Scientists at Meta have developed Brain2Qwerty v2, a system that can convert brain activity into text without requiring brain implants or surgery. Instead, it relies on a non-invasive helmet-like device that detects the brain’s magnetic signals as a person thinks about typing.

The technology uses magnetoencephalography (MEG), a brain imaging technique that records magnetic fields generated by neural activity. The system interprets imagined typing or mental letter selection and converts it into words on a screen, enabling thought-based text input.

Nature Neuroscience Highlights Brain-to-Text Breakthrough

The research was published in the journal Nature Neuroscience, where scientists described how the original Brain2Qwerty system successfully decoded sentences directly from brain signals without physically interacting with the brain.

The latest version, Brain2Qwerty v2, significantly improves performance by using artificial intelligence to process raw brain data automatically and produce text in near real time. This replaces the slower, manually guided decoding methods used in earlier versions.

To train the system, nine healthy volunteers spent approximately ten hours typing sentences while wearing the MEG helmet. During these sessions, the AI learned to associate specific patterns of brain activity with individual letters. It was also combined with a language model, similar to predictive text or smartphone autocorrect, to improve accuracy and reduce typing errors.

Testing showed a major improvement in performance. The system accurately decoded 61% of words on average, with accuracy reaching 78% in the best-performing cases. By comparison, earlier non-invasive brain-to-text methods achieved only about 8% accuracy, highlighting the significant progress made by the new technology.

Currently, helping people who have lost the ability to communicate often requires surgically implanting chips into the brain. The dataset, developed with the Basque Center on Cognition, Brain and Language, is now available for global research and development.

Helmet-Based System Could Restore Communication for People with Paralysis and ALS

A non-invasive solution that only requires wearing a helmet could offer a safer and more accessible alternative. It has the potential to help people living with conditions such as amyotrophic lateral sclerosis (ALS), severe stroke, or other forms of paralysis regain the ability to communicate with loved ones and express their needs, even if they have lost the ability to speak or move.

For now, the biggest limitation is the hardware. The brain-reading equipment is still bulky, expensive, and comparable in size to hospital imaging machines. However, researchers believe advances in sensor technology will make these devices much smaller over time, with the long-term goal of integrating the system into an affordable, wearable cap or similar head-mounted device.

To help speed up research in the field, Meta has made the Brain2Qwerty training code publicly available. Developed with the Basque Center on Cognition, Brain and Language, the dataset is now available for global research and further development.

Developers, scientists, and anyone interested can access technical documentation, demonstrations, and downloadable resources through the project’s official website. The project hub and related files are available at address

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A machine that learned by observing real surgical procedures for the first time successfully performed surgical steps with human-like precision. The study was led by researchers at Johns Hopkins University in Maryland, USA, with contributions from Stanford University experts, and was featured at the Conference on Robot Learning in Munich.

The successful results of the robot’s learning model enable experts to avoid manually programming each individual movement, improve operational efficiency, and move robotic surgery closer to full autonomy.

“Having this model is remarkable—by simply providing camera input, it can determine the robotic actions required for surgery. We see this as an important milestone toward a new era in medical robotics,” said lead author Axel Krieger.

Imitation Learning for Surgical Manipulation

The researchers trained the da Vinci Surgical System–based robot using an imitation learning approach, enabling it to carry out basic tasks like handling a needle, lifting tissue, and stitching. The method blends imitation-based training with a ChatGPT-like framework, but instead of processing text, it relies on kinematics to generate precise physical movements.

The researchers trained the system using a large collection of surgical videos captured from procedures performed with da Vinci prototypes around the world. With nearly 7,000 da Vinci systems in use globally and more than 50,000 surgeons trained on the platform, the extensive dataset allowed the model to learn from a diverse range of surgical techniques and operations.

Although the system is already widely used and marks significant progress toward automated procedures, it still lacks full precision. The study proposes that overcoming this limitation may require training the model to perform relative movements instead of absolute ones, which tend to introduce more errors.

