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hybrid-augmented intelligence： collaboration and cognition

Frontiers of Information Technology & Electronic Engineering 2017年第2期18卷 153-179页

作者： Nan-ning ZHENG Zi-yi LIU Peng-ju REN Yong-qiang MA Shi-tao CHEN Si-yu YU Jian-ru XUE Ba-dong CHEN Fei-yue WANG Institute of Artificial intelligence and Robotics Xi ' an Jiaotong University Xi ' an 710049 China National Engineering Laboratory of Visual Information Processing Applications Xi 'an Jiaotong University Xi'an 710049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

关键词： Human-machine collaboration hybrid-augmented intelligence Cognitive computing Intuitivereasoning Causal model Cognitive mapping Visual scene understanding Self-driving cars

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Human-AI Cooperation in Education: Human in Loop and Teaching as leadership

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教育技术与创新 2022年第1期 14-25页

作者： Feng Chen Wenzhou Medical University Zhejiang Wenzhou

Using the differences and complementarities between human intelligence and artificial intelligence(AI),a hybrid-augmented intelligence,that is both stronger than human intelligence and AI,is created through Human-AI Cooperation(HAC)for teaching and ***-AI Cooperation is infiltrating into all links of education,and recent research has focused a lot on the impact of teaching,learning,management,and evaluation with Human-AI ***,AI still has its limits of intelligence,and cannot cooperate as ***,it is critical to study the obstacles of Human-AI Cooperation in education,as AI plays a role as a partner,not a *** study discussed for the first time how teachers and AI cooperate based on Multiple intelligences of AI proposed by Andrzej Cichocki and puts forward a new Human-AI Cooperation teaching mode:human in the loop and teaching as *** is proposed that humans in the loop and teaching as leadership can solve the problem that AI cannot cope with complex and dynamic teaching tasks in open situations,as well as the limits of intelligence for AI.

关键词： Human-AI Cooperation Education Human in Loop Teaching as leadership Multiagents Multiple intelligences Emotional intelligence Social intelligence Creative intelligence Innovative intelligence Ethical and moral intelligence hybrid-augmented intelligence

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