TECH

AI models can already write code and solve complex tasks. The next challenge is proving they are correct

AI models can already write code, generate text, and solve complex tasks—sometimes with impressive speed. However, one problem continues to challenge experts: certain answers may seem convincing but are actually incorrect.

As this technology begins to enter critical processes and take on tasks autonomously, new concerns arise; in certain contexts, it is not enough for an answer to *seem* right—it must be demonstrated to be correct.

For Amazon Web Services (AWS), the solution to this problem may lie in neuro-symbolic AI, an approach that combines the learning capabilities of generative models with formal mathematical verification methods. "The idea is to use the best of both worlds," explains Leonardo de Moura, a Senior Principal Applied Scientist at AWS.

On one hand, language models operate based on probabilities, capable of producing answers that—while appearing correct—do not necessarily come with a guarantee of accuracy.

On the other hand, automated reasoning follows a different logic. Instead of seeking a probable answer, it allows for the verification of properties defined mathematically. However, this approach only works when it is possible to precisely define what "correct" means.

For instance, in a data compression program, one can mathematically establish a clear property: if a file is compressed and then decompressed, the result must be exactly the original file. In other words, it is possible to specify what the program should do and verify whether that property is met, the manager explains.

However, not all problems work this way. "Imagine, for example, that I want to prove a mathematical model identifies a cat. How do we mathematically define what a cat is?" he asks. For concepts that are vaguer and harder to formalize, formal verification techniques cannot offer the same guarantees. That is why automated reasoning finds particularly relevant applications in areas such as the verification of programs, hardware, or mathematical models of specific processes, notes Leonardo de Moura.

“Automated reasoning has many applications, but it doesn't completely solve the problem,” the executive points out. The answer involves combining the capabilities of generative models with logic-based reasoning systems...“Language models, despite sometimes making mistakes, do incredible things. So, what can we do? The model generates a result, and we use the logic component to verify whether that result is correct or not”... Leonardo de Moura, Senior Principal Applied Scientist at AWS.

How does it work in practice? Returning to the compression program example: instead of asking an AI system simply to write it, one can mathematically specify what needs to be guaranteed.

In other words, the system has the “freedom” to create the program, but generating code that merely *appears* to work isn't enough; it must also construct a proof that the program satisfies the defined property.

Instead of asking for just one thing—like “write the program”—we are asking: “write the program *and* prove that it is correct.”

Combining the ability to generate solutions with formal verification increases confidence in the results, but can the neuro-symbolic AI approach eliminate hallucinations?

According to Leonardo de Moura, the main limitation remains the ability to mathematically define what “correct” means. In the case of the compression program, the property is clear and can be formalized. In other cases, such as identifying a cat, the task is much more difficult.

There are also limits inherent to logic itself. “There are problems that are undecidable,” the expert explains, adding that in such situations, it is impossible to guarantee the existence of a proof for every true statement. However, he acknowledges that these are “extreme cases.”

The next stage of AI...Although the combination of generative AI and automated reasoning is already being used by AWS in areas such as hardware verification, cryptography, and agent control, Leonardo de Moura believes the most significant impact is yet to come.

He argues that, in the coming years, this approach could transform software development, envisioning an AI that is not only capable of writing programs but can also demonstrate how they meet the defined security and functional properties.

He explains that programmers today are sometimes "afraid to implement an optimization because they fear introducing a bug into the code." The alternative would be to manually verify that the changes preserve all properties—a process that can be very time-consuming.

"It is a very time-consuming task. But AI is great at performing tasks that are mechanical and repetitive. It has infinite patience"... "We will have software that is more secure and developed faster, because we are verifying that the code possesses the desired properties. Users will have greater confidence in the software they are using. (...) And I don't think this will happen ten years from now; it will happen much sooner," asserts Leonardo de Moura.

by mundophone

 

TECH

Amazon and Walmart AI ignore 'made in USA' retail fraud, study claims

A Columbia University study published this month reveals that even though Amazon and Walmart’s AI shopping chatbots can easily spot deceptive "Made in USA" claims on their platforms, both retail giants actively choose not to flag the fraud for consumers.