The model learns behaviors that developers never explicitly programmed. For example, if it drops a needle, it can automatically retrieve it and continue the task—something I never directly taught it to do,” Krieger said.

Imitation Learning Expands Toward Fully Autonomous Surgical Procedures

The team is now applying imitation learning not only to simple surgical actions but also to complete procedures, aiming for minimal or even no human intervention. Previously, programming such systems required extensive and rigid planning.

“That approach is very restrictive. What’s different now is that we can simply collect imitation data from various procedures and train a robot within days. This helps speed up autonomy, reduce medical errors, and improve surgical precision,” Krieger added.

The institution and its researchers published a video titled “Robotic Surgery Demonstration” showcasing the robot carrying out the tasks.

Read the original article on: Revista Planeta

Read more: New gel developed by scientist regenerates tooth enamel

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Researchers from the School of Pharmacy and the Department of Chemical and Environmental Engineering at the University of Nottingham in the UK, working with an international team, have developed a bioinspired material that could regenerate tooth enamel, the protective outer layer that shields teeth from damage and decay.

Fluoride-Free Protein-Based Gel That Mimics Natural Enamel Formation

The newly developed gel is applied much like a traditional fluoride treatment but contains no fluoride. Instead, it relies on proteins that replicate the function of those responsible for guiding enamel formation during childhood.

Once applied, the gel creates a thin, durable coating that fills tiny cracks and defects, providing a scaffold for new mineral growth. It draws calcium and phosphate ions from saliva and promotes the controlled formation of enamel crystals through a process called epitaxial mineralization. As these minerals integrate with the existing tooth structure, they rebuild the enamel, restoring its original architecture and protective properties.

Enamel-Like Coating for Dentin Reduces Sensitivity and Improves Bonding

The gel can also be applied to exposed dentin, the inner layer of the tooth, where it forms an enamel-like coating. This may help reduce dentin hypersensitivity while improving the bonding of dental restorations.

Enamel erosion is one of the primary causes of tooth decay, which affects nearly half of the world’s population. Untreated tooth decay can lead to tooth loss and is linked to conditions like diabetes and cardiovascular disease.

Unlike many other tissues, tooth enamel cannot regenerate once it is lost. Existing treatments, including fluoride varnishes, mainly help manage symptoms rather than rebuild the damaged tissue. “Dental enamel has a unique protective structure,” said lead author Abshar Hasan. “Our material promotes crystal growth in an organized manner, restoring the architecture of healthy natural enamel,” he added.

Read the original article on: Dental Press

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If you ate a wild mushroom and suddenly began seeing tiny people, you might naturally assume it contained a known psychedelic compound.

However, that doesn’t seem to be the case with Lanmaoa asiatica—locally called “jian shou qing”—a species sold in markets in Yunnan in southwestern China.

Undercooked Consumption and Lilliputian Hallucinations

When eaten insufficiently cooked, this mushroom can trigger vivid hallucinations of miniature human figures, reminiscent of Gulliver’s encounter with Lilliput.

Hospitals in Yunnan reportedly treat dozens of such poisoning cases every year.

To investigate the cause, mycologists Colin Domnauer and Bryn Dentinger at the University of Utah analyzed the genomes of 53 mushroom samples spanning the broader Lanmaoa genus.

Despite the hallucinations that have been reported, the researchers did not find any genes closely related to those responsible for producing psilocybin or ibotenic acid—two well-known mushroom-derived hallucinogens whose biosynthetic pathways were specifically analyzed in the study.

Genomic Analysis Finds No Known Psychoactive Gene Matches in L. asiatica

The researchers wrote that genome analysis of L. asiatica showed no close matches to genes known to produce psychoactive compounds in mushrooms.

They suggested this finding supports the idea that an as-yet unidentified compound may be responsible for the species’ unusual hallucinogenic effects.

Experiencing visions of tiny people even has a formal term: Lilliputian hallucinations.

Lilliputian hallucinations aren’t the only consequence of eating undercooked L. asiatica; people most frequently report those, though they also experience other effects. Other effects have included dizziness, auditory hallucinations, and physical illness.

