Artificial intelligence is advancing at a dizzying speed. Like many new technologies, it offers significant benefits but also poses safety risks. Recognizing the potential dangers, leading researchers from Google DeepMind, OpenAI, Meta, Anthropic and a coalition of companies and nonprofit groups have come together to call for more to be done to monitor how AI systems “think.”
While roboticists have introduced increasingly advanced systems over the past decades, most existing robots are not yet able to manipulate objects with the same dexterity and sensing ability as humans. This, in turn, adversely impacts their performance in various real-world tasks, ranging from household chores to the clearing of rubble after natural disasters and the assembly or performing maintenance tasks, particularly in high-temperature working environments such as steel mills and foundries, where elevated temperatures can significantly degrade performance and compromise the precision required for safe operations.
Researchers at the University of Southern California recently developed the MOTIF (Multimodal Observation with Thermal, Inertial, and Force sensors) hand, a new robotic hand that could improve the object manipulation capabilities of humanoid robots. The innovation, presented in a paper posted to the arXiv preprint server, features a combination of sensing devices, including tactile sensors, a depth sensor, a thermal camera, inertial measurement unit (IMU) sensors and a visual sensor.
“Our paper emerged from the need to advance robotic manipulation beyond traditional visual and tactile sensing,” Daniel Seita, Hanyang Zhou, Wenhao Liu, and Haozhe Lou told Tech Xplore. “Current multi-fingered robotic hands often lack the integrated sensing capabilities necessary for complex tasks involving thermal awareness and responsive contact feedback.”
Imagine a future where artificial intelligence quietly shoulders the drudgery of software development: refactoring tangled code, migrating legacy systems, and hunting down race conditions, so that human engineers can devote themselves to architecture, design, and the genuinely novel problems still beyond a machine’s reach.
Recent advances appear to have nudged that future tantalizingly close, but a new paper by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and several collaborating institutions argues that this potential future reality demands a hard look at present-day challenges.
Titled “Challenges and Paths Towards AI for Software Engineering,” the work maps the many software-engineering tasks beyond code generation, identifies current bottlenecks, and highlights research directions to overcome them, aiming to let humans focus on high-level design while routine work is automated. The paper is available on the arXiv preprint server, and the researchers are presenting their work at the International Conference on Machine Learning (ICML 2025) in Vancouver.
The Graduate School of Information Science (GSIS) at Tohoku University, together with the Physics and Informatics (PHI) Lab at NTT Research, Inc., have jointly published a paper in the journal Quantum Science and Technology. The study involved studying a combinatorial clustering problem, a representative task in unsupervised machine learning.
Together, the two institutions are researching methods to bring to life a large-scale CIM simulation platform using conventional high-performance computing (HPC). This large-scale CIM will be critical to enabling cyber CIMs that will be widely accessible for solving hard NP, NP-complete and NP-hard problems.
The collaboration kicked off in 2023 with Hiroaki Kobayashi, Professor at the GSIS at Tohoku University, acting as the principal investigator for the joint research agreement (JRA), with PHI Lab Director Yoshihisa Yamamoto joining as the NTT Research counterpart to Kobayashi.
A research team affiliated with UNIST announced the successful development of a novel semiconductor device that uses a new class of materials, known as altermagnetism. This breakthrough is expected to significantly advance the development of ultra-fast, energy-efficient AI semiconductor chips.
Jointly led by Professor Jung-Woo Yoo from the Department of Materials Science and Engineering and Professor Changhee Sohn from the Department of Physics at UNIST, the team succeeded in fabricating magnetic tunnel junctions (MTJs) using altermagnetic ruthenium oxide (RuO2). They also measured a practical level of tunneling magnetoresistance (TMR) in these devices, demonstrating their potential for spintronic applications.
The research was led by Seunghyun Noh from the Department of Materials Science and Engineering and Kyuhyun Kim from the Department of Physics at UNIST. The findings were published in Physical Review Letters on June 20, 2025.
Humanoid robots — long seen as futuristic — are already here, walking, talking, and working among us. Here are 10 advanced examples.
At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don’t have much data. Conversely, successful machine learning (ML) tends to rely on large, high-quality data sets for training. So how can researchers harness AI effectively to support their investigations?
In Physical Review Research, scientists describe an approach for working with ML to tackle complex questions in condensed matter physics. Their method tackles hard problems which were previously unsolvable by physicist simulations or by ML algorithms alone.
The researchers were interested in frustrated magnets— magnetic materials in which competing interactions lead to exotic magnetic properties. Studying these materials has helped to advance our understanding of quantum computing and shed light on quantum gravity. However, frustrated magnets are very difficult to simulate, because of the constraints arising from the way magnetic ions interact.
Canadian researchers from the Weizmann Institute and Intel Labs have presented a new algorithm that allows different AI models unite and work together to increase efficiency and reduce costs.
