as to what it means to do math in the age of AI.

Tech Giants Target the Millennium Problems

In short order, AI has gone from struggling with everyday research-level problems to solving a whole raft of teasers posed by prolific Hungarian mathematician Paul Erdős, then verifying the proof of Fermat’s Last Theorem, and—most recently and famously—OpenAI announcing it had solved the Navier-Stokes existence and smoothness problem. As attendee and young researcher Ailsa Robertson, of the University of Amsterdam, put it: “AI and LLMs set the math community on fire over summer.”

Of the seven extremely difficult Millennium Prize Problems posed in the year 2000 by the Clay Mathematics Institute, only one, the Poincaré conjecture, has been solved by humans so far. OpenAI’s claim to have solved a second, the Navier-Stokes existence and smoothness problem, if verified, represents a watershed moment for automated reasoning.

Fields Medalist Jacob Tsimerman, of the University of Toronto, was keen to recognize this achievement during a press conference at the forum: “We’ve seen rapid increases in capabilities, even faster than many people, including myself, expected,” he said. “Is AI doing something new? I don’t know the details, but…solving Navier-Stokes feels pretty definitive.”

Since this announcement, rumors have swirled about which of the remaining Millennium Problems will be next. One candidate is the Riemann hypothesis. In August, Anthropic quietly employed an unreleased version of Claude to tackle this problem, making important progress on a related problem but not on its main mission. And OpenAI is reportedly focusing on solving the Hodge conjecture. With these tech giants applying the full might of their most advanced unreleased models, many mathematicians see it as inevitable that at least some of these problems will be solved soon.

The tech giants are likely focusing on the Millennium Problems as a way of verifying the capabilities of their advanced AI systems, with the added bonus that solving these famous unsolved problems shows potential users and investors how powerful their technology is. “They’re really just solving these difficult mathematical problems as benchmarks, as some kind of PR stunt,” opined Fields Medalist Peter Scholze, of the University of Bonn, in Germany, during a panel discussion at the event about AI in mathematical research.

Why OpenAI’s Navier-Stokes Solution Became a Controversy

Alongside Scholze and Tsimerman at the panel discussion were thought-leading mathematicians Michael Harris of Columbia University, in New York City, and Geordie Williamson, of the University of Sydney. For them, these and many more problems researchers are now facing stem from the way in which tech giants fail to adhere to the norms and values deeply instilled in the mathematics community. And this was perfectly exemplified by OpenAI’s Navier-Stokes announcement.

“I think that OpenAI behaved extremely poorly, and that we should acknowledge that in the community,” said Williamson. Harris had a front-row seat to this alleged poor behavior. He received what mathematician Tristan Buckmaster, of New York University—who was making significant progress with Anthropic staffer Levent Alpöge on Navier-Stokes—claimed was correspondence between him and OpenAI that appeared coercive, censorious, and even threatening.

“I trusted Tristan’s account of this interaction…and I guess I did my part in promoting his narrative,” Harris said. “But…on social media and traditional media, most reports are consistent with my takeaway; that is, they depict OpenAI as bullying and disrupting disciplinary norms.” OpenAI did not respond to requests for comment prior to publication.

Beyond OpenAI’s sportsmanship (or lack thereof), Williamson is concerned about what AI’s march across mathematics this summer does to the field, both for working mathematicians and for the knowledge that can become useful to the broader society.

“What we want as a mathematical community is understanding, but we measure this against unsolved problems, and the problem is that these two measurements are very, very quickly becoming uncorrelated,” he explained, referring to how AI solutions might give an answer but usually don’t develop new methodology that is understandable or useful. “So now…we suddenly must reevaluate things like how we assess people, who gets jobs, how do we educate people, etc. This is going to be a big challenge.”

Academia Under Pressure

While the tech giants tear up the rule book in their battle for supremacy, it is ordinary human mathematicians that are bearing the consequences. Unbridled access to powerful AI technologies is affecting how mathematicians work across the world.

Young mathematician Mita Ramabulana, of the University of Cape Town, is a case in point. He said that two of the 10 advances in mathematics that OpenAI announced in August overlapped with his own work but left him disappointed “because you spend some time thinking about these things, and these days you don’t know whether someone is just going to plug in a problem that you care about in some LLM and solve it.” He worries that unscrupulous researchers are using LLMs to scoop others or gain professional advantage.

