Researchers developed a mechanochromic skin that converts pressure into color maps for real-time robotic touch.
Category: robotics/AI – Page 8
Ultrafast scanning tunneling microscopy reaches the quantum mechanical space-time limit for the first time
Werner Heisenberg’s famous uncertainty principle describes one of the most intriguing features of quantum physics: certain pairs of physical quantities describing a particle, such as position and momentum, cannot simultaneously be determined with arbitrary precision—not because of imprecise measuring instruments, but because nature forbids it. Between position and time, however, there is no Heisenberg uncertainty principle.
A research team comprising several groups at RUN led by Profs. Jascha Repp, Rupert Huber, Franz Giessibl, and Klaus Richter, as well as a team from the Max Planck Institute in Hamburg led by Angel Rubio, has now observed for the first time that the location and time evolution of an electron cannot be measured with arbitrary precision simultaneously. This so-called space-time limit has important implications for future applications. The work is published in the journal Nature Photonics.
Many future technologies, from green tech and quantum technologies to high-performance electronics for artificial intelligence, require a precise understanding of how matter functions at the microscopic level: how chemical reactions occur, how light interacts with matter, and how electrons move through electronic components. High-resolution still images of the microscopic building blocks of matter are not sufficient for this; rather, time-resolved slow-motion movies from the nanocosmos are needed.
New “Bad Epoll” Linux Kernel Flaw Lets Unprivileged Users Gain Root, Hits Android
A newly disclosed Linux kernel flaw called Bad Epoll (CVE-2026–46242) lets an ordinary user with no special access take full control of a machine as root. It affects Linux desktops, servers, and Android, and a fix is out.
Bad Epoll sits in the same small stretch of kernel code where Anthropic’s most powerful AI model, Mythos, recently found a different bug.
The AI caught one flaw and missed this one. A researcher, Jaeyoung Chung, found it and built a working attack.
Robots can now ‘see’ touch thanks to a new color-changing tactile sensor
Engineers at Queen Mary University of London have built a new color-changing tactile sensor, which allows robots to “see” and touch in real-time. The novel idea was invented by Giacomo Sasso, a postdoctoral researcher at the School of Engineering and Materials Science at Queen Mary University of London, and it works by transforming invisible forces into dynamic color patterns. This enables high-resolution maps of contact, strain and pressure to emerge instantly.
The study is published in the journal Science Advances.
When pressure is applied to a soft sensing surface, the material produces spatially varying structural colors that can be captured immediately using a standard camera, removing the need for complex reconstruction algorithms.
Free-text answers and LLMs reveal hidden reasons behind human choices
Why do people make the choices they do? Researchers from the Center Synergy of Systems (SynoSys) at TUD Dresden University of Technology, the Max Planck Institute for Human Development, and the University of Basel present their new approach to finding answers to that question. The approach combines observed choices with participants’ own descriptions of their decision processes, allowing researchers to study human behavior in greater detail than is possible with behavioral data alone.
The team merged behavioral experiments and free-text explanations to uncover the reasons underlying human decisions with the help of large language models (LLM). Their results are published in the Proceedings of the National Academy of Sciences.
Modern GPU Programming For MLSys
Machine learning systems sit at the heart of modern AI workloads. In these systems, performance often comes down to the quality of a small number of GPU kernels. Attention kernels, LLM prefill and decode kernels, low-precision block-scaled GEMMs, fused MoE layers, and other large fused kernels all directly shape end-to-end speed in both training and serving.
To make these kernels fast, however, we need more than a list of optimization tricks. Modern GPUs are no longer simple variations of the same old design. Recent architectures introduce richer memory spaces, new access patterns, and increasingly specialized execution units. To program them well, we need both a clear mental model of the hardware and a practical understanding of how high-performance kernels are built. This book is about developing both.
The book follows a simple progression: first understand the GPU hardware, then learn the programming model we will use, and finally build state-of-the-art kernels step by step. Our main target is the Blackwell generation, and our main running examples are General Matrix-Matrix Multiplication (GEMM) and FlashAttention. Along the way, we will also study the core ingredients behind GPU optimization: data layout, asynchronous data movement, and asynchronous coordination.
