DIGITAL LIFE

AI is giving bad advice to flatter its users, says new study on dangers of overly agreeable chatbots

Artificial intelligence chatbots are so prone to flattering and validating their human users that they are giving bad advice that can damage relationships and reinforce harmful behaviors, according to a new study that explores the dangers of AI telling people what they want to hear.

The study, published Thursday in the journal Science, tested 11 leading AI systems and found they all showed varying degrees of sycophancy—behavior that was overly agreeable and affirming. The problem is not just that they dispense inappropriate advice but that people trust and prefer AI more when the chatbots are justifying their convictions.

"This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement," says the study led by researchers at Stanford University.

The study found that a technological flaw already tied to some high-profile cases of delusional and suicidal behavior in vulnerable populations is also pervasive across a wide range of people's interactions with chatbots. It's subtle enough that they might not notice and a particular danger to young people turning to AI for many of life's questions while their brains and social norms are still developing.

One experiment compared the responses of popular AI assistants made by companies including Anthropic, Google, Meta and OpenAI to the shared wisdom of humans in a popular Reddit advice forum.

When AI won't tell you you're a jerk...Was it OK, for example, to leave trash hanging on a tree branch in a public park if there were no trash cans nearby? OpenAI's ChatGPT blamed the park for not having trash cans, not the questioning litterer who was "commendable" for even looking for one. Real people thought differently in the Reddit forum abbreviated as AITA, after a phrase for someone asking if they are a cruder term for a jerk.

"The lack of trash bins is not an oversight. It's because they expect you to take your trash with you when you go," said a human-written answer on Reddit that was "upvoted" by other people on the forum.

The study found that, on average, AI chatbots affirmed a user's actions 49% more often than other humans did, including in queries involving deception, illegal or socially irresponsible conduct, and other harmful behaviors.

"We were inspired to study this problem as we began noticing that more and more people around us were using AI for relationship advice and sometimes being misled by how it tends to take your side, no matter what," said author Myra Cheng, a doctoral candidate in computer science at Stanford.

Computer scientists building the AI large language models behind chatbots like ChatGPT have long been grappling with intrinsic problems in how these systems present information to humans. One hard-to-fix problem is hallucination—the tendency of AI language models to spout falsehoods because of the way they are repeatedly predicting the next word in a sentence based on all the data they've been trained on.

Dan Jurafsky, Stanford professor of computer science and linguistics, from left, Myra Cheng, Stanford Ph.D. candidate in computer science, and Cinoo Lee, Stanford postdoctoral fellow in psychology, pose for photos on the university campus in Stanford, Calif., Thursday, March 26, 2026. Credit: AP Photo/Jeff Chiu

Reducing AI sycophancy is a challenge...Sycophancy is in some ways more complicated. While few people are looking to AI for factually inaccurate information, they might appreciate—at least in the moment—a chatbot that makes them feel better about making the wrong choices.

While much of the focus on chatbot behavior has centered on its tone, that had no bearing on the results, said co-author Cinoo Lee, who joined Cheng on a call with reporters ahead of the study's publication.

"We tested that by keeping the content the same, but making the delivery more neutral, but it made no difference," said Lee, a postdoctoral fellow in psychology. "So it's really about what the AI tells you about your actions."

In addition to comparing chatbot and Reddit responses, the researchers conducted experiments observing about 2,400 people communicating with an AI chatbot about their experiences with interpersonal dilemmas.

"People who interacted with this over-affirming AI came away more convinced that they were right, and less willing to repair the relationship," Lee said. "That means they weren't apologizing, taking steps to improve things, or changing their own behavior."

Lee said the implications of the research could be "even more critical for kids and teenagers" who are still developing the emotional skills that come from real-life experiences with social friction, tolerating conflict, considering other perspectives and recognizing when you're wrong.

Finding a fix to AI's emerging problems will be critical as society still grapples with the effects of social media technology after more than a decade of warnings from parents and child advocates. In Los Angeles on Wednesday, a jury found both Meta and Google-owned YouTube liable for harms to children using their services. In New Mexico, a jury determined that Meta knowingly harmed children's mental health and concealed what it knew about child sexual exploitation on its platforms.

Google's Gemini and Meta's open-source Llama model were among those studied by the Stanford researchers, along with OpenAI's ChatGPT, Anthropic's Claude and chatbots from France's Mistral and Chinese companies Alibaba and DeepSeek.