Erie Meyer and Zachary Harris at Columbia Law’s Center for Law and the Economy tested the platforms’ automated shopping assistants, Amazon’s Alexa for Shopping and Walmart’s Sparky, to determine how they handle origin claims. Their findings (first spotted by Reuters) expose a stark divide between what these AI systems know and what they actually tell buyers. In listing after listing, products trumpet "Made in USA" in bold title text while quietly acknowledging imported origins in fine-print specification fields. The underlying AI models effortlessly process this backend data and detect the contradiction, proving that technical limitations are not the problem here.

Rather, the study found that failing to warn shoppers is a calculated commercial strategy. When prompted about "Made in USA" items, Amazon’s chatbot frequently blocked queries or restricted answers, whereas identical questions regarding "Made in China" goods returned detailed responses without issue. When queried about this double standard, Amazon’s own AI shopping agent candidly explained that the restriction reflected a corporate policy designed to shield its overseas seller base from losing sales. Both chatbots acknowledged in conversational exchanges that the silence surrounding fraudulent claims stems from business decisions rather than software defects.

It goes without saying that this kind of selective transparency totally screws with domestic businesses and misleads shoppers willing to pay a premium for American-manufactured goods. For decades, FTC guidelines have required strict adherence to origin claims, mandating that products advertised as American-made contain all or virtually all U.S.-sourced parts and labor. By allowing mislabeled goods to flood their search engines and deploying AI agents that mask these discrepancies, e-commerce giants are able to circumvent established consumer protection regulations.

Meyer and Harris' report also highlights a broader failure of accountability. Both Amazon and Walmart had previously made public commitments to document and disclose the inner workings, capabilities, and safety parameters of their consumer-facing AI models. The study concludes that both corporations have reneged on those promises, and is a larger issue that Congress needs to immediately act on. On a more local level, members of the public, workers and/or whistleblowers are also encouraged to report to their state attorney generals and the U.S. Securities and Exchange Commission, respectively.

The researchers focused on Amazon's Alexa for Shopping and Walmart's Sparky chatbot, concluding that both are able to detect when a 'Made in USA' claim in a product title or listing conflicts with country-of-origin details found elsewhere in that same listing. Researchers also found that "Made in USA" fraud appears to be common on both platforms.

A screenshot of an exchange between an author and Walmart’s Sparky AI chatbot-image above (Credit: Columbia University)

Asked to explain why their parent companies had not acted on the false labeling, both chatbots pointed to business rationale rather than any technical barrier, the study found. On the question of why false "Made in USA" claims continue to appear on its platform, Amazon's Alexa told researchers: "the harm to U.S.-made brands is real and documented, but until that harm creates a financial, regulatory, or reputational cost for Amazon specifically, it remains easier to do nothing."

Walmart's Sparky, when asked why it does not flag suspicious "Made in USA" claims, said the Federal Trade Commission typically enforces such rules against manufacturers rather than retailers. "That's a business calculation, not a legal justification," the chatbot said, according to the study.

The study also found that Amazon blocks answers to questions about "Made in USA" products while permitting equivalent questions about products made in China, and that Amazon's own chatbot described the discrepancy as a choice the company made to protect its overseas seller base.

In a statement, an Amazon spokesperson said the company currently shows country-of-origin information on product detail pages where it is available and is continuing efforts to surface that data more prominently for shoppers. A Walmart spokesperson did not respond to a request for comment, according to Reuters.

Under FTC regulations, a product may carry a "Made in USA" label only if it is "all or virtually all" manufactured domestically. Last year the agency called on both retailers to take action against inaccurate country-of-origin claims by third-party sellers, pointing to the companies' own marketplace rules that obligate sellers to provide truthful product information.

The study is the first published by the Columbia center, which was launched after former FTC Chair Lina Khan returned to the university. "Even as AI tools continue to grow in sophistication and capability, business incentives will shape how these advancements get deployed," Khan said in a statement. "Policymakers and enforcers have a vital role to play to ensure the public doesn't get the short end of the stick."

mundophone

 

TECH

The new generation of hearing aids brings hearing health into the digital age

For decades, hearing aids were viewed primarily as discreet medical devices designed to amplify sound. That definition is no longer sufficient. This new generation of equipment combines digital processing, wireless connectivity, mobile apps, and automatic environmental adaptation systems, bringing hearing technology closer to the world of smart devices.

