Recent artificial intelligence advances have largely focused on text, but AI increasingly shows promise in other contexts, including manufacturing and the service industry. In these sectors, targeted AI improvements can improve product quality and worker safety, according to a new study.
Researchers argue that the problem that has been lurking in the margins of many papers about touch sensors lies in the robotic skin itself.
A next-generation technology developed in 2023, conversational swarm intelligence (CSI), combines the principles of ASI with the power of large language models.
Estimating the pose of hand-held objects is a critical and challenging problem in robotics and computer vision. While leveraging multi-modal RGB and depth data is a promising solution, existing approaches still face challenges due to hand-induced occlusions and multimodal data fusion. In a new study, researchers developed a novel deep learning framework that addresses these issues by introducing a novel vote-based fusion module and a hand-aware pose estimation module.
Researchers developed a more efficient way to control the outputs of a large language model, guiding it to generate text that adheres to a certain structure, like a programming language, and remains error free.
Combining two different kinds of signals could help engineers build prosthetic limbs that better reproduce natural movements, according to a new study. A combination of electromyography and force myography is more accurate at predicting hand movements than either method by itself.
An international team has explored how in future aerial robots could process construction materials precisely in the air -- an approach with great potential for difficult-to-access locations or work at great heights. The flying robots are not intended to replace existing systems on the ground, but rather to complement them in a targeted manner for repairs or in disaster areas, for instance.
New research can transform how hospitals triage, risk-stratify, and counsel patients to save lives.
Inspired by the movements of a tiny parasitic worm, engineers have created a 5-inch soft robot that can jump as high as a basketball hoop. Their device, a silicone rod with a carbon-fiber spine, can leap 10 feet high even though it doesn't have legs. The researchers made it after watching high-speed video of nematodes pinching themselves into odd shapes to fling themselves forward and backward.
After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists combine elements of different methods to improve algorithms or create new ones.
Most people generally are more concerned about the immediate risks of artificial intelligence than they are about a theoretical future in which AI threatens humanity. A new study reveals that respondents draw clear distinctions between abstract scenarios and specific tangible problems and particularly take the latter very seriously.
The invention is a metamaterial, which is a material engineered to feature new and unusual properties that depend on the material's physical structure rather than its chemical composition. In this case, the researchers built their metamaterial using a combination of simple plastics and custom-made magnetic composites. Using a magnetic field, the researchers changed the metamaterial's structure, causing it to expand, move and deform in different directions, all remotely without touching the metamaterial.
A recently created RoboBee is now outfitted with its most reliable landing gear to date, inspired by one of nature's most graceful landers: the crane fly. The team has given their flying robot a set of long, jointed legs that help ease its transition from air to ground. The robot has also received an updated controller that helps it decelerate on approach, resulting in a gentle plop-down.
Neural networks are one typical structure on which artificial intelligence can be based. The term neural describes their learning ability, which to some extent mimics the functioning of neurons in our brains. To be able to work, several key ingredients are required: one of them is an activation function which introduces nonlinearity into the structure. A photonic activation function has important advantages for the implementation of optical neural networks based on light propagation. Researchers have now experimentally shown an all-optically controlled activation function based on traveling sound waves. It is suitable for a wide range of optical neural network approaches and allows operation in the so-called synthetic frequency dimension.
It's a game a lot of us played as children -- and maybe even later in life: unspooling measuring tape to see how far it would extend before bending. But to engineer, this game was an inspiration, suggesting that measuring tape could become a great material for a robotic gripper. The grippers would be a particularly good fit for agriculture applications, as their extremities are soft enough to grab fragile fruits and vegetables, researchers wrote. The devices are also low-cost and safe around humans.