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Researchers launch open-source robotic exoskeleton to help people walk

Imagine a future in which people with disabilities can walk on their own, thanks to robotic legs. A new project from Northern Arizona University is accelerating that future with an open-source robotic exoskeleton.

Right now, developing these complex electromechanical systems is expensive and time-consuming, which likely stops a lot of research before it ever starts. But that may soon change: Years of research from NAU associate professor Zach Lerner’s Biomechatronics Lab has led to the first comprehensive open-source exoskeleton framework, made freely available to anyone worldwide. It will help overcome several huge obstacles for potential exoskeleton developers and researchers.

An effective exoskeleton must be biomechanically beneficial to the person wearing it, which means that developing them requires extensive trial, error and adaptation to specific use cases.

Robots are transforming warehouse automation and ending back-breaking truck loading

The last stronghold of human labor in warehouses – the grueling job of loading and unloading trucks – is rapidly giving way to a new generation of intelligent robots. For decades, logistics companies have struggled to automate this physically demanding and injury-prone work, which often leaves workers battered by heavy lifting and extreme temperatures. Now, breakthroughs in robotics, artificial intelligence, and sensor technology are transforming how goods move in and out of trailers, promising not only greater efficiency but also a fundamental shift in warehouse operations.

At the heart of this revolution is a suite of sophisticated machines from companies like Ambi Robotics, Boston Dynamics, Dexterity AI, and Fox Robotics. Each brings a distinct technical approach to the challenge, as described by The Wall Street Journal.

Ambi Robotics, for example, has developed AmbiStack, a robotic system designed to automate the complex process of stacking items onto pallets or into containers. AmbiStack employs a four-axis gantry robot equipped with advanced cameras and machine vision powered by AI foundation models. This system can analyze, track, and pick each item from a conveyor, performing real-time quality control checks.

“Robots Can Feel Now”: New Color-Changing Skins Let Machines React Instantly Without Wires, Screens, or Human Input

IN A NUTSHELL 🐙 Researchers at the University of Nebraska–Lincoln have developed synthetic skins that mimic the color-changing abilities of marine creatures. ⚙️ These innovative skins utilize autonomous materials that respond to environmental stimuli without the need for traditional electronics. 📱 Potential applications include wearable devices and soft robotics, offering flexibility and adaptability in various

Boson sampling finds first practical applications in quantum AI

For over a decade, researchers have considered boson sampling—a quantum computing protocol involving light particles—as a key milestone toward demonstrating the advantages of quantum methods over classical computing. But while previous experiments showed that boson sampling is hard to simulate with classical computers, practical uses have remained out of reach.

Now, in Optica Quantum, researchers from the Okinawa Institute of Science and Technology (OIST) present the first practical application of boson sampling for image recognition, a vital task across many fields, from forensic science to medical diagnostics. Their approach uses just three photons and a linear optical network, marking a significant step towards low energy quantum AI systems.

It’s elementary: Problem-solving AI approach tackles inverse problems used in nuclear physics and beyond

Solving life’s great mysteries often requires detective work, using observed outcomes to determine their cause. For instance, nuclear physicists at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility analyze the aftermath of particle interactions to understand the structure of the atomic nucleus.

This type of subatomic sleuthing is known as the inverse problem. It is the opposite of a forward problem, where causes are used to calculate the effects. Inverse problems arise in many descriptions of physical phenomena, and often their solution is limited by the experimental data available.

That’s why scientists at Jefferson Lab and DOE’s Argonne National Laboratory, as part of the QuantOm Collaboration, have led the development of an artificial intelligence (AI) technique that can reliably solve these types of puzzles on supercomputers at large scales.

Smart amplifier cuts power consumption, paving way for more qubits and less decoherence

Quantum computers can solve extraordinarily complex problems, unlocking new possibilities in fields such as drug development, encryption, AI, and logistics. Now, researchers at Chalmers University of Technology in Sweden have developed a highly efficient amplifier that activates only when reading information from qubits. The study was published in the journal IEEE Transactions on Microwave Theory and Techniques.

Thanks to its smart design, it consumes just one-tenth of the power consumed by the best amplifiers available today. This reduces decoherence and lays the foundation for more with significantly more qubits and enhanced performance.

Bits, which are the building blocks of a conventional computer, can only ever have the value of 1 or 0. By contrast, the common building blocks of a quantum computer, quantum bits or qubits, can exist in states having the value 1 and 0 simultaneously, as well as all states in between in any combination.

New AI Model Diagnoses Brain Tumors With 99% Accuracy, Without Surgery

An MRI scan revealed a brain tumor located in a difficult area, and performing a biopsy would carry significant risks for the patient, who had initially sought medical help due to double vision. Cases like this, discussed by a multidisciplinary team of cancer specialists, led researchers at Charité – Universitätsmedizin Berlin, along with their collaborators, to search for alternative diagnostic methods.

Their solution is an AI model that analyzes specific features in the genetic material of tumors, particularly their epigenetic fingerprint, which can be obtained from sources such as cerebrospinal fluid. As reported in the journal Nature Cancer, the model classifies tumors both rapidly and with high accuracy.

“AI That Stops Wars”: Former Harvard Scientist Unveils Revolutionary Peace Technology Designed to Prevent Global Conflict Before It Starts

IN A NUTSHELL 🌍 North Star, developed by an ex-Harvard professor, is an AI tool designed to predict and prevent wars by simulating world leaders’ decisions. 🔮 The tool creates digital twins of leaders to foresee outcomes of geopolitical events, offering insights for better decision-making. 💼 Investors see peace tech as a burgeoning market, drawing