The Meta Oversight Board, an independent body funded by Meta, evaluated how major AI models responded to politically sensitive prompts involving countries with different approaches to freedom of expression. In one pattern identified by the report, several models were more likely to refuse or limit responses involving governments that maintain stricter controls on political speech than they were for comparable prompts involving countries with broader protections for public criticism.

The Board said the findings do not suggest that governments directly influenced the AI systems. Instead, the report argues that language models may reflect patterns already present in their training data, which includes online content shaped by national laws, platform moderation practices and cultural norms.

Oversight Board Tests Leading AI Models

The report evaluated 10 commercially available large language models from six AI developers: Meta, OpenAI, Anthropic, Google, DeepSeek and xAI.

To compare model behavior, the Board submitted similar political prompts while changing only the country or political leader referenced. That approach allowed evaluators to determine whether identical requests produced different responses depending on the political environment associated with the subject.

Across the models tested, the Board found measurable differences in how some systems handled politically sensitive requests. It said the results demonstrate how training data and evaluation methods can influence AI-generated responses even when users submit comparable questions.

Transparency Emerges as a Key AI Issue

The findings add to broader discussions about how developers build, test and evaluate generative AI systems before public deployment.

AI assistants now play a growing role in search engines, productivity software, classrooms and consumer applications. As adoption expands, users increasingly expect consistent treatment of factual and political information regardless of geography or language.

The report recommends that AI developers improve transparency around model training and evaluation. It also calls for systematic human rights assessments and broader multilingual testing to identify inconsistencies before public release. According to the Board, those measures could help reduce unintended restrictions on lawful political expression while strengthening public confidence in AI systems.

Training Data Remains Under Growing Scrutiny

The report also highlights a broader challenge facing AI developers. Large language models learn from enormous collections of online material that already reflect political, legal and cultural differences across countries.

The Board noted that multilingual and region-specific datasets can influence how AI systems respond to similar requests. It emphasized that such differences should not automatically be interpreted as intentional censorship. Instead, they illustrate the complexity of training models on diverse information environments with varying legal and cultural standards.

The findings are expected to contribute to ongoing industry discussions about improving dataset documentation, model benchmarking and transparency as generative AI becomes more widely deployed.

AI Governance Extends Beyond Technical Performance

The report comes as governments continue developing regulatory frameworks for artificial intelligence while technology companies face increasing scrutiny over transparency, accountability and responsible AI development.

According to the Oversight Board, evaluating how AI systems respond to politically sensitive information has become an important part of AI governance. As generative AI becomes more deeply integrated into research, education, digital services and everyday information discovery, the report argues that greater transparency in how models are trained, evaluated and tested will be essential for maintaining public trust.

Article Topics: Artificial Intelligence | AI Governance | AI Transparency | Large Language Models | Training Data | Political Speech | Technology Policy | Generative AI

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Developed jointly by World Taekwondo and Singapore-based technology company Refract Technologies, the sport combines traditional taekwondo techniques with virtual reality technology. Athletes compete inside immersive digital environments while performing real physical movements, creating a non-contact competition that organizers say preserves athletic performance while reducing injury risks.

The technology has evolved rapidly since its early demonstrations. After being showcased during Singapore’s Olympic Esports Week in 2023, virtual taekwondo hosted its inaugural World Championships in Singapore in 2024. It is now scheduled to appear at the Asian Games and is expected to be included in the 2027 Southeast Asian Games in Malaysia, signaling broader institutional recognition.

Vietnamese athlete Nguyen Thanh Hien Linh experienced that evolution firsthand. Competing in her first virtual taekwondo event in Singapore in 2024, she said she initially struggled to understand both the technology and competition strategy despite already being an elite national taekwondo champion.

Two years later, Nguyen captured a gold medal at a virtual taekwondo competition in Malaysia after adapting to the distinct tactical and technical demands of the digital format.

How Virtual Reality Changes Competition

Unlike conventional taekwondo, athletes wear virtual reality headsets that place them inside a three-dimensional digital arena while motion-tracking sensors attached to their spine, thighs and shins capture body movements.

Competitors control digital avatars through physical kicks, movement and positioning. Rather than scoring physical contact, successful attacks reduce an opponent’s virtual health bar, with timing, speed and accuracy determining competitive performance.

The format also differs from traditional taekwondo by placing competitors of different ages, genders and weight classes into the same virtual competition environment.