For Robertson, currently studying for a Ph.D. in quantum-safe cryptography at the University of Amsterdam, the problem is even more acute. She has seen all of her mathematics colleagues turn to using LLMs intensively in their research, with many maxing out their Pro subscriptions, and some even spending thousands of euros on additional tokens: “And these are Ph.D. students who don’t have thousands of euros,” she added.

Robertson said that there are colleagues who feel coerced by the tech giants, with the likes of OpenAI announcing in July that it is giving away 100,000 free licenses to its frontier models for researchers in academia. And there are other colleagues who feel they simply have no choice: “If you don’t work at the rate at which you could work with LLMs then you will be behind your peers who will be applying for the same jobs as you.”

This is part of the reason why Robertson’s Ph.D. is now a lot less mathematics-heavy and focused on the societal implications of transitioning to a quantum-safe ecosystem: “Because I don’t want to be in a career where you’re verifying LLM output.”

The spring and summer of 2026 witnessed a string of incidents in which AI agents collaborated on deceptive, unexpected, and sometimes illegal behavior. The most famous example is OpenAI’s hack of AI platform Hugging Face, in which a swarm of roughly 700 AI agents escaped a testing environment and then hacked several companies, searching for information that could help them disguise cheating on a cybersecurity benchmark called ExploitGym.

It was not an isolated failure. The United Kingdom’s AI Security Institute (AISI) and independent researchers have since documented similar cases in which agents created unauthorized channels to communicate and collaborate. AISI found that several agents running Anthropic’s Mythos 5 model turned a GitHub repository into a shared message board. More recently, researchers discovered that OpenAI agents had turned a dormant German programming wiki into a bulletin board to share information about their activities.

Stephen Casper, an assistant professor at Harvard Kennedy School, expects that the incidents that have occurred to date are only the start. He says that, without intervention, the Internet could see a “cyber Cambrian”—an explosive growth of agents collaborating and misbehaving online. “The laundry list of incidents in which AI systems broke out of sandboxes and took unsanctioned actions should suggest to us strongly that today’s frontier AI systems have exceptionally strong cyber capabilities,” he told IEEE Spectrum, “and a penchant for pursuing their own goals.”

Could OpenAI Have Detected Agent Collaboration?

While the recent examples of AI agent misbehavior are various, Casper says most incidents to date have a similar point of failure. “For AI systems to truly get out in the world without humans having meaningful control, they have to either escape, or be released from human-controlled servers.”

In the case of the Hugging Face incident, Casper suspects that better monitoring for out-of-scope activity could have stopped the agents before they breached their testing environment.

This suspicion seems well-founded. By OpenAI’s own account, the activity taken by its agents was so prolific that it crashed Artifactory, a third-party software-package management tool deployed internally that was compromised by the agents and used as a message board. While OpenAI’s internal security detected signs of unusual activity, it didn’t understand the scale of the problem and didn’t stop the offending ExploitGym evaluation run until 16 July—about two months after the first agent posted a message to Artifactory. By that point, agents had posted hundreds of thousands of messages.

“All of this could be monitored for,” says Casper. “Given its knowledge about breakouts, collusion, and unsanctioned internet access dating back to May 26, OpenAI should have been able to anticipate, monitor, and mitigate this type of behavior.”

Engineering Ways to Watch AI Models

Asim Husain is a cofounder at Alterion, a company building tools to control AI agents. These include tools that monitor an agent’s outputs and those that monitor an agent’s actions.

The company’s monitoring solution, Helix, examines the chain of thought and text output of large language models (LLMs). “It’s a network of small language models,” says Husain. “Purpose-built classifiers for a narrower job are cheaper, faster, and more reliable at the specific thing they’re trained to catch.” If an LLM output is flagged as suspicious, Helix steps in. “It acts, blocking the call, killing the session, rolling back the action, whatever the policy calls for,” says Husain.

Alterion’s second solution, Draco, controls the actions agents take. “It sits in the execution path of an agent and enforces policy on what an agent is doing,” says Husain. Rather than examining the model’s chain of thought or text output, Draco evaluates the actions the AI agent takes on a system, like calling a tool or opening a file. It can then stop actions that fall outside those the agent’s pre-defined scope.

Husain says that in addition to monitoring each individual agent’s output and actions, these tools are an effective way to watch for collaboration. “We treat it as the same problem, not a separate one,” says Husain. “Agent-to-agent communication is just another action an agent takes, so it goes through the same control plane as tool calls and data access.”

That’s not to say the challenge of controlling and monitoring AI agents is solved. Reilly Haskins, a member of technical staff at METR, a research nonprofit that evaluates risks posed by AI models, says the methods used to monitor AI models continue to evolve as the models improve and change.