Of leading AI companies, Anthropic has done the most work, at least publicly, in investigating the dangers of sycophancy, finding in a 2024 research paper that it is a "general behavior of AI assistants, likely driven in part by human preference judgments favoring sycophantic responses."

None of the companies directly commented on the Science study on Thursday but Anthropic and OpenAI pointed to their recent work to reduce sycophancy.

The risks of AI sycophancy are widespread...In medical care, researchers say sycophantic AI could lead doctors to confirm their first hunch about a diagnosis rather than encourage them to explore further. In politics, it could amplify more extreme positions by reaffirming people's preconceived notions. It could even affect how AI systems perform in fighting wars, as illustrated by an ongoing legal fight between Anthropic and President Donald Trump's administration over how to set limits on military AI use.

The study doesn't propose specific solutions, though both tech companies and academic researchers have started to explore ideas. A working paper by the United Kingdom's AI Security Institute shows that if a chatbot converts a user's statement to a question, it is less likely to be sycophantic in its response. Another paper by researchers at Johns Hopkins University also shows that how the conversation is framed makes a big difference.

"The more emphatic you are, the more sycophantic the model is," said Daniel Khashabi, an assistant professor of computer science at Johns Hopkins. He said it's hard to know if the cause is "chatbots mirroring human societies" or something different, "because these are really, really complex systems."

Sycophancy is so deeply embedded into chatbots that Cheng said it might require tech companies to go back and retrain their AI systems to adjust which types of answers are preferred.

Cheng said a simpler fix could be if AI developers instruct their chatbots to challenge their users more, such as by starting a response with the words, "Wait a minute." Her co-author Lee said there is still time to shape how AI interacts with us.

"You could imagine an AI that, in addition to validating how you're feeling, also asks what the other person might be feeling," Lee said. "Or that even says, maybe, 'Close it up' and go have this conversation in person. And that matters here because the quality of our social relationships is one of the strongest predictors of health and well-being we have as humans. Ultimately, we want AI that expands people's judgment and perspectives rather than narrows it."

© 2026 The Associated Press

TECH

AI's arrival complicates Big Tech climate goals, and some worry it's locking in more fossil fuels

Six years ago, Google was confident that by 2030 it would power all operations with electricity generated from clean sources, including wind and solar power, and remove as much pollution as it produced. Today it calls those goals a "moonshot." Microsoft says it's still aiming to remove more carbon than it creates by 2030 but now describes the effort as "a marathon, not a sprint."

The race to deploy artificial intelligence is complicating tech companies' commitments to reduce greenhouse gas emissions, most of which come from the burning of gas, oil and coal and drive climate change. They say they must be flexible as they rush to build sprawling data centers that can consume more power than entire cities.

"Even if they haven't officially revised their goals, they are starting to acknowledge that, 'Yeah, we're maybe not on track,'" said Patrick Huang, a senior analyst at Wood Mackenzie.

Now, Huang said, the companies must use whatever kinds of power they can to stay competitive—and increasingly that is natural gas, which is mostly methane, a planet-warming greenhouse gas.

Tech companies bought record amounts of clean energy in 2024 and 2025, according to the Clean Energy Buyers Association.

But total emissions have gone up over roughly the first five years of their climate commitments, according to companies' sustainability reports. Google's emissions jumped nearly 50%. Amazon's rose by 33%, Microsoft's more than 23% and Meta's more than 60%.

Data centers used about 4.6% of total U.S. electricity in 2024, a share that could nearly triple by 2028, according to government estimates. Some analysts predict nationwide electricity use to rise as much as 20% in the next decade, with data centers a big reason.

Meanwhile, a backlog of proposed projects awaiting permission to connect to power grids and efforts by the Trump administration to sideline renewable energy may affect tech companies' climate goals—and prolong reliance on fossil fuels, experts said.

"Each of these alone could be real challenges," said Julie McNamara, associate policy director at Union of Concerned Scientists' Climate & Energy program. "Together, it's just creating a real near-term crunch on the system."

A data center owned by Amazon Web Services, front right, is under construction next to the Susquehanna nuclear power plant in Berwick, Pa., Jan. 14, 2025. Credit: AP Photo/Ted Shaffrey, File

Natural gas use spikes as AI soars...Tech companies say they've made significant progress on emissions through energy-efficiency measures, buying renewable energy credits and power from sources that don't emit greenhouse gases and requiring suppliers to reduce their own emissions.

