PrismML AI compression could give Apple a way to place far more capable artificial intelligence directly inside the iPhone. The Caltech spinout says it reduced a 27-billion-parameter model from approximately 54GB to less than 4GB, small enough to run locally on an iPhone 17 Pro.

That represents a reduction of about 93%, but the more useful number may be memory efficiency. PrismML says its technology can use up to 15 times less memory than conventional versions of the same model. Apple has reportedly held early discussions with the startup about possible uses for the technology.

Neither company has announced a partnership, acquisition or product plan. PrismML’s claims also need to be judged through independent testing, sustained performance and the compromises introduced by heavy compression. Even so, the demonstration points directly at one of Apple’s largest AI challenges: fitting stronger models inside devices without destroying battery life, consuming too much memory or sending every complex request to the cloud.

PrismML AI Compression Changes the Size Problem

Large AI models are difficult to run on phones because their parameters require substantial storage and working memory. A standard model may perform well in a data center equipped with powerful accelerators, large memory pools and constant electricity, then become impractical inside a device designed to last all day on one battery.

PrismML approaches that problem by representing model weights with far fewer bits. The startup has developed what it calls Bonsai models, including 1-bit versions designed to preserve much of the original model’s capability while sharply reducing size and energy use.

The reported iPhone demonstration used a compressed version of Alibaba’s Qwen model with 27 billion parameters. That is far larger than the compact language models normally associated with smartphones. Running it locally suggests that model size may no longer be controlled only by the number of parameters. How efficiently those parameters are represented becomes just as relevant.

Compression is not new. Developers already use quantization, pruning and other methods to make models smaller. PrismML’s claim is that its approach pushes that process much further while retaining useful reasoning and language performance.

That claim is exactly why Apple would be interested.

Why Siri AI Needs Smaller Models

Apple’s AI strategy depends on dividing work between the device and the cloud. Simpler Apple Intelligence requests can run locally, while more demanding tasks may move to Private Cloud Compute. That hybrid structure allows Apple to offer stronger capabilities without forcing the iPhone to carry every model it may ever need.

The cloud layer, however, introduces cost, latency and availability requirements. A request must leave the device, reach a server, be processed and return. Apple has designed Private Cloud Compute around strong privacy protections, but local processing remains faster and easier to explain.

A larger on-device model could improve Siri AI’s language understanding, reasoning and ability to complete multi-step tasks without a network connection. It could also reduce the number of requests sent to Apple’s servers.

That does not mean cloud processing would disappear. Server models can remain larger and support especially demanding requests. PrismML AI compression could instead move the dividing line. Tasks that currently need Private Cloud Compute might become possible directly on a future iPhone.

The benefits could include quicker responses, stronger offline operation and less server demand as Apple Intelligence reaches more users.

Privacy Fits Apple’s Preferred Direction

On-device processing has always been central to Apple’s AI positioning. Personal information can remain on the iPhone rather than moving through a conventional cloud service. The device can interpret a request, use local context and produce an answer without exposing the underlying data beyond the hardware.

A model capable of handling more work locally would reinforce that approach. Siri AI could potentially understand messages, notes, schedules or onscreen content while reducing the need to transfer information elsewhere.

Apple already uses Private Cloud Compute when local processing is insufficient, and the company has built significant safeguards around that system. Yet every task completed on the iPhone removes one additional processing step.

Privacy is not the only advantage. Local models can continue operating when connectivity is poor or unavailable. That could help Siri AI during flights, rural travel, crowded events or other situations where cloud access becomes unreliable.

A capable offline assistant would also feel more like part of the operating system and less like a remote service accessed through an interface.

The iPhone Still Has Physical Limits

Shrinking a model to less than 4GB does not automatically make it ideal for everyday iPhone use. The model must also run quickly enough, avoid excessive heat and leave enough memory for iOS and active apps.

A technical demonstration may complete tasks successfully while consuming more energy or time than users would accept from Siri. Apple would need to optimize the model for its Neural Engine, memory architecture and software frameworks rather than simply loading PrismML’s existing build onto every supported device.

Storage is another consideration. A model occupying several gigabytes would compete with apps, photos, offline media and system files. Apple could reserve more storage for intelligence features, deliver models selectively or use several specialized models instead of one large general system.

Battery use may create the hardest constraint. An AI model can fit in memory and still require too much sustained computation for frequent mobile use. The ideal system needs to be small, fast and efficient enough to disappear into normal iPhone behavior.

