下一代自主机器人代理的人工认知与人工智能

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-03-22 DOI:10.3389/fncom.2024.1349408
Giulio Sandini, Alessandra Sciutti, Pietro Morasso
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引用次数: 0

摘要

工业/服务机器人技术的发展趋势是开发能够与人合作的机器人,以自主、安全和有目的的方式与人互动。这些是第四次和第五次工业革命(4IR、5IR)的基本特征:关键的创新是采用智能技术,从而开发出类似于甚至优于人类的网络物理系统。人们普遍认为,智能可能由人工智能(AI)提供,但这种说法更多的是得到媒体报道和商业利益的支持,而非可靠的科学证据。目前,人工智能的概念相当宽泛,包括 LLM 和许多其他东西,没有任何统一的原则,但在各个领域的成功都是自我激励的结果。目前对人工智能机器人的看法大多遵循一种纯粹的非实体方法,这种方法与老式的笛卡尔身心二元论一致,反映在冯-诺依曼计算架构固有的软件-硬件区分上。本立场文件的工作假设是,通往具有认知能力的下一代自主机器人代理之路,需要一种完全由大脑启发的、具身认知方法,这种方法可避免身心二元论的陷阱,并旨在将 "身体软件"(Bodyware)和 "认知软件"(Cogniware)完全融合在一起。我们将这种方法命名为人工认知(ACo),并以认知神经科学为基础。它特别关注基于人与机器人双向互动的主动知识获取:其实际优势在于提高通用性和可解释性。此外,我们认为,大脑启发的互动网络对于人类与人工认知代理的合作、建立日益增长的个人信任和互惠责任是必要的:这一点在当前的人工智能中显然是缺失的,尽管我们正在积极寻求。ACo 方法是一项正在进行中的工作,它可以利用许多研究线索,其中一些是早期尝试定义人工智能概念和方法的先驱。在本文的其余部分,我们将考虑一些需要在统一框架中重新审视的组成部分:发展型机器人学原理、具有前瞻能力的行动表示方法以及社会互动的关键作用。
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Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents
The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks. Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data. Decoding the application of deep learning in neuroscience: a bibliometric analysis. Optimizing extubation success: a comparative analysis of time series algorithms and activation functions. Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.
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