认知计算及其在农业领域的应用研究
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摘要
认知计算是认知科学、神经科学、数据科学和云计算的交叉学科。数据的急剧增长、算法的不断优化和高性能计算能力的发展加速了认知计算在健康医疗、智慧城市、农业等各个领域的研究和应用。认知计算提供了一种新的模式,是大数据、机器学习、深度学习、自然语言处理、IOT (The Internet of Things)、云计算等不同成熟技术的结合体。在此模式下,研究人员不再满足于继续延用传统的数据分析方法,开始寻求新的方法以期在大规模结构和非结构数据中探索其中模式和相关性。从而认知系统可以提供学习、推理、发现、自然语言交流、决策支持的功能。农业领域的数据量呈现爆发式的增长,认知计算和农业大数据的结合有效促进了智慧农业的发展,但是由于数据不仅包括时空数据、图像、视频等多种类型,其数据质量和地理位置、网络连接和数据来源密切相关。所以对于认知计算在农业领域的应用,这既是机遇也是挑战。基于已有研究工作探讨了认知计算的概念和相关学科;阐述了认知计算的发展历程、不同架构类型和技术体系;总结了近年来认知计算的研究进展;简要介绍了认知计算在农业领域的应用现状,同时对认知计算在农业领域应用中的挑战和发展趋势进行了总结、思考与展望。
Cognitive computing is a nascent interdisciplinary domain, and it is also an evolution of technology that attempts to make sense of a complex world that is drowning in data in all forms and shapes. It is a confluence of cognitive science, neuroscience, date science, and cloud computing, which makes cognitive computing powerful and has the potential for groundbreaking discoveries and advances. We are entering a new era in cognitive comput ing that will transform the way humans collaborate with machines to gain actionable insights in areas such as healthcare, manufacturing, transportation, retail, retail, and financial services. Served as a catalyst for advancing research in cognitive computing, a coherent body of knowledge and recent research in cognitive computing are brought together. First, a deep look was taken at the concept of cognitive computing and an interdisciplinary introduction to cognitive computing, which was to provide a unified view of the discipline. Second, the development procedure was provided. Thirdly, overview of three major categories of cognitive architectures and principal technologies and approaches that are fundamental to a cognitive system were demonstrated. Some of the industries that were early adopters of cognitive com-puting and the types of solutions that were being created were also included. Finally, the applications of cognitive computing in agricultural area was discussed. It covered the applications, the system, the future and its challenges. Cognitive systems can help with the transfer of knowledge and best practices in agricultural area, and using cognitive computing to help decision support services has huge potential. In these use cases, a cognitive system is designed to build a dialog between human and machine so that best practices are learned by the system as opposed to being programmed as a set of rules. It is clear that cognitive computing is in its early stages of maturation. The list of potential uses of a cognitive computing approach will continue to grow over time, and the coming decade will bring many new software and hardware innovations to stretch the limits of what is possible.
Cognitive computing is a nascent interdisciplinary domain, and it is also an evolution of technology that attempts to make sense of a complex world that is drowning in data in all forms and shapes. It is a confluence of cognitive science, neuroscience, date science, and cloud computing, which makes cognitive computing powerful and has the potential for groundbreaking discoveries and advances. We are entering a new era in cognitive comput ing that will transform the way humans collaborate with machines to gain actionable insights in areas such as healthcare, manufacturing, transportation, retail, retail, and financial services. Served as a catalyst for advancing research in cognitive computing, a coherent body of knowledge and recent research in cognitive computing are brought together. First, a deep look was taken at the concept of cognitive computing and an interdisciplinary introduction to cognitive computing, which was to provide a unified view of the discipline. Second, the development procedure was provided. Thirdly, overview of three major categories of cognitive architectures and principal technologies and approaches that are fundamental to a cognitive system were demonstrated. Some of the industries that were early adopters of cognitive com-puting and the types of solutions that were being created were also included. Finally, the applications of cognitive computing in agricultural area was discussed. It covered the applications, the system, the future and its challenges. Cognitive systems can help with the transfer of knowledge and best practices in agricultural area, and using cognitive computing to help decision support services has huge potential. In these use cases, a cognitive system is designed to build a dialog between human and machine so that best practices are learned by the system as opposed to being programmed as a set of rules. It is clear that cognitive computing is in its early stages of maturation. The list of potential uses of a cognitive computing approach will continue to grow over time, and the coming decade will bring many new software and hardware innovations to stretch the limits of what is possible.
