Tech expert Kurt Knutsson discusses how robots and drones are revolutionizing fruit farming with faster picking and smarter handling. Farming is undergoing a remarkable transformation thanks to cutting-edge technologies reshaping how fruit is picked and handled. While autonomous drones like Tevel's Flying Robots are already harvesting fruit globally, innovations like UC San Diego's GRIP-tape gripper represent the next frontier in gentle produce handling. Together, these advancements promise to make fruit production more efficient and precise, though one is a proven solution and the other is a glimpse into farming's future. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW Tevel's Flying Autonomous Robots (FARs) are redefining fruit harvesting by combining artificial intelligence with advanced computer vision.

Twenty-four years ago, the surgeon Santiago Horgan performed the first robotically assisted gastric-bypass surgery in the world, a major medical breakthrough. Now Horgan is working with a new tool that he argues could be even more transformative in operating rooms: the Apple Vision Pro. Over the last month, Horgan and other surgeons at the University of California, San Diego have performed more than 20 minimally invasive operations while wearing Apple's mixed-reality headsets. Apple released the headsets to the public in February, and they've largely been a commercial flop. But practitioners in some industries, including architecture and medicine, have been testing how they might serve particular needs.

Nanoengineers at the University of California San Diego's Jacobs School of Engineering have developed an AI algorithm that predicts the structure and dynamic properties of any material--whether existing or new--almost instantaneously. Known as M3GNet, the algorithm was used to develop matterverse.ai, The project is explored in the Nov. 28 issue of the journal Nature Computational Science. The properties of a material are determined by the arrangement of its atoms. However, existing approaches to obtain that arrangement are either prohibitively expensive or ineffective for many elements.

Isaac Newton's groundbreaking scientific productivity while isolated from the spread of bubonic plague is legendary. University of California San Diego physicists can now claim a stake in the annals of pandemic-driven science. A team of UC San Diego researchers and colleagues at Purdue University have now simulated the foundation of new types of artificial intelligence computing devices that mimic brain functions, an achievement that resulted from the COVID-19 pandemic lockdown. By combining new supercomputing materials with specialized oxides, the researchers successfully demonstrated the backbone of networks of circuits and devices that mirror the connectivity of neurons and synapses in biologically based neural networks. Like biologically based systems (left), complex emergent behaviors--which arise when separate components are merged together in a coordinated system--also result from neuromorphic networks made up of quantum-materials-based devices (right).

According to the National Alliance on Mental Illness and the World Health Organization, depression affects 16 million Americans and 322 million people worldwide. Emerging evidence suggests that the COVID-19 pandemic is further exacerbating the prevalence of depression in the general population. With this trajectory, it is evident that more effective strategies are needed for therapeutics that address this critical public health issue. In a recent study, publishing in the June 9, 2021 online edition of Nature Translational Psychiatry, researchers at University of California San Diego School of Medicine used a combination of modalities, such as measuring brain function, cognition and lifestyle factors, to generate individualized predictions of depression. The machine learning and personalized approach took into account several factors related to an individual's subjective symptoms, such as sleep, exercise, diet, stress, cognitive performance and brain activity.

June 21, 2021--Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow's virtual classrooms – and perhaps future virtual concert halls. UC San Diego music and computer science professor Shlomo Dubnov, an expert in computer music who directs the Qualcomm Institute-based CREL, began developing the new tool to deal with a downside of teaching music over Zoom during the COVID-19 pandemic. "In a music classroom, non-verbal communication such as facial affect and body gestures is critical to keep students on task, coordinate musical flow and communicate improvisational ideas," said Dubnov. "Unfortunately, this non-verbal aspect of teaching and learning is dramatically hampered in the virtual classroom where you don't inhabit the same physical space." To overcome the problem, Dubnov and Ph.D. student Ross Greer recently published a conference paper on a system that uses eye tracking and machine learning to allow an educator to make'eye contact' with individual students or performers in disparate locations – and lets each student know when he or she is the focus of the teacher's attention.

SDSC Director and Distinguished Professor of Physics Michael Norman is a member of the NAIRR Task Force. Director of the San Diego Supercomputer Center, and a Distinguished Professor of Physics at UC San Diego, Michael Norman has been appointed to the National Artificial Intelligence Research Resource (NAIRR) Task Force. This initiative announced recently by the Biden administration supports AI researchers' access to federal data in order to keep the U.S. at the forefront of emerging technology. "UC San Diego's faculty have a history of being called to contribute significantly to our nation's thought leadership on a variety of policy, economic, scientific and social issues," said UC San Diego Chancellor Pradeep K. Khosla. "Professor Norman's expertise in the computer simulation of astronomical phenomena using supercomputers, and the development of the numerical methods to carry them out, will provide the NAIRR taskforce first-hand knowledge to better understand how researchers collaboratively use data analysis and cloud computing in their fields." According to the National Artificial Intelligence Initiative Office, the NAIRR is envisioned as a shared computing and data infrastructure to provide AI researchers and students compute resources and high-quality data, along with appropriate educational tools and user support.

Soft robots are more flexible than traditional machines and have the potential to squeeze into and explore more places. However, most of them need electronic components like circuit boards, valves and pumps to work. Those components are typically heavy, expensive and have to be tethered to the machines outside their body. Now, engineers from the University of California San Diego have developed a four-legged soft robot that doesn't need any of those to work -- in fact, the robot doesn't need any electronic component at all. Their soft robot has an onboard system of pneumatic circuits, which are made up of tubes and soft valves.

CSE Assistant Professor Rose Yu, who recently arrived from Northeastern University in Boston, is developing physics-guided machine learning techniques to model spatiotemporal data. She investigates traffic flows, human mobility and fluid dynamics, but her passion for computer science began more humbly. "I think it was because of my love for computer video games," said Yu. "I played a lot of World of Warcraft in high school." That pastime sparked an early interest in computers and later in machine learning. Yu earned her PhD at USC, where she was honored for best dissertation.

"Essentially, we recreated all the key features that squids use for high-speed swimming," said Michael T. Tolley, one of the paper's senior authors and a professor in the Department of Mechanical and Aerospace Engineering at UC San Diego. "This is the first untethered robot that can generate jet pulses for rapid locomotion like the squid and can achieve these jet pulses by changing its body shape, which improves swimming efficiency." This squid robot is made mostly from soft materials such as acrylic polymer, with a few rigid, 3D printed and laser cut parts. Using soft robots in underwater exploration is important to protect fish and coral, which could be damaged by rigid robots. But soft robots tend to move slowly and have difficulty maneuvering.