Goodman Philip H, Zou Quan, Dascalu Sergiu-Mihai
Department of Medicine and Program in Biomedical Engineering, University of Nevada Reno, USA.
Front Neurosci. 2008 Jul 7;2(1):123-9. doi: 10.3389/neuro.01.007.2008. eCollection 2008 Jul.
Despite decades of societal investment in artificial learning systems, truly "intelligent" systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain "algorithm" itself-trying to replicate uniquely "neuromorphic" dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or "avatars", to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications.
尽管社会在人工智能学习系统上投入了数十年，但真正的“智能”系统尚未实现。这些传统模型基于输入-输出模式优化和/或认知生产规则建模。一种应对方法是社会机器人技术，利用人与机器人的交互来捕捉重要的认知动态，如合作和情感；到目前为止，这些系统仍然采用传统的学习算法。最近，研究人员正专注于大脑“算法”本身的核心假设——试图复制独特的“神经形态”动态，如动作电位发放和突触学习。由于强大的超级计算机的可用性以及从对大脑相互依存的电生理、代谢组学和基因组网络的研究中获得的参数供应不断增加，大规模神经形态模型现在才变得可行。个人计算机技术也使得计算机生成的类人图像或“化身”被接受，以在虚拟现实中代表智能角色。在最近的一篇论文中，我们提出了一种虚拟神经机器人技术（VNR）方法，其中将上述方法（社会情感机器人技术、神经形态大脑架构和虚拟现实投影）进行混合，以快速正向设计并开发出越来越复杂的、具有内在智能的系统。在本文中，我们综合了该领域的研究和相关工作，并为VNR提供了一个框架，对研究和实际应用具有更广泛的意义。