The shift is not merely aesthetic. Modern models can continuously analyze surrounding sound, distinguish speech from background noise, and adjust various parameters in real time. For someone using the device in a restaurant, at a work meeting, or on a busy street, this capability can make a significant difference in understanding conversations.

One of the key advances lies in signal processing. Instead of amplifying all sounds equally, modern devices utilize multiple frequency channels and algorithms capable of prioritizing the human voice. Directional microphones help focus sound capture on the person speaking, while noise-reduction systems aim to limit constant sounds—such as traffic, ventilation, or the movement of people in an enclosed space.

The outcome always depends on the specific model, configuration, and the user's hearing needs. Nevertheless, technological evolution has made the experience more personalized. Today, those looking to compare modern hearing aids will find solutions with various shapes, power levels, and features—ranging from devices that sit almost invisibly inside the ear canal to rechargeable models worn behind the ear.

Artificial intelligence is also beginning to play a more visible role in this sector. In some devices, algorithms recognize acoustic patterns and automatically select the most suitable program. This reduces the need for constant manual adjustments and allows the device to adapt quickly when moving between quiet environments and places with multiple sound sources.

Health monitoring: beyond just hearing...One of the more interesting directions hearing aid technology has taken is the expansion into health monitoring. Hearing aids are worn in the ear for most of the day, which makes them well-placed to track certain health metrics continuously in a way that a wrist-worn device cannot.

Starkey's Omega AI is the furthest ahead here. Starkey states that the device tracks activity levels, includes a balance assessment tool, and monitors respiratory patterns, building a picture of overall well-being alongside hearing performance. Their companion app correlates this data to give users a more complete picture of how their hearing aids and their health are working together.

This is not yet at the level of clinical-grade medical monitoring, and it should not be treated as a substitute for any form of medical assessment. But as an additional layer of insight about how you are moving, resting, and functioning day to day, it is a genuinely interesting development, and one that is likely to become more sophisticated over the next few years.

Bluetooth transforms the hearing aid into a connected device...Connectivity is another area that has significantly changed daily usage. Many devices can connect directly to smartphones, televisions, computers, and other compatible equipment. In practice, phone calls, music, videos, or online meetings can be streamed directly to the user's ears.

This integration brings hearing aids closer to wireless headphones, but with a key difference: the sound is tailored to the individual's hearing profile. For people who work remotely, frequently use the phone, or consume digital content, connectivity has shifted from being a mere extra feature to a key factor in the decision-making process.

Mobile apps offer additional control options. Depending on the manufacturer, users can adjust the volume, switch programs, check battery status, or locate the device. Some platforms also support remote care, allowing certain adjustments to be made without an in-person visit.

The adoption of rechargeable batteries has also made these devices more convenient. Instead of frequently replacing small disposable batteries, users can simply place the devices in a charger at the end of the day. This solution minimizes the handling of tiny components and makes the devices easier to use for individuals with limited manual dexterity.

However, battery life requires careful consideration. Operating time varies based on the model, Bluetooth usage, and the number of hours worn. When traveling or during particularly long days, having a charging case that provides extra power can be a crucial factor.

Purchasing technology is no substitute for a hearing assessment. Despite increasing digitalization, a hearing aid should not be selected based solely on its list of features. Hearing loss can present in various degrees and patterns, and the device must be configured according to audiometric test results. Even a technically advanced model may yield limited results if the fitting does not align with the individual's actual needs.

For this reason, hearing assessments and follow-up care by qualified professionals remain essential. The initial period of use also requires an adjustment phase: sounds that have not been heard for some time may seem intense or unnatural during the first few weeks. Gradual adjustments help the brain relearn how to interpret these auditory cues.

Before purchasing, it is advisable to consider the device's form factor, ease of use, smartphone compatibility, battery life, warranty terms, and the availability of ongoing support. The opportunity to try out the equipment also allows users to understand how it performs in real-world situations, rather than just in a controlled environment.

Hearing health is entering the era of digital services...Online sales and specialized video consultations are transforming how consumers research and purchase hearing technology. Platforms like Clicaudio aggregate information on various models and establish digital communication channels, making the comparison process more accessible for those who prefer to start their research from home.

