Where is the line between firm discipline and child abuse? For millions of families in the United States, the answer depends on where they grew up, what their parents did, and how their culture defines good parenting. A new study published in the Journal of Child and Family Studies tackles this thorny question with an ambitious goal: building a scientific instrument that can measure how people from different backgrounds judge parenting practices that are accepted in some communities but condemned in others. The Parenting Practices and Discipline Scale, or PPDS, developed by Kevin Ortiz Diaz, Maureen C. Kenny, and Helia Baez at Florida International University, is one of the first tools designed specifically to capture culturally situated discipline practices that existing measures simply ignore.

The stakes are enormous. Child abuse is recognized as a global health issue, and in the United States it occurs at what researchers describe as epidemic proportions. Federal data show 546,159 victims of child abuse and neglect in 2023 alone, and the vast majority of these cases, roughly 89 percent, are committed by one or both parents. Children from minority groups are often the most affected, with Black or African American children among the most likely to experience abuse, second only to American Indian or Alaska Native children. Yet the definitions of what constitutes abuse vary dramatically across countries and cultures, creating a legal and ethical minefield for immigrant families who may not realize that practices considered normal at home are illegal in their new country.

The problem is particularly acute for Hispanic families, who make up 19 percent of the American population and represent the largest share of recent immigrants. Hispanic culture is far from monolithic, encompassing communities from South and Central America and the Caribbean with distinct traditions, but certain common threads run through the research. The concept of familismo, a cultural orientation and sense of obligation to family, has been linked to greater parental warmth in some studies. At the same time, physical violence as a disciplinary method remains common in many Hispanic cultures, and the chances that such behaviors are carried over by families immigrating to the United States are high. The result is a potential collision between generations of cultural practice and a legal system, anchored by the Child Abuse Prevention and Treatment Act of 1974, that defines abuse in terms of serious physical or emotional harm.

What makes the new study distinctive is its grounding in intersectionality theory, a framework rooted in feminist scholarship that examines how multiple categories of identity, such as race, ethnicity, gender, and nativity, interact to shape experience. Rather than treating demographic characteristics as independent variables, the researchers designed their analyses to test whether these dimensions jointly predict how individuals classify discipline practices as culturally acceptable or abusive. The framework has practical urgency: racial and ethnic disparities exist in both the reporting and substantiation of child maltreatment, and current child protection policies can disproportionately affect women of color who use social services and face heightened surveillance and risk of reports to child protective services.

The scale itself emerged from a careful two-stage piloting process. First, four experienced mental health clinicians specializing in child abuse, with 10 to 20 years of experience each, reviewed a draft pool of 54 items describing parenting behaviors and case vignettes. Then ten first-generation Hispanic immigrants from a range of nationalities, including Puerto Rican, Cuban, Colombian, Mexican, Dominican, and Nicaraguan participants, examined the refined items and identified which behaviors they would associate with parents in their own cultures and whether they would label those behaviors abusive or acceptable. Consensus required agreement from at least 70 percent of the pilot group. Eighteen items survived, ten identified as culturally acceptable behaviors such as yelling, using the chancla, the slipper famously wielded in many Latino households, or letting a child sleep with parents, and eight identified as abusive behaviors such as spanking, leaving a child alone in a car, or forcing a child to bathe with an adult.

The main study recruited 1,477 undergraduates at a large Hispanic-Serving Institution in Miami, ultimately yielding an analytic sample of 1,089 participants after exclusions for failed attention checks, missing data, and groups too small for meaningful analysis. The final sample was 72.2 percent Hispanic, 16.3 percent non-Hispanic Black, and 11.6 percent non-Hispanic White, nearly 70 percent U.S.-born, and predominantly female at 88.8 percent. Participants rated each of the 18 behaviors on a three-point scale: not abusive, inappropriate but not abusive, or abusive. The choice of a college population was deliberate, since emerging adults are on the cusp of parenthood, attitudes at this life stage predict later parenting intentions, and parenting practices tend to be transmitted intergenerationally.

Psychometrically, the study unfolded in two parts. Study 1 established the scale’s initial properties using exploratory factor analysis with principal axis factoring and oblique rotation on a polychoric correlation matrix, an approach suited to the ordinal, non-normally distributed responses. The analysis supported a two-factor structure: Factor 1 captured discipline and psychological aggression, including spanking, yelling, the chancla, cursing, ignoring, comparing, fighting, and religion, while Factor 2 reflected neglect and physical endangerment, such as using drugs in front of a child, refusing treatment for illness, and allowing smoking. The two factors accounted for 29.5 percent of the variance and were moderately correlated. Internal consistency for the full 18-item measure was acceptable at a Cronbach’s alpha of 0.71, though the abusive subscale showed weaker reliability, a limitation the authors acknowledge candidly.

Study 2 then deployed regression models to test how demographic identities shaped perceptions, and the results were revealing. Gender and parental status emerged as the strongest and most consistent predictors: women and parents were significantly more likely to classify culturally acceptable practices as abusive. Among Hispanic participants, those not born in the United States were more likely than U.S.-born Hispanics to view behaviors like spanking as abusive, a finding that ran counter to the researchers’ hypothesis and suggests that acculturation may shift parenting beliefs toward American child protection norms. Across racial and ethnic groups, Hispanic participants were more likely than Black participants to classify culturally acceptable practices as abusive. Notably, for the more overtly harmful behaviors in Factor 2, parental status was the only significant predictor, suggesting broad agreement that neglecting a sick child or encouraging substance use transcends cultural and gendered expectations.

The effect sizes were small, with the models explaining roughly 1 to 6 percent of the variance, a reminder that unmeasured cultural and contextual factors account for most of the variation in how people judge discipline. Still, the patterns carry real implications for prevention. The authors suggest that father-focused education programs may be valuable, since men were less likely to identify physical discipline as harmful and may therefore be at greater risk of normalizing such practices. Culturally adapted interventions have already shown promise, from brief summer parent training programs benefiting Hispanic children to the multinational Triple P Positive Parenting Program, and the PPDS could help practitioners identify which disciplinary practices are perceived as acceptable within specific communities, allowing messaging to be tailored rather than uniform.