Regardless of the biochemical pathways behind these effects on the brain, scientists have not yet identified the compound responsible—or any of its active counterparts.

Distinct Symptom Profile Compared to Known Mushroom Hallucinogens

The researchers note that their findings align with clinical and observational reports showing that L. asiatica intoxication produces psychological and physical symptoms that differ significantly from those caused by psilocybin or ibotenic acid.

They also suggest that researchers could use the phylogenetic and genomic data presented in the study to support future drug discovery efforts and to improve understanding of how bioactive secondary compounds have evolved and are distributed within the Lanmaoa genus.

The genome sequencing results from this study do more than simply exclude psilocybin or ibotenic acid as explanations.

By identifying 1,515 shared genes across the sampled specimens, the researchers were able to better clarify what genetic traits characterize a mushroom as belonging to the genus Lanmaoa.

The genus now includes 17 recognized species, among them four that were previously unknown. In this study, the researchers formally named two of those new species: Lanmaoa fallax and Lanmaoa carbonilivor.

“The researchers now map the Lanmaoa family and its evolutionary relationships in greater detail and suggest that they may need to reclassify some previously identified specimens.”

One of the challenges in classifying them has been their very similar physical appearances, even when their genetic makeup differs.

Public Health Risks from Misidentification of Lanmaoa Species

“They also point out that widespread trade in Lanmaoa species as edible mushrooms creates a well-known and serious public health risk, because consumers and medical professionals can easily confuse them with toxic lookalikes.”

This, they argue, underscores the importance of improving taxonomic knowledge of the group.

The fact that these mushrooms can induce such a specific type of psychedelic hallucination is intriguing.

Researchers now say the key question is what triggers this effect, suggesting that answering it could reveal deeper insights into how the human mind works.

Mushrooms’ Expanding Scientific Importance

And for those keeping up with mycology research, mushrooms have already proven far more complex than they might seem—showing connections to disease prevention, remarkable evolutionary adaptations, and important roles in Earth’s ecosystems.

Researchers in the Lanmaoa genus still have much to discover.

The researchers write that their study provides a comprehensive genomic basis for Lanmaoa classification, paving the way for future work to more thoroughly investigate the genus’s evolutionary history and its production of secondary chemical compounds.

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OpenAI previewed a new hardware product, though it is not the much-anticipated personal AI device being developed with Jony Ive. Instead, this one targets Codex AI users.

Teaser Reveals Keyboard-Style Device in Collaboration with Work Louder

The OpenAI Developers’ X account shared a teaser on Monday showing a keyboard-style device cycling through rainbow colors, followed by the OpenAI logo and the Work Louder logo, a Canadian-Italian company known for keyboards and accessories.

The caption read, “Your favorite Codex shortcuts are getting an upgrade,” and the post also referenced July 15.

While the full specifications have not yet been revealed, an OpenAI spokesperson told Business Insider that the device will be named “Codex Micro.” They added that it is “designed to supercharge people’s Codex usage.”

Codex Device Mirrors Work Louder’s Creator Micro 2 Design with Branding Changes

Work Louder currently offers a similar product called the Creator Micro 2, a shortcut keyboard that includes a joystick. The two devices have a similar layout, but Codex swaps Work Louder’s branding for OpenAI’s. For instance, the Creator Micro 2 shows “Work Louder © 2025,” while the Codex version reads “OpenAI 2026.”

On the right side, the teaser displays the words “You can just…” before fading into darkness, likely referencing the Codex marketing phrase “You can just build things.”

Dominik Kundel, an OpenAI employee focused on developer experience, also wrote on X that attendees at the AI Engineer World’s Fair might get a chance to “catch a peek” of the device. Some attendees have since shared photos of both the device and its packaging online.

Kundel wrote on LinkedIn that “hardware development moves on very different timelines,” adding that he is “excited to finally see this come to fruition.”