According to coaches involved in the sport, athletes continue to perform authentic taekwondo techniques—including front kicks, turning kicks and spinning kicks—but success depends primarily on execution speed, positioning and decision-making rather than impact force.

Physical Demands Remain Central

Although the matches eliminate physical contact between opponents, athletes and coaches say the discipline remains physically demanding.

Malaysian coach Henry Lee said training begins with improving stamina, muscular endurance and flexibility before progressing to tactical decision-making and movement strategies inside the virtual environment. He said speed effectively replaces striking force as the key competitive advantage.

Athletes must also develop strong spatial awareness because they cannot directly see their opponents outside the virtual display. Instead, they rely on anticipation, movement prediction and rapid reactions while navigating the immersive arena.

Twelve-year-old Malaysian athlete Victoria Siow described the challenge as learning when to move and attack despite not physically seeing the surrounding space, saying the experience feels both game-like and dream-like.

Regional Interest Continues to Grow

Participants and coaches across Southeast Asia say interest has expanded steadily, particularly among younger athletes familiar with digital gaming.

Singapore national athlete Brian Peh said he entered the 2024 championship alongside his son out of curiosity, with both eventually winning gold medals. He now teaches virtual taekwondo at his training hall, arguing the technology channels children’s enthusiasm for gaming into physical activity.

Cambodian coach Vandy Yiv said families have increasingly embraced the discipline because competitors can participate without the injury risks associated with full-contact sparring. During one local tournament earlier this year, he said virtual taekwondo attracted more participants than traditional divisions.

Some newcomers reported experiencing dizziness while adapting to virtual reality headsets, though coaches said athletes generally become accustomed to the immersive environment over time.

Technology Access Remains a Challenge

Malaysia’s national virtual taekwondo coach Tony Lee said the sport remains in its early stages of development despite growing momentum.

He acknowledged that virtual reality equipment remains expensive, potentially limiting access in some parts of the region. However, he said increasing demand is encouraging clubs to invest in the technology, while Malaysia has already introduced national development programs and coaching certification courses to support expansion.

Lee also said younger generations’ familiarity with gaming technologies positions virtual taekwondo for continued growth as competitive virtual sports become more established.

For athletes such as Raja Mardiah Idris, the technology offers opportunities unavailable in conventional competition. She said virtual taekwondo allows older competitors and women to compete on equal terms in a non-contact environment, where success depends on technique, strategy and physical fitness rather than physical impact.

This report is based on reporting by The Associated Press (AP).

Topics: Virtual Taekwondo | Virtual Reality | Asian Games | Sports Technology | Motion Tracking | Refract Technologies | World Taekwondo | Southeast Asia

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Among those advocating a different direction is Yann LeCun, who said existing AI systems such as ChatGPT, Claude and Gemini are effective for tasks including coding, mathematics and text generation but are not designed to achieve human-like or even animal-level understanding of real-world environments.

Speaking during the VivaTech conference, LeCun said these systems primarily learn statistical relationships from vast datasets rather than developing an understanding of how the physical world behaves. He argued that this limitation makes them unsuitable for many robotics applications that require flexible reasoning and adaptation.

LeCun, who previously served as chief AI scientist at Meta before leaving in 2025, now leads Paris-based Advanced Machine Intelligence Labs (AMI Labs), where researchers are developing an alternative AI architecture known as Joint Embedding Predictive Architecture (JEPA).

Earlier this year, AMI Labs announced it had raised more than $1 billion in seed funding. The investment included backing from Nvidia and a fund managing the private wealth of Jeff Bezos, making it one of Europe’s largest seed funding rounds.

Why Researchers Want AI Beyond Language Models

LeCun argues that LLMs are highly capable within structured and predictable domains because they generate responses based on patterns learned during training. However, he said those systems do not possess an underlying model of physical reality.

To illustrate the difference, he described balancing a pen on its tip. While a person instinctively knows the pen will fall without needing to predict the exact direction, an LLM may attempt to generate a statistically likely outcome rather than reasoning about the uncertainty of the situation itself.

According to LeCun, JEPA seeks to address this challenge by creating abstract representations of the physical world. Rather than predicting every possible outcome, the system is intended to identify which information is meaningful while ignoring unnecessary detail, allowing it to reason more efficiently about real-world situations.

