走向通用人工智能,到底是先“理解智能”还是先“制造智能”? 近期,在IJAC优先在线发表的几篇论文中, 北京大学黄铁军 教授的综述成为近两月优先发表论文中的亮点之一。 黄铁军教授在这篇综述中提出的“仿真主义”(Imitationalism)可谓人工智能方法中第五的流派! (熟悉人工智能的小伙伴可能都了解:人工智能的基本思想大致可划分为四个流派:符号主义、链接主义、行为主义和统计主义) 文章不仅阐述了如何打破走向通用人工智能的研究僵局、探索研制类脑神经计算机的新思路,还详细描述了类脑神经计算机三个重要技术层次和国内外研究进展。与IJAC 4月优先在线发表的麻省理工美国人文科学院院士Tomaso Poggio 教授关于深度学习机理的文章一样,本篇综述绝对是 行业宝典 !且此综述已在Twitter被相关学者转发数次。 通用人工智能(Artificial General Intelligence, AGI)指可以像人一样完成各种智能任务的类人智能机器,AGI又称为Srong AI或fullAI. 为了获得通用人工智能,到底是先“理解智能”? (即理解意识现象和功能背后的发生机理) 还是先“制造智能” (即研制类似人脑的具有自我意识的智能机器)? ——这是一个问题! 传统人工智能的思维定式是在“理解智能”机理的基础上设计制造人工智能系统,即先理解智能再模仿智能。然而人类对自身智能的认识还处在初级阶段,在对人类智能的理解还极其有限,“理解智能”这个终极性问题到底数十年还是数百年亦或数千年才能解决?都还是未知数。因此,把“制造智能”寄希望于“理解智能”,实质上把解决问题的方案建立在解决另一个更难问题的基础上,犯了本末倒置的错误。 (图片来自于网络) 如果我们跳出传统思维的窠臼,就会发现通向通用人工智能还有一条“ 新 ” 路线——构建 类脑神经计算机 ,这里称为“仿真主义”(Imitationalism)。说这是一条新路线,是因为它反转了“理解智能”和“制造智能”的前后关系:即我们不再寻求“理解智能”的解,而是先模仿脑,即设计更先进的探测分析工具, 从结构上解析大脑 ,再利用工程技术手段“照葫芦画瓢”式地 构造仿脑装置 ,最后 通过环境刺激和交互训练“仿真大脑”,实现类人智能 。 简言之: 先结构后功能 。 (图片来自于网络) 本篇文章阐述了“先结构后功能”的类脑计算的 三层技术路线 :结构层次模仿脑、器件层次逼近脑,智能层次超越脑;还分析了在获得通用人工智能时, 神经计算机相比传统计算机的优势、生物神经网络相比人工神经网络的优势 ,并综述了国际学者在神经计算机领域的研究进展 。 精彩内容请下载原文阅读,这正是: 人工智能一甲子,结构功能两相争; 符号系统Top down,神经网络向上攻; 进化主义玩互动,机器学习调模型; 欲破智能千古谜,先剖大脑再人工。 ——来源:《中国计算机学会通讯》2017年1月,作者黄铁军 文章免费下载信息: 【 Title 】 Imitatingthe brain with neurocomputer a “new” way towards artificial generalintelligence 【 Author 】 Tie-Jun Huang 【 Abstract 】 To achieve the artificial generalintelligence (AGI), imitate the intelligence? or imitate the brain? This is thequestion! …. To achieve AGI, a practical approach is to build the so-calledneurocomputer, which could be trained to produce autonomous intelligence andAGI. A neurocomputer imitates the biological neural network with neuromorphicdevices which emulate the bio-neurons, synapses and other essential neuralcomponents. The neurocomputer could perceive the environment via sensors andinteract with other entities via a physical body. The philosophy under the“new” approach, so-called as imitationalism in this paper, is the engineeringmethodology which has been practiced for thousands of years, and for manycases, such as the invention of the first airplane, succeeded. This papercompares the neurocomputer with the conventional computer. The major progressabout neurocomputer is also reviewed. 【 Keywords 】 Artificial general intelligence (AGI), neuromorphic computing, neurocomputer, brain-likeintelligence, imitationalism 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1082-y 5-6 月优先在线发表的计算方向论文还有如下,欢迎阅读 【 Title 】 Stability analysis of an underactuatedautonomous underwater vehicle using extended-Routh’s stability method 【 Author 】 Basant Kumar Sahu,Bidyadhar Subudhi, Madan Mohan Gupta 【 Keywords 】 Routh’s stability, extended-Routh’s stability, autonomousunderwater vehicle (AUV), underactuated system, underwater robots 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-0992-4 【 Title 】 Layered software patterns for data analysisin big data environment 【 Author 】 Hossam Hakeem 【 Keywords 】 Big data, data analysis, patterns layered,structure data modelling 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-1043-x 【 Title 】 Multi-sensor data fusion for wheelchairposition estimation with unscented Kalman filter 【 Author 】 Derradji Nada, MounirBousbia-Salah, Maamar Bettayeb 【 Keywords 】 Data fusion, unscented Kalman filter(UKF) , measurement fusion (MF), navigation, state vector fusion(SVF) , wheelchair 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1065-z 【 Title 】 Evaluation method of the gait motion based onself-organizing map using the gravity center fluctuation on the sole 【 Author 】 Koji Makino, MasahiroNakamura, Hidenori, Omori, et al. 【 Keywords 】 Gait motion, self-organizing map (SOM), rehabilitation, evaluationmethod, gravity center fluctuation (GCF) 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-1045-8 【 Title 】 Low-latency data gathering with reliabilityguaranteeing in heterogeneous wireless sensor networks 【 Author 】 Tian-Yun Shi, Jian Li, Xin-ChunJia, et al. 【 Keywords 】 Heterogeneous wireless sensor networks (HWSNs), datagathering tree, multi-channel, power assignment, linkscheduling 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1074-y 【 Title 】 Recursive Bayesian algorithm foridentification of systems with non-uniformly sampled input data 【 Author 】 Shao-Xue Jing, Tian-HongPan, Zheng-Ming Li 【 Keywords 】 Parameter estimation, discrete time systems, Gaussiannoise, Bayesian algorithm, covariance resetting 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1073-z 点击阅读“人工智能”相关推文 【IJAC热文】MITTomaso Poggio教授探讨深度学习机理 http://mp.weixin.qq.com/s/AwmQyhREjpIew0beIuj6yA 【IJAC推文】周志华、吴建鑫等关于循环神经网络的最新研究成果 http://mp.weixin.qq.com/s/S_7TPZ-QiIHkki2l-KAtDg 【IJAC推文】颜水成团队解读“高智商”机器人的终极杀器——深度学习 http://mp.weixin.qq.com/s/KRBTTycNve3GY8T9AkvoSA