New, gelatin-based material could let robots feel everything from a light poke to a deep cut.

As detailed in a new study in Nature Communications, He’s lab brings noninvasive EEG-based BCI one step closer to everyday use by demonstrating real-time brain decoding of individual finger movement intentions and control of a dexterous robotic hand at the finger level.
“Improving hand function is a top priority for both impaired and able-bodied individuals, as even small gains can meaningfully enhance ability and quality of life,” explained Bin He, professor of biomedical engineering at Carnegie Mellon University. “However, real-time decoding of dexterous individual finger movements using noninvasive brain signals has remained an elusive goal, largely due to the limited spatial resolution of EEG.”
There is extensive evidence that brain criticality – the balance between neural excitation and inhibition – enhances its information processing capabilities.
But despite the significance of brain criticality and its potential influence on neurological and psychiatric disorders, the genetic basis of this state had been “largely unexplored”, according to researchers from the Chinese Academy of Sciences’ biophysics and automation institutes. “We demonstrate that genetic factors significantly influence brain criticality across various scales, from specific brain regions to large-scale networks,” the team said in their paper published in the peer-reviewed journal Proceedings of the National Academy of Sciences last month.
They also established a link between criticality and cognitive functions, suggesting a shared genetic foundation.
“These findings position brain criticality as a biological phenotype, opening broad avenues for exploring its implications in brain function and potential dysfunctions,” the team wrote.
Brain criticality is characterised by neuronal avalanches, or cascading bursts of neuron activity in brain networks.
“At the critical state, the brain exhibits scale-free dynamics, with avalanches observed across various scales ranging from local networks of individual neurons to the global network of interacting brain areas,” the paper said.
Amazon is going to put an end to human labour. Yes, it has reached a turning point that will change how we view salaried work forever: robots will outnumber human employees in warehouses around the world. The company that until a few years ago was seen as a major job creator has now said no more human labour, it wants more robots. And yes, it will be the first time that robots outnumber human employees, even though Amazon already has one million machines, from robotic arms to wheeled transporters since 2020.
Layoffs continue and job automation doesn’t seem to be slowing down, because of course, not only does it improve company productivity, but machines don’t get sick, don’t ask for personal days, and don’t demand their labour rights… The data may be very optimistic for Amazon, but workers are seeing their jobs being taken away… and there’s no turning back. Here’s what’s happening inside Amazon’s warehouses.
A new sensing system called SonicBoom could help agricultural robots navigate cluttered environments where visual sensors struggle.
Developed by researchers at Carnegie Mellon University, SonicBoom uses tiny contact microphones to sense sound and localize objects that a robotic arm touches.
Interestingly, these robots could help farmers harvest crops even in increasingly challenging conditions, such as rising temperatures.
Chances are that you have unknowingly encountered compelling online content that was created, either wholly or in part, by some version of a Large Language Model (LLM). As these AI resources, like ChatGPT and Google Gemini, become more proficient at generating near-human-quality writing, it has become more difficult to distinguish between purely human writing from content that was either modified or entirely generated by LLMs.
This spike in questionable authorship has raised concerns in the academic community that AI-generated content has been quietly creeping into peer-reviewed publications.
To shed light on just how widespread LLM content is in academic writing, a team of U.S. and German researchers analyzed more than 15 million biomedical abstracts on PubMed to determine if LLMs have had a detectable impact on specific word choices in journal articles.
D.P. Kroese, Z.I. Botev, T. Taimre, R. Vaisman. Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman and Hall/CRC, Boca Raton, 2019.
The purpose of this book is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.