technology 24 for you

Thursday, October 29, 2026

Consumer Appetite for Personal AI Goes Mainstream

<img src="https://spectrum.ieee.org/media-library/smiling-man-in-headphones-with-on-screen-alerts-for-calendar-emails-and-awareness-mode.png?id=68506456&width=1245&height=700&coordinates=0%2C112%2C0%2C113"/> This article is brought to you by <a href="https://www.qualcomm.com/" target="_blank">Qualcomm Technologies</a>.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 href="https://content.knowledgehub.wiley.com/wp-content/uploads/2026/10/QUL8805_PersonalAI_WhitePaper_0907_v06a.pdf" rel="noopener noreferrer" target="_blank">A Personal AI report</a>, 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:<ul><li>True wireless earbuds – 3 hours</li><li>Wireless headphones – 3 hours</li><li>Smart glasses – 5 hours</li><li>Audio glasses – 6 hours</li><li>Smartwatches – 9 hours</li><li>Smart rings, necklaces and pendants – 8 hours</li></ul>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.<h2>Consumer Trust Is Hard-Won</h2>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.<h2>The Industry Remains Fluid</h2>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.<div class="ieee-sidebar-medium"> <a href="https://content.knowledgehub.wiley.com/wp-content/uploads/2026/10/QUL8805_PersonalAI_WhitePaper_0907_v06a.pdf" target="_blank"></a><a class="shortcode-media-lightbox__toggle shortcode-media-controls__button material-icons" style="background: gray;" title="Select for lightbox">aspect_ratio</a><a href="https://content.knowledgehub.wiley.com/wp-content/uploads/2026/10/QUL8805_PersonalAI_WhitePaper_0907_v06a.pdf" target="_blank"><img alt="Man in suit wearing AI wearable pendant, Snapdragon report cover on personal AI" class="rm-shortcode" data-rm-shortcode-id="67c33ed1fa8f213674863d26b39a2be2" data-rm-shortcode-name="rebelmouse-image" id="3a4c6" loading="lazy" src="https://spectrum.ieee.org/media-library/man-in-suit-wearing-ai-wearable-pendant-snapdragon-report-cover-on-personal-ai.jpg?id=68530945&width=980"/></a><h3 style="padding-top: 16px;">The Rise of Personal AI in Intelligent Wearables</h3>Artificial intelligence is entering a new phase, one that is deeply personal, continuously present, and seamlessly embedded into the devices we wear every day. This evolution, which we define as “Personal AI”, represents a shift away from generic, cloud-only intelligence toward AI that understands individuals in context: their habits, preferences, environments, and intent, in real time.<a href="https://content.knowledgehub.wiley.com/wp-content/uploads/2026/10/QUL8805_PersonalAI_WhitePaper_0907_v06a.pdf" target="_blank">Get the full report</a> →</div><h2></h2> 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.<h2>The Opportunity</h2>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. Reference: https://ift.tt/O6GWUIf

Wednesday, October 28, 2026

Systems Thinking Is Vital in the Age of AI

<img src="https://spectrum.ieee.org/media-library/illustration-of-a-robotic-and-a-human-hand-with-different-color-bands-wrapped-around-both-hands.jpg?id=68528703&width=1200&height=800&coordinates=125%2C0%2C125%2C0"/> 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.<h2>The problem, not the title</h2>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 <a href="https://spectrum.ieee.org/medical-mobile-app-ieee-verified" target="_self">medical device</a>. 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 <a href="https://ieee-pes.org/" rel="noopener noreferrer" target="_blank">energy</a>, 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.<h2>The importance of systems thinking</h2>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.<h2>Human judgment is important</h2>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 <a href="https://spectrum.ieee.org/6-guidelines-governing-ai" target="_self">how to frame the problem,</a> how to ask the right questions, how to test the answers, and when another expert must lead the project.<h2>IEEE’s vital role</h2>The broader form of engineering creates an important role for IEEE. Its <a href="https://www.ieee.org/communities-connection/societies-councils-and-communities/societies" target="_blank">societies</a> already bring together expertise in various technologies, <a href="https://ea.ieee.org/ea-programs" rel="noopener noreferrer" target="_blank">engineering education</a>, <a href="https://standards.ieee.org/" rel="noopener noreferrer" target="_blank">technical standards</a>, and other fields. That makes IEEE well placed to help engineers work across specialties without weakening the value of specialization.IEEE could create more <a href="https://ieee-collabratec.ieee.org/" rel="noopener noreferrer" target="_blank">communities</a> organized around problems that require expertise from several fields. Examples include <a href="https://spectrum.ieee.org/medical-mobile-app-ieee-verified" target="_self">trustworthy clinical AI</a>, 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 <a href="https://spectrum.ieee.org/two-new-ai-ethics-certifications" target="_self">set practical standards</a> 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.<h2>A larger definition of the engineer</h2>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. Reference: https://ift.tt/A3MeoQ8