The researchers are careful to frame their findings as preliminary. The sample was heavily female and drawn from a single Miami institution in a minority-majority city, socioeconomic status was not assessed, and social desirability bias may have influenced responses to questions about parenting. The abusive subscale needs refinement, and future work should apply confirmatory factor analysis and measurement invariance testing across cultural groups. Yet the core contribution stands: a novel measure that captures culturally situated discipline practices invisible to existing tools like the Alabama Parenting Questionnaire or the Child Abuse Potential Inventory. As parenting practices pass from generation to generation and families cross borders into legal systems with different rules, understanding exactly how culture shapes perceptions of abuse may be the first step toward protecting children without criminalizing the traditions their parents brought with them.

Subject of Research: Development and validation of a scale measuring cultural perceptions of parenting and discipline practices

Article Title: Development and Validation of the Parenting Practices and Discipline Scale in an Ethnically Diverse Sample

Article References: Ortiz Diaz, K., Kenny, M. C., & Baez, H. (2026). Development and Validation of the Parenting Practices and Discipline Scale in an Ethnically Diverse Sample. Journal of Child and Family Studies. https://doi.org/10.1007/s10826-026-03370-6

Image Credits: AI Generated

DOI: 10.1007/s10826-026-03370-6

Keywords: child abuse, parenting practices, discipline, Hispanic families, intersectionality, psychometrics, scale development, cultural perceptions, child welfare, corporal punishment, acculturation, exploratory factor analysis

Apple:

Audio Intelligence is a brand-new class of Apple Intelligence features that use the microphones of Apple Watch, the power of Apple silicon, and on-device and cloud-based AI models to help you detect sounds, identify music, and stay present and engaged — all while protecting your privacy at every step.

Hartley Charlton:

• Siri Recap: Uses ambient listening to generate high-level notes from conversations, such as takeaways from a team meeting or a parent-teacher conference, producing an Apple Intelligence-generated title, summary, and key points reviewable in the Siri app.
• Live Rewind: Lets users “go back in time for 15 seconds,” transcribing and displaying what was just said in a conversation with a double press of the Digital Crown.
• Shazam: Automatically identifies music playing nearby and surfaces the song title and artist on the Music Recognition widget in the Smart Stack.
• Sound Recognition: Brings sound alerts to Apple Watch for users who are deaf or hard of hearing, listening for sirens, alarms, doorbells, a baby crying, and more, even when their iPhone isn’t nearby.

Lily Hay Newman:

Apple is emphasizing that all of them are built to prioritize privacy and security using the extensive infrastructure the company has developed to protect its other Apple Intelligence and Siri services. As more and more of these types of features debut, though, the audio intelligence announcements are a reminder that AI-powered tools are becoming increasingly universal in all areas of computing and life.

“These features do not create or store audio recordings, and raw audio used for processing is completely inaccessible to the operating systems, apps, the user, or Apple,” the company wrote in a report shared with WIRED.

[…]

Apple emphasizes that its on-device capabilities for Apple watches have expanded thanks to the company’s new S11 chips. The chips include a special memory-protected space that Apple calls the Secure Exclave. Designed specifically for sensor data, the exclave is an isolated buffer that is inaccessible to the rest of the operating system where Apple Watch can hold and process audio data in a protected space.

[…]

To try to mitigate blatant privacy violations, Apple says that the watch produces an audible chime when a watch owner initiates a Live Rewind transcript to alert other people in the area—even if it is on silent mode or paired with headphones. And the features are built to omit or filter out potentially identifying information about speakers. But the sheer scope and breadth of the tools is extremely noteworthy.

Zac Hall:

If you want to really take a deep dive on privacy and the new features, Apple has published an 11-page privacy paper that goes even further.

Previously:

• Apple Watch Ultra 4
• Apple Watch Series 12
• Pebble Index

Update (2026-09-11): Dr. Drang:

Ron Huang introduced a couple of features of the Series 12 and Ultra 4 that struck me as both disturbing and possibly illegal. They certainly seem at odds with Apple’s commitment to privacy.

[…]

I’m sure Apple will argue that there’s no privacy issue because the recordings aren’t being saved. But if there’s a transcription, isn’t that a type of saving?

And then there’s the question of legality. A great variety of law, both in the United States and across the world, concerns the recording of electronic and in-person conversations. I’m sure Apple has considered this, but I have to think some jurisdictions will frown on Live Rewind. If Apple has to defend it, that’s fine. What’s one more squadron of lawyers to Apple? But if an Apple Watch owner has to defend a transcription made innocently—made with Apple’s encouragement, in fact—that seems like an unfair burden. Maybe Apple should indemnify all future Watch buyers from privacy-related legal action.

[…]

The hoops Apple jumped through to avoid certain words is pretty funny. […] You think your watch records the audio around you? No no no. The audio flows into the Secure Enclave, and flowing is definitely not recording because it’s a different word.

Juli Clover:

Live Rewind and other Audio Intelligence features do not generate audio recordings. Audio flows into the Secure Exclave, is processed, and then deleted.

According to Apple, Live Rewind text snippets “disappear permanently” from the Apple Watch approximately 30 seconds after the Apple Watch display dims, unless the info is saved to the Siri app.

Nick Heer:

But good luck explaining that to anyone around you after you read back an exact transcript of what they just said. Apple says they should be sufficiently notified Live Rewind is active because “an audible chime plays from the speaker on your Apple Watch, even if your Apple Watch is on silent or you have headphones connected”, plus the watch plays an animation, but it is not clear to me when this happens. It is not shown in Apple’s video. The impression I get is that Live Rewind is always running in the background and these notifications are only played after you double-click the Digital Crown to look at the text.