This transformation does not eliminate the clinical component; on the contrary, it reinforces the need to integrate technology, assessment, and follow-up care. True progress lies not merely in manufacturing smaller or more powerful devices, but in creating an experience that supports the user throughout the day and adapts to their habits.

Hearing aids have thus evolved beyond simple amplifiers. They have become connected, customizable devices that are increasingly integrated into our digital daily lives. For millions of people, this evolution can mean greater autonomy in communication, increased confidence in social settings, and a more natural relationship with the sounds of everyday life.

mundophone

NOKIA

The Nokia 1100 teaches a lesson in technology that remains relevant more than 20 years later

While other brands were betting on cameras and novelties, an extremely simple mobile phone won over millions of people with an unexpected solution. The secret to its success remains a lesson for today's technology.

In the early 2000s, the mobile phone industry was in a race for color screens, built-in cameras, and new multimedia features. Yet, one device took the exact opposite path and made history. Instead of impressing with cutting-edge technology, it won over users by solving real, everyday problems. Decades later, its strategy is still considered one of the greatest examples of smart design in mobile telephony.

When the Finnish manufacturer launched the Nokia 1100 in late 2003, it made a decision that seemed to go against the grain of the market. Instead of investing in sophisticated features, it developed a device that was affordable, durable, and extremely easy to use. Its focus was simple: making calls, sending text messages, and withstanding years of heavy use.

The strategy paid off handsomely. Within a few years, the model and its variants surpassed the 200-million-unit sales mark. Later estimates suggest sales reached close to 250 million devices, making the Nokia 1100 the best-selling mobile phone in history.

Much of this success came from emerging markets, where millions of people were buying their first mobile phone. For this audience, cameras, internet access, and multimedia functions were not yet priorities. What really mattered was having a device that was inexpensive, reliable, easy to repair, and capable of lasting a long time.

Although its appearance was quite simple, almost every detail had been carefully planned. The project—known internally as "Penny"—featured a structure designed to minimize dust ingress, a keypad made from a single piece, and rubberized sides that improved grip, even in humid environments. The Nokia 1100 wasn't waterproof, but it withstood the conditions found in workshops, markets, on dirt roads, and in rural areas far better than many of its competitors. Another key feature was the Xpress-on removable cover system; if the device was dropped and the casing damaged, the user could simply replace the outer shell without needing to swap out the entire phone.

The flashlight demonstrated that understanding the user was more important than simply adding features... Among all the Nokia 1100's capabilities, one of the most fondly remembered to this day is actually the simplest. A small flashlight located at the top of the device could be quickly activated via a dedicated button or the system menu.

While this seems commonplace today, in 2003 the idea represented a highly practical solution. In many of the countries where the phone was sold, power outages were frequent and street lighting was limited. In such situations, the small flashlight helped users navigate dark streets, locate objects during blackouts, or light up their surroundings without relying on other light sources.

This detail showed that Nokia had closely observed its consumers' daily lives before developing the product. Instead of creating features merely to grab attention in advertisements, the company sought to address real needs faced daily by millions of people.

The battery life further reinforced this approach. Equipped with the well-known 850 mAh removable BL-5C battery, the Nokia 1100 could stay on for up to 400 hours in standby mode—lasting over two weeks without needing a recharge under ideal conditions.

Furthermore, the device offered everything most users actually needed at the time: calls, SMS messaging, a contact list, an alarm clock, reminders, a stopwatch, a calculator, games, and even a custom ringtone composer.

More than twenty years later, the Nokia 1100 remains memorable because it proved that innovation doesn't always mean adding more technology. The answer to the question posed in the title lies precisely in that philosophy: the device's simplest feature helped turn it into a global phenomenon because it addressed real-world needs that other manufacturers were ignoring. Its story demonstrates that understanding people's everyday lives can be far more important than packing in as many functions as possible.

mundophone

 

TECH

Recycling for the energy transition: Fossil fuel infrastructure provides raw materials for sustainable energy production

To transition from fossil fuels to renewable energy sources, we need to build new infrastructure. Empa researchers show that obsolete fossil fuel infrastructure – such as coal mines, oil and gas platforms, fossil fuel power plants, and pipelines – can provide some of the raw materials needed for the energy transition. In particular, recycling copper and steel would make the energy transition more cost-effective and environmentally friendly.