Apple’s chip design gives it an advantage here. The company controls the processor, Neural Engine, operating system and AI software stack. PrismML could provide the compression technique while Apple handles the deeper hardware integration.

Better AI Without Replacing the Cloud

The strongest use of PrismML’s technology may not be putting one enormous model on every iPhone. Apple could use compression across a family of models designed for different devices and tasks.

A smaller model could handle routine language requests. A more capable local model could be downloaded only to recent Pro devices with sufficient memory. Specialized models could support writing, image generation, app actions or Visual Intelligence. Private Cloud Compute could remain available when the request exceeds local capability.

That would give Apple more flexibility than an all-local or all-cloud design. Each device could carry the intelligence appropriate for its chip, memory and battery capacity.

The approach could also help Macs, iPads and future home products. A compressed model may run more comfortably on a MacBook Air, HomePod or smart home hub without requiring constant server access. Apple could scale the same intelligence foundation across products while adjusting model size to the hardware.

This is especially relevant as Siri AI moves beyond answering questions. App actions, personal context and ambient smart home control require intelligence that remains available throughout the day. Lower memory and energy requirements could make those experiences more practical.

Compression Always Includes Trade-Offs

A 93% reduction is a striking headline, but compression can affect output quality. A smaller representation may lose precision, weaken reasoning or struggle with uncommon information. The real test is not whether the model runs. It is whether it performs well enough across the tasks Apple expects Siri AI to handle.

PrismML says its technology preserves strong performance compared with full-precision models. Apple would still need extensive evaluation across languages, safety categories, tool use and personal-assistant scenarios.

Reliability is especially sensitive for an operating-system assistant. A model used for casual brainstorming can tolerate occasional weakness. A model handling messages, appointments, directions or app actions needs a higher level of consistency.

Apple may therefore use aggressive compression selectively. Some tasks can accept a smaller model, while others may remain on a higher-quality server model. The user would not need to know which model is active as long as the response remains fast and dependable.

Apple Wants the Technology, Not Necessarily the Company

Reports indicate Apple has spoken with PrismML, but early conversations can lead in several directions. Apple could license the startup’s technology, work on a technical integration, hire members of the team or explore an acquisition.

The company has a long history of purchasing smaller firms for specialized technology and engineering talent. Model compression would fit that pattern because it supports Apple’s existing AI strategy rather than creating a separate consumer product.

PrismML could also remain independent and provide its tools to multiple hardware companies. Its technology has potential uses across phones, computers, robotics, vehicles and industrial equipment where cloud access is expensive or unreliable.

Apple’s interest alone gives the startup attention. The larger significance is that efficient models are becoming as valuable as larger ones. The AI industry spent years treating scale as the primary route to improvement. Mobile computing forces a different question: how much intelligence can fit inside the power and memory limits of one personal device?

A Smaller Model Could Make Siri Feel Larger

Apple does not need to place the biggest available model on an iPhone. It needs a model capable enough to improve the interactions users repeat every day.

PrismML AI compression could help Siri understand more context, complete more requests locally and respond without waiting for a server. It could strengthen Apple’s privacy argument while reducing the infrastructure load created by millions of AI-enabled devices.

The technology remains early, and a startup demonstration is not the same as a shipping Apple feature. The 93% figure will mean little if quality, battery life or speed suffer under daily use.

But the direction fits Apple unusually well. The company has spent years designing smaller, more efficient hardware systems that perform beyond their apparent limits. PrismML is proposing a similar strategy for AI models.

If the startup’s claims hold up, the next major Siri improvement may not come from sending a larger model into the cloud. It may come from fitting one into the iPhone without making the iPhone feel the weight.

The post PrismML AI 93% Compression Could Give Siri a Major Boost appeared first on AppleMagazine.

We live in a modern, jet-set, hyper-fast world! When we want to buy something online, boom, zoom, we use our fingerprint or face to approve the transaction, so we can grab the next Segway outta here! We don’t have time to enter a credit card! And can we trust a webpage form? Pfeh!

All right, calm down, 1950s inner voice, it’s not that bad. Most of our transactions involving a payment card or other sensitive data can be safely handled over a secure web connection. Apple Pay in Safari is the highest standard, of course, because the payment process involves encrypted elements, and your card number isn’t disclosed to the merchant. The Wallet app in iOS and the Wallet features in iPadOS and macOS further let us automate the entry of numbers and identifiers on pages we trust.