This isn’t the widely anticipated AI device that OpenAI is developing with iPhone designer Jony Ive. OpenAI CFO Sarah Friar has said the device is expected to launch by the end of the year.

OpenAI isn’t the first AI company to create hardware for “vibe coders.” Cursor had previously distributed standalone “tab” keys as promotional merchandise.

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Technology is reshaping a wide range of professions, and tennis coaching appears to be among its latest targets. The Acemate S10 goes beyond traditional ball machines by both launching and returning shots for a more realistic rally experience.

The S10 uses dual 4K cameras for human-like depth perception and AI tracking for precise 3D ball analysis. It moves on Mecanum wheels with rubber treads and a built-in net, allowing smooth court movement and rallies without wearables or complex setup.

Autonomous Tennis Partner for Continuous Rallies

Designed as an autonomous tennis partner, the Acemate S10 aims to replicate the experience of playing against a human opponent. It moves around the court, tracks incoming shots, catches the ball in its net, and returns it over the net, enabling continuous rallies without the need for another player.

Using dual 4K cameras and AI, the robot tracks the ball in 3D, predicts its landing point, and reacts in as little as 0.15 seconds. It then uses Mecanum wheels to move quickly, collect the ball, and return shots like a human hitting partner.

The Acemate is designed to produce more realistic rallies compared to current tennis robot systems. It adjusts shot placement, speed, spin, and trajectory to support drills like rallies, serves, smashes, and recovery. After training, the S10 provides feedback on placement, accuracy, contact point, and rally consistency. Its companion app takes this further by generating reports that outline strengths and weaknesses and point out areas for improvement.

The author also notes plans to test this advanced model soon, acknowledging that their once-strong tennis skills likely need significant improvement. Still, they look forward to reviewing the performance data. It can also sync with an Apple Watch to show session summaries like duration, shots, and calories burned.

Customizable Training Settings and Multi-Surface Performance

The Acemate app allows users to fine-tune settings such as serve speed, spin, landing zones, and rally difficulty. The robot can deliver flat shots, topspin, slice, and lobs up to 8 meters (26 feet). It works on hard, clay, and grass courts and offers about two hours of battery life per charge.

The S10 weighs 17.8 kg (39.2 lb), and with its net folded, it appears relatively easy to move from a car to the court. However, portability and storage may still be a concern, as tennis training machines of this size can sometimes be cumbersome to handle in practice.

Acemate is offering the S10 for $1,849 (down from $2,499) in a limited-time post–Prime Day deal. Customers can access the discounted price for an additional week by using the code ACEMATE001 at checkout.

The S10 was previously featured during its crowdfunding stage and has since progressed into full production, earning multiple awards along the way.

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Read more:Quantum Entanglement Detected in Visible Crystal

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Quantum entanglement ranks among the most fascinating and mysterious phenomena in physics. Traditionally, scientists believed this quantum behavior appeared only in tiny, isolated systems such as atoms, electrons, or photons. Now, new research shows that entanglement can also exist in objects large enough to see and hold by hand.

Researchers at TU Wien (Vienna University of Technology) have identified strong evidence of quantum entanglement within a centimeter-sized crystal made from a class of materials known as strange metals. Unlike most quantum experiments that require extremely cold temperatures and highly controlled laboratory environments, this crystal maintained its quantum characteristics under ordinary conditions, challenging long-standing assumptions about where quantum effects can occur.

A New Perspective on Strange Metals

Strange metals have puzzled physicists for decades because they do not behave like conventional metals. Their electrical properties cannot be fully explained by existing theories, making them an important subject in condensed matter physics.

The research team believes that quantum entanglement may play a key role in explaining the unusual behavior of these materials. Understanding this connection could help scientists develop more accurate models of how electrons interact inside complex materials.

Measuring the Invisible

To investigate the crystal, the researchers employed a technique based on quantum Fisher information, a mathematical tool that allows scientists to estimate the degree of quantum entanglement within a physical system.

Neutron-scattering experiments at the Institut Laue-Langevin in France independently confirmed the findings. These complementary measurements strengthened the evidence, showing that large-scale entanglement exists inside the crystal rather than remaining confined to microscopic particles.