This feels like a glimpse of how Apple will market its inevitable wear-anywhere glasses product. I do not think it does enough to assuage privacy concerns. I think normal people will continue to react negatively when you tell them you have been passively recording them because, outside Silicon Valley, that is considered gross and invasive.

Saagar Jha:

I have to admit that the Siri Recap stuff all being “documented” using AI slop is really giving me confidence in the level of care that went into designing this feature

Matt Evans (Hacker News):

I’m frankly surprised Apple even went through with this feature at this time, considering the bans and backlash other AI-powered surveillance wearables are getting.

[…]

Apple is keen to evidence its trustworthiness, with on-device AI and Private Cloud Compute and an insistence that it doesn’t record or log audio and transcripts. However, if people think they’re being discreetly recorded without their permission, it’s possible Apple Watch wearers could experience their own slice of the abuse hurled at Meta Glasses wearers.

Dan Sugalski:

I… y’know, Betteridge’s law does, as always, apply.

Glenn Fleishman:

Given that Apple has to warn of this, it’s the surest sign the features shouldn’t be released in this form. Apple repeats the form of the warning I see regularly at my gym… but apparently it isn’t heeding it.

Jack Brewster:

Just called my HSA provider and upfront they say that calls may be recorded and transcribed by AI and then provide no means of opting out. So of course the person who finally answered says I have to give my social to verify my identity.

Update (2026-09-14): See also: Core Intuition and Steven Aquino.

Hoje, com a revelação dos Apple Watches Series 12 e Ultra 4, a Maçã também anunciou um novo conjunto de recursos chamado Inteligência de Áudio (Audio Intelligence), composto pelas funções Reconhecimento de Som (Sound Recognition), Reconhecimento de Música (Music Recognition), Live Rewind e Siri Recap.

Parte da Apple Intelligence e exclusivas desses novos relógios, essas ferramentas analisam o som ambiente — que, em alguns casos, é captado constantemente — para alertar o usuário sobre sons importantes, ajudá-lo a descobrir o título de músicas, recobrar algo que foi falado nos últimos instantes e revisar anotações sobre as suas principais conversas ao longo do dia, respectivamente.

Por mais bem intencionados que esses recursos possam ser, no entanto, é natural que alguns usuários torçam o nariz para eles — afinal, a ideia de ter um dispositivo monitorando tudo o que você fala (e que falam para você) sem interrupções pode ser um tanto… preocupante, principalmente do ponto de vista da privacidade — um assunto tão abordado pela Apple.

E foi muito provavelmente pensando nessas pessoas que a Apple colocou no ar uma extensa página de suporte que detalha o funcionamento dessas ferramentas, além de explicar quais medidas de segurança foram tomadas para proteger usuários que decidirem ativar essas ferramentas. A empresa também publicou um documento [PDF] que mergulha ainda mais nas minúcias dessas novidades, as quais dependem não só do poder de fogo do novo SiP ¹System in a package, ou sistema em um pacote. S11, como também dos seus recursos de segurança embutidos.

Para início de conversa, a Apple deixa claro que todos esses recursos são completamente opcionais; o Reconhecimento de Som, por exemplo, precisa ser configurado nos Ajustes (Settings) do iPhone, de modo que ele só vem ativado por padrão no Apple Watch caso o recurso equivalente do iOS já tenha sido ativado anteriormente pelo usuário no iPhone. Outras ferramentas, como o Reconhecimento de Música, podem ser ativadas diretamente nos Ajustes do Apple Watch (caso já não tenham sido ativadas anteriormente).

O Live Rewind, por sua vez, entra em cena apenas quando o usuário dá um clique duplo na Digital Crown, mostrando em seguida uma transcrição dos últimos 15 segundos de conversa. Ainda segundo a Apple, o relógio para de gerar essa transcrição assim que o último trecho de conversa é exibido, além de não salvar qualquer texto — a menos que o usuário faça uma pergunta sobre o seu conteúdo para a Siri ou escolha salvá-lo no app da assistente, que é onde estão os ajustes dessa ferramenta.

Disponível na Central de Controle, o Siri Recap pode ser ativado e desativado manualmente a qualquer momento, mas também permite que o usuário defina uma programação com base na hora e/ou localização. A Siri também pode ser barrada de realizar qualquer registro em momentos/lugares específicos.

Trechos de texto do Live Rewind e anotações do Siri Recap podem ser visualizados, editados e apagados a qualquer momento no app da Siri, bem como exportados para apps como o Notas (Notes) e o Diário (Journal). Além disso, apagá-los faz com que eles sumam de todos os seus dispositivos sincronizados — lembrando que a Apple os protege com criptografia de ponta a ponta antes de enviá-los ao iCloud.

Proteções adicionais

Segundo a Apple, em todos esses casos, nenhuma gravação de áudio é gerada ou armazenada no Apple Watch. Em vez disso, o áudio captado pelos microfones passa pelo Secure Exclave do SiP S11 — compartimento isolado por hardware que processa dados de maneira separada do resto do sistema, impedindo que aplicativos, o watchOS, a Apple ou até mesmo o próprio usuário possam acessá-lo.

Os resumos da Siri Recap são gerados pelos chamados Apple Foundation Models no Private Cloud Compute, que não salva nenhuma das informações. As anotações também são apagadas automaticamente pelo app da Siri depois de sete dias, caso o usuário escolha não salvá-las ou exportá-las.

A Apple também garante que esses recursos não identificam ou atribuem locutores nos áudios captados — protegendo, assim, a identidade de pessoas no entorno do usuário. A única particularidade aqui é a Siri Recap, que pode registrar nomes citados no conteúdo da conversa, mesmo sem identificar quem é o locutor ou interlocutor; ele também reconhece e censura “conteúdo potencialmente prejudicial, como linguagem que promove automutilação ou discurso de ódio, e informações confidenciais, como dados financeiros, identificadores governamentais, dados de autenticação e certos identificadores pessoais”.