Moving away from oil and gas and toward solar, wind, and hydro power: That is the energy transition. To stop global warming, we must shift our energy infrastructure toward renewable sources in the coming years. The large-scale construction of solar cells and wind turbines requires, among other things, minerals and metals. At the same time, the existing fossil fuel infrastructure is becoming obsolete. So, could we recycle parts of our old energy system to build the new one? Researchers from Empa’s Technology and Society laboratory investigated this question in a study.

Their study was conducted as part of the EU project CircEUlar and was published in the journal Nature Communications. The researchers analyzed the stocks of 22 different materials contained in today’s coal mines, oil and gas platforms, fossil fuel power plants, and large pipelines. “To understand the potential of this ‘urban mine,’ we first need to know what materials are available in it,” says Empa researcher Hauke Schlesier, the study’s lead author.

Two raw materials stood out: steel and copper. Both metals are present in large quantities in fossil fuel infrastructure – and are urgently needed for the energy transition. “Copper is used in transformers and cables, while steel is used for structural elements,” says Schlesier. According to the study, recycling fossil fuel infrastructure could cover the entire steel demand and about one-third of the copper demand for the energy transition. As fossil fuel infrastructure is phased out, these additional material streams would become available for recycling. According to the researchers’ calculations, the capacities of global recycling facilities would be sufficient to recover the copper and steel needed for the energy transition.

More economical and environmentally friendly...But does recycling steel and copper from fossil fuel infrastructure really make sense? The researchers' answer to the question is “yes.” The recycling processes for both metals are significantly more environmentally friendly than their primary extraction. “Steel production generates slag, particulate matter, and large amounts of carbon dioxide, while copper mines produce toxic waste,” says Schlesier. Recycling, on the other hand, primarily requires electricity: Steel is melted down in electric furnaces, while copper can be recovered through an electrochemical process.

From a macroeconomic perspective, repurposing existing steel and copper stocks is highly beneficial. The researchers emphasize that it is worthwhile to begin recycling as early as possible, as this results in the lowest follow-on costs. The primary extraction of raw materials causes environmental and health damage, which entails significant follow-up costs for society (so-called externalized costs). “By recycling steel and copper from fossil fuel infrastructure, we could save between four and eleven trillion U.S. dollars in externalized costs by 2050,” says Schlesier. What's more, recycling itself is no more expensive than the primary extraction of steel and copper and is therefore quite competitive. “In addition, up to two billion tons of CO2 equivalents can be avoided. That corresponds to about 50 years of Swiss emissions,” adds Schlesier.

The main challenge for recycling valuable raw materials from fossil fuel infrastructure is the lack of incentives. “For state-owned fossil fuel companies, reducing societal costs could provide an incentive to phase out fossil fuel infrastructure sooner. This is less likely to apply to privately owned energy companies,” explains Schlesier. “They usually have no economic interest in minimizing externalized costs.” Further targeted incentives from the government could help address this.

Benefits for the energy transition...The recycled steel and copper could then be used worldwide in solar panels, wind turbines, power lines, or electrolysers for hydrogen production. “A promising approach is to use recycled steel instead of aluminum in the mounting systems for solar panels,” says Empa researcher Harald Desing, who co-authored the study. This could reduce the carbon footprint of solar panels by about a third. The amount of steel in the fossil-fuel infrastructure would be sufficient to provide two to five times the amount needed to meet global climate targets for solar power systems.

Recycled steel and copper could also be used in wind turbines. This would likewise reduce the carbon footprint of wind turbines by about a third. If the recycled steel were used exclusively for the construction of wind turbines, it could cover the total steel demand required to meet climate targets through 2050. “It could also be used in power lines and electrolysers for hydrogen production,” says Schlesier. “The key point is that clean energy technologies must gradually replace fossil fuel infrastructure so that the embedded steel and copper can be reused.”

Repurposing fossil fuel infrastructure can save trillions of dollars in global social, economic, and environmental costs during the energy transition. Reusing existing assets accelerates decarbonization, prevents capital waste, and mitigates the impact of unemployment on communities dependent on traditional energy.

