MIR专题征稿 | 类脑机器学习 (2022年4月10日截稿)

Machine Intelligence Research (MIR)专题"Brian-inspired Machine Learning"现公开征集原创稿件，截稿日期为2022年4月10日。专题客座编委：中科院自动化所张兆翔研究员、安徽大学罗斌教授、安徽大学汤进教授、中科院自动化所余山研究员、爱丁堡龙比亚大学Amir Hussain教授。欢迎赐稿！

▼专题 | 类脑机器学习▼Special Issue on Brian-inspired Machine Learning

 专题简介

Powered by new machine learning algorithms, effective large-scale tagging datasets, and superior computing power, AI programs are already outpacing humans in speed and accuracy at specific tasks. However, the human brain still has advantages over artificial intelligence in many aspects. For example, the human brain can multitask, learn autonomously, and generalize learning skills with high efficiency and low energy consumption. Most importantly, the human brain can communicate with each other through natural language; And this kind of language ability is acquired by the human communicative environment as early as early childhood. Understanding the grammar and semantics of human language has been one of the biggest obstacles to the development of artificial intelligence technology with human intelligence since the famous Turing test was developed by Alan Turing. This special issue aims to capture recent advances in the crossing forefront of AI and neuroscience and to point to the next targets in creating brain-like intelligence and further advancing neuroscience.

 征稿范围(包括但不限于)

Brain-inspired Artificial Intelligence

Deep Learning

Lifelong learning

Multi-modal human-computer natural interaction

AI for Neuroscience

Neuromorphic Technologies

 投稿指南

截稿日期：2022年4月10日

投稿地址（已开通）：

https://mc03.manuscriptcentral.com/mir

投稿时，请在系统中选择：

“Step 6 Details & Comments: Special Issue and Special Section”---“Special Issue on Brian-inspired Machine Learning”

 客座编委

张兆翔，中国科学院自动化研究所

罗斌，安徽大学

汤进，安徽大学

余山，中国科学院自动化研究所

Amir Hussain，爱丁堡龙比亚大学

 关于 MIR

Machine Intelligence Research (原刊名：International Journal of Automation and Computing) 由中国科学院自动化研究所主办、Springer海外出版，将于2022年正式出版。期刊立足国内、面向全球，着眼于服务国家战略需求，刊发机器智能领域最新原创研究性论文、综述、评论等，全面报道国际机器智能领域的基础理论和前沿创新研究成果，促进国际学术交流与学科发展，服务国家人工智能科技进步。

MIR主要报道领域包括人工智能基础理论、机器学习、机器感知、模式识别与计算机视觉、类脑智能、自然语言处理、智能机器人、人机协作、知识管理与数据挖掘及应用等。

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