A speech study by a research team from The University of Texas at El Paso has identified an underappreciated aspect of speech in English and Spanish speakers that could lead to improvements in artificial intelligence spoken dialogue systems.

The SETI Institute announced that alliant Global CEO, Dhaval Jadav, joined its Board of Directors. Dhaval brings a deep lifelong passion for space science, a strong commitment to STEM education, and a shared belief in the SETI Institute’s mission to explore one of humanity’s most profound questions: Are we alone in the universe?

This marks the beginning of a strategic partnership that gives the SETI Institute the ability to leverage alliant’s resources and AI capabilities in the search for extraterrestrial life.

“As a kid nothing got me more excited to learn about space than the thought of extraterrestrials being out there,” said Dhaval. “I think we’ve lost some of that sense of wonder, the curiosity that drives people to look beyond their screens and ask big questions about the universe. I wholeheartedly believe in the SETI Institute’s mission, and I hope alliant can help the SETI Institute be a beacon that rekindles that curiosity and inspires people to seek answers to life’s biggest mysteries.”

One of the primary challenges with prosthetic hands is the ability to properly tune the appropriate grip based on the object being handled. In Nanotechnology and Precision Engineering, researchers in China have developed an object identification system for prosthetic hands to guide appropriate grip strength decisions in real time. Their system uses an electromyography sensor at the user’s forearm to determine what the user intends to do with the object at hand.

In APL Bioengineering, researchers use a machine learning algorithm to explore whether electroencephalography could be useful for connecting brain signals with limb movements in patients who have lost some or all their limb function. In tests, the researchers equipped patients with EEG monitors and asked them to perform simple movements, using their algorithm to classify the range of possible signals. They found they could detect the difference between attempted movement and no movement but struggled to differentiate between specific signals.