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Category: robotics/AI

Pea-size liquid-metal pump runs robot butterfly on under 0.1 V

Engineers have invented an ingenious liquid-metal pump that could make future soft robotics and wearable devices much more portable and agile. The innovation, led by the University of Bristol and published in the journal Nature Communications, presents a low-voltage power source with the potential to transform robotic systems for a wide range of applications, from robotic legs to haptic gloves used in medical and industrial settings.

The researchers have demonstrated the varied uses of this innovative technique by creating three prototypes including robotic butterfly wings, a color-changing bracelet, and a haptic fingertip pouch connected to an adjustable wristband which squeezes to simulate natural tactile sensations.w.

Current technologies are powered by bulky compressors or rigid pumps, which limit mobility and flexibility. The small lightweight soft pump—the size of a pea—is powered by liquid metal, which converts electrical energy into fluid motion, creating an efficient, compact power source for next-generation soft robots and adaptive materials such as medical devices and wearable interfaces for virtual reality.

How a shape-shifting tiny rover inspired by Japanese toys autonomously explored the moon

Moon missions come in all shapes and sizes, from car-sized rovers packed with scientific equipment to towering rocket payloads—and now, a small, shape-shifting machine that is about the size of the average palm.

When the Japanese Smart Lander for Investigating Moon (SLIM) touched down on the lunar surface in 2024, a small rover called LEV-2 (nicknamed SORA-Q) rolled out and explored autonomously for nearly two hours. And now, with the publication of a paper in the journal Science Robotics, we are discovering just how this tiny machine navigated the terrain, made its own decisions and what it found.

The advantages of tiny rovers for space exploration include relatively low development costs, lightweight design and the ability to fit into a crowded spacecraft. But building tiny comes with many challenges.

Waymo unveils virtual driver model to test autonomous car crash avoidance

Autonomous vehicles are already a reality on some of our streets and could become a major part of future transportation systems. Safety, of course, is the main concern, as with all vehicles. To help evaluate and improve its autonomous driving technology, U.S. driverless vehicle company Waymo has created a virtual representation of human driver behavior in near-crash situations.

Human drivers avoid collisions by instantly perceiving a hazard, deciding how to react and then executing the maneuver. It all happens in a split second thanks to the central and peripheral nervous systems working together harmoniously.

Currently, testing and training for collision avoidance involve several systems, and each often tests only a specific scenario or metric. For example, one system might only look at what happens when a lead vehicle brakes suddenly. They do not capture the whole sequence of events from detection to actual avoidance.

Light rewrites magnetic memory in one pulse, opening path to lower-power AI chips

As artificial intelligence, cloud computing and digital services continue to expand, the world is facing a growing need for faster and more energy-efficient ways to store and process information. A team led by the National Institutes for Quantum Science and Technology (QST) has developed a new magnetic memory material that can be rewritten using laser light instead of electric current, a step that could help reduce power consumption in data centers and support future high-speed information systems.

The study is published in Applied Physics Letters.

The new material allows magnetic information to be switched by a single ultrashort laser pulse. Because light can reverse magnetic states much faster than electric current, the approach could deliver switching speeds roughly 1,000 times higher than those of conventional electrically driven magnetic memory while also reducing heat generation and energy loss.

From Supernova Physics to Fusion Energy: The Laser Experiments Changing Science — Dr. Mario Manuel

Fusion energy is no longer just science fiction — it’s becoming experimental reality. Dr. Mario Manuel, Ph.D. — General Atomics.

What if we could recreate the inside of a star — not in theory, but inside a laboratory on Earth using the world’s most powerful lasers?

Dr. Mario Manuel, Ph.D. is a plasma physicist and laser-science researcher at whose work sits at the frontier of fusion energy, laboratory astrophysics, high-energy-density physics, and advanced laser diagnostics. Trained in applied plasma physics and aerospace engineering, Dr. Manuel has spent his career developing new ways to visualize and understand the extreme electromagnetic environments created when ultra-powerful lasers interact with matter.

Dr. Manuel’s research has spanned some of the most ambitious scientific efforts underway today — from inertial fusion energy and plasma-instability control to recreating supernova-like shock waves in the laboratory and generating ultra-intense gamma-ray and particle beams using petawatt-class lasers.

Early in his career, Dr. Manuel helped pioneer advanced proton-radiography techniques capable of imaging invisible electric and magnetic fields inside laser-produced plasmas, work that opened new windows into the turbulent physics that can either enable or destroy fusion reactions.

Continue reading “From Supernova Physics to Fusion Energy: The Laser Experiments Changing Science — Dr. Mario Manuel” | >

The Puzzling Success of Overparameterization: Lottery Tickets or Escape Dimensions?

Lotteries and tickets are often used as a didactical analogy to explain the success of overparameterized neural networks: “larger networks succeed because they more likely contain a well-initialized subnetwork that can learn the task in isolation, much like buying more tickets increases the chances of winning a lottery.”

This explanation is intuitive but misleading: it suggests that subnetworks can be treated in isolation from the rest of the network. Following this reasoning leads to interpreting learning in wide networks as a multi-start optimization process, where gradient descent simply conducts a parallel search over subnetworks. We argue that this view is flawed since, among other reasons, winning tickets can be made to fail by perturbing the rest of the network.

Eroding a virtue: AI trains people to expect instant answers — and that’s bad news for patience

Patience is a virtue that researchers have linked to many parts of well-being. But it’s also something that needs a bit of practice and training – and can be undermined by instant, easy gratification.

Annual global migration has nearly tripled since 2000, reshaping where and how people move

Global migration has risen sharply from approximately 13 million people per year in 2000 to around 35 million people per year in 2023. This is according to a new dataset on human migration published in Nature by researchers from the London School of Economics and Political Science (LSE), IIASA and the University of Hong Kong.

This rise in migration outpaces global population growth, showing a true per capita increase in human mobility. The trend is contrary to previous research efforts to quantify global migration flows.

Using deep learning, the researchers built the first dataset of migration flows between all countries for the period 1990–2023, offering a far more detailed picture of global movement than traditional data, which is highly fragmented.

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