He said this capability could eventually make AI systems better suited for robotics, where machines must continually interpret changing environments instead of responding to fixed prompts.

Robotics Continues to Drive AI Research

Improving AI reasoning has become a significant objective for robotics developers, who have invested billions of dollars in humanoid machines capable of operating in human environments.

Although robotic hardware has advanced rapidly, teaching robots to perform everyday household activities such as loading dishwashers or ironing clothing safely remains technically difficult and expensive.

LeCun said current LLM-based approaches are unlikely to solve these challenges effectively because they are not built to interpret complex physical interactions.

World Models Gain Momentum Across the Industry

Other researchers share the view that future AI systems will require more sophisticated reasoning than today’s language models provide.

Ingmar Posner, who directs the Applied AI Lab at the University of Oxford and also serves as an Amazon Scholar, said future AI systems should be capable of explaining cause and effect, identifying what matters in a situation and evaluating alternative actions.

His research group has spent several years developing what he describes as a mechanistic world model, designed to organize knowledge so that information can be efficiently retrieved, combined and modified when solving problems.

World models have existed as a research concept for decades but received renewed attention following influential work published in 2018 by David Ha and Jürgen Schmidhuber. Their research proposed that AI systems could learn by building internal simulations of the world rather than relying solely on memorized patterns.

Since then, several organizations have expanded work in the field. Google has developed Dreamer world models, including a version that learned to collect diamonds in the video game Minecraft by imagining future scenarios during decision-making.

Additional research includes Genie from Google DeepMind, Gaia from Wayve, and work at World Labs, founded in 2023 by Fei-Fei Li.

Commercial Deployment Remains Ahead

Posner said it remains difficult to predict how quickly these newer AI architectures will mature, noting that the rapid arrival of generative AI systems surprised many researchers who had expected such capabilities to take decades longer.

LeCun said AMI Labs plans to continue refining its JEPA-based system through the remainder of this year, with the goal of introducing initial industrial deployments next year if development progresses as planned.

Looking further ahead, he said broader-purpose AI systems capable of performing many tasks with limited additional training remain the long-term objective. Even if those systems eventually exceed human capabilities in certain areas, LeCun argued that people will continue to play the central role in defining goals, asking questions and directing how AI is applied.

Tags: Artificial Intelligence, World Models, Yann LeCun, AMI Labs, Large Language Models, Robotics, Machine Learning, ChatGPT, Google DeepMind, Nvidia, JEPA, AI Research

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The emerging technology is offering comfort to some bereaved families by digitally recreating the appearance and voice of those who have died. At the same time, experts are raising concerns about the psychological and ethical implications of using AI to simulate the dead.

One such customer is 28-year-old office worker Lee Geon Hui, who commissioned an AI-generated video of his late grandfather as a gift for his father. Lee wrote the script himself, imagining the words his grandfather might have wanted to say.

The virtual recreation addressed Lee’s father as “my most precious son,” expressed regret for making him work on the family farm during childhood and apologized for opposing his decision to pursue a career as a hairstylist.

Lee said his father initially refused to watch the video but later viewed it and became emotional.

“My father said he wouldn’t watch the video. But then he did, and he shed tears. So I felt rewarded,” Lee said. “I wrote the script … as it was what I actually wanted to tell my father.”

Lee’s grandfather died in a car accident before Lee was born. He said seeing his father’s emotional reaction highlighted how deeply he still missed his own father decades later.

AI Memorial Videos Become a Growing Industry

South Korea has seen increasing interest in AI-powered memorial services as advances in generative AI make digital recreations more realistic.

Seoul-based startup Vaice says it now serves around 300 customers each month. According to CEO Jeongu Won, most clients are people in their 40s and 50s seeking AI-generated videos of deceased parents, while others create videos of grandparents as gifts for their parents.

The company typically requires several photographs and short voice recordings to recreate a person’s likeness. A standard three- to five-minute video costs approximately 600,000 won (about $390).

Won said families frequently play the videos during traditional memorial ceremonies or major Korean holidays when relatives gather to remember loved ones.

Many of the personalized scripts include expressions of love, unresolved regrets or messages of forgiveness that surviving family members wished they had shared before the person’s death.

Another company, JL Standard, introduced a similar service about five years ago. Executive Choi Yu Ha said public skepticism has eased over time as audiences have become more familiar with AI-generated recreations, including televised appearances featuring digital versions of deceased entertainers.

