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Category: robotics/AI – Page 113

‘Super-Turing AI’ uses less energy by mimicking the human brain

Artificial Intelligence (AI) can perform complex calculations and analyze data faster than any human, but to do so requires enormous amounts of energy. The human brain is also an incredibly powerful computer, yet it consumes very little energy.

As increasingly expand, a new approach to AI’s “thinking,” developed by researchers including Texas A&M University engineers, mimics the and has the potential to revolutionize the AI industry.

Dr. Suin Yi, assistant professor of electrical and computer engineering at Texas A&M’s College of Engineering, is on a team of researchers that developed “Super-Turing AI,” which operates more like the human brain. This new AI integrates certain processes instead of separating them and then migrating huge amounts of data like current systems do.

Brain-like computer steers rolling robot with 0.25% of the power needed by conventional controllers

A smaller, lighter and more energy-efficient computer, demonstrated at the University of Michigan, could help save weight and power for autonomous drones and rovers, with implications for autonomous vehicles more broadly.

The autonomous controller has among the lowest power requirements reported, according to the study published in Science Advances. It operates at a mere 12.5 microwatts—in the ballpark of a pacemaker.

In testing, a rolling robot using the controller was able to pursue a target zig-zagging down a hallway with the same speed and accuracy as with a conventional digital controller. In a second trial, with a lever-arm that automatically repositioned itself, the new controller did just as well.

Cracking the code of private AI: The role of entropy in secure language models

Large Language Models (LLMs) have rapidly become an integral part of our digital landscape, powering everything from chatbots to code generators. However, as these AI systems increasingly rely on proprietary, cloud-hosted models, concerns over user privacy and data security have escalated. How can we harness the power of AI without exposing sensitive data?

A recent study, “Entropy-Guided Attention for Private LLMs,” by Nandan Kumar Jha, a Ph.D. candidate at the NYU Center for Cybersecurity (CCS), and Brandon Reagen, Assistant Professor in the Department of Electrical and Computer Engineering and a member of CCS, introduces a novel approach to making AI more secure.

The paper was presented at the AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI 25) in early March and is available on the arXiv preprint server.

Humans as hardware: Computing with biological tissue

Most computers run on microchips, but what if we’ve been overlooking a simpler, more elegant computational tool all this time? In fact, what if we were the computational tool?

As crazy as it sounds, a future in which humans are the ones doing the computing may be closer than we think. In an article published in IEEE Access, Yo Kobayashi from the Graduate School of Engineering Science at the University of Osaka demonstrates that living tissue can be used to process information and solve complex equations, exactly as a computer does.

This achievement is an example of the power of the computational framework known as , in which data are input into a complex “reservoir” that has the ability to encode rich patterns. A computational model then learns to convert these patterns into meaningful outputs via a neural network.

Liquid robot can transform, separate and fuse like living cells

A joint research team has successfully developed a next-generation soft robot based on liquid. The research was published in Science Advances.

Biological cells possess the ability to deform, freely divide, fuse, and capture foreign substances. Research efforts have long been dedicated to replicating these unique capabilities in artificial systems. However, traditional solid-based robots have faced limitations in effectively mimicking the flexibility and functionality of living cells.

To overcome these challenges, the joint research team successfully developed a particle-armored liquid robot, encased in unusually dense hydrophobic (water-repelling) particles.

Scientists develop dog-inspired robot that runs without motors

Scientists from TU Delft and EPFL have created a quadruped robot capable of running like a dog without the need for motors. This achievement, a product of combining innovative mechanics with data-driven technology, was published in Nature Machine Intelligence and could pave the way for energy-efficient robotics.

“Commercial quadruped robots are becoming more common, but their energy inefficiency limits their operating time,” explains Cosimo Della Santina, assistant professor at TU Delft. “Our goal was to address this issue by optimizing the robot’s mechanics by mimicking the efficiency of biological systems.”

PAWS: Four-legged robot can reproduce animal movement with fewer actuators

Many of the robotic systems developed in the past decades are inspired by four-legged (i.e., quadruped) animals, such as dogs, cheetahs and horses. By replicating the agile movements of these animals, quadruped robots could move swiftly on the ground, crossing long distances on various terrains and rapidly completing missions.

Yet realistically and robustly replicating the fluid motions observed in animals using can be very challenging. While some existing four-legged robots were found to be very agile and responsive to changes in their environment, these systems typically integrate advanced actuators and computational components that consume a lot of energy.

Researchers at EPFL’s CREATE Lab and Delft University of Technology (TU Delft) recently developed a new four-legged robot called PAWS (Passive Automata With Synergies), which could reproduce the fluid and adaptive movements of animals using fewer actuators. This robot, introduced in a paper in Nature Machine Intelligence, leverages so-called motor synergies, which are coordinated patterns of muscle activation that allow animals to perform agile motions consuming less energy.