A schematic representation of in-memory computing using electrochemical memory devices (ECRAMs) arranged in a cross-point array structure, mimicking the way synapses in the brain process information. When voltage is applied to the device, ions move within the channel, enabling simultaneous computation and data storage. This study reveals how ions and electrons behave under applied voltage, uncovering the device’s internal operational dynamics. Credit: POSTECH
As artificial intelligence (AI) continues to advance, researchers at POSTECH (Pohang University of Science and Technology) have identified a breakthrough that could make AI technologies faster and more efficient.
In an experiment reminiscent of the Transformers movie franchise, engineers at Princeton University have created a type of material that can expand, assume new shapes, move and follow electromagnetic commands like a remotely controlled robot even though it lacks any motor or internal gears.
“You can transform between a material and a robot, and it is controllable with an external magnetic field,” said researcher Glaucio Paulino, the Margareta Engman Augustine Professor of Engineering at Princeton.
In an article published April 23 in the journal Nature, the researchers describe how they drew inspiration from the folding art of origami to create a structure that blurs the lines between robotics and materials...
A schematic view of the pump-induced stimulated superradiant Smith-Purcell radiation (PIS-SPR) device, which consists of the electron pre-bunching, electron-compression and harmonic-emission section. Based on PIS-SPR, the free electron beam is well bunched and ultra-narrow spectral linewidth at THz frequency region has been observed. Credit: eLight (2025). DOI: 10.1186/s43593-025-00083-z
Superradiant Smith-Purcell radiation (S-SPR) is a kind of free electron radiation with a train of free electron bunches passing over a periodic grating. In theory, the ultra-narrow spectral linewidth of S-SPR could be realized which would be greatly beneficial to various applications such as imaging, sensing and communication.
However, in the free electron accelerators, customized setups and orotr...
AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server.
“This technique is novel in that it can be used even when you have no idea what spurious correlations the AI is relying on,” says Jung-Eun Kim, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.
“If you already have a good idea of what the spurious features are, our technique is an efficient and effective...
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