That’s all for automated commerce. What about other scenarios where you need to provide information to someone, often a friend or a local business, in order to transfer money or conduct a transaction? How can you be sure no one else is snooping in?

Make a call

Voice is still one of the most secure means of providing data. The landline wired and cellular wireless networks may be fertile ground for government agencies, but if you need to read a credit-card number or provide a PIN, a call is often the safest way to do so. By voice, you can always have the other person verify details that you believe only they know, or you can verify details to them for the same.

Because we live in a cyberpunk dystopian future, I do have to add a proviso. AI-generated voices have been convincing for a couple of years now—long enough that you shouldn’t trust an incoming call from someone you think you know, and certainly not from a bank, credit-card company, or other financial institution. I mean, I wrote “How To Avoid AI Voice Impersonation and Similar Scams” in January 2024! I assume the state-of-the-art scam is even better.

(The biggest takeaway is analog, too. Set a family password that you demand from a family member who calls asking for money or to do some weird phone or computer set of instructions.¹)

Unless recorded, voice calls also lack persistence, making them impossible to recover later. A real-time Bob needs to be either between Alice and Carol or doing an AI impression of Alice or Carol.

Use secure messaging

I could make the argument that using secure messaging carries even less risk than a voice call if you’ve had an ongoing messaging conversation with someone, so you know it’s really them. Are unsecured text messages being intercepted willy-nilly?² No, but there is also something about sending plain text all over the cellular networks that gives me the willies, nillies aside.

Secure messaging systems include:

• iMessage: While iMessage has seen exploits, they’re government-grade ones. Its secure end-to-end infrastructure is creaky, but unbroken. There’s an extra detail I’ll mention below.
• RCS with encryption: Between Apple Messages on iOS 26.5 or later and an Android device with RCS encryption support, you have a similar level of end-to-end protection. Make sure you see RCS encryption in the text field, or it’s just plain text. You can also use RCS encryption between two capable Android devices—as if!
• Signal: The Signal app and ecosystem have the best end-to-end encryption, as it implements some of the most modern approaches to protecting older messages and current conversations available so far.

The extra note on iMessage is complicated. Briefly, if you use Messages in iCloud, an encryption key to retrieve those messages is included in an iCloud-based iPhone or iPad backup. With access to your Apple Account, someone could potentially retrieve your stored messages. However, if you enable iCloud’s Advanced Data Protection, the backup is encrypted and requires endpoint decryption using one of your devices, protecting the Messages in iCloud encryption key. This should be a worry only if someone manages to obtain your Apple Account, can activate a second factor, and the information you passed via Messages is so sensitive that someone would hunt to uncover it.

Avoid using SMS or MMS text messages (unencrypted plain text), unencrypted RCS (in-transit/at-rest encryption), or other messaging systems where you’re not sure how they handle data protection. WhatsApp can be used if you either have backups disabled or have enabled end-to-end encrypted backups.

Don’t use most collaborative tools

I know that I spend a lot of time in shared documents, whether Google Docs, Notes, Pages, or more esoteric apps or web apps. Most lack the highest level of protection. Shared Pages and Numbers files on iCloud Drive, as well as Google Docs, are encrypted in transit and at rest, and granting shared access is relatively easy (very easy in Google Docs). For iCloud Drive documents, this is true even with Advanced Data Protection enabled.

I’d be dubious about pasting my driver’s license or credit-card information into any of them that are shared with someone else. Ditto, don’t take a picture of your payment card or driver’s license and share it via a Shared Album in Photos, as it has the same issue.

There are two notable (heh) exceptions: if you and the other party or parties to a shared Notes entry or an iCloud Shared Photo Library all have Advanced Data Protection enabled, end-to-end encryption is used.

Consider the risk

As with all decisions around privacy, consider how at risk you and your data are, including voice as data. In most cases, ensuring a baseline level of encryption can be enough. Even when the app or server can “see” your unscrambled information, the transport between your device and the server and another person’s device is encrypted. Someone would have to break into the server to sniff data. If stored on the server, the baseline state would be encrypted at rest; however, the server operator would manage those encryption keys.

For peace of mind, if not strictly necessary, I’d encourage you to consider using end-to-end encryption when you can. It’s so easily available. For someone to crack your connection, they would need to obtain one of your devices and be able to unlock it. Otherwise, the path between you and someone else is effectively impregnable.