Reducing capital expenditure (CapEx):

Leveraging gas pipelines: Converting natural gas networks to transport green hydrogen.

Repurposing platforms: Transforming offshore oil structures into foundations for offshore wind energy.

Using depleted wells: Adapting deep oil wells to harness geothermal energy for residential and industrial use.

Existing electrical grids: Connecting solar farms to legacy coal substations to avoid the cost of new transmission lines.

Mitigating environmental and social liabilities:

Avoiding idle assets: Preventing trillions of dollars in physical infrastructure from becoming financial losses (stranded assets).

Preserving jobs: Transitioning refinery operators to roles in biofuel and hydrogen plants.

Carbon storage: Utilizing depleted oil reservoirs for CO₂ capture and storage (CCS) projects.

Revitalizing industrial sites: Converting decommissioned thermal power plants into battery energy storage system (BESS) hubs.

Key financial and technical challenges:

Material incompatibility: Hydrogen can embrittle the steel in old pipelines, necessitating expensive coatings.

Retrofitting costs: The initial investment to repurpose a structure can sometimes approach the cost of building a new one.

Geographic location: Legacy extraction sites do not always align with areas offering the best solar or wind potential.

Regulatory complexity: A lack of clear laws for transferring permits from fossil fuel operations to renewable energy projects.

Empa technology and society laboratory---www.efd.admin.ch

DIGITAL LIFE

From social media to AI: how bots took over the internet

Data from Cloudflare, an internet infrastructure company, estimates that nearly 60% of current online traffic is not generated by humans. In practice, this means that a significant portion of online interactions occurs between automated programs, without any human intervention. For companies relying on audience metrics, digital advertising, or e-commerce, this figure has shifted from a mere technical curiosity to a management challenge.

As a report in *The New Yorker* highlights, the term "bot" gained public prominence during the 2016 US election, when the theory that Russian-linked automated accounts influenced the vote's outcome became widespread. Special Counsel Robert Mueller’s report confirmed the existence of an operation coordinated by the Internet Research Agency—the so-called "troll factory" based in St. Petersburg.

However, *The New Yorker* itself cites studies that put this reach into perspective: a 2023 New York University study, published in the scientific journal *Nature Communications*, showed that just 1% of Twitter users were exposed to 70% of the posts from Russian accounts identified as trolls. An earlier study—conducted by researchers from Dartmouth, Princeton, and the University of Exeter and cited in the report—indicated that the bulk of fake news consumption in 2016 came from conservative voters who likely would have voted for Trump regardless.

Conflicting incentives...The report interviewed Emilio Ferrara, a computer science professor at the University of Southern California (USC), who describes the platforms' operations as a "system of conflicting incentives": bots artificially boost engagement metrics—appealing to advertisers and fueling recommendation algorithms—yet the abundance of toxic content degrades the user experience and can reduce the time spent on the app. *The New Yorker* also cites a study finding that removing toxic posts from a feed reduced the time users spent on the platform—a fact that helps explain why aggressive moderation is not automatically a priority for tech companies.

This balance between engagement and time spent on the platform directly impacts advertising revenue. X’s revenue-sharing program, which pays verified accounts based on organic reach metrics, creates a financial incentive to run bots optimized to maximize likes, reposts, and comments. With the advancement of artificial intelligence, Brown notes, these systems learn to identify which types of content generate the most reaction and automatically adjust their output accordingly.

The cost to companies...This ecosystem has a range of practical effects on businesses outside of social media: bots that buy up concert tickets within seconds of sales opening—only to resell them at markups of hundreds or thousands of dollars; systems that scan real estate listings to fire off automatically generated, artificially low offers to property owners; and automated accounts that wipe out product inventories on e-commerce sites.

The article also revisits the most famous commercial dispute involving bots: Elon Musk’s 2022 purchase of Twitter. According to the report, Musk estimated that 20% of the platform’s accounts were automated—contrasting with the 5% figure stated by the company—and used this argument in an attempt to back out of the deal before the acquisition ultimately went through. The case highlighted how bot measurement can become a due diligence criterion in mergers and acquisitions.

