超大预训练模型(large scale pre-trained model,LSPTM)的发展在人工智能领域产生了意想不到的效果,尤其是在自然语言处理(natural language processing,NLP)上ChatGPT的突破,似乎打通了“人工智能”的任督二脉,在短短的几个月内,其智...超大预训练模型(large scale pre-trained model,LSPTM)的发展在人工智能领域产生了意想不到的效果,尤其是在自然语言处理(natural language processing,NLP)上ChatGPT的突破,似乎打通了“人工智能”的任督二脉,在短短的几个月内,其智能实现了从人类可以理解的智能到无法理解的智能跨越.以ChatGPT为代表的LSPTM即将开启一个全新的硅基智能时代,指挥与控制(command and control,C2)作为人类社会引以为豪的体现智慧的关键要素,会面临什么样的机遇和挑战?以C2过程的基本范式和运行基本模式框架为指导,全面分析LSPTM在C2活动的物理域、信息域、认知域以及社会域各方向潜在的应用,阐述硅基智能时代,人工智能从辅助工具角色向伙伴和替代角色的跨越,C2领域发展的机遇.军事领域对抗性C2的竞争不再局限于技术,而是培育LSPTM的文化底蕴,东西方文化与价值观的差异将决定两种不同LSPTM的智慧与智能.展开更多
The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely prod...The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely produced,and several applications of Level 4 systems to specific situations have also been gradually developed.By improving the automation level and vehicle intelligence,these systems can be further advanced towards fully autonomous driving.However,general development concepts for Level 5 AVs remain unclear,and the existing methods employed in the development processes of Levels 0-4 have been mainly based on task-driven function development related to specific scenarios.Therefore,it is difficult to identify the problems encountered by high-level AVs.The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems.By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving,we put forward a coordinated and balanced framework based on the brain-cerebellum-organ concept through reasoning and deduction.Based on a mixed mode relying on the crow inference and parrot imitation approach,we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning,self-adaptation,and self-transcendence for AVs.From a systematic,unified,and balanced point of view and based on least action principles and unified safety field concepts,we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs,specifically at Level 5.展开更多
文摘超大预训练模型(large scale pre-trained model,LSPTM)的发展在人工智能领域产生了意想不到的效果,尤其是在自然语言处理(natural language processing,NLP)上ChatGPT的突破,似乎打通了“人工智能”的任督二脉,在短短的几个月内,其智能实现了从人类可以理解的智能到无法理解的智能跨越.以ChatGPT为代表的LSPTM即将开启一个全新的硅基智能时代,指挥与控制(command and control,C2)作为人类社会引以为豪的体现智慧的关键要素,会面临什么样的机遇和挑战?以C2过程的基本范式和运行基本模式框架为指导,全面分析LSPTM在C2活动的物理域、信息域、认知域以及社会域各方向潜在的应用,阐述硅基智能时代,人工智能从辅助工具角色向伙伴和替代角色的跨越,C2领域发展的机遇.军事领域对抗性C2的竞争不再局限于技术,而是培育LSPTM的文化底蕴,东西方文化与价值观的差异将决定两种不同LSPTM的智慧与智能.
基金This work was jointly supported by the National Science Fund for Distinguished Young Scholars(51625503)the National Natural Science Foundation of China,the Major Project(61790561)the Joint Laboratory for Internet of Vehicle,Ministry of Education,China Mobile Communications Corporation.
文摘The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely produced,and several applications of Level 4 systems to specific situations have also been gradually developed.By improving the automation level and vehicle intelligence,these systems can be further advanced towards fully autonomous driving.However,general development concepts for Level 5 AVs remain unclear,and the existing methods employed in the development processes of Levels 0-4 have been mainly based on task-driven function development related to specific scenarios.Therefore,it is difficult to identify the problems encountered by high-level AVs.The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems.By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving,we put forward a coordinated and balanced framework based on the brain-cerebellum-organ concept through reasoning and deduction.Based on a mixed mode relying on the crow inference and parrot imitation approach,we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning,self-adaptation,and self-transcendence for AVs.From a systematic,unified,and balanced point of view and based on least action principles and unified safety field concepts,we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs,specifically at Level 5.