For further reading

I cover message security extensively in my book Take Control of FaceTime and Messages, which I recently updated to include the beta release of RCS encryption for Apple-to-Android communication.

For more on Wallet, I wrote an entire book on that seemingly simple app and set of features, Take Control of Apple Wallet. The book arose from the frustrations of finding where things in Wallet (and outside Wallet but related to it) lived.

On the security side of things, Take Control of Securing Your Apple Devices includes detailed advice on the ins and outs of physical security: how to ensure someone with access to your hardware can’t reach your data.

[Got a question for the column? You can email glenn@sixcolors.com or use /glenn in our subscriber-only Discord community.]

The United States is having something of a national referendum on mass surveillance as of late. Embodied most infamously by video surveillance company Flock Safety, Americans from all walks of life are waging war against the proliferation of the company’s automatic license plate readers (ALPR), which the ACLU warns now number in the tens of thousands.

Plenty of communities are organizing campaigns to out-muscle municipal leaders or their cheerleaders on the local police force, and by-and-large, they’re working. Whether in rural towns or sprawling cities, activists who can mobilize citizens to flood council meetings and hold picket signs are finding success. Perhaps most prominently, the Los Angeles Police Department recently allowed its three-year contract with Flock to expire, citing democratic pushback as a significant pressure point.

There’s also another, less-legal side to the anti-Flock sentiment: a rising tide of vandalism. Not content to wait for local officials to strike down ALPR contracts, concerned citizens are taking matters into their own hands by disabling or cutting down Flock camera towers wherever they’re found. But what started as a collection of separate individuals is now rising to the level of a viral phenomenon, as privacy-minded Redditors take to the web to celebrate those who are fighting back with an outpouring of ironic memes and hype reels.

Ground zero for this community appears to be the subreddit r/FlockSurveillance. The forum hosts some 422,000 “activists,” who flood the channel with over 11,000 contributions per week.

Some of the top posts on the subreddit are memes celebrating Flock vandalism: there are crude Photoshop jobs re-imagining covers of the Berenstain Bears as anti-Flock activists, hand-drawn signs encouraging copper thieves to cash in on Flock innards, and an ALPR-themed redesign of the Gadsden flag, to name a few.

Those who’ve been caught and charged with vandalizing ALPRs become digital folk heroes on the subreddit. When news broke that US Air Force engineer Jeffrey Sovern had been charged in connection to the destruction of 13 Flock devices, redditors flooded the post with praise, sharing links to his legal defense fund and encouraging others to donate. (Sovern’s GoFundMe now stands at over $40,000, compared to around $15,440 in early July.)

“I donated to him because those charges are bulls***,” one redditor replied.

In addition to memes, the subreddit has become a destination for serious discussion around ALPRs and surveillance in general. One developer used the forum as a launch pad for their app FlockHopper, a navigation app that helps travelers avoid ALRPs. Others use it to share important developments relevant to the anti-Flock crowd, to get advice on Freedom of Information Act requests, and to dispel misinformation.

“As entertaining a trend as it is, will a few Redditors knocking a few poles over really make a difference?” one redditor asked the group.

“Even a single grain of rice can tip the scales,” a user replied.

More on Flock: Man Trapped in Dystopian Nightmare Thanks to AI Surveillance Cameras Flagging His Every Move

The post Redditors Are Celebrating the Cutting Down of Flock Surveillance Towers With Based Memes appeared first on Futurism.

Unlike a few years ago, modern browsers now arrive with stronger privacy controls. The following are some examples

If you thought private browsing was fully secure, it is time for an update. Basically, it sounds more

Since WIRED reported on Meta’s NameTag face recognition system, company executives have made confusing and conflicting remarks about its very existence.

Rarely has the Los Angeles Police Department found a police toy it doesn’t love. The third-largest police force in the US has major Palantir contracts in place, uses sketchy apps to track minority communities, and routinely sends drones to spy on peaceful protestors.

So when the LAPD ends a contract with a surveillance company because it thinks the technology is too dangerous, you know there’s a problem.

Over the weekend, the police department announced it was abandoning its contract with Flock Safety, the controversial company blanketing the United States in AI-integrated license plate cameras (ALPRs). Though its three-year contract with Flock is just about up, the LAPD says it won’t renew the partnership, citing residents’ concerns over privacy and civil rights, ABC reported.