Yet natural gas in 2024 accounted for more than 40% of electricity powering U.S. data centers, while coal supplied 30% globally, the International Energy Agency said. And the trend doesn't appear to be slowing. Utilities are planning natural gas plants around the country to help supply data centers, while some tech companies plan on-site gas plants built only to feed a data center.

"Companies are scrambling to try to get as much power as they can as quickly as possible," said Lori Bird, director of the U.S. Energy Program at the World Resources Institute. "It's a mad rush and a lot of competition for resources."

Microsoft President Brad Smith told The Associated Press that he is "confident in our ability" to meet the company's 2030 goal to remove more carbon dioxide from the atmosphere than it emits by investing in new sources of carbon-free energy, including nuclear, solar and hydropower.

In Wisconsin, for example, two new natural gas plants to help power a Microsoft data center will be offset by investment in solar elsewhere in the state. Similarly, three natural gas plants will provide electricity to a massive Meta data center in rural Louisiana, while the company invests in solar elsewhere.

Google says it's investing in wind, hydropower, battery storage and advanced nuclear, though it also relies on natural gas. The company plans to buy electricity from a natural gas plant to be built at the Archer Daniels Midland corn processing plant in Decatur, Illinois, where carbon dioxide emissions would be captured and stored underground.

To help meet clean energy goals, tech companies count on such power purchase agreements and buying renewable energy certificates, a tradeable commodity that supports new and existing sources. But that could get more difficult under proposed changes to how greenhouse gases are reported, which would require that sources are in the same region as a company's data center and match hours of operation—for example, solar credits could only be applied to daytime operating hours.

Although some new gas plants will replace dirtier coal plants, it takes about 30 years to recover the investment. That means delaying the overall transition to clean and renewable energy at a time when the United Nations Environment Programme warns that high-emitting countries are unlikely to meet their own targets for reducing greenhouse gas emissions. AI is blamed in part for a 2.4% uptick in U.S. fossil fuel emissions last year, according to a study by the Rhodium Group, an independent research firm.

And though other sectors of the economy also are electrifying, "it is only because of these data centers that these gas plants are being built," McNamara said. "There are no two ways about it."

Trump war on renewables complicates tech goals...Getting enough electricity was challenging even before President Donald Trump took office last year and took aim at renewable energy.

He's canceled grants and permits for solar and wind projects and tax breaks for renewable energy, which advocates say can be built less expensively and more quickly than natural gas or nuclear plants, while ordering that several coal-fired power plants slated for retirement keep running.

Many companies set goals expecting federal tax credits would support wind and solar deployment, said Rich Powell, chief executive officer of the Clean Energy Buyers Association.

But those credits will end in July, after being eliminated by the Republican-controlled Congress and Trump.

Trump, who has called climate change a "hoax," has argued that green energy is unreliable and expensive and could harm national energy independence.

Powell said his association has "been very, very clear with this Congress and this administration that all technology should be on a level playing field and that we're putting both energy affordability and energy reliability at risk if we don't do that."

Josh Parker, sustainability chief for chipmaker Nvidia, said AI eventually will reduce electricity use because it's more efficient than traditional computing. He said curtailing energy development could cause the U.S. to fall behind on AI.

"Our perspective is that we need an all-of-the-above approach to energy," he said.

Tech companies would have been hard-pressed in 2020, when many set goals, to project current energy needs because much of the technology and equipment used to train machine-learning models—which use most data-center electricity—were just being introduced, said Jay Dietrich, who researches AI sustainability for the Uptime Institute and formerly led emissions goal-setting at IBM.

By 2023, he said, tech companies "had a pretty good idea things were going to get a lot more exciting ... and that the numbers were going to grow quickly."

He expects many will extend the timeline for emissions goals, based on a 2025 Uptime Institute survey that saw a 12% drop in the number of operators saying they'd meet a market-based 2030 carbon-neutral goal. However, even with increasing emissions, the largest companies should be able to afford enough renewable energy and offsets to meet carbon-neutral goals.

McNamara said the surge in electricity demand from data centers turned a challenge into "an outright crisis."

"Tech companies are allowing implicitly or explicitly an enormous increase in fossil fuel dependence under their watch and because of their actions," she said.

