Existing numerical computing libraries lack native support for physical units, limiting their application in rigorous scientific computing. Here, the authors developed SAIUnit, which integrates physical units, and unit-aware mathematical functions and transformations into numerical computing libraries for artificial intelligence-driven scientific computing.
“Completely automating labor could generate vast abundance, much higher standards of living, and new goods and services that we can’t even imagine today.”
Neural networks are one typical structure on which artificial intelligence can be based. The term “neural” describes their learning ability, which to some extent mimics the functioning of neurons in our brains. To be able to work, several key ingredients are required: one of them is an activation function which introduces nonlinearity into the structure.
A photonic activation function has important advantages for the implementation of optical neural networks based on light propagation. Researchers in the Stiller Research Group at the MPL and LUH in collaboration with MIT have now experimentally shown an all-optically controlled activation function based on traveling sound waves.
It is suitable for a wide range of optical neural network approaches and allows operation in the so-called synthetic frequency dimension. The work is published in the journal Nanophotonics.
A car accident, football game, or even a bad fall can lead to a serious or fatal head injury. Annually, traumatic brain injuries (TBI) cause half a million permanent disabilities and 50,000 deaths. Monitoring pressure inside the skull is key to treating TBI and preventing long-lasting complications.
Most of these monitoring devices are large and invasive, requiring surgical emplacement. But Georgia Tech researchers have recently created a sensor smaller than a dime. The miniature size offers huge benefits.
“Surgery means extensive recovery time and can significantly impact patient health. Our system doesn’t require surgery because we use a conventional stent, the catheter, as a delivery vehicle,” said W. Hong Yeo, the Harris Saunders Jr. Endowed Professor and an associate professor in the George W. Woodruff School of Mechanical Engineering.
Georgia Tech researchers have developed an almost imperceptible microstructure brain sensor to be inserted into the minuscule spaces between hair follicles and slightly under the skin. The sensor offers high-fidelity signals and makes the continuous use of brain-computer interfaces (BCI) in everyday life possible.
BCIs create a direct communication pathway between the brain’s electrical activity and external devices such as electroencephalography devices, computers, robotic limbs, and other brain monitoring devices. Brain signals are commonly captured non-invasively with electrodes mounted on the surface of the human scalp using conductive electrode gel for optimum impedance and data quality. More invasive signal capture methods such as brain implants are possible, but this research seeks to create sensors that are both easily placed and reliably manufactured.
Hong Yeo, the Harris Saunders Jr. Professor in the George W. Woodruff School of Mechanical Engineering, combined the latest microneedle technology with his deep expertise in wearable sensor technology that may allow stable brain signal detection over long periods and easy insertion of a new painless, wearable microneedle BCI wireless sensor that fits between hair follicles. The skin placement and extremely small size of this new wireless brain interface could offer a variety of benefits over traditional gel or dry electrodes.