Bridging the Quantum and Classical Worlds

The discovery suggests that the boundary separating quantum mechanics from the everyday world may not be as clear as previously believed. Instead of disappearing in larger objects, quantum effects can survive under certain conditions, even in materials visible to the naked eye.

This challenges a fundamental assumption in physics and opens new opportunities for studying quantum behavior in real-world materials.

Potential Impact on Future Technologies

Although the research is primarily fundamental, it may eventually contribute to advances in several emerging technologies. A deeper understanding of entanglement in complex materials could improve the development of:

• More efficient quantum computers
• Advanced quantum sensors
• Next-generation communication systems
• High-temperature superconductors capable of transmitting electricity with minimal energy loss

Scientists caution that practical applications remain a long-term goal, but discoveries like this provide valuable insights into the quantum properties of matter.

Looking Ahead

The ability to observe quantum entanglement in a macroscopic crystal represents an important milestone in modern physics. As researchers continue exploring strange metals and other complex materials, they hope to uncover new principles governing the quantum world and translate those discoveries into technologies that could transform computing, electronics, and materials science.

Source: Researchers at TU Wien, with supporting neutron-scattering experiments conducted at the Institut Laue-Langevin. The findings were reported by the Times of India based on the published research.

Original news article: the Times of India

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Most prosthetic hands still face a fundamental challenge: every amputee has unique needs, yet most devices rely on a one-size-fits-all design. As a result, achieving natural, intuitive control remains difficult, often requiring users to spend significant time learning and adapting.

Even with today’s sophisticated prosthetic technology, many users still struggle to control their devices. Muscle signals can fluctuate because of sweat, changes in skin condition, or routine movements, making it difficult for the prosthesis to consistently interpret the wearer’s intentions. This disconnect can be frustrating and may even cause some users to stop using their prosthetic hand altogether.

Although researchers have improved the way prosthetic systems decode muscle activity, a key obstacle persists: these signals remain inconsistent and difficult to convert into smooth, intuitive movements.

To overcome this limitation, Erik Engeberg, Ph.D., is developing a different approach that prioritizes personalization over one-size-fits-all designs. A professor in Florida Atlantic University’s College of Engineering and Computer Science, Engeberg holds appointments in the Departments of Ocean and Mechanical Engineering and Biomedical Engineering. He also belongs to the FAU Stiles-Nicholson Brain Institute and the FAU Center for Complex Systems, where he develops prosthetic systems that adapt to each user’s unique characteristics.

A Tailored Sleeve Interprets User Intent

The process starts with a 3D scan of the user’s residual limb, which engineers use to design a custom 3D-printed wearable sleeve fitted with soft, flexible magnetic sensors. These sensors rest comfortably on the skin and detect subtle shifts in muscle shape and pressure when the user tries to move their hand or wrist, enabling real-time interpretation of intent.

Engineers fully personalize the system: they adjust the sensor layout to include either 18 or 24 modules depending on limb size and anatomy. Engineers also pair it with an AI model trained specifically for each individual, which learns that person’s unique muscle activity patterns instead of relying on generic datasets.

Consistent Signals During Repeated Use

In tests involving 10 participants, including three upper-limb amputees, the system was able to recognize 19 hand and wrist gestures in real time and convert user intent into control of a dexterous robotic hand. The findings, published in IEEE Transactions on Neural Systems and Rehabilitation Engineering, indicate that performance remained reliable and consistent across repeated trials.

To evaluate long-term durability, the researchers subjected the system to more than 7,500 robotic force cycles over several hours while carefully monitoring sensor responses. The results demonstrated a strong and stable correlation between applied force and output, with accurate pressure detection and no noticeable decline in performance.

After thousands of repeated cycles, the signals remained clear and stable, showing strong separation between noise and meaningful data and only slight variation over time. Overall, the sensors exhibited no significant drift or degradation, preserving the accuracy, repeatability, and responsiveness required for practical prosthetic use.

