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Category: robotics/AI – Page 34

The New Duality: Why This Quantum Discovery Has Even Physicists Questioning Reality

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This quantum duality discovery shows a material acting as both conductor and insulator
 confirmed in a real lab.

A 35 Tesla experiment revealed quantum oscillations inside an insulator’s core. This “conductor-insulator duality” is being compared to wave-particle duality
 raising deeper questions about how reality behaves.

Inside this breakdown:
‱ University of Michigan quantum physics finding
‱ Conductor-insulator duality explained
‱ Wave-particle and observer effect links
‱ Faith and science parallels from Scripture.

If quantum duality keeps expanding
 what does it suggest about how reality actually works?

Computer-designed thermoelectric generator achieves more than 8-fold improvement in efficiency

A thermoelectric generator with a shape that no human designer would likely have imagined has now been created by a computer—and it performs more than eight times better than conventional designs. Rather than relying on intuition or repeated trial and error, the breakthrough was achieved through advanced computational optimization.

A joint research team led by Professor Jae Sung Son of the Department of Chemical Engineering at POSTECH (Pohang University of Science and Technology), in collaboration with Professor Hayoung Chung of the Department of Mechanical Engineering at UNIST (Ulsan National Institute of Science and Technology), has developed a general design framework that enables computers to autonomously identify the optimal structure of thermoelectric generators, which convert waste heat into electricity.

Their work is published online in Nature Communications.

Not all organs age alike: AI unveils the molecular impact of menopause across the female body

Despite affecting half of the world’s population, menopause has historically been understudied and misunderstood, both in biomedical research and clinical practice. However, with the increase in life expectancy, the number of women in the postmenopausal stage continues to grow and, in 2021, those over 50 already represented 26% of the world’s population, according to the WHO.

Its effects go far beyond the reproductive system and are associated with an increased risk of cardiovascular, metabolic, neurodegenerative, and bone diseases. Nevertheless, few studies analyzed in depth how this process affects the female reproductive system as a whole, beyond the ovaries.

In this context, a new study by the Barcelona Supercomputing Center—Centro Nacional de Supercomputación (BSC-CNS), published in Nature Aging, presents the first large-scale atlas of female reproductive system aging, providing a new vision of how this process impacts health.

The Trajectory of Quality of Life in Newly Diagnosed vs Chronic Refractory Focal EpilepsyA Prospective Multicenter Study

The trajectory of quality of life in newly diagnosed vs chronic refractory focal epilepsy: a prospective multicenter study.

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FingerEye bridges touch and vision to improve robot handling before and after contact

To reliably complete various manual tasks, robots should be able to handle a variety of objects, ranging from items found in households to tools used in specific professional settings. While many existing robotic systems can now complete basic manual tasks, such as picking up objects and carrying them to a set location, most systems still struggle with tasks that entail the dexterous manipulation of objects.

The term dexterous manipulation describes the ability to skillfully and precisely move objects in nuanced ways, which is central to the completion of many of the tasks that humans tackle daily. Replicating this ability in robots can be very difficult, as it typically requires gathering and interpreting different types of sensory information.

Conventional approaches for robot manipulation rely on visual sensors, such as cameras, and tactile sensors, devices that pick up tactile information. Yet most existing tactile sensors only provide feedback after a robot touches an object, which makes it difficult to plan manipulation strategies in advance.

Hidden stripe pattern lets microscopes auto-focus across 400 times deeper range

Anyone who has ever used a microscope knows that it takes time to bring a sample into sharp focus. Each time you move the slide, the image blurs, and you have to stop and carefully turn a knob to bring everything back into clear view. For scientists and clinicians, even if the motion is semi-automated, that time quickly adds up as they work with dozens or hundreds of samples.

Now a team of scientists at Caltech has developed an inexpensive, robust fix for this problem that involves little more than a couple of LED lights and some physics-based processing. They describe the new autofocus technique, which they call Digital Defocus Aberration Interference (DAbI), in a paper published in Nature Communications.

The lead authors of the paper are graduate students Haowen Zhou, Ph.D., and Shi “Josh” Zhao, who completed the work in the lab of Changhuei Yang, the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering at Caltech and a Heritage Medical Research Institute Investigator.

AI slashes the time needed to design better heat-harvesting devices

From wearable technology to industrial heat recovery, thermoelectric generators which convert waste heat into electricity have an enormous range of potential applications. So far, however, designing high-performing versions of these devices has remained a painstaking task.

Now, through new research published in Nature, Airan Li and colleagues at the National Institute for Materials Science in Japan have developed an AI-based tool that predicts device performance with greater than 99% accuracy, all while cutting computational time by around 10,000-fold.

Predictive pursuit emerges in high-dimensional recurrent neural networks

Tracking dynamic moving objects in the external world is ethologically important for many organisms. Recent experiments have examined neural dynamics supporting such behaviors by employing visually-guided pursuit in freely moving rodents, yet computational principles underlying this cognitive process are not well understood. To address this, we developed a recurrent neural network model for examining the predictive behaviors and computations that emerge during pursuit. We demonstrate that the model generates internal predictions of the targets future locations, with anticipatory behaviors increasing with exposure to stereotyped trajectories of the target. These internal predictions can be used by the model to pursue a target in a complex environment, and the RNNs emergent strategy is aligned with behavior when tested in rodents. In investigating the computations that underlie the RNNs ability to perform predictive pursuit, we found units sensitive to the position of the target relative to the artificial agent, a representation analogous to egocentric target neurons observed in animals performing pursuit tasks. Ablating these units significantly reduced model performance, establishing a causal role of this functional response type in efficient pursuit. Given the complexity of the task and agent behavior, we hypothesized that RNN models may use high-dimensional neural codes to support predictive pursuit. To test this, we trained models of varying rank and found that anticipatory behavior emerged only when the rank was sufficiently high, despite strong pursuit performance in lower rank models. All RNNs encoded the egocentric location of the target, whereas allocentric self and target locations emerged only in high-dimensional networks. Overall, our results suggest that, unlike commonly studied vision, motor, or memory tasks, predictive pursuit emerges in high-dimensional networks with sufficient resources.

The authors have declared no competing interest.

NVIDIA Launches Nemotron 3 Nano Omni Model, Unifying Vision, Audio and Language for up to 9x More Efficient AI Agents

This approach increases latency through repeated inference passes, fragments context across modalities, and adds cost and inaccuracies over time.

By combining vision and audio encoders within its 30B-A3B, hybrid mixture-of-experts architecture, Nemotron 3 Nano Omni eliminates the need for separate perception models, driving inference efficiency at scale. It pairs this efficiency with strong multimodal perception accuracy, enabling AI systems to achieve 9x higher throughput than other open omni models with the same interactivity. The result is lower costs and better scalability without sacrificing responsiveness or quality.

In agentic systems, Nemotron 3 Nano Omni can work alongside proprietary cloud models or other NVIDIA Nemotron open models — such as Nemotron 3 Super for high-frequency execution or Nemotron 3 Ultra for complex planning — as well as proprietary models from other providers, to power sub-agents for agentic workflows such as computer use, document intelligence and audio-video reasoning.

Tapping your genome with AI and quantum computing could deliver on the promise of personalized medicine — but practical and ethical hurdles remain

Combining AI with quantum computing could enable doctors and researchers to analyze the human body at an unprecedented molecular level, unlocking breakthroughs in personalized medicine. Yet significant quantum technology hurdles remain before this vision becomes reality.

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