The Niels Bohr Institute at the University of Copenhagen has made a groundbreaking discovery that could revolutionize the way we transmit information. Researchers, in collaboration with the University of Konstanz and ETH Zurich, have successfully sent vibrations through an ultra-thin drumhead, measuring only 10 mm wide, with astonishingly low loss – just one phonon out of a million. This achievement is even more impressive than electronic circuit signal handling.

The drumhead, perforated with many triangular holes, utilizes the concept of phonons to transmit signals. Phonons are essentially sound waves that travel through solid materials by vibrating atoms and pushing each other. This phenomenon is not unlike encoding a message and sending it through a material, where signal loss can occur due to various factors like heat or incorrect vibrations.

The researchers’ success lies in achieving almost lossless transmission of signals through the membrane. The reliability of this platform for sending information is incredibly high, making it a promising candidate for future applications. To measure the loss, researchers directed the signal through the material and around the holes, observing that the amplitude decreased by only about one phonon out of a million.

This achievement has significant implications for quantum research. Building a quantum computer requires super-precise transfer of signals between its different parts. The development of sensors capable of measuring the smallest biological fluctuations in our own body also relies heavily on signal transfer. As Assistant Professor Xiang Xi and Professor Albert Schliesser explain, their current focus is on exploring further possibilities with this method.

“We want to experiment with more complex structures and see how phonons move around them or collide like cars at an intersection,” says Albert Schliesser. “This will give us a better understanding of what’s ultimately possible and what new applications there are.” The pursuit of basic research is about producing new knowledge, and this discovery is a testament to the power of scientific inquiry.

In conclusion, the quantum drumhead revolution has brought us one step closer to achieving near-perfect signal transmission. As researchers continue to explore the possibilities of this method, we can expect exciting breakthroughs in various fields, ultimately leading to innovative applications that will transform our understanding of the world.

The Massachusetts General Brigham researchers have developed an innovative artificial intelligence (AI) tool called AI-CAC to analyze previously collected CT scans and identify individuals with high coronary artery calcium (CAC) levels, indicating a greater risk for cardiovascular events. Their research, published in NEJM AI, demonstrated the high accuracy and predictive value of AI-CAC for future heart attacks and 10-year mortality.

Millions of chest CT scans are taken each year, often in healthy people, to screen for lung cancer or other conditions. However, this study reveals that these scans can also provide valuable information about cardiovascular risk, which has been going unnoticed. The researchers found that AI-CAC had a high accuracy rate (89.4%) at determining whether a scan contained CAC or not.

The gold standard for quantifying CAC uses “gated” CT scans, synchronized to the heartbeat to reduce motion during the scan. However, most chest CT scans obtained for routine clinical purposes are “nongated.” The researchers developed AI-CAC, a deep learning algorithm, to probe through these nongated scans and quantify CAC.

The AI-CAC model was 87.3% accurate at determining whether the score was higher or lower than 100, indicating a moderate cardiovascular risk. Importantly, AI-CAC was also predictive of 10-year all-cause mortality, with those having a CAC score over 400 having a 3.49 times higher risk of death over a 10-year period.

The researchers hope to conduct future studies in the general population and test whether the tool can assess the impact of lipid-lowering medications on CAC scores. This could lead to the implementation of AI-CAC in clinical practice, enabling physicians to engage with patients earlier, before their heart disease advances to a cardiac event.

As Dr. Raffi Hagopian, first author and cardiologist at the VA Long Beach Healthcare System, emphasized, “Using AI for tasks like CAC detection can help shift medicine from a reactive approach to the proactive prevention of disease, reducing long-term morbidity, mortality, and healthcare costs.”

Uncovering Human Superpowers: How Our Brains Master Affordances that Elude AI

Imagine walking through a park or swimming in a lake – it’s a natural ability we take for granted. Researchers at the University of Amsterdam have shed light on how our brains process this intuitive knowledge, and the implications are fascinating. By studying brain activity while people viewed various environments, they discovered unique patterns associated with “affordances” – opportunities for action.

In essence, when we look at a scene, our brains automatically consider what we can do in it, whether it’s walking, cycling, or swimming. This is not just a psychological concept but a measurable property of our brains. The research team, led by Iris Groen, used an MRI scanner to investigate brain activity while participants viewed images of indoor and outdoor environments.

The results were striking: certain areas in the visual cortex became active in a way that couldn’t be explained by visible objects in the image. These brain areas not only represented what could be seen but also what you can do with it – even when participants weren’t given an explicit action instruction. This means that affordance processing occurs automatically, without conscious thought.

The researchers compared these human abilities with AI models, including ChatGPT, and found that they were worse at predicting possible actions. Even the best AI models didn’t give exactly the same answers as humans, despite it being a simple task for us. This highlights how our way of seeing is deeply intertwined with how we interact with the world.

The study has significant implications for the development of reliable and efficient AI. As more sectors use AI, it’s crucial that machines not only recognize what something is but also understand what it can do. For example, a robot navigating a disaster area or a self-driving car distinguishing between a bike path and a driveway.

Moreover, the research touches on the sustainable aspect of AI. Current training methods are energy-intensive and often accessible to large tech companies. By understanding how our brains work and process information efficiently, we can make AI smarter, more economical, and more human-friendly.

The discovery of affordance processing in the brain opens up new avenues for improving AI and making it more sustainable. As we continue to explore the intricacies of human cognition, we may uncover even more human superpowers that elude AI – a fascinating prospect indeed.