© 2026 The Associated Press

TECH

Brain-inspired AI hardware helps autonomous devices operate efficiently and independently

The human brain constantly makes decisions. It requires minimal power to move bodies in a desired direction or avoid an object. A Purdue University engineer uses the brain's efficiency as inspiration to help autonomous vehicles, such as drones and robots, make crucial, time-sensitive decisions while operating in the field.

Kaushik Roy, the Edward G. Tiedemann, Jr. Distinguished Professor of Electrical and Computer Engineering in Purdue's Elmore Family School of Electrical and Computer Engineering and director of the Institute of Chips and AI, is developing brain-inspired hardware that enables autonomous devices to efficiently navigate and adapt to their environment. This work is published in Communications Engineering

AI-powered machines have advanced significantly over the past several decades thanks to machine learning, which enables these devices to recognize patterns and make predictions or decisions. But the algorithms that facilitate this learning require immense amounts of energy to operate due to their intensive calculations and the design of the hardware that runs them.

"Today's AI devices are designed with separate processing and memory units," Roy said. "It takes a lot of energy to move the data from the memory to the processing unit and then perform all these complex operations. This is particularly problematic for machines like drones that need to process information quickly and efficiently to avoid obstacles while completing their assigned tasks."

To solve this energy problem, Roy and his team in the Nanoelectronics Research Laboratory are developing a system of sensors, algorithms and hardware that allow autonomous, vision-based vehicles to move from point A to B while avoiding obstacles, optimizing energy use and operating independently.

"From the little we understand of the brain, computation and memory are not separated, essentially making it the most efficient processor imaginable," Roy said. "That's why we're taking more direct cues from the brain and co-designing hardware and algorithms that will optimize a variety of AI devices."

Purdue University engineer Kaushik Roy uses the brain's efficiency as inspiration to help autonomous, vision-based vehicles navigate their surroundings. Roy and his team are developing an energy-efficient system of sensors, algorithms and hardware that enable drones and robots to avoid obstacles. Credit: Purdue University /John Underwood

Algorithms power AI cognition...At the heart of this system are algorithms called spiking neural networks (SNNs). All neural networks are comprised of layers of artificial neurons that activate when presented with information, much like how a biological neuron works within the brain.

However, unlike the brain, all the neurons in a traditional neural network activate with every input of information, thereby expending large amounts of energy with every calculation and every decision or action taken by the network.

On the other hand, the individual neurons in SNNs only fire, or "spike," when they receive important information. What is deemed "important" to a particular neuron is based on an assigned membrane potential—a threshold that determines when a neuron activates.

An input or piece of data must reach that threshold for a neuron to spike and produce a reaction. Therefore, each neuron only processes and stores "memories" relevant to their function.

"The membrane potential of each neuron acts as memory, allowing the network to remember the past, much like biological neurons do," Roy said.

"This behavior turns out to be very useful for sequential and time-based tasks. These are the types of tasks that drones and other autonomous vehicles are performing as they collect information from their environment and use it to make decisions about what to do next."

While a neural network that fires selectively is a strength in terms of processing power, it introduces a weakness in training. Traditional neural networks learn from their mistakes by relying on backpropagation—a constant flow of information through the network's layers of neurons that helps figure out where and how mistakes occurred.

The selective firing of SNNs produces inconsistent activity and less information. And while the timing of a spike is critical to improving an SNN, the backpropagation in a traditional system is designed to track only where errors occur, not when.

To address these problems, Kaushik and his team have developed hybrid neural networks that combine strengths of both traditional neural networks and SNNs. This combination captures timing information effectively while remaining trainable and compact enough for autonomous devices.

Event-based cameras enhance navigation...Two such algorithms, called Spike-FlowNet and Adaptive Safety Margin Algorithm, help special event-based cameras attached to the vehicles more effectively scan and process their environment.

Much like the individual neurons in an SNN, the individual pixels in an event-based camera operate independently, and the camera only records when there's a movement or change happening in the pixels. This differs from traditional cameras, which record an entire scene—all the pixels at all times.

Roy and his team have tested this technology on a drone, with the vehicle successfully navigating around moving rings in real time.

"Using vision sensors only, the drone can avoid stationary and moving objects and reach its target without collision," Roy said. "While doing this, it has to determine how objects move in the visual field, estimate depth and then plan a path. These are time-dependent operations, where understanding how things change over time is critical."