No caso do Live Rewind, um alerta sonoro é reproduzido sempre que o usuário ativa o recurso, não importa se o Apple Watch está com o modo silencioso ligado ou não. Uma animação em tela cheia também é exibida junto com um indicador de uso do microfone; “Isso serve para alertar as pessoas próximas de que o recurso foi ativado”, explica a empresa.

Dependente do Shazam, o Reconhecimento de Música compartilha apenas uma assinatura acústica, e não o áudio bruto, na hora de processar o áudio captado — algo que acontece nos servidores do serviço. A assinatura acústica, ainda segundo a Apple, é gerada em um buffer seguro no Secure Exclave.

Disponibilidade

Todos esses recursos, como dito, requerem um Apple Watch Series 12 ou um Apple Watch Ultra 4 para funcionar.

Indisponíveis para usuários menores de 13 anos, o Live Rewind e a Siri Recap serão liberados como recursos beta ainda neste ano (inicialmente apenas em inglês). O Reconhecimento de Som e o Reconhecimento de Música serão liberados junto com os novos smartwatches.

Notas de rodapé

• 1
  System in a package, ou sistema em um pacote.

The MacVoices Live! panel examines a class action lawsuit over Apple Photos facial recognition, debating whether on-device processing, user consent, and biometric privacy laws justify the legal action. The conversation moves to TV tracking alternatives after TV Time’s shutdown, then shifts to Bending Spoons’ acquisition of Airtable, exploring the company’s growing portfolio, its business strategy, and the future of database tools in an era of AI-assisted coding development.

💾

A security researcher has designed an algorithm that can create computer-generated patterns capable of hiding people, faces, and vehicles from detection by surveillance cameras.

Getting locked out of an account is no fun. Google has a few ways to help you regain access if you happen to forget your password or lose an authenticator, including recovery contacts and backup codes. Now, Google has a completely new option: your face. You can now give Google a video record of your face and sign into your account with a selfie, which sounds like something people are going to just love.

You will have to set this feature up ahead of time if you want the option of regaining account access with a selfie later on. To get started, verify your account type is supported. You won't be able to configure selfie sign-ins for Workspace accounts, child accounts, or any account enrolled in Google's Advanced Protection Program.

Configuring selfie sign-in requires you to record a video, which Google will store on its servers. Google's selfie sign-in landing page includes the typical disclaimers about privacy and data access, promising that the company will keep the video encrypted and won't use it for any other purposes unless you opt in.

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© Google

Getting locked out of an account is no fun. Google has a few ways to help you regain access if you happen to forget your password or lose an authenticator, including recovery contacts and backup codes. Now, Google has a completely new option: your face. You can now give Google a video record of your face and sign into your account with a selfie, which sounds like something people are going to just love.

You will have to set this feature up ahead of time if you want the option of regaining account access with a selfie later on. To get started, verify your account type is supported. You won't be able to configure selfie sign-ins for Workspace accounts, child accounts, or any account enrolled in Google's Advanced Protection Program.

Configuring selfie sign-in requires you to record a video, which Google will store on its servers. Google's selfie sign-in landing page includes the typical disclaimers about privacy and data access, promising that the company will keep the video encrypted and won't use it for any other purposes unless you opt in.

Read full article

Comments

© Google

Om Malik (2023):

One of the key advantages of MacWhisper, as it utilizes the Whisper technology, is its superior accuracy in transcriptions compared to other services I have used. I was an early adopter of Rev.com and eventually switched to Otter.ai, for which I paid approximately $100/year. However, I have since canceled my subscription.

[…]

After comparing the audio transcripts from MacWhisper and Otter, I found that while Otter’s formatting is superior, there is hardly any difference in the transcriptions. I started with the free model, which was sufficient for my needs, and eventually upgraded to the large models. Nowadays, I primarily use MacWhisper to transcribe my voice memos, which I then import into Lex.Page.

Bon Adamson:

Whisper, which is made by OpenAI, has the distinct advantage of allowing you to transcribe audio in almost any language you may ever need to. For the time being, Otter can only transcribe English. The benefits of better accuracy and accurate punctuation that the service already holds over Siri hold up across other languages as well. If you’re looking to transcribe non-English audio accurately, Whisper is definitely the best option available on Mac.

Dan Moren:

By taking advantage of OpenAI’s Whisper framework, Audio Hijack can now take any audio it’s recording and generate a text transcript. While this process was possible before—see Jason’s lengthy post about his workflow—it required a lot of fiddling and several different tools to accomplish; it’s certainly a lot easier to simply drop Audio Hijack’s block into your existing session. If you record multiple sources, it can even appropriately label each one—though if you record multiple people via one input, say the other end of a Zoom call, it can’t distinguish between the various participants.

Inscribe (2026, Hacker News):

Apple’s new SpeechAnalyzer is the most accurate on-device speech engine we tested. It beat every Whisper model we ship, including Whisper Small, on both the clean and the noisy half of LibriSpeech, while running roughly three times faster than Small. And the API it replaces, SFSpeechRecognizer, came last on clean speech: behind even Whisper Tiny, a 40MB model.

[…]

The new API cuts word error rate by 3.5 to 4x on the same audio: from 9.02% to 2.12% on clean speech, and from 16.25% to 4.56% on noisy speech. There is no accuracy trade-off to weigh; the new API wins everywhere we measured, and it produces punctuated, cased text where the legacy engine’s output is rougher.

[…]

All five engines ran comfortably faster than real time: between roughly 12x and 40x on the M2 Pro, meaning an hour of audio transcribes in about 1.5 to 5 minutes on-device. SpeechAnalyzer was about 3x faster than Whisper Small per second of audio while beating it on accuracy.