“There’s a consensus that the most monitorable part of a model is its chain of thought,” he says, but adds that chain-of-thought output can at times be difficult to parse. “Is it gibberish to a human, or is it pretty clear what the model is thinking?” A model might reason in an abstract language and perform better because of it, he says. Examples of this behavior can already be found in the Hugging Face incident, as agents often communicated in shorthand phrases such as
“please_HOLD_swarm_I_prepare_safe_exfil_reply_zz_MAILBOX_JAN183411.”

Solutions Exist, but Enforcement Lags

Engineering systems to monitor and control AI agents which attempt to collaborate, coordinate, and perform out-of-scope tasks is possible, though the methods are evolving.

However, there’s currently no legal framework or industry standardization to enforce, or even recommend, methods for controlling agents that collaborate. That’s where AI governance—new laws, regulations, and industry practices—can come into play.

Noam Kolt, an assistant professor at the Hebrew University of Jerusalem who leads its Governance of AI Lab, says companies should place more emphasis on legal compliance when training and deploying models. In a recent paper, Kolt and a coauthor questioned AI models using a method developed by Tom R. Tyler, author of Why People Obey the Law (Princeton University Press, 2006). When asked abstract questions, models generally told the researchers that the law matters. However, these replies didn’t always translate to actions. “There’s this weird disconnect,” Kolt says. One of his lab’s evaluations asks AI agents to unlawfully edit corporate records. “And well, sometimes they do.”

This behavior was reflected in the Hugging Face incident. OpenAI’s agents sometimes considered the possibility that they were engaged in “unauthorized” activities but proceeded anyway, sometimes with encouragement from other agents.

The solution could include a change to the instructions that guide a model’s behavior. Claude’s constitution, for example, sets out the values Anthropic wants the model to follow. But “perhaps surprisingly, law is just not one of them,” Kolt notes. These instructions could rank legal compliance high in a model’s list of priorities and provide examples of how a model should approach situations where a goal can be achieved only through unlawful conduct.

National governments have yet to issue laws or regulations to clarify how agents are held responsible or enforce standards. Kolt says that under U.S. law, responsibility defaults to an entity that controls an agent. However, the legal understanding of agency hasn’t been revisited in 20 years and may need clarification to address modern AI agents. In another recent paper, Kolt and his coauthors argue that even the European Union, often seen as a leader in AI regulation, hasn’t kept pace with AI agents, as the EU’s AI Act is written to address older models that were less capable of autonomous actions.

Casper, who has coauthored several papers with Kolt, agrees that adoption of new laws and standards is the key. He says the engineering strategies to control agentic behavior and collaboration aren’t perfect, but they already work well enough to provide a benefit. “The main bottleneck is the adoption of best practices,” says Casper. “So yes, I ultimately think that governance is the thing that’s needed the most.”

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Building useful robots starts with understanding the people who use them. For Charlie Kemp, cofounder and chief technology officer of Hello Robot, that means developing assistive robots that can help people with everyday tasks and support greater independence.

In this Robots Guide profile, Kemp shares his path from studying artificial intelligence at MIT to building Stretch, explains how working with people with disabilities has shaped his approach, and offers advice for aspiring roboticists. Read the full profile on IEEE’s Robots Guide.

Space was always supposed to be the final frontier of human exploration. It’s shaping up to be the final frontier for artificial intelligence too.

Last December, NASA’s Jet Propulsion Laboratory used Anthropic’s Claude models to help plan two Mars drives for the Perseverance rover, with human planners checking and adjusting the route before upload. In May, NASA and IBM put a compressed AI model on the International Space Station and a satellite to identify things like floods and clouds from orbit, the first model of its kind demonstrated in space. And in July, astronauts on the ISS tested a large language model to see if it could help with questions on maintenance procedures.

These experiments point to a larger shift in space engineering. For decades, engineers on Earth determined what a machine in space would do, and the machine would do exactly that. Now, researchers are testing whether nondeterministic systems like generative AI can give spacecraft more flexibility to interpret their surroundings, plan tasks, and one day make decisions for themselves. The technology is still far from trustworthy enough to hand over control of a spacecraft, but engineers are starting to ask whether they can afford not to do so as missions become more complex, distant, and numerous.

Why Spacecraft Need True Autonomy

Spacecraft have been operating autonomously for decades. But autonomy has never been the dominant model, in part because space engineers have prized systems whose behavior they can predict.