How Beta Technologies Is Clearing the Runway for Electric Flight

<img src="https://spectrum.ieee.org/media-library/color-photo-of-a-small-gray-airplane-with-a-man-standing-beside-it-and-plugging-in-a-charging-cable.jpg?id=68517917&width=1245&height=700&coordinates=0%2C141%2C0%2C141"/> In the cockpit of Beta Technologies’ Alia CX300, pilot <a href="https://linkedin.com/in/christopher-caputo-80a470157" rel="noopener noreferrer" target="_blank">Chris Caputo</a> steers onto Runway 33 at <a href="https://btv.aero/" rel="noopener noreferrer" target="_blank">Patrick Leahy Burlington International Airport</a> in Vermont. Cleared for takeoff, he releases the brakes and nudges the throttle forward.<div class="rm-embed embed-media"><iframe height="110px" id="noa-web-audio-player" src="https://embed-player.newsoveraudio.com/v4?key=q5m19e&id=https://spectrum.ieee.org/beta-technologies-electric-aircraft&bgColor=F5F5F5&color=1b1b1c&playColor=1b1b1c&progressBgColor=F5F5F5&progressBorderColor=bdbbbb&titleColor=1b1b1c&timeColor=1b1b1c&speedColor=1b1b1c&noaLinkColor=556B7D&noaLinkHighlightColor=FF4B00&feedbackButton=false" style="border: none" width="100%"></iframe></div>At the back of the aircraft, the five-blade pusher propeller snaps awake, and the Alia bolts forward. There’s no shriek of a turbofan spooling up, no shudder of a piston engine, and no wait for thrust. Just the low hum of an electric motor and a shove of instant acceleration.Within a few seconds, we’re at 85 knots (almost 160 kilometers per hour), and Caputo rotates the nose upward. The runway drops abruptly away as we climb. Gaining speed and altitude, we head northwest over Lake Champlain. Through the wraparound canopy, I watch the hunched shoulders of Vermont’s Green Mountains shrink under the right wing, with New York’s taller Adirondacks to the left.Caputo, a former Air Force fighter pilot, flicks the control stick hard left, then right. Despite its 15-meter wingspan, the Alia feels snappy, its fly-by-wire system automatically coordinating aileron and rudder inputs. The machine, he says, is much simpler to manage than the <a href="https://cessna.txtav.com/en/piston/cessna-skylane" rel="noopener noreferrer" target="_blank"> Cessna 182</a> I flew around Australia in my student-pilot days.What’s more, our half-hour flight in the Alia will consume only about US $9 worth of electricity. The same flight in a comparable <a href="https://cessna.txtav.com/en/turboprop/caravan" rel="noopener noreferrer" target="_blank">Cessna 208 Caravan</a> would burn roughly 25 to 30 gallons of Jet A fuel, costing about $210 to $255 at press time. “Even at full throttle, we’re not burning any fuel up here,” says Caputo, as he takes the Alia into a sweeping left turn and heads south over the lake. “We’re not polluting the environment. [There’s] no noise pollution.” <img alt="Color photo of three men building the body of a small airplane that\u2019s sitting on a yellow platform." class="rm-shortcode" data-rm-shortcode-id="36d5d7f2584471d10504b70a2bf1a164" data-rm-shortcode-name="rebelmouse-image" id="2d9ac" loading="lazy" src="https://spectrum.ieee.org/media-library/color-photo-of-three-men-building-the-body-of-a-small-airplane-that-u2019s-sitting-on-a-yellow-platform.jpg?id=68518111&width=980"/>At Beta’s South Burlington, Vt., facility, workers assemble the body of an Alia A250. The company says it has an order and option-to-buy backlog of nearly 1,000 aircraft.Beta TechnologiesThis ride is happening during an unusually lively time for electric aviation. The promise of lower operating costs and cleaner flight has pushed electric aircraft from a fringe experiment to one of the aviation industry’s most closely watched frontiers. After years of renderings, prototypes, and overly optimistic timetables, companies are now flying full-size aircraft, submitting hardware to regulators for certification, building factories, and testing routes with prospective customers.Among the top three U.S. developers of electric planes, <a href="https://www.archer.com/" target="_blank">Archer Aviation</a> and <a href="https://www.jobyaviation.com/" target="_blank">Joby Aviation</a> are concentrating on piloted vertical-takeoff-and-landing air taxis, otherwise known as eVTOLs. <a href="https://beta.team/" target="_blank">Beta Technologies</a> is taking a broader—and in some ways more innovative—path into the market. Rather than betting first on air taxis, Beta is looking to enter electric aviation through short-haul freight planes that use conventional runways, fed by an open charging network that’s designed to make electric aircraft—both Beta’s and its competitors’—part of a working transportation system.To that end, the company is developing two variants of its Alia airframe: the runway-based CX300 I’m flying in and the <a href="https://www.beta.team/aircraft" target="_blank">A250</a>, which adds four lift propellers for vertical takeoff and landing. Beta says its test pilots have flown the Alia family more than 300,000 kilometers, including doing multiple U.S. crossings and overseas demonstrations. The company claims an order and option-to-buy backlog of 991 aircraft.RELATED: <a href="https://spectrum.ieee.org/kyle-clark-profile/kyle-clark" target="_self">From Hockey Enforcer to High-Flying eVTOL CEO</a>“We’re taking a pragmatic stepwise approach for both certification and market entry,” says Beta’s CEO and founder <a href="https://investors.beta.team/governance/leadership-team" target="_blank">Kyle Clark</a>. “We’re ready to go today with medical and cargo aircraft that fly from airport to airport.” Next up, he says, will be passenger aircraft with conventional takeoff and landing, followed by cargo eVTOLs, and finally passenger eVTOLs.<h2>Reducing the Risk of FAA Certification</h2>Beta’s 17,500-square-meter production facility sits beside the Burlington airport and looks more like a giant sunlit workshop than a conventional airplane factory. Geothermal heat pumps help heat and cool the structure. The roof will eventually hold a megawatt of solar panels. On the floor, technicians make composite structures and join aircraft sections beneath American and Pride flags.<h3></h3> <table border-collapse:="" border:="" collapse;="" none;="" style="white-space: unset;" width="100%"><tbody><tr><td style="padding: 24px 16px 18px 16px; border-bottom: 0px solid #000; background-color: #000000; color: #ffffff;">3 Competing Paths to Electric AviationThe top three U.S. electric-aircraft makers are taking different approaches to the same challenge: how to make short-hop flight cleaner, quieter, and commercially useful.</td></tr><tr><td style="padding: 10px; border: 0px solid #ccc; background-color: #DFD5C1"><a class="shortcode-media-lightbox__toggle shortcode-media-controls__button material-icons" style="background: gray;" title="Select for lightbox">aspect_ratio</a><img alt="Illustration of a small airplane viewed from above with 6 propellers." class="rm-shortcode rm-lazyloadable-image" data-rm-shortcode-id="55e60872c085035d08f5578f863d56dc" data-rm-shortcode-name="rebelmouse-image" height="537" id="f3a2b" lazy-loadable="true" src="https://spectrum.ieee.org/media-library/image.png?id=68518506&width=980" width="976"/><style class="image-media media-photo-credit">Chris Philpot</style></td></tr><tr><td style="padding: 10px; border: 0px solid #ccc; background-color: #DFD5C1"><a href="https://www.jobyaviation.com/" target="_blank">Joby Aviation</a>’s piloted, four-passenger S4 aircraft has six tilting propellers to provide both vertical lift and cruise propulsion. It’s optimized for fast, quiet air-taxi service. The company is also working on <a href="https://spectrum.ieee.org/joby-air-taxi-2676635230" target="_self">hydrogen fuel cells for longer-range transport</a>. Joby says the S4 is in the final stage of the Federal Aviation Administration’s certification process and has flown trials in <a href="https://www.jobyaviation.com/news/dubai-air-taxi-network-takes-flight-joby-completes-landmark-flight-and-announces-next-vertiport" target="_blank">Dubai</a>, <a href="https://ir.jobyaviation.com/news-events/press-releases/detail/181/joby-brings-electric-air-taxis-to-new-york-city-in" target="_blank">New York</a>, and <a href="https://www.jobyaviation.com/news/joby-launches-eipp-flights-in-texas" target="_blank">Texas</a>.