Lifeboat Foundation

A smartphone shutting down on a sweltering day is an all-too-common annoyance that may accompany a trip to the beach on a sunny afternoon. Electronic memory within these devices isn’t built to handle extreme heat.

As temperatures climb, the electrons that store data become unstable and begin to escape, leading to device failure and loss of information. But what if gadgets could withstand not just a hot summer day but the searing conditions of a jet engine or the harsh surface of Venus?

In a paper published in the journal Nature Electronics, Deep Jariwala and Roy Olsson of the University of Pennsylvania and their teams at the School of Engineering and Applied Science demonstrated memory technology capable of enduring temperatures as high as 600° Celsius—more than twice the tolerance of any commercial drives on the market—and these characteristics were maintained for more than 60 hours, indicating exceptional stability and reliability.

Washington University in St. Louis scientists have developed a novel material that supercharges innovation in electrostatic energy storage. The material is built from artificial heterostructures made of freestanding 2D and 3D membranes that have an energy density up to 19 times higher than commercially available capacitors.

Electrostatic capacitors play a crucial role in modern electronics. They enable ultrafast charging and discharging, providing energy storage and power for devices ranging from smartphones, laptops, and routers to medical devices, automotive electronics and industrial equipment. However, the ferroelectric materials used in capacitors have significant energy loss due to their material properties, making it difficult to provide high energy storage capability.

The new technology, which was created at the University of Turku and developed by the company CardioSignal, uses a smartphone to analyse heart movement and detect heart failure. The study involved five organisations from Finland and the United States.

Heart failure is a condition affecting tens of millions of people worldwide, in which the heart is unable to perform its normal function of pumping blood to the body.

It is a serious condition that develops as a result of a number of cardiovascular diseases and its symptoms may require repeated hospitalisation.

Google might have invented the transformer models that led to the glut of generative AI we see today, but it wasn’t the first to cash in. The search giant threw its AI work into overdrive in the wake of ChatGPT’s appearance in Microsoft products, adding the Gemini AI to every product it can. A new report claims Gemini is about to come to the Google app on Android, and this may signal the beginning of the end for Assistant.

Gemini is the current brand for all of Google’s commercial AI models—whether it sticks to that is hard to say, but the Bard branding is in the rearview mirror. Gemini has been available on the web, and Google released a mobile app earlier this year. Installing that app on Android prompts you to replace Assistant despite Gemini’s comparative lack of features. Google is just that committed to getting everyone using its AI. Even if you don’t install that app, Google aims to get Gemini in front of your eyes by cramming it into the Google app.

While there’s no official announcement yet, a video of the latest app update is already circulating (see below). The new version has a toggle at the top to switch between search and Gemini. If you’ve seen the iOS Google app recently, it’s essentially the same. There is another wrinkle for Android users, though. That toggle will also switch your phone to use Assistant everywhere, reports 9to5Google. The Google app will now encourage people to switch from Assistant to Gemini, and unlike the new Gemini app, it’s already installed on virtually every Android phone.

The tech giant is building lightweight AI that’s more power-efficient and can run on PCs and phones.

Phones remain undefeated.

The LASSIE project is preparing for a time when people and robots explore space together.

Learn more about how the #space economy can improve life on #Earth from our new insight report, ‘Space: The $1.8 Trillion Opportunity for Global Economic Growth’:

Space is approaching a new frontier. The space economy is expected to be worth $1.8 trillion by 2035 as satellite and rocket-enabled technologies become increasingly prevalent, according to a new report.

Continue reading “Space is booming. Here’s how to embrace the $1.8 trillion opportunity” »

The world’s first fully AI-generated movie has been announced with the trailer for Next Stop Paris predictably containing one too many fingers.

TCLtv+ Studios is a brand new production team and its first release will be a short AI-generated romcom featuring professional voice actors and an original script but the imagery will be generated with AI tools.

Continue reading “The Trailer for the World’s First Fully AI-Generated Film is Here” »

CWI senior researcher Sander Bohté started working on neuromorphic computing already in 1998 as a PhD-student, when the subject was barely on the map. In recent years, Bohté and his CWI-colleagues have realized a number of algorithmic breakthroughs in spiking neural networks (SNNs) that make neuromorphic computing finally practical: in theory many AI-applications can become a factor of a hundred to a thousand more energy-efficient. This means that it will be possible to put much more AI into chips, allowing applications to run on a smartwatch or a smartphone. Examples are speech recognition, gesture recognition and the classification of electrocardiograms (ECG).

“I am really grateful that CWI, and former group leader Han La Poutré in particular, gave me the opportunity to follow my interest, even though at the end of the 1990s neural networks and neuromorphic computing were quite unpopular”, says Bohté. “It was high-risk work for the long haul that is now bearing fruit.”

Spiking neural networks (SNNs) more closely resemble the biology of the brain. They process pulses instead of the continuous signals in classical neural networks. Unfortunately, that also makes them mathematically much more difficult to handle. For many years SNNs were therefore very limited in the number of neurons they could handle. But thanks to clever algorithmic solutions Bohté and his colleagues have managed to scale up the number of trainable spiking neurons first to thousands in 2021, and then to tens of millions in 2023.