Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 1,630

A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan, according to a study published in Radiology: Artificial Intelligence.

“This is the first study to address the most common intracranial tumors and to directly determine the tumor class or the absence of tumor from a 3D MRI volume,” said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, Ph.D., and Daniel Marcus, Ph.D., in Mallinckrodt Institute of Radiology’s Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri.

The six most common intracranial tumor types are high-grade glioma, low-grade glioma, brain metastases, meningioma, pituitary adenoma and acoustic neuroma. Each was documented through histopathology, which requires surgically removing tissue from the site of a suspected cancer and examining it under a microscope.

Read more | >

Commercial prospects for robotaxi services remain uncertain in the near term due to the immaturity of the technology, the absence of legislation to clearly define responsibility in case of a self-driving accident, and persistently high costs associated with the complex self-driving systems.

Baidu’s autonomous driving unit has partnered with the luxury electric vehicle brand of BAIC Group to bring fifth generation Apollo Moon robotaxis to Chinese roads, cutting the cost of the vehicles by two thirds.

Read more | >

Watch developer Plus testing an autonomous truck on the highway without a driver behind the wheel.

Autonomous tech developer Plus has recently completed a real-world demonstration of its Level 4 autonomous truck technology on a traffic-filled highway. The company tested the truck without a driver behind the wheel, and also without any other remote operator who could take control of the truck if needed. The test took place on the Wufengshan highway in the business hub of the Yangtze Delta region, with Plus being the first company to be granted a special permit to test Level 4 vehicles in the country.

Read more | >

New algorithm could enable fast, nimble drones for time-critical operations such as search and rescue.

If you follow autonomous drone racing, you likely remember the crashes as much as the wins. In drone racing, teams compete to see which vehicle is better trained to fly fastest through an obstacle course. But the faster drones fly, the more unstable they become, and at high speeds their aerodynamics can be too complicated to predict. Crashes, therefore, are a common and often spectacular occurrence.

But if they can be pushed to be faster and more nimble, drones could be put to use in time-critical operations beyond the race course, for instance to search for survivors in a natural disaster.

Read more | >

As reported in a new article in Nature Reviews Physics, instead of waiting for fully mature quantum computers to emerge, Los Alamos National Laboratory and other leading institutions have developed hybrid classical/quantum algorithms to extract the most performance—and potentially quantum advantage—from today’s noisy, error-prone hardware. Known as variational quantum algorithms, they use the quantum boxes to manipulate quantum systems while shifting much of the work load to classical computers to let them do what they currently do best: solve optimization problems.

“Quantum computers have the promise to outperform for certain tasks, but on currently available quantum hardware they can’t run long algorithms. They have too much noise as they interact with environment, which corrupts the information being processed,” said Marco Cerezo, a physicist specializing in , quantum machine learning, and quantum information at Los Alamos and a lead author of the paper. “With variational , we get the best of both worlds. We can harness the power of quantum computers for tasks that classical computers can’t do easily, then use classical computers to compliment the computational power of quantum devices.”

Current noisy, intermediate scale quantum computers have between 50 and 100 qubits, lose their “quantumness” quickly, and lack error correction, which requires more qubits. Since the late 1990s, however, theoreticians have been developing algorithms designed to run on an idealized large, error-correcting, fault tolerant quantum computer.

Read more | >

Chip design is a long slog of trial and error, taking years to bring a design to market. Motivo, a five-year-old startup from a chip industry veteran, is creating software to speed up chip design from years to months using AI. Today the company announced a $12 million Series A.

Intel Capital led the round along with new investors Storm Ventures and Seraph Group, as well as participation from Inventus Capital. The company reports it has now raised a total of $20 million with its previous seed funding.

Motivo co-founder and CEO Bharath Rangarajan has worked in the chip industry for 30 years, and he saw a few fundamental trends and issues. For starters, the chip design process is highly time-intensive, taking years to come up with a successful candidate, and typically the first to market wins.

Read more | >