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).
Continue reading “World’s 1st pediatric robotic brain surgery performed on Oklahoma girl” »
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.”
Washington state leads the nation in apple production, and in 2022, the industry contributed more than two billion dollars to the U.S. gross domestic product. Throughout Washington, farms employ anywhere from a dozen to hundreds of workers each year for orchard operations, including for pollination, pruning, flower thinning and fruit harvesting. With an aging population and a decrease in migrant farm workers, however, farmers have struggled to meet their needs for workers during harvest season.
In recent years, researchers have started developing robotic apple harvesting systems, but the ones that have been developed are expensive and complex to use in orchards.
Ninatanta, who grew up in Yakima, Washington, picked fruit alongside his parents during his childhood. When he began his work with Luo on a robotic apple gripper, he had his parents videotape their work, so he could model his gripper on their handiwork.