Experts Warn of Emotional and Ethical Risks

While supporters believe the technology can provide emotional closure, researchers caution that recreating deceased individuals also raises difficult questions about consent, privacy and mental health.

Yong Man Ro, an AI researcher at the Korea Advanced Institute of Science and Technology, described the technology as both beneficial and potentially disruptive because it directly affects human emotions.

“It’s a double-edged sword, as it deals with human emotions,” Ro said. “As AI technologies become part of people’s lives, they can also bring about cultural experiences and shocks that we have never experienced.”

Legal experts argue that clearer regulations are needed as AI becomes capable of producing increasingly lifelike digital replicas.

Choung Wan, an emeritus professor at Kyung Hee University Law School, said laws should protect the dignity and posthumous rights of deceased individuals. He suggested AI recreations should not be permitted if the individual explicitly opposed such use before death and called for limits on commercial use of a person’s image and voice.

Future AI ‘Griefbots’ Present New Challenges

Researchers believe ethical concerns could become even more complex if AI systems evolve beyond one-way video messages into interactive “griefbots” capable of carrying on extended conversations with bereaved family members.

Several startups are already experimenting with conversational AI designed to simulate deceased individuals.

Choung warned that healthy grieving typically involves accepting the reality of loss and that prolonged conversations with AI replicas could interfere with that process.

“Psychologically, a healthy mourning involves a process to acknowledge the absence of the deceased and pass through the pains of their losses,” Choung said. “But speaking with an AI system simulating a living person could undermine the process of accepting deaths and rather cause a negative effect of leaving bereaved families trapped in a fantasy.”

Won said Vaice is proceeding cautiously regarding conversational AI because unsupervised interactions could create unforeseen ethical issues.

Technology Advances Continue

AI-generated memorials are becoming increasingly realistic as image generation and voice synthesis technologies improve.

Choi said today’s systems can reproduce details as fine as facial wrinkles and skin texture, leading many customers to feel the digital recreations closely resemble their loved ones.

Ro, whose own parents died last year, created a one-minute AI-generated video that he shared with his siblings during a family gathering. The digital versions of their parents encouraged them not to worry and to take care of themselves.

Although the experience was deeply moving, Ro said the family viewed the video only once.

“One time was enough to watch it to honor our late parents who were quite elderly. We moved on,” he said.

Tags: Artificial Intelligence, AI Memorials, Generative AI, South Korea, Digital Afterlife, AI Ethics, Grief Technology, Emerging Technology, Digital Humans, AI Regulation

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The announcement was made by WhatsApp on Monday through an official company blog post, where the Meta-owned messaging platform said users can begin reserving unique usernames ahead of the feature’s broader release later this year. The company said the update is designed to reduce the need to expose personal phone numbers when initiating conversations.

With more than 3 billion users worldwide, WhatsApp has traditionally relied on phone numbers as the primary method for identifying and contacting accounts. The upcoming system introduces a different approach by allowing users to be discovered through a unique username instead.

Alice Newton-Rex, WhatsApp’s vice president of product, said the feature was designed with privacy as its primary objective.

According to Newton-Rex, users who enable the option will be reachable only by their exact username rather than their phone number. She also clarified that WhatsApp will not maintain a searchable username directory or provide username suggestions while people search, reducing the likelihood of unsolicited contact.

The company has not announced an exact launch date beyond saying the feature will become available over the coming months.

Usernames Designed to Limit Personal Information Sharing

The introduction of usernames addresses a longstanding privacy limitation on WhatsApp, where anyone possessing another person’s phone number could attempt to initiate contact.

Current privacy tools on the platform include blocking individual users, silencing unknown callers, and displaying optional profile names within group chats for participants who have not saved one another’s contact details. Those measures do not remove the requirement to use phone numbers as account identifiers.

Under the new system, users will have the option of sharing a username instead of their personal mobile number when communicating with new contacts.

Reservation Process Opens Before Global Rollout

WhatsApp said it is opening username reservations ahead of the public launch to give users an opportunity to secure preferred names before the feature becomes widely available.

Newton-Rex acknowledged that desirable usernames are expected to attract significant interest, which influenced the company’s decision to allow early reservations.

Businesses, organizations and creators that already operate verified or established accounts on Meta’s other platforms, including Facebook and Instagram, will also be eligible to reserve matching usernames for WhatsApp.