Lynnette Ng, a researcher who studied bot networks at Carnegie Mellon University, explains to *The New Yorker* why these accounts are so effective at spreading content: bots repeatedly exploit cognitive biases, making it difficult for people to reason critically about the information they receive. A *New York Times* investigation into actress Sydney Sweeney’s advertising campaign for American Eagle revealed how a small number of critical posts—once they went viral—were amplified by coordinated accounts and bots, creating the appearance of a broader consensus than actually existed.

The next chapter: autonomous agents...We are entering new territory: artificial intelligence agents capable of performing tasks autonomously. The report details the case of Summer Yue, a security researcher at Meta, who set up an AI agent on the OpenClaw platform to organize her email inbox and suggest messages for deletion. According to the researcher’s own account on social media, the agent began deleting emails on its own—at a pace she was unable to halt remotely.

The underlying issue: training data...The root of this problem lies in the vast amounts of web content used to train these models. This extensive training data is what makes these chatbots so efficient and knowledgeable. However, it’s this very efficiency that poses a threat. Martin Vechev, a renowned computer science professor, rightly points out the gravity of the situation, stating,

“This is very, very problematic.”

The challenge is not just identifying the issue but finding a solution that doesn’t compromise the chatbot’s functionality.

A goldmine for scammers and advertisers...The potential misuse of this inferred information is vast. Scammers, always on the lookout for vulnerabilities, could exploit chatbots to harvest sensitive data from unsuspecting individuals. But it doesn’t stop there. The advertising industry, known for its relentless pursuit of detailed user profiles for targeted marketing, could leverage this capability to a frightening degree. As Vechev suggests, this could usher in a new era of advertising where chatbot interactions play a pivotal role in building intricate user profiles.

The balance of power: machines vs. human intuition...While large language models are adept at picking up subtle clues from conversations, there’s an ongoing debate about their efficiency compared to human intuition. Tramèr speculates on the balance of power, suggesting that while chatbots might excel in some areas, human intuition and experience might still outperform in others. However, the very fact that we’re comparing machine efficiency to human intuition in this context is a testament to the profound impact and potential risks of these chatbots.

In conclusion: a call for caution...The advancements in chatbot technology, while commendable, come with significant privacy concerns. As users, it’s crucial to approach these interactions with caution, fully aware of the potential implications. As for the tech giants, the onus is on them to ensure that the pursuit of innovation doesn’t come at the cost of user privacy.

mundophone

 

TECH

Unwanted neighbor: Unbearable 24/7 data center noise sparks lawsuit from furious residents

For residents of Dowagiac, Michigan, home no longer equals quiet refuge, it now sounds like a clogged vacuum cleaner running full blast in the living room, 24 hours a day. Frustrated by a relentless, high-pitched hum emitted by a nearby server facility, local homeowners have filed a lawsuit against the site’s parent company, Hyperscale Data, seeking legal relief from round-the-clock noise pollution.

The site, operated by subsidiary Alliance Cloud Services, started serving as a cryptocurrency mining center in 2021 and then graduated to an AI data data center in 2024, which required continuous, industrial-scale cooling fans, unleashing an unrelenting wall of sound that neighboring residents describe as psychological torture. 

"It sounds like someone set up a vacuum, like in your living room, and the vacuum filter is clogged up, so it's a high-pitched whining," resident Lindy Valenzuela explained in an interview with WXYZ.TV, noting that it feels as if operators simply turned the machinery on and walked away. Fellow neighbor Billy Finn compared the living conditions to a movie scene where prisoners are tortured with sound inside a cell.

In response to growing public outcry, the city of Dowagiac instituted an industrial noise ordinance setting strict limits of 65 dB during the day and 55 dB at night. City inspectors subsequently issued fines against the facility for violating those thresholds, though Hyperscale Data continues to challenge both the citations and the municipal sound-measuring methodology. In the video above, one can see that the reporter's noise meter is hovering just above 50 dB, although we believe noise levels and pitch (i.e. whine) are two separate metrics that need to be accounted for here.

