虽然科幻小说传说人类会以"小绿人"的形式与外星生活相会，但这个万圣节起发的细节是：寻找外星智能与AI一起向前迈出了一大步，它越来越关注人类为外星人所建造的AI agents.
一些科学家提出，地外文明如果存在，可能比人类年长数十亿。 这些文明可能已经发展出远远超出人类的技术，包括各种形式的人工智能。
突破倾听和SETI研究所正在将特工AI带入日常搜索外星情报。 在北加州的艾伦望远镜阵列上，agents现在可以引导仪器，增强并校正所接入的数据，并将AI模型应用到所接入的流中——全部来自单一的自然语言请求。 NVIDIA Holoscan平台处理大量的传感器数据流，使agents活数据能够采取行动。
结果是更快，更能适应地搜索来自外星生命和技术的潜在信号。
称 Andrew Siemion、SETI研究所SETI的伯纳德·M·奥利弗(Bernard M. Oliver)和突破倾听的主要调查员。 但这不是值得担心的事。 而是翻译方面的突破。
Terabits 二，飞行中处理
射电望远镜产生大量数据，给科学家带来挑战，他们需要随着观测的展开而识别出潜在的有趣信号。
在艾伦望远镜阵列上，一个Holoscan平台库DAQIRI使望远镜能够将每秒传感器数据中每秒的terabits直接拉入GPU内存，从而在飞行中处理观测，而不是填充硬盘。 CyberEther是SETI研究所开发的一个信号处理框架，使用包括cuFFT和cuBLAS在内的NVIDIA CUDA-X库进行数学。
Nexus是一款基于网络的天文台管弦乐器，作为agent拉带。 它给agents一个仪器状态的单一真理源，计算资源和管线，让研究人员监测包丢失，读取活光谱并重写信号处理块——都通过用自然语言来提示。 该系统可在NVIDIA RTX PRO 6000 Blackwell GPU上处理每秒2.4个terabits，在仪器旁边的场地上进行推断。
综合起来，这个系统使agent能够引导望远镜的天线，校准和聚焦一个干净的信号的仪器，捕获并固定数据错误并绘制结果。 Agents将人类需要几周或几年的时间压缩成小时。
科学事实，不是虚构
管道已经证明了自己，在"蟹状星云"中首次实现了实时 AI探测到一个脉冲星——一个能像宇宙灯塔一样脉冲的快速旋转的星状残余。
接下来，该小组正在评估开放模型，包括数据位置和转换最重要的案例的NVIDIA Nemotron，以及某一天可能在月球最远一端断线的观测站。
为科学学习更多NVIDIA AI.
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
While science fiction lore tells us that humans will meet extraterrestrial life in the form of little green men, the detail that should raise hairs this Halloween is this: the search for extraterrestrial intelligence took a major leap forward with AI, and it increasingly focuses on AI agents built by humans listening for AI agents built by aliens.
Some scientists have suggested that extraterrestrial civilizations, if they exist, could be billions of years older than humanity. Such civilizations might have developed technologies far beyond humanity’s, including forms of AI.
Breakthrough Listen and the SETI Institute are bringing agentic AI into the daily search for extraterrestrial intelligence. At the Allen Telescope Array in Northern California, agents can now steer the instrument, enhance and correct incoming data, and apply AI models to the incoming stream — all from a single natural language request. The NVIDIA Holoscan platform processes the massive streams of sensor data, giving agents live data to act on.
The result is a faster, more adaptable search for potential signals from extraterrestrial life and technologies.
“As our new agentic observatories reach out into the universe, what they find may have much more in common with themselves than with the humans who created them,” said Andrew Siemion, Bernard M. Oliver chair for SETI at the SETI Institute and principal investigator of Breakthrough Listen. “But this isn’t something to fear. Rather, it’s a breakthrough in translation.”
Terabits a Second, Processed in Flight
Radio telescopes generate enormous volumes of data, creating a challenge for scientists who need to identify potentially interesting signals as observations unfold.
At the Allen Telescope Array, DAQIRI, a Holoscan platform library, enables the telescope to pull terabits per second of sensor data straight into GPU memory so observations are processed in flight rather than filling hard drives. CyberEther, a signal-processing framework developed at the SETI Institute, does the math using NVIDIA CUDA-X libraries including cuFFT and cuBLAS.
Nexus, a web-based observatory orchestrator, acts as the agent harness. It gives agents a single source of truth for instrument state, compute resources and pipelines and lets researchers monitor packet loss, read live spectrograms and rewrite signal-processing blocks — all by prompting in natural language. The system can handle 2.4 terabits per second on NVIDIA RTX PRO 6000 Blackwell GPUs, with inference running on premises, next to the instrument.
Put together, the system enables an agent to steer a telescope’s antennae, calibrate and focus the instrument for a clean signal, catch and fix data errors and plot the result. Agents compress what would take humans weeks or years into hours.
Science Fact, Not Fiction
The pipeline has already proven itself, achieving the first real-time AI detection of a pulsar — a rapidly spinning stellar remnant that pulses like a cosmic lighthouse — in the Crab Nebula.
Next, the team is evaluating open models including NVIDIA Nemotron for cases where data locality and turnaround matter most and for observatories that may someday run offline on the far side of the moon.
Learn more about NVIDIA AI for science.
Auto-fetched and translated by AI Gem Box. All rights belong to the original author.