Computation and memory converge in specialized hardware...Hardware, the final component of Roy's system, is currently under development. He aims to harness in-memory computing to eliminate what is termed the von-Neumann bottleneck—the pathway that data must travel between a computer's central processing unit and memory, often resulting in computational lags.

The hardware in development effectively eliminates that pathway by mapping computational operations and processes directly onto a memory chip.

One device, an electronic synapse that mimics how the brain learns, works by sending an electrical current through a layer of metal that then produces an effect called spin-orbit torque.

Spin-orbit torque works by moving regions of a magnetic layer in different directions depending on the timing and strength of the current. The device learns when the electrical current physically reshapes the magnetic structure, influencing how strongly the current passes in the future.

Devices like the electronic synapse reduce power consumption, increase energy efficiency and, most importantly, operate without internet connections—crucial for autonomous devices out in the field.

While the demonstrations use drones, the same brain-inspired architectures could apply to ground robots, autonomous vehicles, wearables and other embedded AI systems that need real-time perception and decision-making under energy constraints.

Provided by Purdue University

KODAK

The Kodak PixPro AZ653 is a new bridge camera with a robust 65x optical zoom

Today's Kodak – as in the third party who has licensed the legendary name — specializes in blast-from-the-past tech. Case in point, the 'new' PixPro AZ653 bridge camera, which follows the PixPro AZ652 from 2019.

(Most of) its specs look too good to be true — a stabilized 65x optical zoom that covers everything from wide-angle landscapes to distant wildlife, 1cm macro focusing, 20MP stills, DSLR-style handling and an articulated screen — all for just $450 / £350.

This is an affordable do-it-all camera, except for one important part — quality. Like most bridge cameras (with few exceptions such as the Sony Cyber-Shot RX10 IV), it packs a tiny 1/2.3-inch sensor — the type you get in cheap smartphones, which means image quality is lacking.

In bright light, highlights will blow out and shadows lack depth. In low light, detail will be mushy. It's the price you pay for such versatility, and personally it's not a compromise I'd be willing to take for a special vacation like a safari, where I'd want the best possible quality.

That said, the overall package, especially considering the price, could be worth it for general use. And now Kodak has released this newer version which adds USB-C charging to bring the series up to date, even if the other features are now considered dated.

I'm a little disappointed there are no other upgrades — for example, the PixPro AZ653's video recording tops out at Full HD (so no 4K), while burst shooting is only up to 5fps.

For sure, it's the 65x zoom lens, spanning 24-1560mm focal lengths, which is the big sell, and along with the price I expect it'll be a popular camera.

So-called bridge cameras are named because they 'bridge' the gap between compact cameras and DSLRs. They are bulky do-it-all 'compact' cameras with one important catch — a compromise in image quality.

They very much had their heyday in the 2000's, but 20 years later the market is a shadow of its former self – my best bridge cameras guide now has just three entries, one of which was recently discontinued.

That said, when Nikon relaunched its own take last year, the Coolpix P1100 with its 125x zoom, it got plenty of interest, despite its much steeper price tag.

Panasonic got in on the act in 2024 by refreshing its own affordable bridge camera, the Lumix FZ80D / FZ82D, which also modernized the series with USB-C charging, but otherwise features dated tech.

The surprise for me is how close the Lumix model is in price to Kodak's, and given it shoots 4K video (albeit with a lesser 60x zoom), it feels like the better pick of the two. For context, we gave that underwhelming Lumix camera a 2.5 star rating. Kodak says its PixPro AZ653 will hit stores in April.

The AZ653 targets an audience looking for an "all-in-one" camera without breaking the bank. Its main highlight is the 65x stabilized optical zoom, covering an equivalent focal length of 24mm to 1560mm, ideal for photographing everything from landscapes to distant wildlife. In addition, the camera offers:

Macro focus at 1cm

20MP sensor

Articulated screen and DSLR-like body

USB-C charging (the main upgrade compared to its predecessor, the AZ652)

Despite the versatile lens, the AZ653 suffers from the main drawback of inexpensive bridge cameras: the 1/2.3-inch BSI CMOS sensor, a size already easily found in affordable smartphones. With these characteristics, the model tends to capture little light, compromising performance in low-light scenarios, and struggles to handle very bright areas well.

