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CLC number: TP391.4

On-line Access: 2017-01-20

Received: 2016-12-31

Revision Accepted: 2017-01-09

Crosschecked: 2017-01-11

Cited: 4

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Citations:  Bibtex RefMan EndNote GB/T7714


Fei Wu


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.1 P.3-14


Challenges and opportunities: from big data to knowledge in AI 2.0

Author(s):  Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yzhuang@zju.edu.cn, wufei@zju.edu.cn, chenc@zju.edu.cn, panyh@cae.cn

Key Words:  Deep reasoning, Knowledge base population, Artificial general intelligence, Big data, Cross media

Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan. Challenges and opportunities: from big data to knowledge in AI 2.0[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3-14.

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A1 - Yue-ting Zhuang
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A1 - Chun Chen
A1 - Yun-he Pan
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DOI - 10.1631/FITEE.1601883

In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI, as follows: from shallow computation to deep neural reasoning; from merely data-driven model to data-driven with structured logic rules models; from task-oriented (domain-specific) intelligence (adherence to explicit instructions) to artificial general intelligence in a general context (the capability to learn from experience). Motivated by such endeavors, the next generation of AI, namely AI 2.0, is positioned to reinvent computing itself, to transform big data into structured knowledge, and to enable better decision-making for our society.


概要:本文对大数据时代人工智能领域近期出现的若干理论和技术进展进行了综述。我们认为,将数据驱动机器学习方法与人类的常识先验与隐式直觉有效结合起来,可实现可解释、更鲁棒和更通用的人工智能。AI 2.0时代大数据人工智能具体表现为:从浅层计算到深度神经推理;从单纯依赖于数据驱动的模型到数据驱动与知识引导相结合学习;从领域任务驱动智能到更为通用条件下的强人工智能(从经验中学习)。下一代人工智能(AI 2.0)将改变计算本身,将大数据转变为知识以支持人类社会作出更好决策。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Abadi, M., Agarwal, A., Barham, P., et al., 2016. Tensor-linebreak Flow: large-scale machine learning on heterogeneous distributed systems. ePrint Archive, arXiv:1603.04467.

[2]Auer, S., Bizer, C., Kobilarov, B., emph et al., 2007. DBpedia: a nucleus for a web of open data. Proc. 6th Int. Semantic Web Conf. & 2nd Asian Semantic Web Conf., p.722-735.

[3]Bahdanau, D., Cho, K., Bengio, Y., 2014. Neural machine translation by jointly learning to align and translate. ePrint Archive, arXiv:1409.0473.

[4]Baudisch, P., Good, N., Bellotti, V., et al., 2002. Keeping things in context: a comparative evaluation of focus plus context screens, overviews, and zooming. Proc. SIGCHI Conf. on Human Factors in Computing Systems, p.259-266.

[5]Bergstra, J., Breuleux, O., Bastien, F., et al., 2010. Theano: a CPU and GPU math compiler in Python. Proc. 9th Python in Science Conf., p.1-7.

[6]Bollacker, K., Evans, C., Paritosh, P., et al., 2008. Freebase: a collaboratively created graph database for structuring human knowledge. Proc. ACM SIGMOD Int. Conf. Management of Data, p.1247-1250.

[7]Brill, E., 1992. A simple rule-based part of speech tagger. Proc. Workshop on Speech and Natural Language, p.112-116.

[8]Carlson, A., Betteridge, J., Kisiel, B., et al., 2010. Toward an architecture for never-ending language learning. Proc. 24th AAAI Conf. on Artificial Intelligence, p.3-11.

[9]Cho, K., Courville, A., Bengio, Y., 2015. Describing multimedia content using attention-based encoder-decoder networks. IEEE Trans. Multim., 17(11):1875-1886.

[10]Collobert, R., Bengio, S., Mariéthoz, J., 2002. Torch: a Modular Machine Learning Software Library. IDIAP Research Report No. IDIAP-RR 02-46, Dalle Molle Institute for Perceptual Artificial Intelligence, Martigny, Switzerland.

[11]Gordo, A., Almazan, J., Revaud, J., et al., 2016. End-to-end learning of deep visual representations for image retrieval. ePrint Archive, arXiv:1610.07940.

[12]Harris, Z.S., 1954. Distributional structure. In: Hiz, H. (Ed.), Formal Linguistics Series. Springer Netherlands, Houten, Netherlands.

[13]He, K.M., Zhang, X.Y., Ren, S.Q., et al., 2015. Deep residual learning for image recognition. ePrint Archive, arXiv:1512.03385.

[14]Hu, Z.T., Ma, X.Z., Liu, Z.Z., et al., 2016. Harnessing deep neural networks with logic rules. ePrint Archive, arXiv:1603.06318.

[15]Ip, C.Y., Varshney, A., 2011. Saliency-assisted navigation of very large landscape images. IEEE Trans. Visual. Comput. Graph., 17(12):1737-1746.

[16]Jia, Y.Q., Shelhamer, E., Donahue, J., et al., 2014. Caffe: convolutional architecture for fast feature embedding. Proc. 22nd ACM Int. Conf. on Multimedia, p.675-678.

