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These Seven AI Rings Translate Sign Language in Real Time
The wireless rings read 100 common signs from two sign languages and “autocomplete” sentences.
At the turn of the 20th century, William Hoy transformed Major League Baseball. The most prominent deaf player in history, he taught his team American Sign Language (ASL) to communicate on the field while keeping opponents in the dark. His silent speech, a legacy well over a century old now, also inspired umpires to make calls using hand gestures.
ASL is one of some 300 sign languages used today by roughly 70 million deaf people worldwide. But only a sliver of society understands signs. Everyday tasks, like ordering at a restaurant or meeting people at social events can be difficult. To bridge the gap, a South Korean team developed smart rings to translate finger motions into text.
Older devices usually require a jungle of cables to connect sensors. But the new rings are wireless, freeing people to use natural hand motions. The rings also stretch to fit different finger sizes. These upgrades make them more comfortable and reliable, wrote the team. Each ring is powered by a replaceable 12-hour battery.
Fluent signers can communicate at speeds of around 100 to 150 signs per minute, similar to spoken conversation. Devices need to keep up with that speed to avoid uncomfortable pauses. So the team developed AI-based “autocomplete” for the system that, like typing, guesses the next word based on what’s already been signed to generate phrases and sentences on the fly.
Trained on 100 common words in ASL and International Sign Language (ISL), the wearable was over 88 percent accurate in tests, even for users with no experience.
The rings are a step toward “seamless interaction between signers and non-signers,” wrote the team.
Let’s ChatThere are a variety of devices that translate sign language into text or speech, some already on the market.
One design is a bit like virtual reality gaming. It uses cameras and computer vision software to recognize hand gestures. The approach is reasonably fast and accurate in the lab, but struggles in simulated real-world scenarios, where changes in lighting or background confuse the system.
Devices worn by users are more reliable. WearSign, for example, uses sensors to capture the electrical activity of muscles during signing and translates it into text. Often, these devices need to be tailored to the user, a hurdle that limits use, as some can’t commit to the training.
Engineers have also tried embedding tracking sensors in a smart glove. The sensors send signals through cables to a shared wireless transmitter. But it’s a bit like using tools wearing a heavy winter glove. The devices limit natural movement and are uncomfortable for daily use.
They also usually come in only one size with fixed sensor placements, wrote the team. So, depending on hand size, the sensors may be out of place, reducing accuracy.
Put a Ring on ItTo overcome these problems, the team built AI rings to track the seven most dominant fingers in signing. (The right pinkie, left middle finger, and thumb didn’t make the cut.) The rings are worn right below the second knuckle to allow natural movement.
Each device is made of stretchy material to accommodate different finger sizes and looks more like a translucent Band-Aid than a typical ring. A tiny accelerometer captures movements like bending, curling, and holding still. The sensors are cheap, low-power, and already used in Apple Watches, Fitbits, and other wearables. There are also onboard chips to manage power use, wafer-thin Bluetooth transmitters, and common replaceable batteries that last nearly 12 hours.
The rings broadcast signals to a host device, which processes the data and maintains a timeline of each movement so incoming signs aren’t scrambled in translation.
To identify words, the system matches gestures to a database of 100 ASL and ISL signs. For example, closing both open palms into fists means “want.” The rings can also pick up signs in motion, like “dance” or “fly,” and those with fingers held still, like “I” and “you.” In first-time users, the system was 88 percent accurate for both ASL and ISL.
To make sure that conversations flow naturally, the team added an AI to track conversations and predict what word comes next. In tests, the system autocompleted simple phrases, like “family want beautiful animal.”
While still experimental, the rings could also translate between sign languages. Because the AI learns from gestures alone, with enough training data, it could eventually turn into a kind of Google Translate for signing.
But finger gestures fail to capture the full spectrum of sign language. Facial expressions, mouth movements, shoulder and body posture, speed, and rhythm all carry critical information, including meaning and emotion. Without this context, the system could easily miscommunicate intent. Some efforts are now returning to older video-based systems to better capture the entire signing experience, this time with sleeker hardware and far more processing power.
The team thinks the rings might be useful elsewhere too, like for use in virtual or augmented reality, touchless computer interfaces, and tracking hand movements in rehabilitation.
The post These Seven AI Rings Translate Sign Language in Real Time appeared first on SingularityHub.
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WWDC: From NeXTStep for Apple to Apple’s next step for AI
As Apple heads toward next month’s Worldwide Developer Conference (WWDC), cast your mind back almost 30 years. That’s when something happened that arguably put events in motion that led to Apple becoming the company it is today. That was when Apple co-founder Steve Jobs returned to the top job at WWDC 1997 — the first such event after Apple acquired NeXT.
The big debt to NSIt took until 2000 to fully realize what the NeXT purchase meant; that’s when the Mac OS X Public Beta was released. The operating system has seen many twists and turns since then, but the NeXTStep OS acquisition forms the basis on which the Apple software ecosystem has been built. Mac, iPhone, iPad – even Apple Watch and Vision Pro – all share elements of it.
You can see its traces each time you use an application that makes use of a macOS API that uses the NS — ‘NeXTStep’ prefix. That means you’re using NeXT when you work in SwiftUI, use Apple’s core frameworks, or write code for use across different platforms in the current ecosystem. Despite the many names for Apple’s platforms, they all have a little NeXT in common.
The need for a new, modern operating system was critical at the time to Apple. The company had fallen into the doldrums with its classic Mac OS operating system and competitors had forged ahead, at least in marketshare. Among others, Michael Dell, Time Magazine, and almost everyone else expected the company to collapse. NeXT was the salvation, Jobs the icon, and history the prize.
The next challenge nowToday’s Apple faces a fresh existential challenge, and while much of it feels media-driven, the company does need to introduce an intelligence layer around and upon its platforms, alongside the tools developers need to exploit AI within their applications.
Apple knows this, too, which is why it already offers Apple Intelligence APIs to developers to use in their apps. The company also knows they need a way to market those software ideas and get them into the hands of end users; that’s what the App Store provides.
When Apple wove NeXT into its operating system, it somehow managed to provide developers with better tools, modern, enduring foundations, frameworks and everything else needed to build an ecosystem that extends across multiple product families at a range of prices and technological advancement — from the $499 MacBook Neo to the $3,499 Vision Pro. You can build applications for any or all of these platforms using components Apple provides, along with what you bring yourself. To a great extent, all of this potential was unlocked by the acquisition of NeXTStep and its use in OS X at the turn of the century.
Telling storiesNo doubt, developers are eager to discover the extent to which Apple has managed to join the circle of AI development on its platforms. They surely hope for powerful new APIs to enhance their products with a new intelligence layer, even while Apple itself needs to offer developers the same thing to keep them loyal to its platforms.
If you squint just a little bit, the same challenges that haunted Apple in the late ‘90s echo again today. Apple wants to reinvent itself for AI without sacrificing all the benefits of its existing ecosystem. It wants to do so while making sure its developer community buys into its chosen direction. To help achieve this, it can lean heavily into its inherent hardware advantage: Not only can its products run the apps developers build, but they are also fantastic platforms to build on in the first place. All the same, it needs to convince them with a narrative that resonates, which means that while WWDC in 1997 was all about NeXTStep, WWDC 2026 is all about which steps Apple takes next.
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