引文
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[41]Xiao,T.and S.Lan.Knowledge Reduction based on Cognitive Model of Granular Computing[J].Advances in Information Sciences and Service Sciences.2013,5(6):733.
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[45]Eden,C.On the nature of cognitive maps[J].Journal of management studies.1992,29(3):261-265.
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[47]Wang,Y.Inference algebra(IA):A denotational mathematics for cognitive computing and machine reasoning(I)[J].International Journal of Cognitive Informatics and Natural Intelligence(IJCINI).2011,5(4):61-82.
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[50]Merolla,P.,et al.A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm[C].in 2011 IEEEcustom integrated circuits conference(CICC).IEEE.2011.
[51]Esser,S.K.,et al.Cognitive computing systems:Algorithms and applications for networks of neurosynaptic cores[C].in The 2013 International Joint Conference on Neural Networks(IJCNN).IEEE.2013.
[52]Yelick,K.,et al.Productivity and performance using partitioned global address space languages[C].in Proceedings of the 2007 international workshop on Parallel symbolic computation.ACM.2007.
[53]Merolla,P.A.,et al.A million spiking-neuron integrated circuit with a scalable communication network and interface[J].Science.2014,345(6197):668-673.
[54]Amir,A.,et al.Cognitive computing programming paradigm:a corelet language for composing networks of neurosynaptic cores[C].in The 2013 International Joint Conference on Neural Networks(I-JCNN).IEEE.2013.
[55]Djurfeldt,M.The connection-set algebra-a novel formalism for the representation of connectivity structure in neuronal network models[J].Neuroinformatics.2012,10(3):287-304.
[56]Djurfeldt,M.The Connection-set Algebra:a formalism for the representation of connectivity structure in neuronal network models,implementations in Python and C++,and their use in simulators[J].BMC neuroscience.2011,12(1):80.
[57]Lawniczak,A.T.and B.N.Di Stefano,Computational intelligence based architecture for cognitive agents[J].Procedia Computer Science.2010,1(1):2227-2235.
[58]ANDERSON,I.R.and B.I.REISER,THE LISP TUTGR[M].1985.
[59]Koedinger,K.R.,et al.Intelligent tutoring goes to school in the big city[J].International Journal of Artificial Intelligence in Education(IJAIED).1997,8:30-43.
[60]Seo,M.,et al.Solving geometry problems:Combining text and diagram interpretation[C].in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015.
[61]Matsuda,N.,et al.Cognitive anatomy of tutor learning:Lessons learned with SimStudent[J].Journal of Educational Psychology.2013,105(4):1152.
[62]Zadrozny,W.W.,V.de Paiva,and L.S.Moss.Explaining watson:polymath style[C].in Twenty-Ninth AAAI Conference on Artificial Intelligence.2015.
[63]Martinez,P.,H.Bakardjian,and A.Cichocki.Multi-command realtime brain machine interface using SSVEP:feasibility study for occipital and forehead sensor locations[C].in Advances in Cognitive Neurodynamics ICCN 2007.2008,783-786.
[64]Negueruela,C.,et al.Brain-computer interfaces for space applications[J].Personal and Ubiquitous Computing.2011,15(5):527-537.
[65]Hua,X.-S.,M.Ye,and J.Li.Mining knowledge from clicks:MSR-Bing image retrieval challenge[C].in 2014 IEEE International Conference on Multimedia and Expo Workshops(ICMEW).IEEE.2014.
[66]Bai,Y.,et al.Bag-of-words based deep neural network for image retrieval[C].in Proceedings of the 22nd ACM international conference on Multimedia.ACM.2014.
[67]Dong,J.,X.Li,and C.G.Snoek,Word2VisualVec:Cross-media retrieval by visual feature prediction[J].arXiv preprint arXiv:1604.06838,2016.
[68]Orozco,H.,D.Thalmann,and F.Ramos.Making empathetic virtual humans in human-computer interaction scenarios[C].in Proc.of the 11th Computer Graphics International.2010.
[69]Costa,P.T.and R.R.McCrae.Normal personality assessment in clinical practice:The NEO Personality Inventory[J].Psychological assessment.1992,4(1):5.
[70]Ekman,P.,W.V.Friesen,and P.Ellsworth.Emotion in the human face:Guidelines for research and an integration of findings[M].Elsevier.2013,11.
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