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Category: robotics/AI – Page 81

Teen builds advanced robotic hand from LEGO parts

A talented teenager from the UK has built a four-fingered robotic hand from standard Lego parts that performs almost as well as research-grade robotic hands. The anthropomorphic device can grasp, move and hold objects with remarkable versatility and human-like adaptability.

Jared Lepora, a 16-year-old student at Bristol Grammar School, began working on the hand a couple of years ago with his father, who works at the University of Bristol. Called the Educational SoftHand-A, it is made entirely of LEGO MINDSTORMS components and is designed to mimic the shape and function of the human hand. The only non-LEGO parts are the cords that act as tendons.

The hand’s four (an index, middle, pinkie and opposing thumb) and twelve joints (three on each finger) are driven by two motors that control two sets of tendons. One tendon opens the hand while the other closes it, similar to the push-pull system of our own muscles.

Beyond electronics: Optical system performs feature extraction with unprecedented low latency

Many modern artificial intelligence (AI) applications, such as surgical robotics and real-time financial trading, depend on the ability to quickly extract key features from streams of raw data. This process is currently bottlenecked by traditional digital processors. The physical limits of conventional electronics prevent the reduction in latency and the gains in throughput required in emerging data-intensive services.

The answer to this might lie in harnessing the power of light. Optical computing—or using light to perform demanding computations—has the potential to greatly accelerate feature extraction. In particular, optical diffraction operators, which are plate-like structures that perform calculations as light propagates through them, are highly promising due to their and capacity for parallel processing.

However, pushing these systems to operating speeds beyond 10 GHz in practice remains a technical challenge. This is mainly due to the difficulty of maintaining the stable, coherent light needed for optical computations.

Unified memristor-ferroelectric memory developed for energy-efficient training of AI systems

Over the past decades, electronics engineers have developed a wide range of memory devices that can safely and efficiently store increasing amounts of data. However, the different types of devices developed to date come with their own trade-offs, which pose limits on their overall performance and restrict their possible applications.

Researchers at Université Grenoble Alpes (CEA-Leti, CEA List), Université de Bordeaux (CNRS) and Université Paris-Saclay (CNRS) recently developed a new memory device that combines two complementary components typically used individually, known as memristors and ferroelectric capacitors (FeCAPs). This unified memristor-ferroelectric memory, presented in a paper published in Nature Electronics, could be particularly promising for running artificial intelligence (AI) systems that autonomously learn to make increasingly accurate predictions.

“The ‘ideal’ memory would be high-density, non-volatile, capable of non-destructive readout, and offer virtually infinite endurance,” Elisa Vianello, senior author of the paper, told Tech Xplore.

Supercomputer-developed AI learns the intricate language of biomolecules

Scientists at the University of Glasgow have harnessed a powerful supercomputer, normally used by astronomers and physicists to study the universe, to develop a new machine learning model which can help translate the language of proteins.

In a new study published in Nature Communications, the cross-disciplinary team developed a (LLM), called PLM-Interact, to better understand interactions, and even predict which mutations will impact how these crucial molecules “talk” to one another.

Early tests of PLM-interact, a protein language model (PLM), show that it outperforms competing models in understanding and predicting how proteins interact with one another. The team’s research demonstrates PLM-interact could help us better understand key areas of medical science, including the development of diseases such as cancer and .

Quantum Echo: Nobel Prize in Physics Goes to Quantum Computer Trio (Two from Google) Who Broke Through Walls Forty Years Ago

Editor’s Note: EDRM is proud to publish Ralph Losey’s advocacy and analysis. The opinions and positions are Ralph Losey’s copyrighted work. All images in the article are by Ralph Losey using AI. This article is published here with permission.]

The Nobel Prize in Physics was just awarded to quantum physics pioneers John Clarke, Michel H. Devoret, and John M. Martinis for discoveries they made at UC Berkeley in the 1980s. They proved that quantum tunneling, where subatomic particles can break through seemingly impenetrable barriers, can also occur in the macroscopic world of electrical circuits. So yes, Schrödinger’s cat really could die.

AI restores James Webb telescope’s crystal-clear vision

Two Sydney PhD students have pulled off a remarkable space science feat from Earth—using AI-driven software to correct image blurring in NASA’s James Webb Space Telescope. Their innovation, called AMIGO, fixed distortions in the telescope’s infrared camera, restoring its ultra-sharp vision without the need for a space mission.

AI teaches itself and outperforms human-designed algorithms

Like humans, artificial intelligence learns by trial and error, but traditionally, it requires humans to set the ball rolling by designing the algorithms and rules that govern the learning process. However, as AI technology advances, machines are increasingly doing things themselves. An example is a new AI system developed by researchers that invented its own way to learn, resulting in an algorithm that outperformed human-designed algorithms on a series of complex tasks.

For decades, human engineers have designed the algorithms that agents use to learn, especially reinforcement learning (RL), where an AI learns by receiving rewards for successful actions. While learning comes naturally to humans and animals, thanks to millions of years of evolution, it has to be explicitly taught to AI. This process is often slow and laborious and is ultimately limited by human intuition.

Taking their cue from evolution, which is a random trial and error process, the researchers created a large digital population of AI agents. These agents tried to solve numerous tasks in many different, complex environments using a particular learning rule.

The Day My Smart Vacuum Turned Against Me

Would you allow a stranger to drive a camera-equipped computer around your living room? You might have already done so without even realizing it.

It all started innocently enough. I had recently bought an iLife A11 smart vacuum—a sleek, affordable, and technologically advanced robot promising effortless cleaning and intelligent navigation. As a curious engineer, I was fascinated by its workings. After leaving it to operate for the entire year, my curiosity got the better of me.

I’m a bit paranoid—the good kind of paranoid. So, I decided to monitor its network traffic, as I would with any so-called smart device.

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