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Category: robotics/AI – Page 54

A Cornell-led research team has developed an artificial intelligence-powered ring equipped with micro-sonar technology that can continuously—and in real time—track fingerspelling in American Sign Language (ASL).

In its current form, SpellRing could be used to enter text into computers or smartphones via fingerspelling, which is used in ASL to spell out words without corresponding signs, such as proper nouns, names and technical terms. With further development, the device—believed to be the first of its kind—could revolutionize ASL translation by continuously tracking entire signed words and sentences.

The research is published on the arXiv preprint server.

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We move thanks to coordination among many skeletal muscle fibers, all twitching and pulling in sync. While some muscles align in one direction, others form intricate patterns, helping parts of the body move in multiple ways.

In recent years, scientists and engineers have looked to muscles as potential actuators for “biohybrid” robots—machines powered by soft, artificially grown . Such bio-bots could squirm and wiggle through spaces where traditional machines cannot. For the most part, however, researchers have only been able to fabricate artificial muscle that pulls in one direction, limiting any robot’s range of motion.

Now MIT engineers have developed a method to grow artificial muscle tissue that twitches and flexes in multiple coordinated directions. As a demonstration, they grew an artificial, muscle-powered structure that pulls both concentrically and radially, much like how the iris in the human eye acts to dilate and constrict the pupil.

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International Iberian Nanotechnology Laboratory (INL) researchers have developed a neuromorphic photonic semiconductor neuron capable of processing optical information through self-sustained oscillations. Exploring the use of light to control negative differential resistance (NDR) in a micropillar quantum resonant tunneling diode (RTD), the research indicates that this approach could lead to highly efficient light-driven neuromorphic computing systems.

Neuromorphic computing seeks to replicate the information-processing capabilities of biological neural networks. Neurons in rely on rhythmic burst firing for sensory encoding, , and network synchronization, functions that depend on oscillatory activity for signal transmission and processing.

Existing neuromorphic approaches replicate these processes using electrical, mechanical, or thermal stimuli, but optical-based systems offer advantages in speed, energy efficiency, and miniaturization. While previous research has demonstrated photonic synapses and artificial afferent nerves, these implementations require additional circuits that increase power consumption and complexity.

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The use of artificial intelligence (AI) scares many people as neural networks, modeled after the human brain, are so complex that even experts do not understand them. However, the risk to society of applying opaque algorithms varies depending on the application.

While AI can cause great damage in democratic elections through the manipulation of social media, in astrophysics it at worst leads to an incorrect view of the cosmos, says Dr. Jonas Glombitza from the Erlangen Center for Astroparticle Physics (ECAP) at Friedrich-Alexander Universität Erlangen-Nürnberg (FAU).

The astrophysicist uses AI to accelerate the analysis of data from an observatory that researches cosmic radiation.

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About 100 million metric tons of high-density polyethylene (HDPE), one of the world’s most commonly used plastics, are produced annually, using more than 15 times the energy needed to power New York City for a year and adding enormous amounts of plastic waste to landfills and oceans.

Cornell chemistry researchers have found ways to reduce the environmental impact of this ubiquitous —found in milk jugs, shampoo bottles, playground equipment and many other things—by developing a machine-learning model that enables manufacturers to customize and improve HDPE materials, decreasing the amount of material needed for various applications. It can also be used to boost the quality of recycled HDPE to rival new, making recycling a more practical process.

“Implementation of this approach will facilitate the design of next-generation commodity materials and enable more efficient polymer recycling, lowering the overall impact of HDPE on the environment,” said Robert DiStasio Jr., associate professor of chemistry and chemical biology in the College of Arts and Sciences (A&S).

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Scientists from the Beijing-based NOETIX Robotics have developed a new meter-tall humanoid robot that is capable of performing near-perfect continuous backflips. Called the NOETIX N2, this 4.2 foot tall robot features innovative hardware to ensure stability while performing the feat.

According to Jiang Zheyuan, technical leader of the development team, performing a backflip is harder compared to a frontflip as human feet are longer in the front. To enable the robot’s backflip action faultlessly, the team came up with innovative hardware designs to ensure the robot’s stability. For example, the heavy joints of the humanoid’s limbs are placed closer to its crotch to make it easier to rotate in the air.

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The battle for artificial intelligence supremacy hinges on microchips. But the semiconductor sector that produces them has a dirty secret: It’s a major source of chemicals linked to cancer and other health problems.

Global chip sales surged more than 19% to roughly $628 billion last year, according to the Semiconductor Industry Association, which forecasts double-digit growth again in 2025. That’s adding urgency to reducing the impacts of so-called “forever chemicals” — which are also used to make firefighting foam, nonstick pans, raincoats and other everyday items — as are regulators in the U.S. and Europe who are beginning to enforce pollution limits for municipal water supplies. In response to this growing demand, a wave of startups are offering potential solutions that won’t cut the chemicals out of the supply chain but can destroy them.

Per-and polyfluoroalkyl substances, or PFAS, have been detected in every corner of the planet from rainwater in the Himalayas to whales off the Faroe Islands and in the blood of almost every human tested. Known as forever chemicals because the properties that make them so useful also make them persistent in the environment, scientists have increasingly linked PFAS to health issues including obesity, infertility and cancer.

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One belief underlying the power-hungry approach to machine learning advanced by OpenAI and Mistral AI is that an artificial intelligence model must review its entire dataset before spitting out new insights.

Sepp Hochreiter, an early pioneer of the technology who runs an AI lab at Johannes Kepler University in Linz, Austria, has a different view, one that requires far less cash and computing power. He’s interested in teaching AI models how to efficiently forget.

Hochreiter holds a special place in the world of artificial intelligence, having scaled the technology’s highest peaks long before most computer scientists. As a university student in Munich during the 1990s, he came up with the conceptual framework that underpinned the first generation of nimble AI models used by Alphabet, Apple and Amazon.

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The European Commission is raising $20 billion to construct four “AI gigafactories” as part of Europe’s strategy to catch up with the U.S. and China on artificial intelligence, but some industry experts question whether it makes sense to build them.

The plan for the large public access data centers, unveiled by European Commission President Ursula von der Leyen last month, will face challenges ranging from obtaining chips to finding suitable sites and electricity.

“Even if we would build such a big computing factory in Europe, and even if we would train a model on that infrastructure, once it’s ready, what do we do with it?” said Bertin Martens, of economic think tank Bruegel. It’s a chicken and egg problem. The hope is that new local firms such as France’s Nvidia-backed Mistral startup will grow and use them to create AI models that operate in line with EU AI safety and data protection rules, which are stricter than those in the U.S. or China.

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