“Autonomy often does not have a deterministic outcome, which means that how you got into a certain situation changes the behavior,” says Robert Ambrose, the former chief of NASA’s Software, Robotics, and Simulation Division. “So if you come into the same situation but from different paths, the outcome could be different. And so engineers hate that.”

Ambrose spent much of his career working on autonomous systems at NASA, including autonomy for the Orion spacecraft, NASA’s deep space and lunar orbiter spacecraft, and Robonaut 2, a humanoid robot designed to work alongside astronauts that went to space in 2011. With Orion, he saw how quickly the testing problem could multiply. Engineers had to consider not just what the spacecraft might do, but all the different ways it could have arrived at a decision.

But Ambrose says engineers found ways to manage that complexity, including automating the testing itself. “We fought the challenges of autonomy using autonomy,” he says. “That actually works.”

The need for autonomy becomes even more obvious the farther a mission travels from Earth. Ambrose points to a possible mission to Europa, Jupiter’s ice-crusted moon, where a spacecraft could dive through a water plume erupting from beneath the surface. The plume could appear too quickly for engineers on Earth to direct the spacecraft into it.

“It’s up to the spacecraft to make a decision, and we’ll be watching what happened an hour ago,” he says. A mission like that, he added, is “totally impossible” without giving the machine real autonomy.

Icarus Robotics engineers gather valuable training data for a free-flying robotic system—destined for the International Space Station—during a zero-g flight.Icarus Robotics

Adapting Robotics to Microgravity Physics

As AI and robotics advance on Earth, a commercial space boom is creating new opportunities to put those technologies to work in orbit. But what works on Earth does not necessarily work in space.

Icarus Robotics is developing what it calls a robotic labor force for space. This includes Joy, a free-flying robotic system. Joy recently finished zero-gravity testing in Canada ahead of a planned deployment to the ISS, where one of its first tasks would be moving cargo bags between modules. The company plans to start with teleoperation, using that data to eventually train the robots to work on their own.

“What the rollout will probably look like is something much closer to beginning with partial autonomy, so you still have human supervision in the loop at all times,” says Jamie Palmer, Icarus’s cofounder and CTO.

A robot trained on Earth learns from the physics of the environment around it, Palmer says, and in orbit, the physics is entirely different.

“If you take the newest Gemini robotics model, or you take the newest physical intelligence model, and you put it in zero-g there, it’s just going to fail immediately,” he says. On Earth, for example, a robot learns that when it pushes something off a table, the object falls. In orbit, it keeps moving.

That leaves Icarus with a problem the terrestrial robotics industry also faces, but in a more extreme form: There is very little real-world data from the environment where its robots will operate. The company is combining demonstrations from its robots in microgravity with simulations and tests on Earth to build its own dataset. “I wish there was” an available dataset Icarus could download and use, says Ethan Barajas, the company’s cofounder and CEO. “But there’s not today, not in a meaningful way.”

Managing Autonomous Spacecraft Risk

The challenge is not simply teaching spacecraft to act on their own. It is figuring out how to manage the risks of giving them more freedom.

“It has been mind-boggling to me how little autonomy we have in space applications,” says Ufuk Topcu, an engineering professor at the University of Texas at Austin and the director of the Center for Autonomy. “Because it’s exactly the place where human involvement is extremely hard, the stakes are high, and you need to act fast.”

Topcu says that the goal cannot be to guarantee that autonomous systems will never do anything wrong. They’re most useful in situations humans cannot anticipate, he says, so instead researchers need to start with restricted applications, learn how the systems behave, and gradually expand where and how they are used. The real question, in his view, is how well the risk of deployment is managed, not whether they can be fully eliminated.

That may become increasingly important as the space industry changes. For most of the space age, a small number of government agencies designed missions that could take decades to develop and operate. Commercial companies are now putting more spacecraft into orbit, and new missions can be developed and launched much faster.

“Space used to have very slow innovation cycles,” Topcu says. “They would think of a mission concept and spend 10 or 15 years on it. It’s not like that anymore. Everything is evolving faster now.”

Have you ever read a paper in Science or Nature and thought, “Man, that research was so cool. I wish I could try that method on my own data,” only to spend a week wrestling with someone else’s undocumented repo, broken dependencies, and half-finished readme.txt?

Well, now you can, more or less.

Say hello to Paper2Agent, a new open-source framework that transforms academic reports into interactive AI agents you can talk to. Give it a paper, along with the accompanying codebase, data, or other supplementary material, and the system automatically extracts the core workflows, then spins up a tested, runnable toolkit that you can use on your own datasets.

