Lifeboat Foundation

Open AI, the research company founded by Elon Musk, has just discovered that their artificial neural network CLIP shows behavior strikingly similar to a human brain. This find has scientists hopeful for the future of AI networks’ ability to identify images in a symbolic, conceptual and literal capacity.

While the human brain processes visual imagery by correlating a series of abstract concepts to an overarching theme, the first biological neuron recorded to operate in a similar fashion was the “Halle Berry” neuron. This neuron proved capable of recognizing photographs and sketches of the actress and connecting those images with the name “Halle Berry.”

Now, OpenAI’s multimodal vision system continues to outperform existing systems, namely with traits such as the “Spider-Man” neuron, an artificial neuron which can identify not only the image of the text “spider” but also the comic book character in both illustrated and live action form. This ability to recognize a single concept represented in various contexts demonstrates CLIP’s abstraction capabilities. Similar to a human brain, the capacity for abstraction allows a vision system to tie a series of images and text to a central theme.

For decades, there has been a trend in microelectronics towards ever smaller and more compact transistors. 2D materials such as graphene are seen as a beacon of hope here: they are the thinnest material layers that can possibly exist, consisting of only one or a few atomic layers. Nevertheless, they can conduct electrical currents—conventional silicon technology, on the other hand, no longer works properly if the layers become too thin.

However, such materials are not used in a vacuum; they have to be combined with suitable insulators—in order to seal them off from unwanted environmental influences, and also in order to control the flow of current via the so-called field effect. Until now, hexagonal boron nitride (hBN) has frequently been used for this purpose as it forms an excellent environment for 2D materials. However, studies conducted by TU Wien, in cooperation with ETH Zurich, the Russian Ioffe Institute and researchers from Saudi Arabia and Japan, now show that, contrary to previous assumptions, thin hBN layers are not suitable as insulators for future miniaturized field-effect transistors, as exorbitant leakage currents occur. So if 2D materials are really to revolutionize the semiconductor industry, one has to start looking for other insulator materials. The study has now been published in the scientific journal Nature Electronics.

Scientists have taken a major step forward in harnessing machine learning to accelerate the design for better batteries: Instead of using it just to speed up scientific analysis by looking for patterns in data, as researchers generally do, they combined it with knowledge gained from experiments and equations guided by physics to discover and explain a process that shortens the lifetimes of fast-charging lithium-ion batteries.

It was the first time this approach, known as “scientific machine learning,” has been applied to battery cycling, said Will Chueh, an associate professor at Stanford University and investigator with the Department of Energy’s SLAC National Accelerator Laboratory who led the study. He said the results overturn long-held assumptions about how lithium-ion batteries charge and discharge and give researchers a new set of rules for engineering longer-lasting batteries.

The research, reported today in Nature Materials, is the latest result from a collaboration between Stanford, SLAC, the Massachusetts Institute of Technology and Toyota Research Institute (TRI). The goal is to bring together foundational research and industry know-how to develop a long-lived electric vehicle battery that can be charged in 10 minutes.

North Carolina State University engineers continue to improve the efficiency of a flexible device worn on the wrist that harvests heat energy from the human body to monitor health.

In a paper published in npj Flexible Electronics, the NC State researchers report significant enhancements in preventing heat leakage in the flexible body heat harvester they first reported in 2017 and updated in 2020. The harvesters use heat energy from the human body to power wearable technologies —think of smart watches that measure your heart rate, blood oxygen, glucose and other health parameters—that never need to have their batteries recharged. The technology relies on the same principles governing rigid thermoelectric harvesters that convert heat to electrical energy.

Flexible harvesters that conform to the human body are highly desired for use with wearable technologies. Mehmet Ozturk, an NC State professor of electrical and computer engineering and the corresponding author of the paper, mentioned superior skin contact with flexible devices, as well as the ergonomic and comfort considerations to the device wearer, as the core reasons behind building flexible thermoelectric generators, or TEGs.

From flat battery to full charge in just five minutes—an Israeli start-up has developed technology it says could eliminate the “range anxiety” associated with electric cars.

Ultra-fast recharge specialists StoreDot have developed a first-generation lithium-ion battery that can rival the filling time of a standard car at the pump.

“We are changing the entire experience of the driver, the problem of ‘range anxiety’… that you might get stuck on the highway without energy,” StoreDot founder Doron Myersdorf said.

Non-rigid point set registration is the process of finding a spatial transformation that aligns two shapes represented as a set of data points. It has extensive applications in areas such as autonomous driving, medical imaging, and robotic manipulation. Now, a method has been developed to speed up this procedure.

In a study published in IEEE Transactions on Pattern Analysis and Machine Intelligence, a researcher from Kanazawa University has demonstrated a technique that reduces the computing time for non-rigid point set registration relative to other approaches.

Previous methods to accelerate this process have been computationally efficient only for shapes described by small point sets (containing fewer than 100000 points). Consequently, the use of such approaches in applications has been limited. This latest research aimed to address this drawback.

In recent years, videogame developers and computer scientists have been trying to devise techniques that can make gaming experiences increasingly immersive, engaging and realistic. These include methods to automatically create videogame characters inspired by real people.

Most existing methods to create and customize videogame characters require players to adjust the features of their character’s face manually, in order to recreate their own face or the faces of other people. More recently, some developers have tried to develop methods that can automatically customize a character’s face by analyzing images of real people’s faces. However, these methods are not always effective and do not always reproduce the faces they analyze in realistic ways.

Researchers at Netease Fuxi AI Lab and University of Michigan have recently created MeInGame, a deep learning technique that can automatically generate character faces by analyzing a single portrait of a person’s face. This technique, presented in a paper pre-published on arXiv, can be easily integrated into most existing 3D videogames.

Algorithms are meaningless without good data. The public can exploit that to demand change.

Hence, while the fast particles are abundant in molecular systems, they are practically lacking in active matter. These fast particle can overcome the attraction forces of the molecular surrounding and give rise to the gaseous phase; the lack of fast particles in active matter results in the lack of the gas, coexisting with the liquid phase. If the particle density is still larger and the repulsive forces are strong, the active matter falls into a solid phase, Fig. 1 d, where the particles mobility is suppressed.

In some range of parameters a novel swirlonic phase is formed, Fig. 1e. It is comprised of swirlons —“super-particles” with many astonishing properties which we address below. Individual active particles in swirlons perform a swirling motion around their common center. As we demonstrate in what follows, the swirlons are formed when local fluctuating force exceeds the critical force, which characterizes the ability of an active particle to move against an applied load.

Finally, for large density and very strong attractive forces a collapsed state is observed, Fig. 1 f. In the collapsed state active particle also move around the common center, however the character of the motion is rather irregular. In the present study we will focus on the gaseous, liquid and especially on the swirlonic phase.