Rules Intended to Reduce Impersonation Risks

WhatsApp said usernames must contain between three and 35 characters.

To help reduce impersonation, the company plans to reserve certain usernames associated with celebrities, public figures, government organizations and other high-profile entities rather than making them immediately available for public registration.

The company has not provided additional technical details on how those protected usernames will be managed.

Although WhatsApp remains less widely used than traditional text messaging services in the United States, it continues to serve as one of the world’s largest messaging platforms across Europe, Asia and many other regions. The username system represents one of the platform’s most notable identity and privacy updates by reducing dependence on phone numbers while maintaining existing account security.

Tags: WhatsApp, Meta, Usernames, Privacy, Messaging Apps, Digital Identity, Online Privacy, Meta Platforms, Mobile Apps, Technology

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While the technology promises to make dating more efficient, many users remain uncertain whether algorithms can truly replace the emotional instincts and authentic connections that define successful relationships.

Marie Lansley, 36, recently relocated to San Francisco for a new job while also searching for a romantic partner. As she explored various dating platforms, she began using AI chatbots to assist with starting conversations, an aspect of online dating she finds more difficult than meeting people face-to-face.

Lansley regularly alternates between OpenAI’s ChatGPT and Anthropic’s Claude to help generate conversation starters. Although she appreciates the convenience, she believes technology has clear limitations.

“AI is great at making dating more efficient,” Lansley said. “But the chemistry — that’s always going to be analog.”

AI Expands Its Role in Digital Dating

Consumers are increasingly adopting artificial intelligence across different stages of the dating process. Some rely on AI-powered matchmaking services, while others use chatbots to improve dating profiles or draft responses to potential matches.

Among the most common uses is asking AI to write opening messages, suggest replies during conversations, or explain confusing interactions with prospective partners.

In addition to ChatGPT and Claude, some users are experimenting with Google’s Gemini, Elon Musk’s Grok, and other generative AI assistants. Dating platforms and AI developers have also embraced the trend by publishing examples on social media demonstrating personalized dating advice generated by their systems.

Dating coach Carey Gaynes compared today’s AI assistants to the literary character Cyrano de Bergerac, who famously helped another man express romantic feelings through carefully crafted words.

She acknowledged that AI can be useful but cautioned that relying too heavily on technology risks replacing a person’s authentic voice during the early stages of a relationship.

Dating Apps Continue Integrating AI Features

Artificial intelligence has already become deeply integrated into several major dating platforms.

Lansley said she was impressed by the conversational depth displayed by an AI matchmaker during an onboarding session with the dating app Known. She felt the chatbot asked more thoughtful questions than many traditional dating services, although the first suggested match ultimately was not an ideal fit.

Other users view AI as a helpful assistant during the often awkward opening exchanges.

Mason Naung, a 25-year-old student in Los Angeles, said AI-generated suggestions could make it easier to create initial icebreakers on dating platforms such as Hinge. However, he believes continuing entire conversations with AI-written messages could become a warning sign.

Artificial intelligence is also being used to help end relationships more respectfully.

Dani Cohen, a business owner in San Diego, said she would rather receive a thoughtfully written AI-assisted message explaining someone’s decision to stop dating than experience being “ghosted” without any explanation.

She argued that if AI encourages clearer and kinder communication, it may offer practical value despite concerns about authenticity.

Questions Over Authenticity Remain

Despite increasing adoption, many users remain skeptical about allowing artificial intelligence to play a significant role in their personal relationships.

Several people interviewed said they have established personal boundaries regarding when AI assistance becomes inappropriate, particularly once conversations move beyond initial introductions.

Clara Sullivan, a student in Los Angeles, said she would be reluctant to continue communicating with someone if she discovered their messages were primarily written by AI.

She expressed concern that growing dependence on chatbots could reduce people’s willingness to think creatively and communicate independently.

Public opinion reflects similar concerns.

According to a 2025 survey by the Pew Research Center cited in the report, 53% of U.S. adults believe artificial intelligence will reduce people’s ability to think creatively, while half believe it could weaken the ability to build meaningful relationships.

Dating Industry Bets on Artificial Intelligence

Despite reservations, the online dating industry continues investing heavily in AI-powered features.

Dating platforms have introduced tools designed to recommend compatible profiles, generate conversation starters, and provide profile-writing assistance.