</td></tr><tr><td style="padding: 10px; border: 0px solid #ccc; background-color: #E9E3D6"><img alt="Illustration of a small airplane with 12 propellers, not all of which are visible." class="rm-shortcode rm-lazyloadable-image" data-rm-shortcode-id="484e33c9e0069dd459fc2ac7579752ea" data-rm-shortcode-name="rebelmouse-image" height="549" id="02b51" lazy-loadable="true" src="https://spectrum.ieee.org/media-library/image.png?id=68529864&width=980" width="976"/><style class="image-media media-photo-credit">Chris Philpot</style></td></tr><tr><td style="padding: 10px; border: 0px solid #ccc; background-color: #E9E3D6"><a href="https://archer.com/" target="_blank">Archer Aviation</a>’s piloted, four-passenger Midnight has 12 propellers—six tilting rotors and six stationary rotors. The aircraft, which Archer has been testing in both vertical and conventional takeoffs and landings, is aimed at air-taxi service in urban areas. The company says Midnight is in Phase 4 of the FAA certification process and has completed trial flights.</td></tr><tr><td style="padding: 10px; border: 0px solid #ccc; background-color: #DFD5C1"><img alt="Illustration of two small airplanes, the one in the foreground having propellers and the one in the background having no propellers." class="rm-shortcode rm-lazyloadable-image" data-rm-shortcode-id="5e545723288d28d78b4f5f8d2835f2d8" data-rm-shortcode-name="rebelmouse-image" height="549" id="16c3b" lazy-loadable="true" src="https://spectrum.ieee.org/media-library/image.png?id=68529944&width=980" width="976"/><style class="image-media media-photo-credit">Chris Philpot</style></td></tr><tr><td style="padding: 10px; border: 0px solid #ccc; background-color: #DFD5C1"><a href="https://beta.team/" target="_blank">Beta Technologies</a>’ piloted Alia aircraft comes in <a href="https://beta.team/aircraft" target="_blank">two flavors</a>: The A250 eVTOL has separate vertical-lift rotors and a rear cruise propeller, while the CX300 is a conventional-takeoff fixed-wing plane. The two models use the same pusher motor, battery pack, flight control computer, and fixed-pitch propeller. The company is initially focusing on cargo and medical transport, regional logistics, and airport-to-airport operations, with passenger service later. Beta expects <a href="https://www.flyingmag.com/beta-technologies-electric-aircraft-media-day/" target="_blank">FAA certification</a> for the CX300 in 2027 and for the A250 a year later.</td></tr></tbody></table><h3></h3> <a href="https://spectrum.ieee.org/kyle-clark-profile/kyle-clark" target="_self">Kyle Clark</a>, a native of Vermont, founded Beta in 2017 to realize his long-held dream of building a hybrid-electric aircraft. (For his bachelor’s thesis at Harvard University, he designed a flight-control system for such an aircraft.) <a href="https://achievement.org/achiever/martine-rothblatt-ph-d/" rel="noopener noreferrer" target="_blank">Martine Rothblatt</a>, CEO of the biotech company <a href="https://www.unither.com/home" rel="noopener noreferrer" target="_blank">United Therapeutics</a> and cofounder of <a href="https://www.siriusxm.com/" rel="noopener noreferrer" target="_blank">SiriusXM </a>satellite radio, invested $48 million in Beta to fund its first prototype, with the eventual goal of using the aircraft to transport organs for transplantation.The company has kept much of its design and manufacturing in-house, partly because the pieces are closely coupled and partly because suitable aviation components didn’t exist when development began. Vertical integration gives Beta’s engineers control over the interfaces and any failure data. But it also means the company must be competent at several businesses simultaneously: airframes, electric motors, inverters, batteries, software, chargers, and aircraft production.The production line can build both the fixed-wing CX300 and the vertical-lift A250, which share the same fuselage, wings, pusher motor (for horizontal propulsion), batteries, avionics, and much of the tooling. This results in some one-size-fits-all decisions: The CX300’s twin booms, which connect the wing to the tail surfaces, look superfluous until you see the A250’s four lift propellers mounted on similar structures.On the CX300, those booms add weight and drag even though they serve no lift function. But the trade-off—a simpler overall design—is part of Beta’s central strategy: Rather than applying for FAA certification for the more novel and complex eVTOL A250, with its multiple lift systems and transitions between hover and wing-borne flight, the company is bringing the runway version forward first. It’s certifying the motor, batteries, fly-by-wire controls, and much of the airframe in the simpler aircraft; it will then reuse evidence and manufacturing experience when it certifies the A250.The other big reason to start with the CX300 is logistical: The United States has more than 5,000 public-use airports, so the CX300 can operate from infrastructure that already exists. The cargo and medical flights that the aircraft will initially tackle offer tightly defined missions in which lower energy and maintenance costs matter more than cabin amenities. Hence the company’s decision to leave cabin air-conditioning off the CX300, at least for now. “Things that don’t exist on an airplane don’t weigh anything,” Clark says. “They can’t break. You don’t have to service them…. You don’t have to certify them.”“This strategy, at the beginning, was seen as a failure of their VTOL,” says analyst <a href="https://www.linkedin.com/in/sergiocecutta/?isSelfProfile=false" target="_blank">Sergio Cecutta</a>, a managing partner at SMG Consulting. “But I think a lot of people were very myopic. It was a way for [Beta] to break up the certification in two pieces, and so they have retired a lot of the risk. [A majority] of the [CX300’s] parts are shared with the VTOL, and that means that a lot of the documentation for certification can be easily reused on the VTOL side when that certification starts.”<h2>How Redundancy Enhances Safety</h2>A decade and a half after electric cars began making significant inroads into the automotive market, electric airplane technology is advancing rapidly. In some ways, an electric car has more complicated power-engineering requirements than an electric aircraft. An electric car’s power demands change constantly, while an electric aircraft operates in a narrower set of power regimes: takeoff, level flight, and descent.But if your car’s drivetrain fails, you can just pull over to the side of the road. That’s not an option for a pilot 1,000 meters up. The hurdle isn’t that electric motors are inherently fragile; it’s that aviation safety standards are extraordinarily demanding. Catastrophic failure conditions generally must be held to roughly one chance in a billion flight hours or less.<h3></h3> <img alt="Color photo taken from above showing a gray and white small airplane in a high-ceilinged space, being worked on by people on ladders." class="rm-shortcode" data-rm-shortcode-id="7d631a484d9dc840ba3c5ee8aa65a1f2" data-rm-shortcode-name="rebelmouse-image" id="9810c" loading="lazy" src="https://spectrum.ieee.org/media-library/color-photo-taken-from-above-showing-a-gray-and-white-small-airplane-in-a-high-ceilinged-space-being-worked-on-by-people-on-lad.jpg?id=68518124&width=980"><h3></h3> Because individual electrical components cannot meet that standard on their own, electric aircraft rely on layered redundancy: duplicate power paths, independent inverters, fault isolation, monitoring, and backup capacity. Each layer helps close the safety gap, but each also adds weight and complexity.To close the reliability gap and satisfy regulators who have little experience with electric aircraft, Beta engineers have built redundancy into <a href="https://beta.team/motor" rel="noopener noreferrer" target="_blank">the motor</a> itself. When I visit the company’s motor shop, technicians are assembling the H500A pusher motor, which splits up critical components like the inverters and stator sections.