Hybrid-augmented intelligence: collaboration and cognition

Abstract

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.

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?

“…Zheng et al assert that the long-term goal of AI is to enable machines to think like humans. Therefore, the question remains, can any computer truly replace the function of the human mind [62]?…”

“…Furthermore, AI is not suited for situations where end goals and inputs are not well defined, thereby prompting IA to play a crucial role [60]. This is further explored by Zheng et al [62], who argue that owing to high levels of uncertainties and vulnerabilities in human life, machines are unable to completely replace humans. Interestingly, They propose the concept of human-in-the-loop augmented intelligence through human-computer collaboration.…”

From Chance to Serendipity: Knowledge Workers’ Experiences of Serendipitous Social Encounters

“…We propose that this role could be best realized through cooperative synergy between a data-driven analytics system focused on computational analysis and a user capable of making personally relevant interpretations and focusing on the qualitative aspects of social matching. us, ICT-enhanced social serendipity could be aimed at achieving human-in-the-loop analytics [69] and augmented intelligence [70,71] rather than artificial intelligence, that is, supplementing human intelligence with computational capabilities through new forms of HCI. e innate differences between human decision-making and computational logic necessitate careful design of the interplay of these two types of intelligence [72].…”

Autonomous driving: cognitive construction and situation understanding

“…Kuo et al [124] showed that the neurons in the insula and anterior cingulate cortex of the brain are more active during intuitive reasoning, wheras the median, inferior parietal, and anterior torso are more active in logical reasoning, demonstrating the independence of intuition and logical reasoning systems from the perspective of neuroscience. Zheng et al [125] also pointed out that a human's intuitive reasoning is closely related to the abstract ability and strong generation ability of the human brain to prior knowledge, rather than simple mechanical memory. It was further pointed out that human intuition can rapidly allow decisions to be made with regard to risk avoidance based on the world model of the human brain owing to this high degree of generalization.…”

“…It was further pointed out that human intuition can rapidly allow decisions to be made with regard to risk avoidance based on the world model of the human brain owing to this high degree of generalization. The authors [125] also provided a basic method of computational realization of intuitive reasoning. In conclusion, it is insufficient to rely on logical reasoning for autonomous vehicles to drive in open traffic scenarios, and intuitive reasoning must therefore be introduced into the computing framework of self-driving cars.…”