Senior author Engeberg said, “We cannot standardize prosthetic control because every person has a unique movement pattern shaped by their anatomy, injury background, and the way their remaining muscles operate.” “For these systems to be effective in everyday use, they must be tailored to the individual. By integrating 3D-printed wearable sensors with personalized AI models, we are getting closer to prosthetics that respond naturally and instantly to a user’s intent, instead of requiring users to adapt to the device’s constraints.”

There Is No Single Sensor Configuration that Fits Everyone

The results also revealed that there is no universal sensor configuration that works best for everyone. Some participants reached higher accuracy with fewer sensors, while others needed a greater number, with the optimal setup depending on individual anatomy, injury history, and the condition of the remaining muscles. In several cases, participants achieved over 90% accuracy across multiple gestures only when engineers customized the sensor arrangement to match each user’s residual muscle patterns.

“Our findings show that prosthetic performance strongly depends on how well sensor placement and density are adapted to the individual,” said Engeberg. “This points toward a future where prosthetists can adjust sensor configurations much like a medical prescription, optimizing both comfort and functionality for each user.”

The study also generated a shared dataset from both amputee and non-amputee participants, offering a useful resource for further research in the field.

The Extent of the Unmet Need

“This research addresses a very practical goal: directly improving people’s quality of life,” said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. “When engineering advances are aligned with real-world needs—especially for individuals who rely on prosthetic devices for independence—the impact extends well beyond the laboratory. It is about restoring function, rebuilding confidence, and enabling people to interact with the world more naturally.”

In the United States, about 2.1 million people are living with limb loss, with roughly 185,000 amputations performed each year. Worldwide, more than 50 million people are affected, and this number is expected to rise due to diabetes, vascular disease, trauma, and conflict-related injuries. Restoring upper-limb function is particularly difficult because of the intricate complexity of natural hand and finger movements.

The study’s co-author is Wen-Yu “Marty” Cheng, a graduate student and Ph.D. candidate in Florida Atlantic University’s College of Engineering and Computer Science.

Read the original article on: Tech Xplore

Read more: A new smart bicycle can detect when a rider intends to turn and when they might be losing balance

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Two-wheeled vehicles that use conventional stability-control systems need to lean to steer, which makes it challenging for rider-assistance technologies to tell whether a rider is deliberately turning or beginning to lose balance and risk a fall. To overcome this issue, researchers at Shibaura Institute of Technology (SIT) in Japan have created a rider-intent-aware control system that can differentiate between intentional cornering and instability, activating stabilization support only when necessary.

The study was led by Associate Professor Hiroaki Kuwahara from the Department of Machinery and Control Systems at Shibaura Institute of Technology (SIT), Japan, alongside second-year master’s student Shota Tsukase from the Graduate School of Systems Engineering and Science at the same institution. The team aimed to address a major drawback in conventional stability-control systems, which typically react only to vehicle motion and can sometimes interfere with a rider’s intended actions. They published their research on June 19, 2026, in IEEE/ASME Transactions on Mechatronics.

“We thought haptic technology could go beyond simple force feedback and actually help interpret rider intent,” Kuwahara said. “By studying how the rider and vehicle interact, we set out to design a mobility system that intervenes only when necessary.”

A Bicycle Designed to Interpret Rider Intention

To accomplish this, the researchers built a steer-by-wire bicycle in which no mechanical connection links the handlebars to the front wheel, unlike a traditional bike. Instead, the connection is electronic, enabling the system to monitor steering inputs and rider–vehicle interactions while still preserving natural steering feel through haptic feedback, which provides force-based sensations that reflect how the bicycle is responding.

The researchers combined the platform with a machine-learning-based system that classifies rider intent. This system relies on a long short-term memory (LSTM) neural network, a machine-learning model designed to detect patterns in time-series data. Prior to training, the researchers applied K-means clustering, an unsupervised learning method, to group riding data into three categories: straight-line riding, cornering, and instability.