“You could lose an entire half of the system, and it would still function the same,” says <a href="https://www.flyingmag.com/beta-technologies-electric-aircraft-media-day/" rel="noopener noreferrer" target="_blank">Adam Lowe</a>, Beta’s flight operations lead.The H500A’s broad diameter gives the magnetic forces greater leverage, producing high torque at low rotational speed, a useful trait when turning large propellers. The H500A weighs about 75 kilograms and produces about 640 kilowatts of power. The motor achieves 97.5 percent efficiency, based on internal testing; typical conventional turboprops have an efficiency range of 25 to 35 percent.Beta had aimed to have its motor certified in the first half of 2026, a deadline that passed without a public announcement. Although the company’s certification strategy is aimed at streamlining the process, getting clearance to fly from the <a href="https://www.faa.gov/" target="_blank">Federal Aviation Administration</a> is probably still its biggest obstacle. For Beta, and any company trying to bring an electric aircraft into service, the FAA must sign off first on the propeller, then the motor, then the aircraft. After that, the manufacturer must pursue a production certificate from the FAA, proving that the company can build the same approved aircraft again and again, with the right tools, records, inspections, quality controls, supplier oversight, training, calibration, and parts tracking.<h3></h3> <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/ohwqxK0BCtM?rel=0" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe><h3></h3> Beta has found a narrow on-ramp to get the A250 flying before full certification. Under the <a href="https://www.faa.gov/newsroom/future-aviation-here-trumps-transportation-secretary-sean-p-duffy-and-faa-unveil-eight" target="_blank">FAA’s eVTOL Integration Pilot Program</a>, companies and public agencies can run FAA-supervised, real-world trials to test how advanced air-mobility aircraft might operate in the national airspace. For Beta, that included July flights across a Virginia-to-Maryland corridor carrying, among other things, a manufactured transplantable organ in a medical containment system. Beta has also been chosen for six other FAA-backed trials.For the fixed-wing Alia CX300, customer trials are moving in parallel. In March, Beta’s CX300 successfully <a href="https://investors.beta.team/news-events/press-releases/detail/113/loganair-adds-beta-technologies-alia-ctol-aircraft-to-its-fleet-following-successful-demonstrations-in-united-kingdom" target="_blank">completed 23 flights</a> over 10 days on Loganair routes linking six Scottish cities. The airline has since signed a <a href="https://www.loganair.co.uk/news/loganair-adds-beta-technologies-alia-ctol-aircraft-to-its-fleet-following-successful-demonstrations-in-united-kingdom/" rel="noopener noreferrer" target="_blank">term sheet</a> for five Beta aircraft, with options for five more and service expected to start in 2029.<h2></h2><h2>Innovation in eVTOLs</h2>Beta is pushing so hard for the A250 because the case for electric flight gets even more interesting with vertical-lift aircraft. Helicopters and other VTOLs have a less orderly life than fixed-wing planes do. They must hover, sidestep, turn in place, settle, climb, and hold themselves in the air by main force. But conventional helicopters are built around a rotor speed that varies only a few percent because the engine, transmission, and rotor system are all designed around one narrow operating point.“One of the dreams of vertical flight has been to vary the rotor rpm,” says<a href="https://aero.umd.edu/clark/faculty/525/Anubhav-Datta" rel="noopener noreferrer" target="_blank"> </a><a href="https://aero.umd.edu/clark/faculty/19/Anubhav-Datta" rel="noopener noreferrer" target="_blank">Anubhav Datta</a>, an aerospace engineering professor at the University of Maryland, College Park, who studies electric propulsion and vertical flight. Electric motors, he argues, have advanced enough to make that practical. A rotor that can slow down or speed up as conditions change can waste less energy, make less noise, and better match the odd assortment of tasks that vertical flight demands.Electric propulsion also lets designers break up the work with multiple motors. The weight of an electric motor is tied closely to its torque. If you spread that torque among several motors, each of the motors can shrink, meaning that their combined weight is less than would be needed for one big engine. This is one reason why so many electric aircraft designs appear to have sprouted propellers wherever an engineer found a place to put one.There is a downside to using multiple smaller motors: If one of the motors quits, the rotor it’s driving has little stored energy compared to the inertia in a conventional helicopter’s one large rotor. This inertia can come in handy if the engine dies, because the rotor will continue to spin and allow the pilot to descend under control, in an emergency technique known as autorotation.<h3>Beta Technologies, Made in Vermont</h3> <img alt="Large group of employees gathered in an aircraft hangar with planes in the background." class="rm-shortcode" data-rm-shortcode-id="b32dbd0a462cdc08f4c34062ac724dc0" data-rm-shortcode-name="rebelmouse-image" id="5f01c" loading="lazy" src="https://spectrum.ieee.org/media-library/large-group-of-employees-gathered-in-an-aircraft-hangar-with-planes-in-the-background.jpg?id=68530775&width=980">According to Beta Technologies founder and CEO Kyle Clark, his native Vermont—known more for its maple syrup and outdoorsy lifestyle than its technological achievements—is a big part of the secret to Beta’s success at attracting talent. “The recruiting strategy is this: You find anybody who has vacationed in Vermont or went to school here. And you say, ‘You can actually live here and have a high-paying job in a mission-driven business, doing something that you care about with great people.’ ” The company currently employs about 1,500 people, most of them in South Burlington, Vt., and is actively hiring for its engineering, manufacturing, software, flight operations, and commercial teams.As befits an aviation company, Beta offers flying lessons for all employees—a perk that is intended to give engineers, software developers, pilots, and mechanics a common language. The company also operates a health clinic for employees and their families and is building a day care center. Those programs have an idealistic Vermont flavor that is shaped by Clark and his wife, <a href="https://investors.beta.team/governance/leadership-team" target="_blank">Katie Clark</a>, who is Beta’s head of brand, creative, culture and spaces. But they also serve a practical purpose: attracting and retaining the best people in a small labor market. —T.C.</img></img>Electric-aircraft makers compensate with redundant motors and electrical channels, but most leading designs also have wings to allow gliding. In Beta’s A250, that means that if all else fails, the pilot still has a shot at reaching a suitable landing spot.That helps explain the convergence in today’s eVTOL designs. <a href="https://www.jobyaviation.com/aircraft/" target="_blank">Joby Aviation</a>’s S4 uses six propellers that tilt between lift and cruise. <a href="https://www.archer.com/aircraft" target="_blank">Archer Aviation’s Midnight</a> has six tilting propellers and six dedicated lift props. Beta’s A250 keeps its four lift propellers horizontal and uses a separate pusher motor for forward flight.