Lifeboat Foundation

The wing dynamics of flying animal species have been the inspiration for numerous flying robotic systems. While birds and bats typically flap their wings using the force produced by their pectoral and wing muscles, the processes underlying the wing movements of many insects remain poorly understood.

Researchers at Ecole Polytechnique Fédérale de Lausanne (EPFL, Switzerland) and Konkuk University (South Korea) recently set out to explore how herbivorous insects known as rhinoceros beetles deploy and retract their wings. The insight they gathered, outlined in a paper published in Nature, was then used to develop a new flapping microrobot that can passively deploy and retract its wings, without the need for extensive actuators.

“Insects, including beetles, are theoretically believed to use thoracic muscles to actively deploy and retract their wings at the wing bases, similarly to birds and bats,” Hoang-Vu Phan, the lead author of the paper, told Tech Xplore. “However, methods of recording or monitoring muscular activity still cannot determine which muscles beetles use to deploy and retract their wings nor explain how they do so.”

Over the past couple of decades, computer scientists have developed a wide range of deep neural networks (DNNs) designed to tackle various real-world tasks. While some of these models have proved to be highly effective, some studies found that they can be unfair, meaning that their performance may vary based on the data they were trained on and even the hardware platforms they were deployed on.

For instance, some studies showed that commercially available deep learning–based tools for facial recognition were significantly better at recognizing the features of fair-skinned individuals compared to dark-skinned individuals. These observed variations in the performance of AI, in great part due to disparities in the training data available, have inspired efforts aimed at improving the fairness of existing models.

Researchers at University of Notre Dame recently set out to investigate how hardware systems can contribute to the fairness of AI. Their paper, published in Nature Electronics, identifies ways in which emerging hardware designs, such as computing-in-memory (CiM) devices, can affect the fairness of DNNs.

The trailer featured quotes from famous film critics panning Francis Ford Coppola’s previous films that appear to be made up by ChatGPT.

Organoids, $500/month, last up to 100 days, used by select universities:

Designed by FinalSpark, biocomputers are a more efficient and low-energy alternative for training AI models.

Researchers at Argonne have developed an innovative technique that creates “fingerprints” of different materials that can be read and analyzed by a neural network to yield previously inaccessible information — https://bit.ly/3LCklZw.

The goal of the AI is just to treat the scattering patterns as…

Study shows how materials change as they are stressed and relaxed.

Continue reading “Scientists develop new artificial intelligence method to create material ‘fingerprints’” »

Have you ever wonder how SNNs work and their difference from traditional neural networks? Or how SNNs play an important role in computing beyond the Moore’s Law?

What is SNN?
Spiking neural network (SNN) is a new form of neural networks with biologically realistic mechanisms designed to emulate the efficiency and effectiveness of the biological brain.

Researchers worldwide can now create highly realistic brain cortical organoids — essentially miniature artificial brains with functioning neural networks — thanks to a proprietary protocol released this month by researchers at the University of California San Diego.

The new technique, published in Nature Protocols (“Generation of ‘semi-guided’ cortical organoids with complex neural oscillations”), paves the way for scientists to perform more advanced research regarding autism, schizophrenia and other neurological disorders in which the brain’s structure is usually typical, but electrical activity is altered. That’s according to Alysson Muotri, Ph.D., corresponding author and director of the UC San Diego Sanford Stem Cell Institute (SSCI) Integrated Space Stem Cell Orbital Research Center. The SSCI is directed by Dr. Catriona Jamieson, a leading physician-scientist in cancer stem cell biology whose research explores the fundamental question of how space alters cancer progression.

The newly detailed method allows for the creation of tiny replicas of the human brain so realistic that they rival “the complexity of the fetal brain’s neural network,” according to Muotri, who is also a professor in the UC San Diego School of Medicine’s Departments of Pediatrics and Cellular and Molecular Medicine. His brain replicas have already traveled to the International Space Station (ISS), where their activity was studied under conditions of microgravity.

In a study published in Cell Reports Physical Science (“Electro-Active Polymer Hydrogels Exhibit Emergent Memory When Embodied in a Simulated Game-Environment”), a team led by Dr Yoshikatsu Hayashi demonstrated that a simple hydrogel — a type of soft, flexible material — can learn to play the simple 1970s computer game ‘Pong’. The hydrogel, interfaced with a computer simulation of the classic game via a custom-built multi-electrode array, showed improved performance over time.

Dr Hayashi, a biomedical engineer at the University of Reading’s School of Biological Sciences, said: Our research shows that even very simple materials can exhibit complex, adaptive behaviours typically associated with living systems or sophisticated AI.

This opens up exciting possibilities for developing new types of ‘smart’ materials that can learn and adapt to their environment.

Brain-inspired navigation technologies combine environmental perception, spatial cognition, and target navigation to create a comprehensive navigation research system. Researchers have used various sensors to gather environmental data and enhance environmental perception using multimodal information fusion. In spatial cognition, a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition map. However, existing models face challenges in achieving high navigation success rate and efficiency. In addition, the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further exploration.