Using data gathered from riding experiments, the LSTM model evaluated variables including steering angle, vehicle speed, roll angle, lateral acceleration, and reaction torque. These inputs allowed the system to understand both the bicycle’s dynamic state and the interaction between rider and machine. By integrating these datasets, the model was able to identify riding conditions in real time.

Distinguishing Intentional Turns from Instability Issues

The results showed that the system was able to reliably classify different riding situations and, most importantly, separate deliberate cornering from unstable motion—even though both involve similar leaning behavior. This ability is essential, since intervening during a normal turn can interfere with the rider’s control, while intervening at the right moment during instability can help prevent a crash.

“Two-wheeled vehicles naturally lean when turning, so it is important to tell the difference between intentional maneuvers and conditions that could lead to a fall,” Kuwahara explains. Our system analyzes both vehicle dynamics and rider interaction to distinguish between them and delivers stabilization support only when it is truly needed.

Once it identified the riding state, the control system reacted based on the situation. In cases of deliberate steering or cornering, the stabilization function stayed off, allowing the rider to fully maintain control. However, when it detected signs of instability, the system automatically engaged stabilization assistance to help the rider regain balance. Experimental results indicated that the method could accurately detect riding conditions and deliver support at the right time without affecting natural bike handling.

Assistance Without Overriding Control

The researchers suggest that developers could ultimately adapt the technology for use in electric bicycles, electric motorcycles, shared bike systems, and delivery vehicles. It may also offer particular benefits for older adults and novice riders by providing added stability while preserving a natural riding feel.

“Our aim is to go beyond traditional automation and develop human-cooperative control,” Kuwahara says. “Instead of replacing the rider, the system interprets their intentions and intervenes only when instability is detected. We believe this approach can help create safer and more user-friendly next-generation mobility.”

In future work, the team aims to extend the system so it can identify a broader variety of riding scenarios and environmental factors, such as different road conditions. Ultimately, they hope to create intelligent rider-assistance systems that cooperate with the rider, improving safety while maintaining full control and maneuverability.

Read the original article on: Tech Xpore

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LONDON (AP) — WhatsApp is introducing a new privacy-focused feature that will let users connect using unique usernames instead of sharing their phone numbers.

The messaging platform announced on Monday that users can now begin reserving unique usernames ahead of the feature’s official rollout later this year. Once available, people will be able to communicate through these usernames rather than exposing their phone numbers.

With more than 3 billion users worldwide, WhatsApp has traditionally required a phone number for others to initiate contact. The new option aims to give users greater control over their privacy.

Rollout Planned in the Coming Months, Exact Launch Date Unclear

Owned by Meta Platforms, WhatsApp said in a blog post that the username feature will roll out in the coming months, though it has not given an exact release date.

“We have designed this as a core privacy feature,” said Alice Newton-Rex, WhatsApp’s vice president of product, during a media briefing.

To further protect privacy, WhatsApp confirmed there will be no searchable username directory, and the app will not suggest usernames while users are typing.

Users will need to know a person’s exact username before they can start a conversation with them, Newton-Rex explained.

At present, WhatsApp’s privacy tools mainly allow users to block specific contacts or mute calls from unknown numbers. Although the app lets people set a profile name, it is only visible in group chats to participants who have not already saved that user’s phone number in their contacts.

Regional Usage Patterns of WhatsApp and SMS

Although traditional text messaging remains more popular than WhatsApp in the United States, the app has a strong user base across Europe, Asia, and many other parts of the world.

As unique online usernames are often in high demand, many users are expected to rush to secure their preferred handles.

“I think a lot of people will want to claim their usernames, which is why we decided to open reservations early,” Newton-Rex said.

Businesses, organizations, and content creators with existing accounts on Meta’s platforms, including Instagram and Facebook, will also have the opportunity to reserve matching usernames on WhatsApp.

Usernames must contain between three and 35 characters. To reduce the risk of impersonation, WhatsApp said it will reserve usernames associated with well-known individuals and organizations, such as celebrities, public figures, and government agencies.

Read the original article on: apnews

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