<h2>Beta’s Approach to Batteries</h2>In Beta’s home state of Vermont, the electricity produced in the state comes almost entirely from renewable sources. Of course, electric aviation’s climate advantage depends not only on how clean the power grid is, but on how efficiently the aircraft uses energy. The turbines in conventional jet engines are marvelous machines, but they throw away a lot of energy as heat. Plus they need to carry the hardware to manage that heat—inlets, ducts, radiators, oil coolers—and the drag that comes with pulling air through all of it. <img alt="A photo showing a bearded white man wearing a helmet and piloting an aircraft" class="rm-shortcode" data-rm-shortcode-id="de9c1c1df73c31dfba408a119d8b6f6e" data-rm-shortcode-name="rebelmouse-image" id="c7d60" loading="lazy" src="https://spectrum.ieee.org/media-library/a-photo-showing-a-bearded-white-man-wearing-a-helmet-and-piloting-an-aircraft.jpg?id=68518211&width=980"/> <img alt=" A photo showing a small white plane flying above a shoreline" class="rm-shortcode" data-rm-shortcode-id="51aab9f28f2a85e3bd828a40cb59c27e" data-rm-shortcode-name="rebelmouse-image" id="514ad" loading="lazy" src="https://spectrum.ieee.org/media-library/a-photo-showing-a-small-white-plane-flying-above-a-shoreline.jpg?id=68518209&width=980"/>Kyle Clark [in cockpit], founder and CEO of Beta Technologies, pilots the company’s Alia CX300, an electric plane that Beta expects to receive FAA certification in 2027. Clark began designing a hybrid electric plane while still in college.Beta TechnologiesBy contrast, an electric design, with no combustion, gearbox, or oil circuits, avoids much of that energy burden. The result is an aircraft that is more than twice as “slippery” in terms of its lift-to-drag ratio. Batteries will never come close to the fuel density of petroleum fuels. But because electric drivetrains are much more efficient than combustion engines, a battery doesn’t necessarily need to match the energy density of petroleum fuel to be useful.While the motors on an electric aircraft provide the drama at takeoff and landing, the batteries set nearly every boundary: the range, payload, charge time, cooling load, reserve energy, and, ultimately, useful life. For now, the hardest limit on electric aviation is not imagination, software, or even motors. It is the stubborn weight of stored energy.“One thing you notice about [electric] airplanes is that they are all small,” says Cecutta, the aviation analyst. “They are not small because someone has done a market study and decided that is the optimal size. They are small because you cannot make them big. This limitation is from the batteries.”In a conventional aircraft, fuel accounts for a significant percentage of the takeoff weight. But that weight gradually disappears during flight. In an electric plane, the battery packs also account for a lot of the weight; in Beta’s CX300, they’re close to half the aircraft’s mass. But a drained battery at landing weighs the same as a fully charged one at takeoff. That means the rest of the aircraft must be light enough to lift a power source that never gets lighter, yet strong enough to endure vibration, turbulence, hard landings, and years of fatigue. The more engineers push to save weight, Datta notes, the more flexible the airframe becomes, making the wings and other structures more prone to bending, twisting, and vibration. In that sense, the battery problem becomes a structural problem, too.Beta’s CX300 carries five liquid-cooled <a href="https://beta.team/battery" target="_blank">battery packs</a> of about 45 kilowatt-hours each, giving the aircraft roughly 225 kWh of rated capacity, though not all of that may be usable in flight. Each pack weighs 255 kg and contains 2,772 cylindrical cells. Altogether, about 14,000 cells sit beneath the occupied cabin, each one a tiny storehouse of energy, heat, and potential trouble.The burden of the battery explains much of what I see elsewhere in Beta’s aircraft: the 15-meter wingspan, the composite structure, and the almost doctrinal effort to leave out every piece of hardware and creature comfort that’s not essential to the mission. The CX300 even does without flaps, which most aircraft use to generate extra lift at low speeds, so it must take off and land unusually fast for an aircraft of its size.On Beta’s battery line, commercially supplied standard 21-millimeter-diameter, 70-mm-long cells are tested and assembled. Cells become modules, modules become subpacks, and eleven subpacks become a 45-kWh pack. Cooling plates manage heat during normal operation, while passive barriers and electrical isolation are designed to contain the ugly edge cases that Beta’s testing process intentionally triggers.The modular design of the packs will give Beta’s planes a path to better batteries as the cells and battery chemistry improve. Caputo tells me that the company is already evaluating higher-density cells at its battery testing facility in nearby St. Albans, Vt. More watt-hours per kilogram could eventually mean more range, more payload, or both.<h2>Beta Technologies’ Electric Ecosystem</h2>What sets Beta Technologies apart from its competitors is the breadth of its ambitions: It’s not just building electric aircraft. It’s also cultivating a broader ecosystem to nurture the growth of electric aviation.For instance, Beta intends its motor business to extend beyond its own aircraft. The company has delivered lift motors for <a href="https://www.eveairmobility.com/" target="_blank">Eve Air Mobility</a>’s proof-of-concept aircraft and has a contract to supply up to <a href="https://www.eveairmobility.com/eve-air-mobility-selects-beta-technologies-as-pusher-motor-supplier/" target="_blank">$1 billion in pusher motors</a> as Eve’s program develops. In addition, Beta is selling flight-control computers and software to <a href="https://www.horizonaircraft.com/" target="_blank">Horizon Aircraft</a>. It is also producing propulsion systems for the military, as well as the <a href="https://beta.team/defense" target="_blank">MV250</a>, a hybrid-electric autonomous aircraft for defense missions. <img alt='Color photo of two cubelike structures, one labeled "charge" and the other labeled "thermal."' class="rm-shortcode" data-rm-shortcode-id="94f447b0439786defc0c96e106c7ed75" data-rm-shortcode-name="rebelmouse-image" id="61648" loading="lazy" src="https://spectrum.ieee.org/media-library/color-photo-of-two-cubelike-structures-one-labeled-charge-and-the-other-labeled-thermal.jpg?id=68518063&width=980"/>Beta’s fast-charging stations [left] have been installed at 138 locations. The thermal-management station [right] keeps the battery from overheating during charging. The company uses the open-standard Combined Charging System (CCS) so that other aircraft can use its chargers. Beta TechnologiesFurther evidence of Beta’s far-ranging plans is visible near the battery line in the South Burlington facility, where workers are readying a few of the company’s 1.5-meter-tall <a href="https://beta.team/charge-cube" target="_blank">Charge Cubes</a> for shipping. Intended for installation at airports, each cube takes 480-volt AC from the grid and can deliver up to 320 kW at as much as 1,000 volts DC. According to Beta, charging either model of its Alia aircraft takes about 35 minutes, from the minimum charge required under visual flight rules to full state. A separate thermal-management cube circulates water-glycol coolant through the aircraft to keep the batteries from overheating during charging.The charge cubes use the Combined Charging System <a href="https://www.charin.global/technology/ccs-specification/" target="_blank">(CCS)</a>, an open standard increasingly shared across the electric transportation industry. Beta’s strategy resembles Tesla’s decision to treat <a href="https://www.tesla.com/supercharger" target="_blank">charging</a> as part of the vehicle business, with one important difference: Beta is courting other aircraft makers, airports, and operators to use its technology. Archer,<a href="https://www.signatureaviation.com/" target="_blank"> </a><a href="https://www.atlanticaviation.com/" target="_blank">Atlantic Aviation</a>, <a href="https://www.signatureaviation.com/" target="_blank">Signature Aviation</a>,<a href="https://www.atlanticaviation.com/" target="_blank"> </a>and government agencies have purchased or installed Beta’s charging equipment. As of October, Beta has 138 fast-charge locations, with additional sites in permitting or construction.For charging beyond the fixed network, Beta offers a wheeled 40-kW <a href="https://beta.team/mini-cube?hsCtaAttrib=184705145623" target="_blank">Mini Cube</a> and a 65-kW Mini Cube+ that can ride inside an Alia and plug into more modest electrical service. The Mini Cube can also charge cars, buses, and trucks—any vehicle that uses the CCS standard.Back above Lake Champlain, the CX300 levels off, and the motor recedes to a faint hum. Then Caputo switches it off, and there’s nothing but the rush of wind around the canopy. The aircraft glides on its long wings while the coasting propeller turns the motor into a generator, feeding about 9 kW back to the battery.Then we turn to the east and slip into the landing queue behind a National Guard C-130 Hercules, and Caputo powers and slides the throttle forward. The motor springs to life again, pushing the airplane, and perhaps aviation itself, into the future. Reference: https://ift.tt/4h5rMvq