“This contract is not being renewed because of serious concerns around civil liberties and civil rights issues, particularly around privacy and the data that is being collected from these cameras,” LAPD’s chief information officer Dean Gialamas told ABC in a statement. “The LAPD had to make a difficult decision, in this case discontinuing using Flock services until we can get those data, privacy, security and sharing concerns ironed out through a contractual relationship.”

The news comes just days after our sister publication The Drive‘s Joel Feder reported how he was suddenly swarmed by armed cops while test driving a Range Rover with his wife in Minnesota. Officers said they had been tracking him for days using Flock’s AI-integrated cameras, which had erroneously tagged his car as stolen.

After the latest Flock contract news broke, 404 Media revealed that a July 10 audit by the LAPD Office of the Inspector General caught the department’s ALPR cameras generating 161 false stolen-vehicle alerts in just two months — each one ending with officers pulling over an innocent driver. Factoring in 337 alerts which “resulted in the recovery of stolen vehicles,” the LAPD’s cameras carry an error rate of 32.3 percent, effectively giving officers a one-in-three chance at pulling an innocent person over.

“During the review period, officers acknowledged 161 alerts as accurate license plate matches; however, subsequent investigations determined the vehicles were not stolen,” the report reads. “In addition to creating an inconvenience for vehicle owners, these inaccuracies can affect individual liberty interests, erode public trust, and potentially create substantial legal and financial liability concerns.”

The OIG report notes that officers are required to “attempt to verify all records prior to conducting an investigative stop,” though it didn’t disclose how many of the 161 wrongful stops were the result of an officer’s failure to do so. Instead, investigators blamed license plate records it says were “not updated in a timely manner,” which “may” lead cops to “act on inaccurate or outdated information, increasing the risk of unnecessary enforcement actions, including vehicle stops and wrongful detentions, or a confrontation with serious consequences.”

As 404 flagged, the LAPD approaches potentially stolen vehicles as “high-risk” stops. For the LAPD, these stops require drastic shows of force, including “calling for back up, air support, and a supervisor and ordering the suspect out of their vehicle,” the report reads.

It doesn’t take a large language model to figure out that this is an incredibly dangerous scenario for Black people in Los Angeles, who account for 19.5 percent of those killed by the LAPD, despite making up only nine percent of the city’s population.

Whatever way you slice it, it seems one of the most notorious police departments in the US has finally met its match.

More on surveillance: Flock Cameras Screw Up, Swarm Innocent Man With Armed Police

The post LAPD Abandons Flock Contract After Making a Horrifying Discovery appeared first on Futurism.

Users and Hollywood agencies raised privacy and copyright concerns about the new tool, Muse Image.

Flock surveillance cameras are blanketing the country, and to no one’s surprise, innocent people are being caught in their dragnet.

At our sister publication The Drive, automotive journalist Joel Feder reports how he was suddenly swarmed by armed cops while test driving a Range Rover with his wife in Minnesota. The police used four squad cars to box him in in a coordinated maneuver; when he demanded an explanation, the officers said that they had been tracking him for days using Flock’s AI-integrated cameras, which had tagged his car as stolen — erroneously, as it turned out.

It was a startling example of how far reaching the controversial surveillance system had already become, never mind how flawed its conclusions can be.

“Whether you’ve actually stolen a car or are just rolling down the road having done nothing wrong, like me, once these systems have you in their crosshairs, there’s pretty much only one way it can go,” Feder wrote. “Welcome to the future. It’s scary out here.”

While still on the scene — every on-the-run criminal’s favorite place to lie low, a Kohl’s parking lot — the cops explained that the license plates on Feder’s Range Rover had been reported stolen by a dealership in Los Angeles. This made “absolutely no sense” to Feder, since car companies are extremely thorough about who in the media they loan their cars to. One officer presented photos taken by the cameras in the Flock app showing his SUV, plate number 34 10 DTM, confirming they had the right car — at least according to Flock.

But when Feder got Jaguar Land Rover on the phone, the automaker said that the reported plate number was 34 03 DTM, not 34 10 DTM. On New Jersey plates, the middle number is written smaller than the others, and for whatever reason, the number entered into the Flock system was recorded as 34 DTM.

This apparently drove Flock’s AI tech haywire, according to Feder. It “wasn’t registering that non-standard little number when it began picking up the Range Rover around town,” he wrote. “It just saw 34 DTM in large type and started alerting the local police.”