[17]Kalchbrenner, N., Grefenstette, E., Blunsom, P., 2014. A convolutional neural network for modelling sentences. ePrint Archive, arXiv:1404.2188.

[18]Karpathy, A., Joulin, A., Li, F.F.F., 2014. Deep fragment embeddings for bidirectional image sentence mapping. Proc. Advances in Neural Information Processing Systems, p.1889-1897.

[19]Kim, Y.M., Varshney, A., 2006. Saliency-guided enhancement for volume visualization. IEEE Trans. Visual. Comput. Graph., 12(5):925-932.

[20]Kitcher, P., 1988. Marritem s computational theory of vision. Philos. Sci., 55(1):1-24.

[21]Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. 26th Annual Conf. on Neural Information Processing Systems, p.1097-1105.

[22]Lee, C.Y., Xie, S., Gallagher, P., et al., 2015. Deeply-supervised nets. Artificial Intelligence and Statistics Conf., p.562-570.

[23]Li, J.W., Monroe, W., Ritter, A., et al., 2016. Deep reinforcement learning for dialogue generation. ePrint Archive, arXiv:1606.01541.

[24]Liu, Y., Sun, C.J., Lin, L., et al., 2016. Learning natural language inference using bidirectional LSTM model and inner-attention. ePrint Archive, arXiv:1605.09090.

[25]Low, Y.C., Gonzalez, J.E., Kyrola, A., et al., 2014. GraphLab: a new framework for parallel machine learning. ePrint Archive, arXiv:1408.2041.

[26]Mackinlay, J., Hanrahan, P., Stolte, C., 2007. Show me: automatic presentation for visual analysis. IEEE Trans. Visual. Comput. Graph., 13(6):1137-1144.

[27]Marrinan, T., Aurisano, J., Nishimoto, A., et al., 2014. SAGE2: a new approach for data intensive collaboration using scalable resolution shared displays. Int. Conf. on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), p.177-186.

[28]McCarthy, J., Minsky, M.L., Rochester, N., et al., 2006. A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag., 27(4):12-14.

[29]Mikolov, T., Chen, K., Corrado, G., et al., 2013. Efficient estimation of word representations in vector space. ePrint Archive, arXiv:1301.3781.

[30]Neal, R.M., 2012. Bayesian Learning for Neural Networks. Springer Science & Business Media, Berlin, Germany.

[31]Pan, Y.H., 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.

[32]Rezende, D.J. Mohamed, S., Danihelka, I., et al., 2016. One-shot generalization in deep generative models. ePrint Archive, arXiv:1603.05106.

[33]et al.}2003]russell2003artificial Russell, S.J., Norvig, P., Canny, J., et al., 2003. Artificial Intelligence: a Modern Approach. Prentice Hall, Upper Saddle River, USA.

[34]Sacha, D., Stoffel, A., Stoffel, F., et al., 2014. Knowledge generation model for visual analytics. IEEE Trans. Visual. Comput. Graph., 20(12):1604-1613.

[35]Sarjant, S., Legg, C., Robinson, M., et al., 2009. All you can eat ontology-building: feeding Wikipedia to Cyc. Proc. Int. Joint Conf. on Web Intelligence and Intelligent Agent Technology, p.341-348.

[36]Schroeder, W.J., Lorensen, B., Martin, K., 2004. The Visualization Toolkit: an Object-Oriented Approach to 3D Graphics. Kitware, New York, USA.

[37]Shijia, E., Jia, S.B., Yang, X., et al., 2016. Knowledge graph embedding for link prediction and triplet classification. China Conf. on Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data, p.228-232.

[38]Shneiderman, B., 1996. The eyes have it: a task by data type taxonomy for information visualizations. Proc. IEEE Symp. on Visual Languages, p.336-343.

[39]Shojaee, S.M., Baghshah, M.S., 2016. Semi-supervised zero-shot learning by a clustering-based approach. ePrint Archive, arXiv:1605.09016.

[40]Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. ePrint Archive, arXiv:1409.1556.

[41]Sutskever, I., Vinyals, O., Le, Q.V., 2014. Sequence to sequence learning with neural networks. Conf. on Neural Information Processing Systems, p.3104-3112.

[42]Szegedy, C., Liu, W., Jia, Y.Q., et al., 2015. Going deeper with convolutions. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1-9.

[43]Vrandevčić, D., Krőtzsch, M., 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM, 57(10):78-85.

[44]Weston, J., Chopra, S., Bordes, A., 2014. Memory networks. ePrint Archive, arXiv:1410.3916.

[45]Wu, F., Yu, Z., Yang, Y., et al., 2014. Sparse multi-modal hashing. IEEE Trans. Multim., 16(2):427-439.

[46]Wu, F., Jiang, X.Y., Li, X., et al., 2015. Cross-modal learning to rank via latent joint representation. IEEE Trans. Imag. Process., 24(5):1497-1509.

[47]Zhuang, Y.T., Song, J., Wu, F., et al., 2016. Multi-modal deep embedding via hierarchical grounded compositional semantics. IEEE Trans. Circ. Syst. Video Technol.

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