Yuki:

SpeechAnalyzer’s ReportingOption.fastResults reads like a knob you’d reach for when you want to trade power for latency. That framing is what cost me the afternoon.

[…]

If a recording matters — and in a notes app, a lost meeting is the worst thing that can happen to a user — then the container you write while recording has to be PCM in CAF. Not m4a, and not AAC-in-CAF either.

[…]

AVAudioEngine’s inputNode silently ignores your device selection

[…]

I wanted summarization to run on-device. Everything else in Nib’s transcription path does, the audio never leaves the machine, and it would have been a much cleaner story to tell.

But 4,096 tokens for the Foundation Models isn’t enough.

Previously:

• Apple Foundation Models in appleOS 27
• WhisperPad Rejected From the Mac App Store
• Overcast Transcripts
• Generated Transcripts in Pocket Casts
• Apple Podcasts Transcripts
• Dictation and Predictive Text in macOS Sonoma and iOS 17
• macOS vs. Dragon Dictation

Meta executives claimed that a controversial facial recognition feature didn’t “exist.” Fast forward a few weeks later, and they’re describing the feature in public — and in detail.

In June, a Wired investigation revealed that Meta had quietly infused unreleased facial recognition tech into its “AI Glasses,” the tech giant’s chatbot and camera-equipped smart glasses. While the feature, dubbed NameTag, was inaccessible to consumers, it was designed to register faces encountered by Meta glasses wearers “into unique biometric signatures, commonly known as faceprints, and check each one against faceprints stored on the user’s phone,” according to the magazine.

It’s safe to say that Meta executives were furious after Wired published its investigation. After all, the company has a long track record of data privacy violations. The revelation that its “pervert glasses,” as some have taken to calling Meta’s controversial smart glasses, could soon be using facial recognition tech — not unlike the eyewear being used by Immigration and Customs Enforcement (ICE) agents — fueled an existing PR disaster.

Andy Stone, the tech giant’s spokesperson and VP of communications, declared on X that Wired’s reporting was “intellectually dishonest” and “advocacy-driven click bait.” What’s more, according to Stone, NameTag was fully nonexistent.

“Here’s a thing: Wired reports Meta didn’t answer several questions about how this will work,” Stone added in a follow-up post. “How could we? The feature doesn’t exist!”

In response to Stone, another Meta executive, chief technology officer Andrew “Boz” Bosworth, jumped in.

“Incredibly misleading from Wired,” he wrote. “Absolutely dishonest.”

Yet despite company leaders reassuring us that the feature doesn’t exist, Bosworth discussed it in detail during an interview with The Atlantic CEO and podcaster Nick Thompson. Bosworth’s comments in the interview, which was published last week, seemingly contradicted his fellow executives, revealing that the feature did indeed exist.

NameTag, Bosworth told Thompson, would be “encrypted locally to your device,” and would catalog people who smart glasses wearers “met in person, with your glasses on, who introduced themselves, or you said, ‘okay, this is David, remember this person.'”

This information is “only available to you, when you are wearing your glasses,” Bosworth added. He emphasized that the data captured by NameTag wouldn’t be stored in a centralized database.

“This is a person you’ve met before. Here’s their name. They’re right in front of you. That’s… what we call a NameTags feature,” said Bosworth. “I think people get confused because they hear face recognition and they think, ‘oh, there’s a central face database that everyone’s being scanned constantly into.’ It’s not that.”

In addition to supporting blind or low-vision smart glasses wearers, said Bosworth, NameTag could solve what Meta calls “the cocktail party problem,” saving wearers from any awkwardness that may arise when encountering people at parties who they’re sure they’ve met before, but can’t quite place.

“It’s a very universally human problem,” Bosworth said. “Nearly every human I talk to, no matter what their status of their vision or their memory, understands that this is a problem that would help them feel more comfortable in social situations if they had it.”

To be clear, Wired never suggested that the facial recognition feature it uncovered was being used to build a “central face database” by way of Meta’s smart glasses. Indeed, in line with what Bosworth said in the podcast appearance, Wired reported that “code discreetly added to Meta’s AI app over multiple updates this year shows that the feature, internally called ‘NameTag,’ identifies people captured by the glasses’ camera and, when activated, alerts the wearer when it recognizes someone.” The story also repeatedly noted that the feature was unreleased and inaccessible to glasses wearers.

When journalists and others called out the contradiction between Meta’s past and recent statements, Stone — the comms executive who said that Wired couldn’t answer questions about the feature because it “doesn’t exist” — jumped back in the comments section.

“We have said for months we’re exploring such features, even as nothing has shipped to consumers,” the spokesperson said in response to a post by The Intercept journalist Sam Biddle. “No secret there despite the conspiratorial thinking.”

There’s a big difference between a feature not existing and a feature that exists — if still unfinished — and has yet to be released. Meta appears to be arguing that if a feature remains inaccessible to consumers, it’s technically nonexistent, even if the company has gone as far as to issue multiple updates inserting code for the feature into products that are already in consumers’ hands. (Meta quietly deleted the NameTag code after its existence was made public.)

During the podcast interview, Bosworth took care to emphasize that Meta is considering the privacy implications of a facial recognition-powered feature like NameTag. He noted that it’s likely illegal in states like Illinois and Texas, and that it “has to be done in a way that people feel comfortable.”

Meanwhile, Meta continues to fight an ongoing consumer harm lawsuit, prompted by an investigation by a pair of Swedish newspapers. The publications found that Meta collected recordings by smart glasses wearers and passed them along to human data labelers, despite promising consumers that their recordings would be stored only on their devices.

“Designed for privacy,” reads a webpage for Meta’s smart glasses, “controlled by you.”

More on Meta’s smart glasses: Meta Furious Over Bombshell Smart Glasses Revelation

The post Top Meta Exec Describes Controversial Facial Recognition Feature in Detail After the Company Claimed It Didn’t Exist appeared first on Futurism.

