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Category: robotics/AI – Page 22

How a fabric patch uses static electricity in your clothes to let you chat with AI and control smart devices

There could soon be a new way to interact with your favorite AI chatbots—through the clothing you wear. An international team of researchers has developed a voice-sensing fabric called A-Textile. This flexible patch of smart material turns everyday garments into a kind of microphone, allowing you to speak commands directly to what you’re wearing. This lets you communicate with AI systems such as ChatGPT or smart home devices.

Wearable devices that sense and interact with the world around us have long been the stuff of science fiction dreams. However, traditional sensors currently in use are often bulky, rigid and uncomfortable. They also lack sensitivity, meaning they struggle to hear soft or normal speaking voices, making it hard for AI to understand commands.

The researchers addressed this issue by exploring triboelectricity, the principle behind static electricity. A-Textile is a multi-layered fabric, and as you move the layers, they rub together to create a tiny electrostatic charge on the fabric. When you speak, the cause the charged layers to vibrate slightly, generating an that represents your voice. To boost the signal, the team embedded flower-shaped nanoparticles into the fabric to help capture the charge and prevent it from dissipating. This ensures it is clear enough to be recognized by AI.

Quantum crystals offer a blueprint for the future of computing and chemistry

Imagine industrial processes that make materials or chemical compounds faster, cheaper, and with fewer steps than ever before. Imagine processing information in your laptop in seconds instead of minutes or a supercomputer that learns and adapts as efficiently as the human brain. These possibilities all hinge on the same thing: how electrons interact in matter.

A team of Auburn University scientists has now designed a new class of materials that gives scientists unprecedented control over these tiny particles. Their study, published in ACS Materials Letters, introduces the tunable coupling between isolated-metal molecular complexes, known as solvated electron precursors, where electrons aren’t locked to atoms but instead float freely in open spaces.

From their key role in energy transfer, bonding, and conductivity, electrons are the lifeblood of chemical synthesis and modern technology. In , electrons drive redox reactions, enable bond formation, and are critical in catalysis. In technological applications, manipulating the flow and interactions between electrons determines the operation of electronic devices, AI algorithms, photovoltaic applications, and even . In most materials, electrons are bound tightly to atoms, which limits how they can be used. But in electrides, electrons roam freely, creating entirely new possibilities.

Tron: Ares | Official Trailer

Watch the brand-new trailer for Tron: Ares and experience it in theaters, filmed for IMAX, October 10.

Tron: Ares follows a highly sophisticated Program, Ares, who is sent from the digital world into the real world on a dangerous mission, marking humankind’s first encounter with A.I. beings. The feature film is directed by Joachim Rønning and stars Jared Leto, Greta Lee, Evan Peters, Hasan Minhaj, Jodie Turner-Smith, Arturo Castro, Cameron Monaghan, with Gillian Anderson, and Jeff Bridges. Sean Bailey, Jeffrey Silver, Justin Springer, Jared Leto, Emma Ludbrook and Steven Lisberger are the producers, with Russell Allen serving as executive producer.

Grammy®-award winning rock band Nine Inch Nails composed the score for “Tron: Ares,” and today, the band dropped \.

Leveraging AI in the Early Detection of Pancreatic Cancer | Tomorrow’s Cure Season 2 Episode 7

A recent breakthrough from Mayo Clinic researchers offers new hope. Using the world’s largest imaging dataset, Mayo Clinic’s team has developed a cutting-edge AI model capable of detecting pancreatic cancer on standard CT scans—when surgery is still an option. This breakthrough represents a leap forward in the fight against pancreatic cancer, with the potential to save lives. Learn more about this life-changing innovation in early cancer detection. Featured experts include Ajit Goenka, M.D., radiologist and professor of radiology at Mayo Clinic’s Comprehensive Cancer Center and Suresh Chari, M.D., professor, Department of Gastroenterology, Hepatology, and Nutrition in the Division of Internal Medicine at MD Anderson Cancer Center. Subscribe to Tomorrow’s Cure wherever you get your podcasts. Visit tomorrowscure.com for more information.

This podcast is for informational purposes only and should not be relied upon as professional, medical or legal advice. Always consult with a qualified health care provider for any medical advice. The appearance of any guest does not imply an endorsement of them, their employer, or any entity they represent. The views and opinions are those of the speakers and do not necessarily reflect the views of Mayo Clinic. Reference to any product, service or entity does not constitute an endorsement or recommendation by Mayo Clinic.

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Physics-informed AI excels at large-scale discovery of new materials

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A KAIST research team has introduced a new technique that combines physical laws, which govern deformation and interaction of materials and energy, with artificial intelligence. This approach allows for rapid exploration of new materials even under data-scarce conditions and provides a foundation for accelerating design and verification across multiple engineering fields, including materials, mechanics, energy, and electronics.

Professor Seunghwa Ryu’s research group in the Department of Mechanical Engineering, in collaboration with Professor Jae Hyuk Lim’s group at Kyung Hee University and Dr. Byungki Ryu at the Korea Electrotechnology Research Institute, proposed a new method that can accurately determine material properties with only limited data. The method uses physics-informed machine learning (PIML), which directly incorporates physical laws into the AI learning process.

In the first study, the researchers focused on hyperelastic materials, such as rubber. They presented a physics-informed neural network (PINN) method that can identify both the deformation behavior and the properties of materials using only a small amount of data obtained from a single experiment. Whereas previous approaches required large, complex datasets, this research demonstrated that material characteristics can be reliably reproduced even when data is scarce, limited, or noisy.

A new method to build more energy-efficient memory devices could lead to a sustainable data future

A research team led by Kyushu University has developed a new fabrication method for energy-efficient magnetic random-access memory (MRAM) using a new material called thulium iron garnet (TmIG) that has been attracting global attention for its ability to enable high-speed, low-power information rewriting at room temperature. The team hopes their findings will lead to significant improvements in the speed and power efficiency of high-computing hardware, such as that used to power generative AI.

The work is published in npj Spintronics.

The rapid spread of generative AI has made the power demand from data centers a global issue, creating an urgent need to improve the energy efficiency of the hardware that runs the technology.

The Role of Artificial Intelligence in Early Cancer Diagnosis

Diagnosing cancer at an early stage increases the chance of performing effective treatment in many tumour groups. Key approaches include screening patients who are at risk but have no symptoms, and rapidly and appropriately investigating those who do. Machine learning, whereby computers learn complex data patterns to make predictions, has the potential to revolutionise early cancer diagnosis. Here, we provide an overview of how such algorithms can assist doctors through analyses of routine health records, medical images, biopsy samples and blood tests to improve risk stratification and early diagnosis. Such tools will be increasingly utilised in the coming years.

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