Because Flock’s surveillance network is so sprawling, this error was now a nationwide issue. “Anywhere a police department has a partnership with Flock,” Feder wrote, “any other JLR-owned car with the same plate structure is going to get flagged as stolen.” Later, Feder discovered that the car they were looking for wasn’t stolen after all; its license plate had been accidentally misplaced during a photo shoot.

Flock has surged to national attention in the past year, as police departments across the country partner with the firm to deploy its license plate readers and video cameras that can track vehicles and pedestrians. Often taking the form of towering pylons, it’s as if Flock wants residents to know they’re being watched, and its highly visible presence has served as a lightning rod for anxieties over privacy, surveillance, and police overreach.

Residents have fiercely protested against the deployment of Flock cameras, overrunning town meetings. Others have resorted to covering the cameras with trash bags or vandalizing them. Last month, a US Air Force engineer won public sympathy after being charged with sawing down over a dozen Flock cameras, with his legal defense’s GoFundMe page quickly raising tens of thousands of dollars.

On top of being widespread, Flock cameras are hooked into a centralized network, meaning the troves of footage they collect can easily be accessed by cops with little oversight. Flock and police departments argue that the cameras will only be used to investigate serious crimes, but there’re already mounting cases of cops being arrested for using Flock to stalk women. An investigation last year revealed that ICE was tapping into Flock’s network to conduct searches.

Flock’s overreach is already so vast that, in Feder’s case, he was warned to drive straight home and not cross into the neighboring towns — where their own Flock cameras would wrongly flag his car and sic the cops on him, too.

“You’re lucky we’re in Plymouth,” the officer who warned him said. “If you were in Minneapolis, they definitely would’ve come at you with guns drawn.”

More on surveillance: Woman Surprised When Flock Surveillance Tower Appears in Her Yard Without Warning

The post Flock Cameras Screw Up, Swarm Innocent Man With Armed Police appeared first on Futurism.

Meta’s Mouse-Tracking Pause Meta has switched off a program that quietly watched its own staff work. On Monday,

The private events group, cofounded by Peter Thiel, says a “criminal” hacker is behind a breach that exposed members’ personal details. WIRED found no evidence a break-in was needed to access the files.

An anonymous reader shared this report from the Associated Press: Officials in Kansas City, Missouri, are preparing to equip cameras on some public buses with facial recognition software capable of identifying passengers who appear on a list of banned riders or missing persons. Supporters and opponents alike view the effort as a major litmus test for tapping the AI-powered software on a U.S. public transportation system, positioning Kansas City as the latest epicenter of a fierce debate over whether the safety benefits of artificial intelligence are worth the privacy costs. "The idea of running face recognition on a camera that is pointed on live spaces in public is a line that until recently has never really been crossed in the last 25 years," said Jay Stanley, senior policy analyst for the Project on Speech, Privacy and Technology at the American Civil Liberties Union. The state of Missouri declined to help fund the project as expected due to concerns with the facial recognition component. Still, the city is pushing ahead with local and federal money, said Tyler Means, chief mobility and strategy officer at the Kansas City Transportation Authority. "Privacy is always a tricky thing," Means said. "We've always had cameras on our buses. It's just new technology. I think in time it'll smooth over and people will realize, 'Well, it didn't really feel any different'...." Images captured by cameras aboard the buses would immediately be checked against any active alerts, generated when a missing person, banned rider or someone on a law enforcement watch list designated by the transportation authority is identified... After the buses return to the depot, the transportation authority would archive the regular video footage on a local server for up to five years. The company partnering with Kansas City to run the cameras "started using live facial recognition years ago to alert nursing homes when residents left the building," according to the article, and then "brought the technology to correctional institutions and schools." But this is its first attempt at bringing its cameras onto public transportation. The article also includes this quote from Will Owen, communications director for the Surveillance Technology Oversight Project. "City residents should not be guinea pigs for transit systems to test Silicon Valley's latest unproven, biased surveillance tech."

Read more of this story at Slashdot.

Age verification is consuming the Internet. From social media bans in Australia to porn restrictions in half of US states, for many having to prove their age to access websites is becoming an everyday requirement. But one of the key technologies underpinning many of these age checks is about to seep into the offline world—with potentially life-changing consequences for people having their age predicted by AI.

Starting next year, the British government is planning to introduce facial age estimation—where AI scans your face and suggests how old you are—to help determine the age of asylum seekers arriving at the United Kingdom’s border. The move is believed to be the first time that a so-called facial age estimation (FAE) system has been used in this way. Many asylum seekers arriving in the UK will not have documents proving their age, and if children are incorrectly classed as adults, they can be stripped of some legal protections and placed in adult-only detention centers.