The concept may sound a little like Google’s NotebookLM (now called Gemini Notebook), which lets you upload documents and chat with an AI about what’s in them. But Paper2Agent aims to go a step further: Rather than simply answering questions about a paper, its agents can actually run the methods described in it—and potentially combine those methods with tools from other papers.

The goal, explains Stanford computer scientist James Zou, is to change what a scientific paper fundamentally is. “Knowledge should not be static records,” Zou says. “It really should be dynamic and interactive—and this has many benefits, including making knowledge more reproducible but also enabling all sorts of new kinds of discovery.”

Zou and his colleagues described the tool 16 September in Nature. They tested Paper2Agent across diverse disciplines including statistics, econometrics, and astrophysics. However, the researchers focused their proof-of-concept demonstrations on computational biology, where turning published methods into usable tools can be particularly cumbersome.

From Paper to Prompt

The team started with AlphaGenome, a deep-learning model that predicts how mutations in DNA affect gene regulation. (A companion resource unveiled earlier this month, the AlphaGenome Atlas, cataloged the model’s predictions for all 9 billion possible single-letter changes in the human genome.)

The researchers fed Paper2Agent the corresponding documentation and code. About 45 minutes later, with no human intervention, the AI had produced 22 tools covering different aspects of AlphaGenome’s functionality, all on a personal laptop and for less than US $15 in computing costs. One tool, for example, could predict how a DNA change might affect gene activity, while others could compare those effects across tissues or analyze multiple variants at once.

“The idea of making papers more dynamic and executable through an agentic interface is quite compelling.” —Dongping Chen, University of Maryland

All 22 tools passed automated validation, thanks to a testing agent working behind the scenes to run the various AlphaGenome sub-tools against reference results. If a test failed, the agent would diagnose the problem and try to fix the tool, with up to six attempts per function. If that didn’t work, it could drop the tool altogether.

The validated tools were then packaged into a Model Context Protocol server and connected to Claude Code (though any compatible chat-based AI assistant could do). The result was a user-facing AlphaGenome agent that could take questions in plain English, run the appropriate analyses, and spit back results and visualizations.

The team then put the agent through its paces, hitting it with a battery of questions, ranging from simple requests to open-ended research problems. According to the researchers’ analysis, it outperformed both standard Claude given the AlphaGenome codebase and a specialist AI co-scientist tool called Biomni.

Multi-Agent Research Paper Collaboration

Going one step further, the researchers turned a couple of more papers into interactive agents and linked them with the AlphaGenome agent. (One of the additional papers was on how inherited DNA variants linked to autoimmune disease disrupt cell function, while the other was a more systematic exploration of how silencing every expressed gene alters immune cells.)

Prompted to investigate the genetic basis of psoriasis, an itchy skin disease, the three agents collectively zeroed in on a little-understood gene called GPR137 as a likely causal factor. What’s more, the AI proposed 10 ways to validate this inference. A human researcher selected one, and the resulting analysis found that silencing GPR137 produced changes in gene activity strikingly similar to those caused by the psoriasis-linked variant in immune cells.

“These agents, because they’re able to directly collaborate and communicate, can facilitate all these kinds of collaborations,” says Zou.

Other researchers see plenty of potential as well. “The idea of making papers more dynamic and executable through an agentic interface is quite compelling,” says Dongping Chen, a computer scientist at the University of Maryland in College Park.

“Agentification itself is a useful certificate that says, ‘This work is relatively complete and well documented.’” —James Zou, Stanford University

Olivier Elemento, a computational biologist who directs the Englander Institute for Precision Medicine at Weill Cornell Medicine in New York City, sees the approach as having broader implications for how researchers share their work.

“It’s a real advance in terms of how we think about the publication process,” he says, “with AI at the center and in a way that makes publications more interactive.” (Elemento peer-reviewed the study for Nature.)

The potential applications extend beyond the research side of academia, too. Artur Skowroński, head of application development at the Polish software company VirtusLab, noted in a blog post that Paper2Agent could help bring scientific papers to life in classrooms. For example, students could use the agent to play with methods described in the literature instead of merely reading about them.

The Future of Agentified Research Papers

With Paper2Agent now up and running, Zou and his colleagues have begun turning more of their own research papers into agents. Just one day after publishing their Nature paper on Paper2Agent, they unveiled the Virtual Biotech, a multi-agent platform modeled on a drug development company.

They described the system in Science and, at the same time, posted a Paper2Agent-generated incarnation of the paper.