Tinder offers an AI-powered feature called Chemistry that recommends profiles based on users’ interests, while Hinge has introduced AI tools aimed at improving conversations and profile quality.

Bumble is also preparing broader AI-driven matchmaking capabilities. Founder and Chief Executive Officer Whitney Wolfe Herd recently said the company plans to move away from its well-known swipe interface in favor of artificial intelligence designed to make finding compatible partners more personalized.

Following criticism of the announcement, Herd said the company’s objective is to ensure technology makes relationships “feel more human, not less.”

Balancing Convenience With Human Connection

Some users continue to question whether AI can offer meaningful guidance in matters of romance.

Mohammed Nizami, 23, said he uses artificial intelligence in other areas of his life but intentionally avoids relying on it for dating. He believes authentic emotional connection requires direct communication without technological filters.

He also questioned whether AI systems always provide balanced advice, noting that chatbots often prioritize supportive responses that may not necessarily be the most helpful.

Jake Clay, a content creator in New York City, described the growing use of AI in dating as both helpful and concerning.

Although he appreciates receiving fewer requests from friends to interpret dating messages because many now consult AI instead, he believes outsourcing such personal interactions reflects broader changes in how people approach relationships.

For many singles, artificial intelligence may become another tool in the search for love. Yet even as technology reshapes modern dating, many users continue to believe genuine chemistry and emotional connection remain qualities that no chatbot can fully replicate.

Tags: Artificial Intelligence, AI Chatbots, Online Dating, Dating Apps, ChatGPT, Claude AI, Bumble, Tinder, Digital Relationships, Generative AI

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Kempf is the founder of Kyber, a Paris-based startup developing an infrastructure layer designed to synchronize video, audio, sensor data, and control inputs with minimal latency. The system is positioned for use cases where operators and machines are physically separated from the underlying compute and execution environment.

The company has raised a $5 million funding round led by venture capital firm Lightspeed, which has also backed companies including Anthropic and Mistral AI. Lightspeed framed its investment in the context of “physical AI,” stating that such systems depend heavily on robust underlying infrastructure.

Kyber’s core product is an SDK that focuses on real-time coordination between remote operators and distributed devices. Kempf describes the system as addressing environments “where the person who’s operating is not in the same place as the compute, which is not in the same place as the action.”

The startup’s technical approach draws heavily from streaming architecture. Kempf previously worked on cloud gaming infrastructure at Shadow, and Kyber’s design reflects that background in low-latency video transmission combined with IoT device optimization.

From Video Streaming to Physical AI Infrastructure

The company’s origins are rooted in video streaming systems, a lineage that traces back to Kempf’s work on VLC and later cloud gaming platforms. Kyber extends those principles into physical systems where latency becomes operationally critical.

Kempf has emphasized that in real-world machine control, timing constraints are strict. “If you control things in the real world, every millisecond matters,” he said, underscoring the importance of minimizing lag between operator input and machine response.

The company argues that while similar systems exist in niche deployments such as remote driving, most are limited in scale. Kempf noted that existing implementations often manage only a few thousand vehicles, whereas future deployments may require coordination across millions of devices.

Scaling Remote Operations and Device Observability

Kyber’s architecture also targets a challenge that becomes more complex at scale: observability. As fleets of autonomous systems grow, ensuring that devices are functioning correctly and responding as expected becomes a core engineering requirement.

The company positions its system as applicable to a range of scenarios, from remote software updates to full operational control of distributed hardware fleets. This includes robotics, drones, and remote IT infrastructure management.

Kyber is also exploring the remote IT access space, a market often associated with enterprise tools such as Citrix. Kempf has indicated that Kyber aims to extend beyond traditional remote desktop models into broader infrastructure control systems.

Open Source Foundations and Enterprise Deployment

Kyber is built on a hybrid model. Its core software remains open source, reflecting Kempf’s long-standing involvement in community-driven development. Alongside this, the company offers enterprise-grade deployments and services, including forward-deployed engineering support.

Forward-deployed engineers form a significant portion of Kyber’s approximately 25-person team. The startup operates from Paris, with additional offices in San Francisco and Singapore, reflecting its target of global enterprise deployment.

According to the company, Kyber is already in commercial use across sectors including defense, telecommunications, robotics, and artificial intelligence systems.

