A simulated fault scenario marked the end of the first phase of testing for software designed to enable autonomous operations of a spacecraft’s operating and robotic systems. The software’s name is ISAAC – the Integrated System for Autonomous and Adaptive Caretaking system.
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.
AI ethics is about more than just bias. That’s why Red Hat’s Noelle Silver is dedicated to spreading AI literacy.
Real-time capabilities will allow a customer to plug an existing TensorFlow or Pytorch neural network into streaming data and have it continuously re-trained, for uses such as recommender systems.
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 classical computers 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 computing, quantum machine learning, and quantum information at Los Alamos and a lead author of the paper. “With variational quantum algorithms, 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.
The Google subsidiary solved a fundamental problem in biology but didn’t promptly share its solution. So a University of Washington team tried to re-create it.
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.
Materials that change their properties in response to certain stimuli could come to occupy a valuable space in many fields, ranging from robotics, to medical care, to advanced aircraft. A new example of this type of shape-shifting technology is modeled on ancient chain mail armor, enabling it to swiftly switch from flexible to stiff thanks to carefully arranged interlocking particles.
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Engineers at Caltech and JPL
The Jet Propulsion Laboratory (JPL) is a federally funded research and development center managed for NASA by the California Institute of Technology (Caltech). The laboratory’s primary function is the construction and operation of planetary robotic spacecraft, though it also conducts Earth-orbit and astronomy missions. It is also responsible for operating NASA’s Deep Space Network. JPL implements programs in planetary exploration, Earth science, space-based astronomy and technology development, while applying its capabilities to technical and scientific problems of national significance.
Synopsys, which sells software for designing semiconductors to dozens of companies, is adding artificial intelligence to its arsenal.