Other aspects that complicate the life of the Kodak device are the limited recording to Full HD at 30 FPS, without 4K support, and continuous shooting with a range of only 5 FPS. For those planning to record unique trips, such as a safari, the savings on hardware may come at the cost of the best memories.

Considering that similar rivals, such as the Panasonic Lumix FZ80D/FZ82D line, at least offer 4K videos, and that advances in smartphones, especially from Chinese brands with robust sensors, guarantee better specifications, choosing the AZ653 may be difficult. Still, the price may be the most attractive factor.

by mundophone

 

TECH

Siemens unveils heavy battery locomotive with 2 MWh capacity and pantograph

Siemens Mobility, a technology and sustainability leader in rail and Akiem, Europe’s leading provider of locomotive and passenger train leasing and maintenance, have signed a framework agreement for the purchase of 80 Vectron locomotives, with a firm order of 50, which includes the launch of the new Vectron Dual Mode Electric/Battery locomotives and an option for 30 additional units. With the new locomotive, Siemens Mobility is further developing its proven Vectron Dual Mode platform and expanding the Vectron family with a battery-based solution. The locomotives can operate both under overhead line power and on nonelectrified sections using traction batteries. 

Siemens Mobility will deliver the first locomotives from 2029/2030, enabling Akiem, as the launch customer, to offer them for lease to the market. The agreement builds on the long-standing partnership between the two companies. Prior to this latest order, Akiem had already placed several firm orders with Siemens Mobility for a total of 120 Vectron and Vectron Dual Mode locomotives since 2021. With the new battery-based Vectron Dual Mode, Siemens Mobility supports rail decarbonization by enabling efficient, climate-friendly operations on routes without end-to-end electrification, further strengthening sustainable rail freight as demand continues to grow.

The new Dual Mode is a mainline locomotive intended to haul trains over longer distances. Typically, dual-mode Vectrons are used in freight transport. In Germany, the electric/diesel-electric version of the Dual Mode is also needed for ICE trains. The new diesel ICE L can hit speeds of 99 mph with a Vectron. The battery version also reaches 99 mph. This makes it significantly slower than regular Vectrons, which Siemens also offers with top speeds of 124 mph and 143 mph.

This suggests that the new battery Vectron will likely be used for freight transport, where high speeds are not a priority. However, it is certainly possible that the new locomotive will also be used for passenger trains. Especially in Germany, there are still many important passenger routes that lack an overhead wire. Additionally, routes are frequently out of service because they require general renovations.

“Akiem’s decision is a strong vote of confidence in our technology, and we greatly value that trust. With the Vectron Dual Mode Electric/Battery, we are taking a major development step based on the proven Vectron platform, adding a new fully electric member to the Vectron family,” said Andre Rodenbeck, CEO Rolling Stock, Siemens Mobility. “For customers, this means greater operational flexibility on routes where electrification is not continuous, while supporting the transition to more sustainable rail operations.”

“Akiem and Siemens’ teams shared a common vision that Vectron Dual Mode Electric/Battery will meet the vast majority of our customer needs when operating on non-electrified lines, significantly reducing costs of operations against existing market solutions, while reducing CO2 emission and noise,” said Fabien Rochefort, CEO Akiem. “Vectron Dual Mode Electric/Battery is a natural and reliable continuity of the proven Dual Mode platform. We are delighted to extend our Vectron portfolio, while innovating and differentiating to the benefit of our customers everywhere in Europe.”

Vectron Dual Mode Electric/Battery: innovation based on proven concept...The new Vectron Dual Mode Electric/Battery is built on the proven Vectron Dual Mode platform and is designed for a similar range of operations. Instead of a diesel engine, it uses a modular traction battery system, allowing the locomotive to run both under overhead lines and on routes without continuous electrification. It is planned with flexible battery configurations of up to more than 2 MWh and is designed to deliver up to 2,400 kW to the wheels in both battery mode and when operating under AC overhead line power. The new Vectron Dual Mode Electric/Battery is also designed for speeds of up to 160 km/h, a maximum tractive effort of 300 kN, a weight of approximately 90 tons, and comes with a train supply power of 480 kVA.