However, not every study they threw at the tool could be converted into an agent. Of the 100 computational biology papers they tried, 26 failed to make the leap to agent form, often because of incomplete code, missing documentation, or other software packages that couldn’t be made to work.

But Zou sees that as a feature, not necessarily a bug. When the system gets stuck, it can expose missing information, errors in the code, or discrepancies between the paper and its implementation—problems that might otherwise go unnoticed. As Zou puts it: “Agentification itself is a useful certificate that says, ‘This work is relatively complete and well documented.’”

Human scientists, Zou says, will still have the final say. But he envisions agents becoming part of what it means to publish a paper. Today, papers come with data and code availability statements. Tomorrow, he suggests, they could come with an “agent availability” statement: a virtual corresponding author available around the clock, in any language, to answer the questions that real authors never have time to field.

Naturally, Zou and his colleagues decided to try the idea on their own study. They fed the Paper2Agent manuscript into Paper2Agent, creating an agent that now lives at paper2agent.ai. In other words, a paper about turning papers into agents has turned itself into an agent. The recursion, it seems, has already begun.

This article is brought to you by CoolIT, an Ecolab Company.

Beyond 250 kW a server rack can no longer be cooled by a hybrid approach of liquid and air. At this density a 70/30 liquid-air split leaves 75 kW of air load. The air cooling system needed to move it brings cost and complexity few operators will accept. The answer is near-total heat capture. Liquid takes effectively all the heat, air falls below 1 percent of the load, allowing the server to run fanless.

CoolIT builds these loops today from modular coldplate blocks proven across six generations of fanless designs. Processor thermal design power (TDP) keeps climbing generation over generation. This rising heat load is now cascading into the memory, networking, storage, and power components that once ran comfortably on air.

The heat escaped the chip

For years the story stayed simple. Cool the processor and let air handle the rest. That balance has shifted. As TDP climbs, heat spreads outward from the processor and cascades into the components around it. Memory, networking, storage, and power now run hot enough to demand liquid of their own. Engineers designing the next generation of AI servers face a board where heat capture rises with every launch.

Beyond 250 kW per rack, air cooling becomes the bottleneck. Near-total liquid heat capture enables fanless AI server designs built for the next generation of computing.

New parts, new rules

Unlike processors, which are cooled as flat rectangular packages, these peripherals come in a wide range of shapes, sizes, and mounting requirements, each with its own thermal limits. Some run cooler than the processor case temperature, others run hotter, which leaves them sensitive to a design tuned only for CPUs and GPUs. Operators need purpose-built solutions here, matched to the part rather than stretched across the board.

CoolIT engineers meet this with a deep toolkit. Conductive plates, vapor chambers, heat pipes, and thermal transfer plates move heat from components closer to the liquid path. Riding coldplates enable pluggable components. Each solution stays true to the component it serves.

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One loop, one server

Cooling the parts is one challenge. Uniting them is the real work. Full heat capture means folding every one of these solutions into a single server loop that distributes coolant effectively and remains easy to install. Connection reliability, coolant routing, and the time it takes to assemble the loop at rack integration determine whether a design thrives in production or stalls on the bench. CoolIT builds these loops from proven modular blocks, so operators gain performance and deployment speed within the same solution.

Density forces the decision

Rack power continues to climb toward 1 MW, and the case for liquid grows stronger at every step. A 70/30 split of liquid to air holds comfortably at lower density. Past roughly 250 kW it stops working. The 30 percent left to air becomes a 75 kW load inside a single rack, and moving that much heat demands a parallel air system whose cost and footprint few operators will accept. Adding density only widens the gap.

As rack power continues to climb toward 1 MW, CoolIT’s modeling places full heat capture as the standard server design for flagship rack-scale products through 2028.

The simpler, more efficient answer is to capture the heat in liquid and drop air to less than 1 percent of the total load. True 100 percent remains almost impossible to reach in the strictest sense, so the honest and achievable target is near-total capture. That distinction matters to engineers who value precision, and the direction stays clear either way. Full heat capture moves from a premium option to a mainstream requirement as density rises, and CoolIT’s modeling places it as the standard server design for flagship rack-scale products through 2028.

CoolIT delivers it

CoolIT scales heat capture all the way to 100 percent using modular coldplate building blocks proven across six generations of fanless server designs. Engineering teams are already working on designs for the maximum density racks coming next. As the cascade spreads and racks grow denser, near-total heat capture becomes the design that keeps AI running.

Talk to CoolIT about building a server loop engineered for total heat capture.

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