Tuesday, October 27, 2026

Code-execution flaw in UEFI firmware affects most servers on planet Earth

Defenders across the world face the burden of responding to yet another botched code-execution vulnerability, this time in boot-time firmware used by most of the world’s server vendors and data centers. The vulnerability, with a severity rating of 9.5 out of 10, resides in the TianoCore <a href="https://www.tianocore.org/tianocore-wiki.github.io/reference/external-resources/edk_ii.html">EDK II</a>, an open source codebase that hardware makers and cloud providers use to create the UEFI firmware that runs during the boot-up sequence. Last year, a representative of the UEFI Forum <a href="https://uefi.org/sites/default/files/resources/State%20of%20UEFI_UEFI%202025%20DevCon_Final.pdf">called</a> EDK2 the "preeminent solution / implementation of the UEFI specifications." Virtually all UEFI suppliers (sometimes still called IBVs or independent BIOS vendors), including AMI, Insyde, and Phoenix, use the reference code extensively. Project Mu, Microsoft's hardened EDK2 fork, used the code containing the vulnerable component as well. The vulnerability, tracked as CVE-2026-97631, is a memory-corruption flaw in an <a href="https://en.wikipedia.org/wiki/Internet_Control_Message_Protocol">ICMP</a> component that attackers can exploit to execute malicious code that runs before the operating system and firmware can implement defenses like <a href="https://uefi.org/sites/default/files/resources/UEFI_Secure_Boot_in_Modern_Computer_Security_Solutions_2019.pdf">Secure Boot</a>, which establishes a chain of trust for all code, including the <a href="https://www.trustedcomputinggroup.org/work-groups/tpm">Trusted Platform Module</a> (TPM), loaded during boot. While final payloads delivered by CVE-2026-97631 have no persistence—meaning they run only once—they can give attackers a wide opening to install other payloads that also run each time early in the boot process.<a href="https://arstechnica.com/security/2026/10/code-execution-flaw-in-uefi-firmware-threatens-most-servers-on-planet-earth/">Read full article</a> <a href="https://arstechnica.com/security/2026/10/code-execution-flaw-in-uefi-firmware-threatens-most-servers-on-planet-earth/#comments">Comments</a> Reference : https://ift.tt/slNniFe

Your Robot’s Safety Functions Already Work. What If the Input Lies?

