Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001124S0029) for the Artificial Intelligence Quantified (AIQ) project.
AIQ seeks to find ways of assessing and understanding the capabilities of AI to enable mathematical guarantees on performance. Successful use of military AI requires ensuring safe and responsible operation of autonomous and semi-autonomous technologies.
Learn about Sleepy Pickle, a new threat exploiting machine learning models via pickle files. Protect your data now!
Researchers have developed a technique that allows artificial intelligence (AI) programs to better map three-dimensional spaces using two-dimensional images captured by multiple cameras. Because the technique works effectively with limited computational resources, it holds promise for improving the navigation of autonomous vehicles.
The discovery process involved extensive fieldwork and the use of advanced technology. Researchers utilized high-resolution aerial photographs and an optimized artificial intelligence model to accurately map the habitat and health of trees across the region, indirectly leading to the identification of the new viper species. This method allowed the researchers to cover a vast area with unprecedented precision, enhancing their understanding of the ecosystem and its inhabitants.
One of the most intriguing aspects of the Ovophis jenkinsi is its behavior. Unlike many snakes that prefer to flee when threatened, this viper exhibits aggressive defensive tactics.
“It is usually slow-moving but shows great aggression when disturbed,” the researchers wrote. “When threatened, these snakes inflate their bodies to make themselves appear larger and strike quickly.”
Find out why the multihead attention layer is showing up in all kinds of machine learning architectures. What does it do that other layers can’t?
Patreon: / animated_ai.
Animations: https://animatedai.github.io/
The lawsuit is one of the most direct challenges to Musk’s decision to start xAI, and it comes on the heels of his threat to develop AI outside of Tesla unless he is awarded more voting control over the company.
The suit was also filed just a few hours before Tesla is scheduled to host its annual…
Tesla shareholders are suing CEO Elon Musk and the company’s board over the creation of xAI, a new artificial intelligence company, as reported by TechCrunch.
A Finnish startup called Flow Computing is making one of the wildest claims ever heard in silicon engineering: by adding its proprietary companion chip, any CPU can instantly double its performance, increasing to as much as 100x with software tweaks.
If it works, it could help the industry keep up with the insatiable compute demand of AI makers.
Flow is a spinout of VTT, a Finland state-backed research organization that’s a bit like a national lab. The chip technology it’s commercializing, which it has branded the Parallel Processing Unit, is the result of research performed at that lab (though VTT is an investor, the IP is owned by Flow).
Karleigh Fry pioneers robotic neurosurgery:
An 8-year-old girl from Oklahoma has become the world’s first pediatric patient to undergo robotic deep brain stimulation (DBS).
The announcement was made by two collaborating hospitals, Oklahoma Children’s Hospital OU Health and Bethany Children’s Health Center.
This groundbreaking procedure not only marks a significant milestone in pediatric neurosurgery but also holds promise for future treatments of severe neurological disorders in children.
Researchers have demonstrated a new intelligent photonic sensing-computing chip that can process, transmit and reconstruct images of a scene within nanoseconds. This advance opens the door to extremely high-speed image processing that could benefit edge intelligence for machine vision applications such as autonomous driving, industrial inspection and robotic vision.
Edge computing, which performs intensive computing tasks like image processing and analysis on local devices, is evolving into edge intelligence by adding artificial intelligence (AI) driven analysis and decision-making.
“Capturing, processing and analyzing images for edge-based tasks such as autonomous driving is currently limited to millisecond-level speeds due to the necessity of optical-to-electronic conversions,” said research team leader Lu Fang from Tsinghua University in China. “Our new chip can perform all these processes in just nanoseconds by keeping them all in the optical domain. This could be used to significantly enhance, or even replace, the traditional architecture of sensor acquisition followed by AI post-processing.”
