Videos from the world’s largest robot trade show in Tokyo last week show Kawasaki’s four-legged robot, Bex, in action.
In today’s world, autonomous machines play a major role in our lives, yet it is still difficult to establish trust between humans and machines. Aside from concerns about unexpected disruptions, robots do not yet communicate exactly the way humans do. Researchers have revealed that independent computer systems are able to increase trust in robots, increase collaboration, and streamline task execution.
Humans tend to rely more on robots that provide self-assessment while performing their tasks, according to the study. Communication is essential for establishing trust in a human working environment. Humans and autonomous machines may have a gap of understanding, which can result in a robot performing an action incorrectly, and even misuse or exploitation of the robot’s capabilities.
In a study conducted by researchers from Draper and the University of Colorado Boulder in the USA, researchers examined how autonomous robots using probability models are capable of calculating and expressing self-assessment skills, forming a kind of machine-self-confidence. The models were developed to predict their behavior and provide a perspective on their mission before the event occurs.
Scientists identified and observed cells in the human brain that act as filing systems for everything we observe. Could we use this for AI? property= description.
Researchers at Duke University have demonstrated the first attack strategy that can fool industry-standard autonomous vehicle sensors into believing nearby objects are closer (or further) than they appear without being detected.
The research suggests that adding optical 3D capabilities or the ability to share data with nearby cars may be necessary to fully protect autonomous cars from attacks.
The results will be presented Aug. 10–12 at the 2022 USENIX Security Symposium, a top venue in the field.
Circa 2021
Seoul National University Hospital completed a liver transplant procedure using a robot and a laparoscope that left no huge abdominal scars for both the donor and recipient.
Suh Kyung-suk, a professor on the liver transplant team, noted that the new surgical procedure also reduces complications associated with the lungs and scars and shortens the recovery time.
Continue reading “Seoul hospital completes robotic liver transplant surgery” »
Over the past decade or so, many researchers worldwide have been trying to develop brain-inspired computer systems, also known as neuromorphic computing tools. The majority of these systems are currently used to run deep learning algorithms and other artificial intelligence (AI) tools.
Researchers at Sandia National Laboratories have recently conducted a study assessing the potential of neuromorphic architectures to perform a different type of computations, namely random walk computations. These are computations that involve a succession of random steps in the mathematical space. The team’s findings, published in Nature Electronics, suggest that neuromorphic architectures could be well-suited for implementing these computations and could thus reach beyond machine learning applications.
“Most past studies related to neuromorphic computing focused on cognitive applications, such as deep learning,” James Bradley Aimone, one of the researchers who carried out the study, told TechXplore. “While we are also excited about that direction, we wanted to ask a different and complementary question: can neuromorphic computing excel at complex math tasks that our brains cannot really tackle?”
A Microsoft Research team has introduced a “simple yet effective” method that dramatically improves stability in transformer models with just a few lines of code change.
Large-scale transformers have achieved state-of-the-art performance on a wide range of natural language processing (NLP) tasks, and in recent years have also demonstrated their impressive few-shot and zero-shot learning capabilities, making them a popular architectural choice for machine learning researchers. However, despite soaring parameter counts that now reach billions and even trillions, the layer depth of transformers remains restricted by problems with training instability.
In their new paper DeepNet: Scaling Transformers to 1,000 Layers, the Microsoft team proposes DeepNorm, a novel normalization function that improves the stability of transformers to enable scaling that is an order of magnitude deeper (more than 1,000 layers) than previous deep transformers.
Researchers at Johns Hopkins University have developed a new shock-absorbing material that is super lightweight, yet offers the protection of metal. The stuff could make for helmets, armor and vehicle parts that are lighter, stronger and, importantly, reusable.
The key to the new material is what are known as liquid crystal elastomers (LCEs). These are networks of elastic polymers in a liquid crystalline phase that give them a useful combination of elasticity and stability. LCEs are normally used to make actuators and artificial muscles for robotics, but for the new study the researchers investigated the material’s ability to absorb energy.
The team created materials that consisted of tilted beams of LCE, sandwiched between stiff supporting structures. This basic unit was repeated over the material in multiple layers, so that they would buckle at different rates on impact, dissipating the energy effectively.