During a tense city council meeting, Hyperscale Data CEO William Horne attempted to reassure the community by highlighting planned sound mitigation efforts, including the purchase of adjacent acreage to serve as a natural acoustic buffer. Horne then made a sweeping proposal to dissatisfied neighbors: if mitigation efforts failed to make their homes enjoyable, the company would buy their properties outright.

That buyout offer did little to soothe anger in the room. For many families, leaving is neither simple nor acceptable. One plaintiff noted that their family has lived in the neighborhood for nearly a century, supported by generational roots that money cannot replace. Another neighbor, caring for a 17-month-old toddler with a second baby on the way, emphasized the sheer disruption of relocating. Residents repeatedly asked executive leadership why community complaints went unanswered for two years before lawsuits were filed.

The Dowagiac battle reflects a broader clash across the country as tech firms scramble to build energy- and cooling-intensive AI infrastructure near residential zones. Similar class-action lawsuits have surfaced in states like Wisconsin, where residents living near massive server complexes report not just audible noise, but infrasound—inaudible, low-frequency vibrations that travel hundreds of feet through the ground, vibrating homes and causing physical discomfort. Granted, some companies, such as Microsoft, have been making inroads to reduce the impact their data centers have on neighborhoods.

Microsoft’s $7.3 billion AI data center...Earlier this year, Microsoft CEO Satya Nadella proudly touted his company’s $7.3 billion Fairwater data center in Mount Pleasant, Wisconsin, as the “world’s most powerful AI data center,” connecting “hundreds of thousands” of power-hungry chips “into a single seamless cluster.”

However, local residents aren’t even remotely as enthusiastic about the facility cropping up in their neighborhood. Last week, three residents of the village of Sturtevant, which is adjacent to Mount Pleasant, filed a class-action lawsuit, as The Milwaukee Journal Sentinel reports, alleging that Microsoft’s flashy data center is emitting “unreasonable and excessive noise onto Plaintiffs’ properties, thereby causing property damages through private nuisance and negligence.”

The noise being generated by “diesel generators and heating, ventilation, and air conditioning (HVAC) systems, including chillers, cooling towers, air-handling units, and condenser fans,” is “consistent and pervasive,” per the lawsuit. The residents claim that Microsoft failed to mitigate the noise through “adequate acoustic barriers, shields, or walls.”

The lawsuit highlights a fierce battle that’s been brewing as major tech companies continue to encroach residential areas with enormous and extremely resource-intensive data centers. The backlash has grown into a major bipartisan issue that could even affect the upcoming midterm elections. Companies say they need the sprawling facilities to fuel a technological revolution, despite major environmental concerns, massive water consumption and noise pollution.

Just last week, a resident in a small town in Southwest Michigan measured the noise level from a nearby data center from his porch, registering a very irritating 60 decibels.

Microsoft’s facility in Wisconsin is emitting a noise “similar to the whirring engine of a freight train parked nearby” around the clock, as Amy Cimbalnik, one complainant in the class-action lawsuit, told The Milwaukee Journal Sentinel.

“We heard it 24 hours a day, and eventually realized it was coming from the Microsoft campus,” she added.

The tech giant acknowledged the lawsuit, telling the publication that it’s “committed to being a good neighbor in the communities where we build, own, and operate our data centers.”

Microsoft also claimed last month that it had already solved the issue, but considering the latest lawsuit was filed on July 1, it doesn’t seem to have appeased the upset residents.

According to a June 18 update on Microsoft’s official blog, the company said that it was investigating the “source of the sound,” claiming that it had “conducted tests, and put noise mitigations in place.” Blog entries from mid-April detail a “tonal humming sound,” which was being caused by “cooling fans that are currently operating at high speeds.”

“Several neighbors confirmed what our independent monitoring showed: that these mitigations fully resolved the issue,” the June update reads.

Village of Mount Pleasant communications director Sean Ryan told The Milwaukee Journal Sentinel that he had not received any complaints since Microsoft made changes to its property in mid-April to “resolve a humming sound.”

“Village officials stand ready to respond if people do reach out to us, and will continue to ensure Microsoft is a good corporate citizen and a good neighbor,” he added.

But that may be easier said than done. Considering Microsoft is looking to build a whopping 15 data centers in Mount Pleasant alone, the company will have to tread carefully to avoid more litigation from furious neighbors.

mundophone