Market Position and Technical Direction

Kyber’s strategic focus is concentrated on three main areas: robotics, drone systems, and remote IT access. Each reflects different aspects of distributed machine control, unified by the requirement for low-latency synchronization between operators and devices.

The company’s underlying premise is that future AI systems will increasingly interact with physical environments, making real-time infrastructure as critical as model performance. Lightspeed described this dependency in its investment announcement, noting that “physical AI is only as good as the underlying systems running it.”

While still early in scale, Kyber’s positioning reflects a broader industry shift toward infrastructure designed for embodied AI systems rather than purely digital applications.

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Her remarks came during a Bloomberg interview, as reported by TechCrunch on June 20, 2026. The discussion focused on privacy and the growing role of AI assistants such as ChatGPT and Claude.

“These are not your friends. These are not conscious beings. These are not sentient interlocutors,” Whittaker said.

She also noted that she uses AI tools in limited ways. For instance, she relies on them to format documents. However, she avoids deeper interaction. “I don’t ask them questions,” she said. She added that she prefers to work through ideas without machine-generated responses shaping her thinking.

As a result, she raised concerns that frequent reliance on chatbots could shift how people develop ideas and structure thought.

Privacy concerns around expanding AI systems

Whittaker also addressed comments from Microsoft AI CEO Mustafa Suleyman about future AI agents that could handle tasks like holiday shopping.

In that scenario, she said, such systems would require broad access to personal data across multiple services. Because of this, she warned that deeper integration could create serious privacy risks.

She referenced systems like Microsoft Copilot as examples of tools that could operate across platforms and user accounts.

“What you’ve just described is a system with very pervasive access across multiple applications and services,” she said. She added that, in Signal’s case, this level of access would resemble a “backdoor.”

Her concern focused on the permissions required for AI agents to reach messaging apps, email, calendars, and financial data.

Balancing convenience and control

The comments highlight an ongoing debate in the tech industry. On one side, companies are building AI systems that automate tasks across apps and services. On the other, privacy advocates warn about the risks of granting such broad access.

In addition, Whittaker said these systems may improve convenience but also expand the amount of sensitive data processed by a single platform.

Therefore, she urged caution in how much control users give up in exchange for automation.

The debate reflects growing tension between AI-driven convenience and data protection as assistants become more embedded in daily digital life.

Tags: Signal, Meredith Whittaker, AI chatbots, Privacy, Cybersecurity, Microsoft Copilot, ChatGPT, Claude AI, Data security, Agentic AI

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While Apple’s AI-powered Siri overhaul drew attention at its Worldwide Developers Conference earlier this month, the company is positioning a wider approach that embeds Apple Intelligence directly into existing apps and workflows rather than relying on a single conversational interface.

The result, based on Apple’s developer materials and early beta behavior described by TechCrunch, is a series of incremental automation tools aimed at reducing manual input across everyday tasks such as messaging, payments, browsing, password management, and device notifications.

Rather than introducing a standalone AI product experience, Apple is integrating machine learning functions into system utilities and core apps already used by iPhone customers.

AI embedded into everyday iPhone workflows

One of the most visible additions is a bill-splitting feature integrated with Apple Cash. The system can extract receipt data from photos or uploads, identify line items, and calculate shared costs including tax and tip. Users can then assign items to individuals within a group chat in Messages before initiating payment.

In security-focused updates, Apple is also introducing an automated password update function. The feature identifies weak or compromised credentials—such as those exposed in data breaches—and can navigate supported websites to replace them with stronger alternatives without requiring manual intervention.

Messages is also gaining contextual suggestions powered by Apple Intelligence. These one-tap prompts are designed to interpret conversation intent and recommend actions such as adding reminders, sharing photos from events, or creating calendar entries based on discussion context.

System-level context and automation features

Another addition, described as “Call Context,” surfaces relevant information during phone calls with customer service representatives. For example, confirmation codes linked to travel bookings stored in Mail may appear directly on the call screen. Apple’s design approach, as outlined in the TechCrunch reporting, emphasizes on-device processing for these features.

The Calendar app is also receiving natural language input support, allowing users to create or modify events through conversational descriptions. Apple Intelligence extracts relevant details such as dates, locations, and contacts to populate event fields automatically.

Meanwhile, the Shortcuts automation tool is being redesigned to accept natural language instructions. Instead of building workflows manually, users can describe desired outcomes—such as automating routines based on calendar schedules or triggering actions when connecting accessories.