<img src="https://spectrum.ieee.org/media-library/humanoid-robot-and-robot-dog-meet-a-woman-on-a-sunny-terrace-at-sunset.jpg?id=68002098&width=1245&height=700&coordinates=0%2C87%2C0%2C87"/> This article is brought to you by <a href="https://vicone.com/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" rel="noopener noreferrer" target="_blank">VicOne</a>.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 <a href="https://vicone.com/blog/can-a-robot-do-everything-right-and-still-be-unsafe/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">deliberate manipulation</a>.At <a href="https://www.linkedin.com/showcase/vicone-lab-r7/posts/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">VicOne LAB R7</a>, we examine how manipulated inputs can change robot behavior — and what that means for the systems meant to keep people safe.<h2>How an untrusted input can change a robot’s behavior</h2>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 <a href="https://arxiv.org/abs/2411.13587" target="_blank">study of vision-language-action (VLA) models</a>, a patch in a camera’s view reduced task success in simulated robot tests. <a href="https://arxiv.org/abs/2509.19870" target="_blank">FreezeVLA</a> 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.<a href="https://www.linkedin.com/showcase/vicone-lab-r7/posts/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">VicOne LAB R7</a> 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. <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/d9lPOKP1mos?rel=0" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe> 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<h2>Safety-related decisions have inputs too</h2>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. <a href="https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/son" rel="noopener noreferrer" target="_blank" title="https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/son">Researchers demonstrated</a> 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. <img alt="Table comparing AI safety risks (bugs, failures) with security risks (attacks, hijacks)." class="rm-shortcode" data-rm-shortcode-id="3e7e106e37e2bcc689c01dcc811a861b" data-rm-shortcode-name="rebelmouse-image" id="571fc" loading="lazy" src="https://spectrum.ieee.org/media-library/table-comparing-ai-safety-risks-bugs-failures-with-security-risks-attacks-hijacks.jpg?id=68002090&width=980"/> 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<h2>Deliberate attacks challenge safety assumptions</h2>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 <a href="https://arxiv.org/abs/2106.09249" target="_blank">autonomous driving research</a>, 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.<h2>Security testing strengthens robot safety evidence</h2>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 <a href="https://vicone.com/blog/from-perception-to-action-what-gbt-45502-2025-signals-for-service-robot-security/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">GB/T 45502-2025</a> addresses information security for service robots. <a href="https://webstore.iec.ch/en/publication/69228" target="_blank">IEC TS 63074</a> examines security threats that could affect safety-related control systems. The <a href="https://vicone.com/blog/when-ai-moves-why-the-eu-machinery-regulation-matters-for-physical-ai-security/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">EU Machinery Regulation</a>, 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. <img alt="Diagram of AI robot security lifecycle from development to operation monitoring" class="rm-shortcode" data-rm-shortcode-id="77bf57b0d0c4b4d22f15a6f08f08e2ca" data-rm-shortcode-name="rebelmouse-image" id="f3777" loading="lazy" src="https://spectrum.ieee.org/media-library/diagram-of-ai-robot-security-lifecycle-from-development-to-operation-monitoring.jpg?id=68002095&width=980"/> 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.VicOneBefore 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 <a href="https://robotichackingcommunity.com/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">Robotic Hacking Community</a>, VicOne LAB R7 works with researchers to investigate how cyber threats can change robot behavior. <a href="https://vicone.com/products/radeis/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">VicOne’s Radeis</a> helps teams validate potential effects before deployment, while <a href="https://vicone.com/products/rthena/?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">Rthena</a> provides visibility into risks and behavior during operation.<h2>Five questions safety and security teams should answer together</h2>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 “<a href="https://info.vicone.com/why-robot-safety-depends-on-cybersecurity-vicone?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-10" target="_blank">Why Robot Safety Depends on Cybersecurity: Protecting the Information That Safety Decisions Rely On</a>.” Reference: https://ift.tt/H4eMKSO

