The brain’s astounding cellular diversity and networked complexity could show how to make AI better.
Category: robotics/AI – Page 194
AI model based on neural oscillations delivers stable, efficient long-sequence predictions
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence (AI) model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data.
AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model called “state-space models” has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges—they can become unstable or require a significant amount of computational resources when processing long data sequences.
To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators—a concept deeply rooted in physics and observed in biological neural networks.
ZF wins brake-by-wire tech business for 5 million vehicles
Featuring the Electro-Mechanical Brake and by-wire technology on the rear brakes, the project will also include ZF’s Integrated Brake Control and traditional front calipers, creating a ‘hybrid’ braking system of by-wire and hydraulics that offers increased flexibility to the manufacturer. The agreement will also provide significant steering technology with ZF’s Electric Recirculating Ball Steering Gear. This cutting-edge braking technology combined with traditional braking systems and innovative steering tools further solidifies ZF’s position as the industry leader in providing complete chassis solutions to its customers while providing a major customer win.
“We are all proud to see ZF’s technology leadership in the Chassis segment providing tangible value for our customers. Our goal when combining our steering, braking, dampers and actuators as well as corresponding software businesses into a single division was to create the world’s most comprehensive Chassis Solutions product and system offering,” said Peter Holdmann, Board of Management member at ZF and head of Division Chassis Solutions. “This combined center of expertise allows us to offer comprehensive solutions that integrate advanced engineering, innovative design, and cutting-edge technology to deliver unparalleled performance and safety.”
The road to the software-defined vehicle With the Electro-Mechanical Brake (EMB) as a key component of the brake-by-wire technology, ZF lays the foundation for the software-defined vehicle that will lead to new functions and features, many that emphasize safety as much as driving comfort. One such feature being explored with by-wire technology is the ability for the vehicle to autonomously brake and steer in a crash situation.
ZF’s Electro-Mechanical Brake provides premium performance for automatic emergency braking, full energy recuperation and redundant fallback options up to full automated driving for passenger car and light truck segments.
System converts fabric images into complete machine-readable knitting instructions
Recent advances in robotics and machine learning have enabled the automation of many real-world tasks, including various manufacturing and industrial processes. Among other applications, robotic and artificial intelligence (AI) systems have been successfully used to automate some steps in manufacturing clothes.
Researchers at Laurentian University in Canada recently set out to explore the possibility of fully automating the knitting of clothes. To do this, they developed a model to convert fabric images into comprehensive instructions that knitting robots could read and follow. Their model, outlined in a paper published in Electronics, was found to successfully realize patterns for the creation of single-yarn and multi-yarn knitted items of clothing.
“Our paper addresses the challenge of automating knitting by converting fabric images into machine-readable instructions,” Xingyu Zheng and Mengcheng Lau, co-authors of the paper, told Tech Xplore.
Robotics researchers develop algorithms that make mobile navigation more efficient
Delivery robots made by companies such as Starship Technologies and Kiwibot autonomously make their way along city streets and through neighborhoods.
Under the hood, these robots—like most mobile robots in use today—use a variety of different sensors and software-based algorithms to navigate in these environments.
Lidar sensors—which send out pulses of light to help calculate the distances of objects—have become a mainstay, enabling these robots to conduct simultaneous localization and mapping, otherwise known as SLAM.
Michael Levin: The New Era of Cognitive Biorobotics | Robinson’s Podcast #187
Patreon: https://bit.ly/3v8OhY7
Michael Levin is a Distinguished Professor in the Biology Department at Tufts University, where he holds the Vannevar Bush endowed Chair, and he is also associate faculty at the Wyss Institute at Harvard University. Michael and the Levin Lab work at the intersection of biology, artificial life, bioengineering, synthetic morphology, and cognitive science. Michael also appeared on the show in episode #151, which was all about synthetic life and collective intelligence. In this episode, Michael and Robinson discuss the nature of cognition, working with Daniel Dennett, how cognition can be realized by different structures and materials, how to define robots, a new class of robot called the Anthrobot, and whether or not we have moral obligations to biological robots.
The Levin Lab: https://drmichaellevin.org/
OUTLINE
00:00 Introduction.
02:14 What is Cognition?
08:01 On Working with Daniel Dennett.
13:17 Gatekeeping in Cognitive Science.
25:15 The Multi-Realizability of Cognition.
31:30 What are Anthrobots?
39:33 What Are Robots, Really?
59:53 Do We Have Moral Obligations to Biological Robots?
Robinson’s Website: http://robinsonerhardt.com
Robinson Erhardt researches symbolic logic and the foundations of mathematics at Stanford University. Join him in conversations with philosophers, scientists, weightlifters, artists, and everyone in-between.
Quantum-Neural Hybrid Solves Impossible Math
The worlds of quantum mechanics and neural networks have collided in a new system that’s setting benchmarks for solving previously intractable optimization problems. A multi-university team led by Shantanu Chakrabartty at Washington University in St. Louis has introduced NeuroSA, a neuromorphic architecture that leverages quantum tunneling mechanisms to reliably discover optimal solutions to complex mathematical puzzles.
Published March 31 in Nature Communications, NeuroSA represents a significant leap forward in optimization technology with immediate applications ranging from logistics to drug development. While typical neural systems often get trapped in suboptimal solutions, NeuroSA offers something remarkable: a mathematical guarantee of finding the absolute best answer if given sufficient time.
“We’re looking for ways to solve problems better than computers modeled on human learning have done before,” said Chakrabartty, the Clifford W. Murphy Professor and vice dean for research at WashU. “NeuroSA is designed to solve the ‘discovery’ problem, the hardest problem in machine learning, where the goal is to discover new and unknown solutions.”