An investigation by WIRED and Lighthouse Reports, in collaboration with The Independent, has obtained an internal UK government report detailing its tests of FAE technologies. It shows how the systems regularly mistake children for adults and appear to contain serious bias problems, which directly impact the largest group of migrants subject to age assessments in 2025, according to data from the Home Office. The investigation raises questions about the effectiveness of the technology and whether it should be deployed in such high-stakes scenarios.

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Leaked files show the invite-only network grades members by their money and fame, shaping who’s in, who’s out, and who pays.

Leaked files show the invite-only network grades members by their money and fame, shaping who’s in, who’s out, and who pays.

Artist Morry Kolman will be livestreaming feeds of the NBA champions’ ticker-tape parade from NYC’s traffic cameras—and this time, the city’s Department of Transportation isn’t demanding he stop.

Internal Home Office tests of age-verification technology show the risks of life-altering errors. It’s moving forward anyway.

Reuters reports a cyber extortion group has claimed responsibility for breaching Novo Nordisk's network, stealing roughly 1.3 terabytes of data, including source code, drug research, clinical-trial records, employee and physician information, production-system details, and internal AI model data. The group says it's exploring selling parts of the data after unsuccessfully demanding $25 million from the company. From the report: FulcrumSec, a cyber extortion group that emerged in October 2025, said in a long message posted to its website that it spent more than two months in Novo Nordisk's networks stealing data. It said that data included company source code, proprietary information on released and unreleased drugs, trial data, employee, doctor and patient data, information related to company processing facilities and internal AI model information. [...] FulcrumSec told Reuters in an email that Novo Nordisk representatives contacted the group on June 3, roughly 48 hours after the group's initial contact to unnamed company executives. The company used a random Proton Mail email address sent to email addresses that FulcrumSec used in its initial outreach, and confirmed it was the company by requesting specific files for verification only the company would know about. The FulcrumSec representative also said that the group would prefer not to sell data, "as open sourcing it is a more effective deterrent for future companies to avoid paying." [...] FulcrumSec said it would not share some of the data it stole, including information on thousands of company employees and physicians, and roughly 11,500 pseudonymized clinical trial patients. The group said it also would withhold data related to operational technology and software used to interact with sensors and machinery at Novo Nordisk production facilities as part of its "harm-reduction strategy." A Novo Nordisk spokesperson said in an email that the company "is aware of claims that data allegedly copied externally without authorization from our systems has been published online. We take this matter seriously and maintain continued operations of our main platforms. We are in contact with the relevant authorities."

Read more of this story at Slashdot.

In a groundbreaking development addressing the burgeoning concern of privacy in modern medical technology, researchers at the University of Kansas have unveiled a transformative artificial intelligence (AI) model designed specifically to safeguard sensitive biometric data embedded within electrocardiograms (ECGs). This innovation is poised to reshape how medical institutions share and analyze vital cardiovascular data without compromising patient confidentiality. Electrocardiograms have long been renowned for their role in capturing the heart’s electrical activity to diagnose and monitor cardiac health; however, as AI integration deepens, these signals inadvertently reveal more than just clinical information — exposing personal attributes such as sex, age, race, and even uniquely identifiable biometric markers.

The research team at Kansas, led by doctoral candidate Fairuz Shadmani Shishir in collaboration with the KU Medical Center, has pioneered the privacy-preserving variational autoencoder (PP-VAE). This novel AI architecture is specially engineered to retain the clinical utility of ECG data while systematically obfuscating sensitive biometric features that might otherwise be inferred through advanced AI analysis. The dual objective of PP-VAE is both technically sophisticated and ethically imperative, ensuring that predictive diagnostic accuracy remains uncompromised while minimizing the risk of privacy breaches inherent in sharing raw biomedical signals.

Electrocardiograms, traditionally viewed as a straightforward diagnostic tool, have grown in complexity with modern AI techniques that can extrapolate a multiplicity of patient traits beyond the cardiovascular parameters they were designed to measure. Shishir notes that these advanced systems can uncover “soft biometrics” such as demographic information, which raises pressing data governance and patient privacy challenges. By developing PP-VAE, the researchers aim to reimagine ECG data processing whereby sensitive attributes are masked without degrading the signal quality necessary for important medical prognoses, such as identifying patients at risk of left ventricular ejection fraction (LVEF) abnormalities, a critical indicator linked to heart failure and mortality risk.