Designers say that as well as offering a degree of protection from surveillance, their clothes make a powerful fashion statement about the importance of privacy

As facial recognition technology is rolled out across Britain’s public spaces, a new generation of designers say privacy could be the next big fashion trend.

Companies have started incorporating “adversarial patterns” in their garments – carefully designed arrangements of shapes, colours and repeated motifs said to exploit weaknesses in some computer vision systems.

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© Photograph: PR

© Photograph: PR

© Photograph: PR

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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In recent years, the advent of artificial intelligence (AI) has reshaped the landscape of medical research and clinical practice, ushering in an era where machines replicate—and increasingly surpass—human cognitive abilities in various domains. Among the most intriguing developments is the capability of AI systems to assimilate vast troves of real-world data, allowing algorithms to accurately recognize complex patterns within clinical environments. This includes the identification of intricate pathological features, accurate labeling of disease states, and support in making critical healthcare decisions. Central to this revolution is the notion of synthetic data generation, a cutting-edge technology poised to tackle longstanding barriers impeding medical innovation, particularly in the realm of gastrointestinal (GI) medicine.

Synthetic data generation refers to the process through which artificial datasets are created by AI algorithms, mimicking the statistical properties and underlying structures of real-world clinical data without exposing sensitive patient information. This technique promises to solve two critical challenges: preserving patient privacy and reducing the often exorbitant costs and logistical complexities associated with curating and annotating healthcare datasets. By producing realistic, unbiased, and privacy-preserving synthetic data, researchers and clinicians could unlock unprecedented opportunities for training sophisticated AI models and improving clinical decision support systems tailored to gastrointestinal health.

In the context of gastrointestinal diseases, where early detection and precise diagnosis are often paramount, synthetic data offers a transformative potential. GI disorders encompass a broad spectrum of conditions, such as inflammatory bowel disease, colorectal cancer, and liver cirrhosis, each demanding nuanced understanding and timely intervention. Traditional approaches to gathering data for AI training encounter significant roadblocks, including patient consent restrictions, heterogeneous data quality, and the inherent rarity of some disease presentations. Generating synthetic equivalents of such data not only bridges these gaps but also enables the continuous refinement of diagnostic algorithms through virtually unlimited and diverse data augmentation.

From a technical standpoint, generating high-fidelity synthetic GI data entails leveraging advanced generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models. GANs, for example, pit two neural networks against each other in a zero-sum game—the generator strives to create realistic data indistinguishable from original datasets, while the discriminator attempts to detect artificiality. This adversarial process gradually improves the quality of synthetic outputs, rendering them increasingly suitable for downstream clinical applications. However, the GI domain presents unique challenges due to the complexity and variability inherent in endoscopic images, histopathological slides, and multi-omics data.

A profound technical hurdle lies in ensuring that synthetic gastrointestinal datasets retain clinical validity and biological relevance. Unlike generic image synthesis, medical data synthesis must preserve subtle yet critical diagnostic details, such as mucosal texture alterations, microvascular patterns, and lesion morphology. Obtaining this level of fidelity mandates incorporating domain-specific constraints and annotation-guided synthesis protocols. Furthermore, the synthetic data must demonstrate robustness to common sources of variability: differences in imaging devices, procedural inconsistencies, and patient-specific anatomical variations. Without these safeguards, downstream AI models risk being trained on data that poorly represents true clinical scenarios and yield unreliable predictions.

Another pivotal aspect of using synthetic data in gastrointestinal medicine is its contribution to overcoming privacy legislation hurdles like HIPAA or GDPR. Traditional anonymization methods risk compromising data utility or may be insufficient in preventing re-identification attacks. Conversely, by training generative models on real patient data and producing artificial yet representative datasets, synthetic data generation preserves privacy inherently, as no direct personal identifiers or actual patient samples are included. This feature fosters a more open collaboration across institutions and research groups, catalyzing the development of multipurpose AI-driven tools capable of detecting early-stage GI malignancies, monitoring disease progression, and predicting therapeutic responses.

Despite the tremendous promise of synthetic data, translating this innovation from research laboratories to clinical workflows demands meticulous validation and regulatory compliance. Clinical decision support systems powered by synthetic data-driven AI must undergo rigorous testing against gold standards to verify their safety and effectiveness. Interpretability also remains a significant concern, as clinicians must trust and understand AI recommendations to incorporate them confidently in patient care. Efforts to integrate synthetic data into regulatory frameworks, combined with transparent reporting of generative model performance metrics, will be crucial to achieving widespread clinical acceptance.

Synthetic data not only enhances diagnostic AI but opens new frontiers for medical education and professional training in gastroenterology. Trainees often face limited exposure to rare or complex cases, which impedes skill acquisition and confidence-building. High-quality synthetic datasets can provide diverse and unlimited case simulations, including pathologies that might be encountered infrequently but require high diagnostic acumen. This approach supports immersive, hands-on learning without compromising patient safety or privacy, helping to shape the next generation of gastroenterologists equipped with AI-enhanced diagnostic abilities.

Moreover, the adoption of synthetic data dovetails with the broader trend of precision medicine and personalized healthcare. By complementing real patient data with synthetic counterparts generated under controlled conditions, AI models can better capture the heterogeneity of gastrointestinal diseases, including genetic, environmental and lifestyle factors. This richer informational substrate empowers clinicians to tailor interventions more precisely, leading to improved patient outcomes, reduced adverse effects, and optimized resource utilization. Synthetic data thus acts as a cornerstone technology in enabling AI systems to realize their full potential in individualized care pathways.

Nevertheless, emerging research highlights several critical challenges that warrant attention for synthetic data to reach its full utility. One major issue is preventing the inadvertent introduction of bias during data synthesis, which could propagate and amplify healthcare disparities if not carefully addressed. Ensuring equitable representation across demographics, comorbidities, and clinical settings is imperative. Additionally, synthetic data generation must grapple with the potential for overfitting or hallucination, where models produce plausible yet clinically inaccurate artifacts. Developing rigorous validation metrics, cross-institutional benchmarking, and consensus guidelines will be necessary steps to mitigate these risks effectively.

