Large Language Models (LLMs) Overview
STAR.VISION▎Definition and Purpose:
Large Language Models (LLMs) are artificial intelligence models that leverage machine learning techniques to comprehend and produce human language.
They offer extensive value in automating and enhancing communication and data processing within businesses and organizations.
▎Operational Mechanism:
LLMs function through neural network-based models and often employ Natural Language Processing (NLP) techniques to generate outputs.
They are capable of performing tasks such as text analysis, sentiment analysis, language translation, and speech recognition.
▎Learning Process:
LLMs learn from extensive datasets containing billions of words and phrases through unsupervised learning.
Pretraining phases like those for models such as GPT-3 and BERT are foundational in the development of LLMs.
Without explicit human instructions, they extract information and learn language patterns from data.
▎Application in Space Technology:
LLMs are gaining significance in the realm of space technology.
They can aid in remote data acquisition and retrieval, thereby enhancing the capabilities of personnel operating at the frontiers of space.
▎STAR.VISION's Contribution:
STAR.VISION successfully launched the "STAR.VISION Smart Emergency Satellite No. 1," marking China's first AI payload-based smart emergency satellite equipped with an intelligent operating system.
This satellite boasts a peak onboard computing power of 80TOPS, facilitated by the "String" Edge AI Platform developed by STAR.VISION.
It serves a wide range of applications, including forest disaster reduction, water quality monitoring, soil moisture assessment, and pest monitoring.
▎Benefits in Space:
LLMs can assist astronauts in addressing complex issues and accessing crucial information in space, independent of Earth's network connections.
Their development and deployment in space demonstrate the potential of LLMs in extreme environments and their contribution to the exploration and utilization of space resources by humans.