AI Robots Working Inside International Space Station
Introduction
NASA’s Astro B program aims to test the feasibility of using AI robots to perform various tasks and aid astronauts in their daily duties. The program aims to allow autonomous robots to perform maintenance on spacecraft and enhance the safety and cost-effectiveness of space flights.
Two AstroBee robots have recently operated independently side by side with humans in separate modules of the International Space Station. Bumble tested its navigation ability in the Harmony module, while Queen captured its first 360-degree panoramic image of the interior of the orbital laboratory. These mapping and imaging experiments are part of the Integrated System for Autonomous and Adaptive Caretaking Project managed at NASA’s Ames Research Center in California.
AstroBee System
The project uses the AstroBee system, a set of three cube-shaped robots, plus a docking station designed and built by NASA’s Ames Research Center. The AstroBees, which first launched to the Space Station in 2018, can operate fully autonomously or under remote control by astronauts or ground operators. The AstroBee AI robots are being taught to autonomously support spacecraft monitoring, maintenance, and other tasks, making space flights safer and more cost-effective.
Artificial Intelligence Driven Clinical Sepsis Screening Approach Saves Lives
Researchers from Bayesian and Johns Hopkins University have demonstrated the deployment of artificial intelligence-driven clinical sepsis screening approach, which reduced mortality, morbidity, and length of hospital stay patients with sepsis. Early recognition and treatment are critical to successful outcomes, and an automated sepsis early warning system can help clinicians recognize sepsis as early as possible. The technology has the potential to make a significant difference in hospital mortality rates from sepsis.
New Breakthrough in AI for High Volume Unstructured Text
Cortical.eo has announced its breakthrough prototype for classifying high volumes of unstructured text, making large-scale classification use cases commercially viable for the first time. The benchmark results show that the operations cost can be reduced from several dollars per classifier to a fraction of a cent, making hate speech detection for nearly three billion Facebook users possible.
Efficiency is the New Precision in Artificial Intelligence
Efficiency is essential for the future of green computing, and high-efficiency AI capabilities are the way forward. While large industries aim to use less energy, the AI and ML industry has gone in the opposite direction. Large-scale classification use cases require significant energy inputs, but AI and ML companies are working on increasing efficiency to reduce their carbon footprint.
Conclusion
The Astro B program, which is being managed at NASA’s Ames Research Center in California, is testing the feasibility of using AI robots to perform various tasks and aid astronauts in their daily duties. These robots, known as AstroBees, can operate autonomously or under remote control and enhance the safety and cost-effectiveness of space flights.
The deployment of an AI-driven clinical sepsis screening approach has shown promise in reducing mortality and morbidity for patients with sepsis. Similarly, the breakthrough prototype for classifying high volumes of unstructured text has opened possibilities for new use cases, such as hate speech detection for nearly three billion Facebook users.
Efficiency is the new precision in artificial intelligence, ensuring a future for green computing. AI and ML companies are striving to make large-scale classification use cases commercially viable while reducing their carbon footprint.