In parallel, fostering multidisciplinary collaboration among clinicians, computer scientists, data privacy experts, and ethicists will accelerate the adoption of synthetic data paradigms. This convergence of expertise will enable the design of sophisticated pipelines that uphold ethical standards, comply with legal mandates, and optimize clinical relevance. It will also stimulate innovation in enhancing generative model architectures, integrating multi-modal data sources, and streamlining model deployment within real-time endoscopy or pathology workflows.

Reflecting on the current trajectory, the era of synthetic data in gastrointestinal medicine signals a paradigm shift from data scarcity and privacy constraints toward unprecedented data abundance and accessibility. As AI algorithms evolve and healthcare systems embrace digital transformation, synthetic data will be pivotal in democratizing access to high-quality datasets, unleashing creativity in algorithm development, and fostering robust, scalable solutions for early disease detection and personalized treatment. The implications extend beyond GI diseases, serving as a blueprint for AI-driven clinical innovation across medical specialties.

In conclusion, the synthesis of artificial yet clinically meaningful datasets represents one of the most promising frontiers in AI and medicine. While significant challenges remain—technical, ethical, and regulatory—the momentum and early successes within gastrointestinal medicine are compelling. Future research should focus on refining generative approaches, validating AI models trained on synthetic data across diverse populations, and embedding these technologies seamlessly into clinical practice. Ultimately, the judicious application of synthetic data holds the key to unlocking the full transformative power of artificial intelligence for improved patient care and health system efficiency worldwide.

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Subject of Research: Synthetic data generation and its application in gastrointestinal medicine.

Article Title: Synthetic data generation: challenges and perspectives for gastrointestinal medicine.

Article References:
Gatoula, P., Iakovidis, D.K., Diamantis, D.E. et al. Synthetic data generation: challenges and perspectives for gastrointestinal medicine. Nat Rev Gastroenterol Hepatol (2026). https://doi.org/10.1038/s41575-026-01216-6

Image Credits: AI Generated

In a groundbreaking study poised to redefine cancer immunotherapy paradigms, researchers have unveiled a programmable RNA engineering platform that restores immune recognition in notoriously immune-resistant tumors such as prostate cancer. This innovative approach, centered on precise manipulation of mRNA alternative polyadenylation (APA), revives the expression of major histocompatibility complex class I (MHC-I) molecules — the pivotal sentinels of immune surveillance routinely downregulated in these tumors. The findings, detailed in Nature Biomedical Engineering, shed light on an elusive molecular mechanism of immune evasion and herald a new frontier in translational cancer immunology.

Prostate cancer and analogous immune-cold tumors have long bedeviled clinicians by resisting immune checkpoint inhibitor therapies, a frontline strategy in modern oncology. A primary culprit in this resistance is the diminished presentation of tumor-specific antigens on MHC-I molecules, molecules intrinsically responsible for flagging abnormal cells to cytotoxic CD8 T cells. Without adequate MHC-I display, these malignant cells evade immune detection, rendering checkpoint blockade largely ineffective. Despite the clinical significance, no therapies have yet emerged that selectively restore MHC-I expression, underscoring a critical unmet medical need.

The investigative team addressed this gap by engineering the 3′UTR CRISPR/dCas13 system (3′UTRCES), an advanced RNA-editing platform designed to modulate mRNA 3′ untranslated region (3′UTR) dynamics in vivo. Unlike conventional gene editing tools that alter DNA sequences, 3′UTRCES uniquely targets RNA transcripts’ polyadenylation sites to manipulate APA patterns — a post-transcriptional regulatory mechanism influencing mRNA stability, localization, and translation. Leveraging a catalytically inactive Cas13 (dCas13) fused to programmable effectors, this system reprograms 3′UTR architecture to reinstate normal gene expression profiles critical for immune function.

Central to their discovery is the identification of tumor-specific 3′UTR shortening in the E3 ubiquitin ligase adaptor SPSB1, a molecular player hitherto not linked to immune modulation. Through sophisticated analyses, the researchers demonstrated that aberrant SPSB1 3′UTR truncations promote enhanced ubiquitination and subsequent degradation of MHC-I molecules. This selective downregulation disrupts the immune system’s ability to recognize and eliminate malignant cells without affecting programmed death-ligand 1 (PD-L1), an immune checkpoint protein. These insights decode a novel mechanism by which tumors suppress antigen presentation, bypassing immune control.

Armed with this mechanistic understanding, the team deployed lipid nanoparticle (LNP) delivery of the 3′UTRCES constructs directly into syngeneic mouse models harboring prostate tumors. This therapeutic intervention reversed the aberrant SPSB1 3′UTR shortening, effectively restoring MHC-I expression on tumor cells. The restored antigen presentation reinvigorated the infiltration of CD8 cytotoxic T lymphocytes, amplifying antitumor immune responses and sensitizing tumors to subsequent immune checkpoint therapies. This combinatorial strategy outperformed existing modalities, offering a tangible path to overcome immune resistance.

The implications of this study extend beyond prostate cancer, representing a conceptual shift in targeting post-transcriptional gene regulation to modulate tumor immunity. By precisely tailoring RNA isoforms through 3′UTRCES, it becomes feasible to rectify pathological gene expression patterns without permanent genomic alterations, minimizing off-target effects and paving the way for tunable, reversible therapies. This technological stride potentially positions LNP-3′UTRCES as a versatile platform for broad-spectrum cancer immunotherapy.

