Conceptualization and Framework of Hybrid Intelligence Systems

• URL: http://arxiv.org/abs/2012.06161v1
• Date: Fri, 11 Dec 2020 06:42:06 GMT
• Title: Conceptualization and Framework of Hybrid Intelligence Systems
• Authors: Nikhil Prakash and Kory W. Mathewson
• Abstract summary: This article provides a precise definition of hybrid intelligence systems and explains its relation with other similar concepts. We argue that all AI systems are hybrid intelligence systems, so human factors need to be examined at every stage of such systems' lifecycle.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As artificial intelligence (AI) systems are getting ubiquitous within our society, issues related to its fairness, accountability, and transparency are increasing rapidly. As a result, researchers are integrating humans with AI systems to build robust and reliable hybrid intelligence systems. However, a proper conceptualization of these systems does not underpin this rapid growth. This article provides a precise definition of hybrid intelligence systems as well as explains its relation with other similar concepts through our proposed framework and examples from contemporary literature. The framework breakdowns the relationship between a human and a machine in terms of the degree of coupling and the directive authority of each party. Finally, we argue that all AI systems are hybrid intelligence systems, so human factors need to be examined at every stage of such systems' lifecycle.

Related papers

• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• Ontology in Hybrid Intelligence: a concise literature review [3.9160947065896803]
  Hybrid Intelligence is gaining popularity to refer to a balanced coexistence between human and artificial intelligence. Ontology improves quality and accuracy, as well as a specific role to enable extended interoperability. An application-oriented analysis has shown a significant role in present systems (70+% the cases) and, potentially, in future systems.
  arXiv  Detail & Related papers  (2023-03-30T09:55:29Z)
• The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems [0.0]
  We identify the need for developing socio-technological ensembles of humans and machines. We present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design.
  arXiv  Detail & Related papers  (2021-05-07T16:10:44Z)
• On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS) [87.3520234553785]
  Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences.
  arXiv  Detail & Related papers  (2021-02-11T05:44:25Z)
• Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
  We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
  arXiv  Detail & Related papers  (2020-08-07T21:00:13Z)
• Synergetic Learning Systems: Concept, Architecture, and Algorithms [4.623783824925363]
  We describe an artificial intelligence system called the Synergetic Learning Systems'' The system achieves intelligent information processing and decision-making in a given environment through cooperative/competitive synergetic learning. It is expected that under our design criteria, the proposed system will eventually achieve artificial general intelligence through long term coevolution.
  arXiv  Detail & Related papers  (2020-05-31T06:23:03Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)
• A Structured Approach to Trustworthy Autonomous/Cognitive Systems [4.56877715768796]
  There is no generally accepted approach to ensure trustworthiness. This paper presents a framework to exactly fill this gap. It proposes a reference lifecycle as a structured approach that is based on current safety standards.
  arXiv  Detail & Related papers  (2020-02-19T14:36:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.