I Used AI to Fake a Scientific Paper. Here’s What Happened

<img src="https://spectrum.ieee.org/media-library/man-in-glasses-at-night-his-face-reflected-in-a-window-beside-him.jpg?id=68515605&width=1500&height=2000&coordinates=0%2C0%2C0%2C0"/> <img alt="“text”" class="rm-shortcode" data-rm-shortcode-id="f5e039f57b814b41112b4d42fa7dd3ec" data-rm-shortcode-name="rebelmouse-image" id="623fc" loading="lazy" src="https://spectrum.ieee.org/media-library/text.png?id=67033315&width=980"/>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 <a href="https://www.pnas.org/doi/10.1073/pnas.2605754123" target="_blank">57 percent of all published academic articles</a> exhibited detectable evidence of large language model (LLM) influence, a massive jump from 12 percent in 2023. What’s more, <a href="https://arxiv.org/html/2602.00319v2" target="_blank">over 12 percent of Nature Communications reviews</a> were found to be completely AI-generated in 2025.<div class="rm-embed embed-media"><iframe height="110px" id="noa-web-audio-player" src="https://embed-player.newsoveraudio.com/v4?key=q5m19e&id=https://spectrum.ieee.org/ai-generated-scientific-papers&bgColor=F5F5F5&color=1b1b1c&playColor=1b1b1c&progressBgColor=F5F5F5&progressBorderColor=bdbbbb&titleColor=1b1b1c&timeColor=1b1b1c&speedColor=1b1b1c&noaLinkColor=556B7D&noaLinkHighlightColor=FF4B00&feedbackButton=false" style="border: none" width="100%"></iframe></div>Given that for every 40 papers showing statistical evidence of AI usage in 2025, <a href="https://www.pnas.org/doi/10.1073/pnas.2526734123" target="_blank">only one formally disclosed it</a>, 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 <a href="https://www.linkedin.com/in/sulicz/" target="_blank">Amanda Sulicz</a>, 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?<h3></h3> <div class="rm-embed embed-media"><div class="flourish-embed flourish-pictogram" data-src="visualisation/30581736?602891"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img alt="pictogram visualization" src="https://public.flourish.studio/visualisation/30581736/thumbnail" width="100%"/></noscript></div></div> <div class="rm-embed embed-media"><div class="flourish-embed flourish-chart" data-src="visualisation/30581953?602891"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img alt="chart visualization" src="https://public.flourish.studio/visualisation/30581953/thumbnail" width="100%"/></noscript></div></div><h3></h3> 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 <a href="https://www.linkedin.com/in/jenny-mahoney-b03a683/" target="_blank">Jenny Mahoney</a>, managing editor of the multidisciplinary and open-access journal <a href="https://ieeeaccess.ieee.org/" target="_blank">IEEE Access</a>, 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.<h2>Round 1: Creating the Forgery</h2>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.<h3></h3> <img alt="Person working at a desk in a lit garden office at night, seen through window" class="rm-shortcode" data-rm-shortcode-id="24c1245ba8eab872e05ff7e8b19d025e" data-rm-shortcode-name="rebelmouse-image" id="bb404" loading="lazy" src="https://spectrum.ieee.org/media-library/person-working-at-a-desk-in-a-lit-garden-office-at-night-seen-through-window.jpg?id=68516072&width=980"><h3></h3> 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.<h2>Round 1: Attempt to Publish</h2><h3>Two Ways to Make a Paper With AI</h3> <img alt="Illustration of digital papers overlaid on a blue circuit board pattern." class="rm-shortcode" data-rm-shortcode-id="90521eaeabcb87e85e4f74c80e45cd7e" data-rm-shortcode-name="rebelmouse-image" id="ce350" loading="lazy" src="https://spectrum.ieee.org/media-library/illustration-of-digital-papers-overlaid-on-a-blue-circuit-board-pattern.png?id=68516003&width=980">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 + GEMINI1. 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. <div class="horizontal-rule"></div>ROUND 2: FARS1. 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.<h3></h3> 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 <a href="https://stm-assoc.org/what-we-do/strategic-areas/research-integrity/integrity-hub/" target="_blank">STM Integrity Hub</a>, she picked up 12, and another checker from <a href="https://molecularconnections.com/" rel="noopener noreferrer" target="_blank">MolecularConnections</a> 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 <a href="https://optow.ele.uniroma3.it/assanto.php" rel="noopener noreferrer" target="_blank">Gaetano Assanto</a>, 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.<h2>The AI Arms Race</h2>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.<h3></h3> These tools aim to address a serious bottleneck to scientific discovery: us. <h3></h3> 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 <a href="https://www.linkedin.com/in/luigi-longobardi-38390819/" target="_blank">Luigi Longobardi</a>, 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 <a href="https://ncses.nsf.gov/pubs/nsb202333" target="_blank">over 3 million papers</a> 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.<h2>Burden on Academics</h2>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.<h3>What Gave Away My AI Papers </h3> Round 1: The Fake PaperCreated with free versions of ChatGPT and GeminiGhost 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 PaperCreated by the autonomous research system FARSToo 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.<h3></h3> There are two high-profile homes for this important and completely volunteer-led post-publication review work. <a href="https://pubpeer.com/" target="_blank">PubPeer</a> is a place where researchers, often called scientific sleuths, can anonymously identify flaws in the literature. And <a href="https://retractionwatch.com/" target="_blank">Retraction Watch</a> 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. <a href="https://www.linkedin.com/in/ivanoransky/" target="_blank">Ivan Oransky</a>, 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 <a href="https://link.springer.com/article/10.1007/s40199-025-00580-z" rel="noopener noreferrer" target="_blank">journal article</a> 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.”<h2>Blurred Lines</h2>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 <a href="https://arxiv.org/abs/2606.31651" rel="noopener noreferrer" target="_blank">FARS (Fully Automated Research System)</a>, <a href="https://github.com/aiming-lab/AutoResearchClaw" rel="noopener noreferrer" target="_blank">AutoResearchClaw</a>, <a href="https://github.com/sakanaai/ai-scientist-v2" rel="noopener noreferrer" target="_blank">The AIScientist-v2</a>, <a href="https://www.intology.ai/" rel="noopener noreferrer" target="_blank">Zochi</a>, <a href="https://arxiv.org/abs/2505.18705" rel="noopener noreferrer" target="_blank">AI Researcher</a>, and <a href="https://deepscientist.cc/" rel="noopener noreferrer" target="_blank">DeepScientist</a> all aim to address a serious bottleneck to scientific discovery: us.<h3></h3> What happens when AI isn’t simply fabricating a paper but conducting research that blurs the line between legitimate science and AI fabrication? <h3></h3> 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. <a href="https://insilico.com/news/tmikccj2f1-advancing-drug-discovery-from-automation" target="_blank">LabClaw</a>, 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 <a href="https://arxiv.org/abs/2606.31651" target="_blank">166 papers in 417 hours</a>, was developed by the Singapore-based startup <a href="https://www.crunchbase.com/organization/analemma" rel="noopener noreferrer" target="_blank">Analemma</a>. “We built FARS because research is still constrained by human attention, fragmented tools, scarce technical labor, and slow experimental iteration,” explains <a href="https://www.linkedin.com/in/suxiwen/" rel="noopener noreferrer" target="_blank">Xiwen Su</a>, 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 <a href="https://baike.baidu.com/fr/item/Analemme/3430299" rel="noopener noreferrer" target="_blank">acquired by Baidu</a> 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.<h2>Round 2: Using an AI Science Engine</h2>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.<h2>Round 2: Attempt to Publish</h2>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.<h2>Automation Changing the Landscape</h2>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 <a href="https://www.nature.com/articles/s41586-026-10265-5" rel="noopener noreferrer" target="_blank">AI Scientist academic paper</a> and <a href="https://www.autoscience.ai/blog/meet-carl" rel="noopener noreferrer" target="_blank">papers from the Autoscience Institute</a> 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.”<h3></h3> <img alt="Man in glasses working at a desktop computer in a dimly lit home office." class="rm-shortcode" data-rm-shortcode-id="268f6ad6525d1d767578ab0a73ec5181" data-rm-shortcode-name="rebelmouse-image" id="42e61" loading="lazy" src="https://spectrum.ieee.org/media-library/man-in-glasses-working-at-a-desktop-computer-in-a-dimly-lit-home-office.jpg?id=68530754&width=980"/><h3></h3> 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?”<h2>A Human-Centric or AI-Driven Future?</h2>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 <a href="https://cches.co.uk/electronic-lab-notebooks/" target="_blank">electronic lab notebook</a> 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.<h3>See the AI-Generated Papers</h3> Benjamin Skuse created two papers for this experiment, both screened by IEEE Access with the publisher’s cooperation. Neither was accepted for publication.<a href="https://spectrum.ieee.org/files/114035/AI-generated_paper_round_1.pdf" target="_blank">Paper 1: The AI-Generated Forgery</a> Created with ChatGPT and Gemini, this paper describes research that never happened.<a href="https://spectrum.ieee.org/files/114041/AI-generated_paper_round_2.pdf" target="_blank">Paper 2: The AI-Generated Research Paper</a> 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.</img></img> Reference: https://ift.tt/5o27WRe