Delving deeper into the molecular underpinnings, the research highlights how alternative polyadenylation — once considered a subtle nuance of gene regulation — plays an outsized role in immune evasion. APA events that truncate key regulatory 3′UTRs can rewire protein expression profiles, impacting ubiquitin-mediated degradation pathways. In SPSB1’s case, 3′UTR shortening shields MHC-I from stable expression, underscoring the delicate interplay between RNA processing and immune visibility. These revelations chart a new investigative avenue for uncovering APA-driven oncogenic mechanisms.

The study harnessed state-of-the-art transcriptomic and proteomic methodologies to validate the APA signatures and subsequent functional outcomes. Single-cell RNA sequencing and cross-linking immunoprecipitation unveiled the precise RNA-protein interactions modulated by 3′UTR alterations. Meanwhile, in vivo tumor growth assays meticulously quantified the therapeutic efficacy of 3′UTRCES-mediated intervention. The robust experimental design marries molecular biology with immuno-oncology, reinforcing the translational promise of RNA engineering.

Beyond prostate cancer, the researchers posit that similar APA-driven immune escape mechanisms may underpin resistance in other malignancies characterized as immune-cold or immunologically excluded. Tumor heterogeneity presents obstacles, yet the adaptability of the 3′UTRCES platform offers a pathway to customize RNA editing interventions tailored to tumor-specific APA landscapes. This precision medicine approach aligns with emerging oncology paradigms striving to unify genetic and epigenetic therapeutic targeting.

Crucially, this work confronts a longstanding conceptual bottleneck — the absence of clinically viable modalities to restore MHC-I expression selectively. Immune checkpoint inhibitors, though transformative, rely heavily on intact antigen presentation machinery to elicit durable responses. By restoring the tumor’s antigen-presenting capacity, 3′UTRCES synergizes with immune checkpoint blockade, reinvigorating exhausted cytotoxic T cells and promoting tumor clearance. This paradigm underscores the importance of targeting upstream immune suppression pathways in cancer.

The LNP delivery mechanism employed is equally noteworthy, showcasing advances in nanomedicine that enable precise nucleic acid transport to tumor sites. Lipid nanoparticles offer biocompatibility, customizable surface chemistry, and efficient endosomal escape — attributes critical for clinical translation. As the field gravitates toward RNA-based therapeutics post-COVID-19, leveraging these delivery systems in oncology sets a precedent for future innovations combining synthetic biology and immunotherapy.

Looking ahead, the scalability and safety of 3′UTRCES-mediated therapies warrant thorough exploration through preclinical and clinical studies. Immunogenicity of RNA-guided effectors, off-target RNA interactions, and durability of therapeutic responses remain important considerations. Nevertheless, the current findings provide a compelling proof of concept, invigorating translational research efforts to combat immune escape mechanisms effectively.

This pioneering research not only reframes our understanding of tumor immune evasion but also exemplifies how RNA technology can be harnessed to unlock therapeutic opportunities in recalcitrant cancers. By transforming APA from a molecular signature of immune suppression into a therapeutic target, the study opens vistas for next-generation cancer treatments that restore immune competence at the RNA level. This convergence of molecular biology, immunotherapy, and RNA engineering heralds a new dawn in oncology innovation.

In summary, the programmable editing of mRNA 3′UTRs via the 3′UTRCES platform revitalizes MHC-I expression in prostate tumors, counteracting immune suppression driven by SPSB1-mediated degradation pathways. This novel mechanistic insight, coupled with effective lipid nanoparticle delivery and demonstrated synergy with immune checkpoint inhibitors, marks a transformative advance in overcoming tumor immune resistance. The study exemplifies how precision RNA engineering can recalibrate tumor immunogenicity and redefine therapeutic potentials in cancer treatment.

As research continues to unravel the complexities of RNA-mediated regulation of immune visibility in cancer, the integration of programmable RNA tools like 3′UTRCES with established immunotherapies holds immense promise. These synergistic strategies may soon accelerate the development of personalized cancer therapies designed to circumvent immune escape and foster more durable clinical remissions. The future of oncology may indeed lie at the interface of RNA engineering and immunomodulation.

This landmark work not only elucidates a previously unrecognized molecular mechanism of immune evasion but also delivers a versatile, programmable therapeutic platform for precise post-transcriptional gene regulation in cancer. Its translational implications inspire optimism for expanding the repertoire of RNA-based interventions that can be tailored to diverse tumor contexts, ultimately enhancing patient outcomes and transforming cancer immunotherapy at its molecular roots.

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Subject of Research: Programmable RNA engineering to restore MHC-I expression and overcome immune evasion in prostate cancer.

Article Title: Programmable mRNA 3′UTR engineering restores MHC-I and overcomes immune evasion in prostate cancer.

Article References:
Huang, F., Yuan, F., Li, K. et al. Programmable mRNA 3′UTR engineering restores MHC-I and overcomes immune evasion in prostate cancer. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-026-01720-9

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41551-026-01720-9

A Florida crabber spent months fighting a child-luring charge that, according to a new federal lawsuit, should never

One day after WIRED revealed that Meta had quietly embedded an unreleased face-recognition system into an app installed on more than 50 million phones, the company removed it, according to a WIRED analysis of the latest version’s code.

The most recent version of Meta AI, a companion app for its line of smart glasses, strips out the unactivated software components that powered the system Meta internally called NameTag. The version published the day of WIRED’s report included several code libraries explicitly named for face recognition. Friday’s release includes none of them.

Andy Stone, Meta's vice president of communications, told WIRED on Monday that the feature is purely exploratory, adding: “No final decision has been made on what to do here, if anything.”

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Believers are sealed by the Holy Spirit for redemption. The mark of God signifies salvation, contrasting with Satan's mark, which represents servitude and slavery. The coming digital ID system potentially fulfills the mark of the beast prophecy. Don't accept any digital ID!

The Digital ID Act 2024 in Australia is a vehicle for extensive surveillance, potentially linking personal data such as biometrics to financial accounts and digital currency. Governmental assurances of voluntary participation do not help, fearing that such measures may become mandatory, leading to total control and loss of individual freedoms.