The technical underpinning of PP-VAE involves training independent convolutional neural networks to disentangle and suppress identifiable biometric markers while preserving clinically relevant features within the ECG signal. This balancing act required careful engineering to ensure that the encoded ECG data remained diagnostically rich, capable of supporting predictions related to conditions such as left ventricular hypertrophy and forecasting five-year mortality risk. The research showcased that their method consistently outperformed or competed with other state-of-the-art AI models, marking a significant stride in machine learning’s ability to harmonize privacy with clinical efficacy.

Importance of such privacy-preserving strategies becomes particularly clear against the backdrop of the healthcare industry’s increasing reliance on data sharing for collaborative research, AI model development, and multi-institutional patient care coordination. The KU team articulates that unrestricted data sharing without privacy controls introduces tangible risk vectors for patient re-identification and misuse. PP-VAE proposes a scalable approach toward enabling secure exchange of ECG data, thus fostering innovation and improving medical outcomes while staunchly protecting individual privacy rights.

This research also touches on the profound issue of bias within AI-driven medical diagnostics, which has historically contributed to disparities in healthcare delivery among marginalized populations. The team consciously incorporated balanced datasets reflective of gender and racial diversity in their model training to mitigate bias and improve generalized performance across varied demographic groups. While initial validations were primarily conducted using data from KU Medical Center and publicly available datasets, future work is projected to expand this cross-regional training, enhancing the robustness and impartiality of the model when applied globally.

Another compelling facet of this breakthrough is the planned public release of the PP-VAE model, reflecting the researchers’ commitment to transparency, collaboration, and democratization of AI tools in medicine. By allowing institutions worldwide to access and further refine the model with local datasets, the innovation promises to catalyze a new era of secure, privacy-conscious healthcare analytics. Adoption of PP-VAE could alleviate prevalent trust issues among patients concerned about the confidentiality of their biometric health information—a critical barrier to broader acceptance of AI-driven healthcare technologies.

Furthermore, the researchers discuss the broader implications for data-driven diagnosis, illustrating how balancing the protection of sensitive attributes with clinical utility could become a standard principle in biomedical AI applications beyond cardiology. This approach offers a blueprint for addressing ethical and privacy challenges that increasingly pervade the intersection of health data science and artificial intelligence.

The University of Kansas team’s work is also distinguished by its multidisciplinary nature, featuring collaboration between electrical engineering, computer science, and cardiovascular medicine experts. Such an integrated approach enabled the synthesis of advanced machine learning expertise with intimate clinical insight, culminating in a pragmatic solution tailored precisely to real-world healthcare needs.

This pioneering study was recently published in Scientific Reports, showcasing detailed methodology, experimental evaluations, and comparative performance analyses that underscore the efficacy and potential transformative impact of PP-VAE. Supported by the American Heart Association, this research is an exemplar of innovative technological stewardship addressing one of the most pressing challenges in digital health.

As AI continues to proliferate across medical diagnostics, models like PP-VAE will undeniably play a critical role in shaping ethical frameworks and technological architectures for future patient data management. By protecting sensitive biometric identifiers embedded in ECG data, the model not only preserves patient autonomy but also enhances the potential for AI-driven insights to be safely harnessed for better clinical outcomes worldwide.

The journey ahead involves validating and deploying this technology in diverse healthcare environments globally to ensure scalability, efficacy, and fairness remain intact as data heterogeneity increases. Yet, with the foundational work from the University of Kansas, a robust and privacy-conscious approach to ECG analysis and beyond appears well within reach, heralding a new era of secure AI-driven cardiovascular care.

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Subject of Research: Privacy-preserving AI models for electrocardiogram data analysis
Article Title: Safeguarding Patient Privacy in Electrocardiogram Analysis with AI-driven Models
Web References: Scientific Reports Article
Image Credits: Fairuz Shadmani Shishir / University of Kansas

Keywords

Privacy-preserving AI, Electrocardiogram (ECG), Variational Autoencoder, Convolutional Neural Networks, Biomedical Data Security, Cardiovascular Diagnostics, Left Ventricular Ejection Fraction, Patient Privacy, Machine Learning Bias, Medical Data Sharing, Artificial Intelligence in Healthcare, Clinical Decision Support

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