Lifeboat Foundation

Recent advances in the field of artificial intelligence (AI) and computing have enabled the development of new tools for creating highly realistic media, virtual reality (VR) environments and video games. Many of these tools are now widely used by graphics designers, animated film creators and videogame developers worldwide.

One aspect of virtual and digitally created environments that can be difficult to realistically reproduce is fabrics. While there are already various computational tools for digitally designing realistic fabric-based items (e.g., scarves, blankets, pillows, clothes, etc.), creating and editing realistic renderings of these fabrics in real-time can be challenging.

Researchers at Shandong University and Nanjing University recently introduced a new lightweight artificial neural network for the real-time rendering of woven fabrics. Their proposed network, introduced in a paper published as part of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ‘24, works by encoding the patterns and parameters of fabrics as a small latent vector, which can later be interpreted by a decoder to produce realistic representations of various fabrics.

Carbon nanotube networks made with high purity and ultraclean interfaces can be used to make a tensor processing unit that contains 3,000 transistors in a systolic array architecture to improve energy efficiency in accelerating neural network tasks.

Constructing spatial maps from sensory inputs is challenging in both neuroscience and artificial intelligence. Gornet and Thomson show that as an agent navigates an environment, a self-attention neural network using predictive coding can recover the environment’s map in its latent space.

The “neural processing unit” is being pushed as the next big thing for “AI PCs” and “AI smartphones,” but they won’t eliminate the need for cloud-based AI.

Nvidia, the world’s largest company by value, is reportedly developing a new artificial intelligence (AI) chip based on its flagship product B200 for the China market.

The mass production of the new chip, which may be called B20, will commence later this year while shipments will start in the second quarter of next year, Reuters reported, citing sources familiar with the matter.

The report said Nvidia will work with Inspur, one of its distributors in mainland China. However, Inspur said it has not started any business and cooperation related to B20 as of now. It said the Reuters report is not true.

Propulsive motion in soft robotic systems requires the power amplification of stored energy. An accumulated strain energy-fracture power-amplification method is used to create light-driven soft robotic systems with a controlled launching ability.

Both the predictive power and the memory storage capability of an artificial neural network called a reservoir computer increase when time delays are added into how the network processes signals, according to a new model.

A reservoir computer—a type of artificial neural network—can use information about a system’s past to predict the system’s future. Reservoir computers are far easier to train than their more general counterpart, recurrent neural networks. However, researchers have yet to develop a way to determine the optimal reservoir-computer construction for memorizing and forecasting the behavior a given system. Recently, Seyedkamyar Tavakoli and André Longtin of the University of Ottawa, Canada, took a step toward solving that problem by demonstrating a way to enhance the memory and prediction capabilities of a reservoir computer [1]. Their demonstration could, for example, allow researchers to make a chatbot or virtual assistant, such as ChatGPT, using a reservoir computer, a possibility that so far has been largely unexplored.

For those studying time-series-forecasting methods—those that can predict the future outcomes of complex systems using historical time-stamped data—the recurrent neural network is king [2]. Recurrent neural networks contain a “hidden state” that stores information about features of the system being modeled. The information in the hidden state is updated every time the network gains new information about the system and is then fed into an algorithm that is used to predict what will happen next to the system.

People who struggle with facial recognition can find forming relationships a challenge, leading to mental health issues and social anxiety. A new study provides insights into prosopagnosia or face blindness, a condition that impairs facial recognition and affects approximately 1 in 50 people.

The researchers scanned the brains of more than 70 study participants as they watched footage from the popular TV series “Game of Thrones.” Half of the participants were familiar with the show’s famously complex lead characters and the other half had never seen the series.

When lead characters appeared on screen, MRI scans showed that in neurotypical participants who were familiar with the characters, brain activity increased in regions of the brain associated with non-visual knowledge about the characters, such as who they are and what we know about them.

Did life ever exist on Mars, and if so, how did it get there? This is the goal of NASA’s Curiosity rover, which has traversed Gale Crater on Mars since 2012. But a recent finding by the car-sized robotic explorer could help bring scientists one step closer to answering these questions as Curiosity sent back images of yellow crystals revealed to be deposits of elemental sulfur, along with an entire field of them. This finding was accidentally “un-earthed” as Curiosity drove over them during its excursions. While scientists didn’t anticipate finding elemental sulfur in this region, this finding could hold the potential to help piece together the geologic history of Gale Crater and whether life once existed there.

Recent image of elemental sulfur crystals obtained by NASA’s Curiosity rover on Mars. (Credit: NASA/JPL-Caltech/Malin Space Science Systems)

“Finding a field of stones made of pure sulfur is like finding an oasis in the desert,” said Dr. Ashwin Vasavada, who is a project scientist on Curiosity at NASA’s Jet Propulsion Laboratory. “It shouldn’t be there, so now we have to explain it. Discovering strange and unexpected things is what makes planetary exploration so exciting.”