Siemens Mobility is one of the leaders in alternative traction technologies for rail and is consistently advancing decarbonization with proven battery and hybrid solutions – for example, by further developing the Vectron platform toward battery-enabled variants such as the Vectron with Battery Power Module. In regional rail, Siemens Mobility is also enabling low-emission operations on partially or non-electrified routes with the Mireo Plus B (battery) and the Mireo Plus H (hydrogen). At the same time, Siemens Mobility continues to expand its portfolio of alternative traction solutions to provide operators with additional options for climate-friendly rail services.

DIGITAL LIFE

Beware of quishing: fake QR codes steal your money

The gesture has become so natural that we don't even think about it anymore. You sit on a restaurant terrace, take your cell phone out of your pocket, open the camera and point it at the small black and white square stuck to the table. In a couple of seconds, the menu appears on your screen. This extreme convenience, however, is being transformed into a silent weapon. The National Republican Guard (GNR) recently issued a warning about a new wave of scams in Portugal that exploits exactly this blind trust: "quishing".

To understand the dimension of this threat, it is useful to go back in time a little. The QR (Quick Response) code is not a new technology. It was invented in 1994 by the Japanese company Denso Wave for a very specific purpose: to track automotive components during the manufacturing process. Unlike traditional barcodes, which store information only in a horizontal line, the QR code saves data in two dimensions (horizontal and vertical). This allows it to contain a substantial amount of information, such as a complete web address.

The pandemic catapulted this technology into our daily lives, replacing physical menus, paper tickets, and payment terminals. The big technical problem is that a QR code is essentially "blind" and passive; it doesn't have any native security layer or encryption to validate the destination it's sending you to.

The term "quishing" comes from the fusion of "QR" and "phishing" (the classic social engineering technique used in fake emails to fish for your data). However, while a fraudulent email often ends up in the spam folder or has obvious spelling errors, a malicious QR code is visually indistinguishable from a legitimate one.

The attack usually begins in a surprisingly analog way. Criminals generate a code that points to a server they control. Then, they print this code on a high-quality sticker and physically stick it over a real code in a public space. Imagine a municipal parking meter. You park your car, see the signal to conveniently pay with your cell phone, and scan it.

When your camera processes this counterfeit sticker, your screen is immediately redirected to a webpage that perfectly mimics the parking company's official website. Unsuspecting, you enter your credit card details to pay a two-real fee. In reality, you've just given a scammer direct access to your bank account.

Beyond the fake payments, the danger extends to infecting the device itself. Some of these links are programmed to force the download of malicious software. Once installed, this malware operates invisibly on your smartphone's processor, capable of intercepting passwords, reading your messages (including two-factor authentication codes sent by your bank), and monitoring your activity. A simple moment of distraction can cost you your digital identity and your savings.

How to protect your phone and your wallet...The success of this scam doesn't depend on complex flaws in your device's operating system, but on each user's behavior. We've been conditioned to associate these squares with speed and usefulness, lowering our defenses. To navigate this scenario without compromising your safety, you should adopt a more skeptical stance.

Before scanning any code in a public space, apply these basic rules(below):

Physically inspect the surface: Run your finger over the code to check if there is a sticker superimposed on the original material of the poster or machine.

Analyze the link preview: Nowadays, any smartphone camera shows the web address before opening it; read the URL carefully and look for spelling errors or domains that do not match the official brand.

Evaluate the context: If you find an isolated code on a utility pole promising big prizes or free Wi-Fi access, the likelihood of it being a trap is enormous.

Prioritize official apps: For mobility payments, such as scooters or parking meters, directly open the app of the service you already have installed, instead of scanning generic codes posted on the street.

Technology is designed to make our routine easier, but convenience should never outweigh the protection of your personal data. An extra second of attention is all you need to avoid a huge headache.

by mundophone 

TECH

QuWAN Express: QNAP's "VPN" that eliminates the need for routers

QuWAN Express is a lightweight VPN networking solution that enables encrypted connectivity between NAS devices without the need for additional routers. The product expands on the existing QuWAN SD-WAN architecture, focusing on scenarios where simplicity of deployment is prioritized over the complexity of a complete network infrastructure.

Not all organizations have sufficient infrastructure to justify installing dedicated routers at each location. Small branch offices, temporary offices, or environments that only need to connect two or three remote NAS systems were previously beyond the practical reach of QuWAN SD-WAN.

