Lifeboat Foundation

It could improve limits on information transfer speed but is made of a super expensive ingredient that might make it financially infeasible.

Researchers at Columbia University in the US have developed the fastest and most efficient superconductor that works at room temperature, a press release said. The superconductor is made of superatomic material only known by its chemical formula, Re6Se8Cl2.

In a short span of time, silicon has become an integral part of most modern-day equipment ranging from cell phones to cars, computers to smart homes. However, scientists have found that silicon will soon reach its limits. This is because of the atomic structure of the semiconductor.

In America, roughly 40 million Americans have diabetes and about 95% of them have type 2 diabetes. Type 2 diabetes occurs when the body cannot correctly process sugar and fuel cells. More specifically, the body does not produce enough insulin to break down sugar into glucose for the cells to use. In this case, treatment includes insulin shots or a pump in addition to a strict diet excluding sweets or high fat meals. Treatment limitations disrupt patient quality of life. Some researchers have been working on better detection for diabetic retinopathy with artificial intelligence (AI), but research is limited on how to better detect diabetes itself. Thus, many researchers are working to detect diabetes early on and discover better treatments.

Klick labs, located in multiple cities across the world, is trying to detect type 2 diabetes by having a patient speak into a microphone for 10 seconds. Klick labs believes this technology can better detect diabetes and help patients get treatment earlier. The study was published in Mayo Clinic Proceedings: Digital Health, which details how patients spoke for 10 seconds and combined with health data, including age, sex, height, and weight, created an AI model that discerns whether a person has type 2 diabetes or not. After further tests, scientists determined it has 89% and 86% accuracy for women and men, respectively.

In the study, Klick Labs collected voice recordings of 267 people, either non-diabetic or diabetic. The participants were asked to record a phrase into their smartphones six times a day for a total of 2-weeks. Over 18,000 recordings were taken and analyzed to distinguish 14 acoustic features that helped distinguish non-diabetic to type 2 diabetic individuals. The research highlights specific vocal variations in pitch and intensity that could lead to how the medical community screens for early-onset diabetes. A major barrier to early detection includes time, travel, and cost, which many people do not have. Voice diagnosis can help eliminate those barriers and improve detection and treatment in diabetic patients.

Acoustic resonators, found in devices like smartphones and Wi-Fi systems, degrade over time with no easy way to monitor this degradation. Researchers from Harvard SEAS and Purdue University have now developed a method using atomic vacancies in silicon carbide to measure the stability of these resonators and even manipulate quantum states, potentially benefiting accelerometers, gyroscopes, clocks, and quantum networking.

Acoustic resonators are everywhere. In fact, there is a good chance you’re holding one in your hand right now. Most smartphones today use bulk acoustic resonators as radio frequency filters to filter out noise that could degrade a signal. These filters are also used in most Wi-Fi and GPS

GPS, or Global Positioning System, is a satellite-based navigation system that provides location and time information anywhere on or near the Earth’s surface. It consists of a network of satellites, ground control stations, and GPS receivers, which are found in a variety of devices such as smartphones, cars, and aircraft. GPS is used for a wide range of applications including navigation, mapping, tracking, and timing, and has an accuracy of about 3 meters (10 feet) in most conditions.

Acoustic resonators are everywhere. In fact, there is a good chance you’re holding one in your hand right now. Most smart phones today use bulk acoustic resonators as radio frequency filters to filter out noise that could degrade a signal. These filters are also used in most Wi-Fi and GPS systems.

Acoustic resonators are more stable than their electrical counterparts, but they can degrade over time. There is currently no easy way to actively monitor and analyze the degradation of the material quality of these widely used devices.

Now, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with researchers at the OxideMEMS Lab at Purdue University, have developed a system that uses atomic vacancies in silicon carbide to measure the stability and quality of acoustic resonators. What’s more, these vacancies could also be used for acoustically-controlled quantum information processing, providing a new way to manipulate quantum states embedded in this commonly-used material.

The trial has revealed that Google was concerned about losing its monopoly to Spotlight, an in-house search engine made by Apple.

Google and Apple compete on several fronts – operating systems, email, app stores, cloud computing, and photo apps. While Google leads in the market share of its phone operating system, Apple boasts of a line of very cool hardware tech. But they remain partners in one key area, which is also currently in the eye of the storm.

Google pays Apple for its search engine to be the default selection on iPhones. Its parent company, Alphabet, pays the iPhone maker upwards of $20 billion annually as part of the deal. In 2016, Apple reportedly was presented with a lucrative billion-dollar offer by Microsoft to replace Google with Bing in its phones. But Apple didn’t budge.

Still, experts caution that Chinese firms remain years behind in producing the lithography systems needed to make real progress.

China’s top memory chip maker, Yangtze Memory Technologies Corp (YMTC), has achieved a “surprise” breakthrough in producing the “world’s most advanced” 3D NAND memory chip, which is used in consumer devices like laptops and smartphones, a report by TechInsights.

Breaking the US sanctions barrier

Continue reading “How China’s YMTC defied US sanctions with a chip breakthrough” »

No AI announcements expected at Apple event on Monday.

Apple is reportedly spending a billion dollars a year in a major push for artificial intelligence. Over the last year, the AI boom has seen many of its tech adversaries investing millions and billions of dollars into large language models (LLMs) and conversational platforms.

Although the iPhone maker is hush about what is cooking in its AI laboratory, Interesting Engineering reported earlier that the company may be looking to revamp Siri with generative AI capabilities. Much like how OpenAI’s ChatGPT (Plus and Enterprise) can now generate content from voice commands, iPhone users could use Siri similarly.

Motorola unveiled a flexible phone with an adaptive display that you can wear like a bracelet or watch yesterday at Lenovo Tech World ‘23 in Austin. The full HD phone can be bent into multiple shapes, including one that props it up on a table or desk for viewing, or a full U-shaped bracelet that straps on to your wrist.

The phone offers a 6.9 display when flat and a 4.6 display when curved to sit on a flat surface like a bedside clock or a standalone viewing screen. The flexible screen appears to be paired with a fabric backing, unusual in a smartphone. The phone does not just friction-fit to your wrist, which would be likely to slip off with movement, but magnetically clings to an… More.

The full HD flexible phone can be bent into multiple shapes, including one that props it up for viewing, or a full U-shaped bracelet that straps on to your wrist.

Continue reading “Motorola Teases Flexible Phone You Wear Like A Bracelet” »

As the utility of AI systems has grown dramatically, so has their energy demand. Training new systems is extremely energy intensive, as it generally requires massive data sets and lots of processor time. Executing a trained system tends to be much less involved—smartphones can easily manage it in some cases. But, because you execute them so many times, that energy use also tends to add up.

Fortunately, there are lots of ideas on how to bring the latter energy use back down. IBM and Intel have experimented with processors designed to mimic the behavior of actual neurons. IBM has also tested executing neural network calculations in phase change memory to avoid making repeated trips to RAM.

Now, IBM is back with yet another approach, one that’s a bit of “none of the above.” The company’s new NorthPole processor has taken some of the ideas behind all of these approaches and merged them with a very stripped-down approach to running calculations to create a highly power-efficient chip that can efficiently execute inference-based neural networks. For things like image classification or audio transcription, the chip can be up to 35 times more efficient than relying on a GPU.