Science and technology

Humanity’s farthest spacecraft presses onward in quiet solitude beyond the bounds of the solar system, and to sustain its journey, engineers now face tough decisions about which instruments must be powered down. Every choice demands a careful trade‑off between preserving the craft and pursuing new insights at space’s outer frontier.As it continues its trek through interstellar space, Voyager 1 has moved into a fresh operational phase focused on preserving limited resources instead of expanding capabilities, and in mid-April, NASA engineers issued a command to power down one of the spacecraft’s scientific instruments to conserve energy and prolong its working life,…

Humanity’s most distant spacecraft continues its solitary voyage beyond the solar system’s edge, and engineers must now make difficult calls about which instruments should be shut down to prolong its travels. Each decision involves a delicate balance between safeguarding the craft and uncovering fresh discoveries at the universe’s remote frontier.As it journeys farther into interstellar space, Voyager 1 has shifted into a new operational stage, one centered on conserving resources rather than expanding capabilities, and in mid-April, NASA engineers sent a command to shut down one of the probe’s scientific instruments to save power and extend its functional lifespan, a…

Artificial intelligence workloads have reshaped how cloud infrastructure is designed, deployed, and optimized, prompting serverless and container-driven platforms once focused on web and microservice applications to rapidly evolve to meet the unique demands of machine learning training, inference, and data-intensive workflows; these needs include extensive parallel execution, variable resource usage, ultra‑low‑latency inference, and frictionless connections to data ecosystems, leading cloud providers and platform engineers to rethink abstractions, scheduling methods, and pricing models to better support AI at scale.Why AI Workloads Stress Traditional PlatformsAI workloads differ greatly from traditional applications across several important dimensions:Elastic but bursty compute needs: Model training can…

Vision-language-action models, commonly referred to as VLA models, are artificial intelligence frameworks that merge three fundamental abilities: visual interpretation, comprehension of natural language, and execution of physical actions. In contrast to conventional robotic controllers driven by fixed rules or limited sensory data, VLA models process visual inputs, grasp spoken or written instructions, and determine actions on the fly. This threefold synergy enables robots to function within dynamic, human-oriented settings where unpredictability and variation are constant.At a high level, these models connect camera inputs to semantic understanding and motor outputs. A robot can observe a cluttered table, comprehend a spoken instruction…