QuWAN Express does not replace the existing architecture—it maintains QHora routers as the basis for larger-scale deployments—but fills a gap that previously required more complex alternative solutions. QNAP positions it as a complement, not as an internal competitor.

The service uses Super Node, a QNAP cloud-based relay system, to establish automatic VPN connections between NAS devices. The solution works even without public IP addresses and traverses multiple firewall layers without manual port configuration or IP addressing conflict management.

Ruby Chan, Product Manager at QNAP, explains the scope of the offering: “With the introduction of QuWAN Express, we are expanding the flexibility of the QuWAN architecture to storage-centric scenarios. This allows organizations to integrate NAS systems into the QuWAN connectivity framework at different stages and scales of deployment, without compromising security or incurring unnecessary network overhead.

As multi-site operations become the norm, businesses increasingly demand reliable data transfer between different locations and external backup resources. However, not all use cases justify the costs and complexity of a complete network infrastructure.” To address this need, QNAP Systems, Inc. today announced the launch of QuWAN Express, a lightweight VPN networking solution that extends QNAP's QuWAN SD-WAN architecture to deliver encrypted point-to-point, NAS-to-NAS connectivity without additional routers, simplifying data transfer between locations and remote backup.

Complementing use cases without recreating the architecture...In existing QuWAN SD-WAN deployments, centralized network backbones based on QHora routers remain the foundation for enterprise-wide network management and security. QuWAN Express is not intended to replace this architecture, but rather to complement it, addressing deployment cases that require more flexibility. This includes temporary locations, smaller branch offices, or environments where NAS systems need to be integrated into the QuWAN ecosystem to provide mutual connectivity and direct access to NAS resources across the organization.

“By launching QuWAN Express, we are expanding the flexibility of the QuWAN architecture for storage-focused scenarios,” said Ruby Chan, product manager at QNAP. “This allows organizations to integrate NAS systems into the QuWAN connectivity framework at different stages and scales of deployment, without compromising security and avoiding additional network complexity.”

By reducing connectivity barriers between NAS in different locations...For organizations that do not use QNAP routers but still need secure data transfer or backup between remote NAS systems, QuWAN Express offers a more straightforward and simple approach. Through QNAP's Super Node cloud relay service, NAS devices can automatically establish secure VPN connections, even without public IP addresses or in environments with multiple layers of firewalls, eliminating the need for manual port forwarding, firewall configuration, or IP address conflict management, and significantly reducing deployment time and operational complexity.

QuWAN Express supports point-to-point connectivity for up to three NAS devices, making it ideal for cross-region data exchange and remote backup. The service includes 15 GB of free data transfer per month, and upon reaching the limit, traffic is reduced to maintain continuity of essential connections. Additional capacity and bandwidth can be flexibly incorporated through the QNAP Software Store.

For companies structuring distributed data architectures or seeking to simplify off-site backup and branch synchronization workflows, QuWAN Express offers a lightweight and rapidly deployable option. By reducing friction in network deployment, it allows NAS connectivity to keep pace with growing operational demands without imposing restrictions on infrastructure planning.

Limits and Costs...QuWAN Express supports point-to-point connections between up to three NAS devices simultaneously. The service includes 15 GB of free monthly data transfer; when this limit is reached, traffic is managed by throttling to ensure the continuity of critical connections, instead of interrupting them. Additional capacity can be purchased from the QNAP Software Store.

For organizations with external backup workflows or cross-region data synchronization, the cost-to-implement ratio is the main selling point. The absence of mandatory additional hardware significantly reduces startup time and cost.

QNAP's QuWAN Express is a software-based point-to-point (P2P) VPN solution that allows you to securely connect QNAP NAS devices in different locations without the need for additional routers. It simplifies the creation of mesh networks for backup and data transfer, offering automatic configuration, ideal for SMBs.

Key features and benefits (below):

Routerless connection: Connects NAS devices directly over the internet, eliminating the need for routers or complex firewalls.

Agile deployment: Simplifies configuration, reducing installation time and the need for IT technical knowledge.

Secure mesh VPN: Creates a secure virtual private network for data interconnection between branch offices or locations.

Ideal for NAS: Focused on storage scenarios, facilitating remote backups and data synchronization between multiple NAS devices.

P2P capability: Supports point-to-point connections for up to three NAS devices, ideal for cross-regional data exchange.

Additional benefits: Offers 15 GB of free data transfer per month.

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