Interface cleanup and background intelligence

Apple is also applying AI-driven grouping to reduce notification overload in the Home app. Instead of sending multiple alerts for related smart home events, the system consolidates them into a single contextual notification describing the overall activity.

The Photos and Home integration also enables improved search and clip retrieval for events such as deliveries or home activity footage.

In Safari, Apple Intelligence introduces tab organization that groups open pages based on inferred topics. For example, browsing sessions related to travel planning may be automatically clustered into a single tab group, improving navigation across sessions.

Apple emphasizes that, according to TechCrunch, Safari’s organization features are designed with privacy considerations in mind and avoid exposing browsing data externally, including to Apple.

A distributed approach to consumer AI

Taken together, the iOS 27 updates reflect a shift toward distributed AI functionality embedded across the operating system rather than centered in a single assistant interface. While Siri remains a headline feature in Apple’s AI roadmap, these incremental tools suggest a broader attempt to normalize automation within everyday device interactions.

The features are currently undergoing developer testing, with wider availability expected later in the year.

Tags: Apple Intelligence, iOS27, Siri, Artificial Intelligence, Mobile Automation, iPhone Features, Privacy Computing, Software Updates, Consumer AI

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The work is opening access to historical records stored in archives, libraries, and private collections across Europe. Many of these documents contain coded messages, damaged text, or unfamiliar writing systems that have challenged scholars for generations.

The growing use of AI highlights how advanced computing is expanding beyond traditional technology fields and supporting historical research.

AI Expands Historical Research

Research teams are applying machine learning systems to a wide range of historical materials. These include diplomatic correspondence, medical records, personal letters, and political communications.

The technology helps scholars process large collections of documents more quickly. Tasks that once required years of manual work can now move forward at a faster pace.

Researchers use AI primarily for pattern recognition, transcription, and text analysis. The technology supports the work of historians rather than replacing them.

The Borg Cipher Offers a Key Example

One notable project focuses on the Borg cipher, a manuscript preserved in the Vatican Library.

The document is more than 400 years old and contains hundreds of pages filled with unusual symbols mixed with Roman characters. Researchers combined AI-assisted analysis with traditional cryptographic techniques to study the manuscript.

Their work helped decode parts of the text that had remained unclear for centuries. The manuscript contained medical remedies and treatment instructions.

The project demonstrated how AI can support investigations into complex historical records.

Hidden Messages in Historical Documents

Cipher research remains a specialized area of study. Scholars believe many archived documents around the world still contain encrypted content.

These records often include diplomatic intelligence, administrative information, private correspondence, religious communications, and scientific observations. Many authors intentionally concealed sensitive information through encryption.

Modern AI systems can identify recurring patterns in symbols, handwriting styles, and language structures. Those capabilities help researchers locate clues that might otherwise remain hidden.

Several projects have already revealed new details about important historical figures and events.

For example, researchers studying coded letters connected to Mary, Queen of Scots uncovered information about political plotting during her imprisonment in England. Other decrypted archives have provided new insight into personal relationships and diplomatic tensions that public records did not fully explain.

Challenges Remain for Researchers

Despite recent progress, historical ciphers still present major challenges.

Many manuscripts use multiple encryption methods at the same time. Some contain symbol substitutions, misleading characters, or unknown languages. Physical damage also complicates the work. Age, fading, and deterioration have left many documents incomplete.

To overcome these obstacles, research teams are building databases of historical ciphers. They are also training machine learning models to recognize linguistic and structural patterns.

Some projects combine computer vision with language-processing technology. These systems can reconstruct damaged symbols before researchers attempt decryption.

Human Expertise Remains Essential

Researchers hope future systems will analyze manuscript images and decode encrypted content with fewer intermediate steps.

Such tools could help scholars access large collections of historical records that remain difficult to study because of the time and expertise required.

Even so, experts stress that human oversight remains critical.

Historical interpretation requires cultural, political, and linguistic knowledge that AI cannot independently verify. While machine learning can identify likely solutions, historians must confirm findings and place them in the proper historical context.

As more institutions digitize their collections, researchers expect AI-assisted cryptanalysis to play a larger role in historical scholarship. The technology is helping experts examine documents that remained hidden from modern understanding for centuries, offering new opportunities to explore the past.

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