nach oben

Frontiers of Information Technology & Electronic Engineering

Erschienen in:

01.01.2017 | Review

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

verfasst von: Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan

Erschienen in: Frontiers of Information Technology & Electronic Engineering | Ausgabe 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

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.

Vorheriger Artikel Special issue on artificial intelligence 2.0

Nächster Artikel Crowd intelligence in AI 2.0 era

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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

Auer, S., Bizer, C., Kobilarov, B., 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. http://dx.doi.org/10.1007/978-3-540-76298-0_52

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

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. http://dx.doi.org/10.1145/503376.503423

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.

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. http://dx.doi.org/10.1145/1376616.1376746

Brill, E., 1992. A simple rule-based part of speech tagger. Proc. Workshop on Speech and Natural Language, p.112–116. http://dx.doi.org/10.3115/1075527.1075553 CrossRef

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.

Cho, K., Courville, A., Bengio, Y., 2015. Describing multimedia content using attention-based encoder-decoder networks. IEEE Trans. Multim., 17(11):1875–1886. http://dx.doi.org/10.1109/TMM.2015.2477044 CrossRef

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.

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.

Harris, Z.S., 1954. Distributional structure. In: Hiz, H. (Ed.), Formal Linguistics Series. Springer Netherlands, Houten, Netherlands. http://dx.doi.org/10.1007/978-94-017-6059-1_36

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

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

Ip, C.Y., Varshney, A., 2011. Saliency-assisted navigation of very large landscape images. IEEE Trans. Visual. Comput. Graph., 17(12):1737–1746. http://dx.doi.org/10.1109/TVCG.2011.231 CrossRef

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. http://dx.doi.org/10.1145/2647868.2654889

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

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.

Kim, Y.M., Varshney, A., 2006. Saliency-guided enhancement for volume visualization. IEEE Trans. Visual. Comput. Graph., 12(5):925–932. http://dx.doi.org/10.1109/TVCG.2006.174 CrossRef

Kitcher, P., 1988. Marr’s computational theory of vision. Philos. Sci., 55(1):1–24.MathSciNetCrossRef

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.

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

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

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.

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

Mackinlay, J., Hanrahan, P., Stolte, C., 2007. Show me: automatic presentation for visual analysis. IEEE Trans. Visual. Comput. Graph., 13(6):1137–1144. http://dx.doi.org/10.1109/TVCG.2007.70594 CrossRef

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. http://dx.doi.org/10.4108/icst.collaboratecom.2014.257337

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.

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

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

Pan, Y.H., 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409–413. http://dx.doi.org/10.1016/J.ENG.2016.04.018 CrossRef

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

Russell, S.J., Norvig, P., Canny, J., et al., 2003. Artificial Intelligence: a Modern Approach. Prentice Hall, Upper Saddle River, USA.MATH

Sacha, D., Stoffel, A., Stoffel, F., et al., 2014. Knowledge generation model for visual analytics. IEEE Trans. Visual. Comput. Graph., 20(12):1604–1613. http://dx.doi.org/10.1109/TVCG.2014.2346481 CrossRef

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. http://dx.doi.org/10.1109/WI-IAT.2009.60

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

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. http://dx.doi.org/10.1007/978-981-10-3168-7_23 CrossRef

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. http://dx.doi.org/10.1109/VL.1996.545307

Shojaee, S.M., Baghshah, M.S., 2016. Semi-supervised zeroshot learning by a clustering-based approach. ePrint Archive, arXiv:1605.09016.

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

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

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.

Vrandečić, D., Krötzsch, M., 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM, 57(10):78–85. http://dx.doi.org/10.1145/2629489 CrossRef

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

Wu, F., Yu, Z., Yang, Y., et al., 2014. Sparse multi-modal hashing. IEEE Trans. Multim., 16(2):427–439. http://dx.doi.org/10.1109/TMM.2013.2291214 CrossRef

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. http://dx.doi.org/10.1109/TIP.2015.2403240 CrossRef

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. http://dx.doi.org/10.1109/TCSVT.2016.2606648

Titel: Challenges and opportunities: from big data to knowledge in AI 2.0
verfasst von: Yue-ting Zhuang
Fei Wu
Chun Chen
Yun-he Pan
Publikationsdatum: 01.01.2017
Verlag: Zhejiang University Press
Erschienen in: Frontiers of Information Technology & Electronic Engineering / Ausgabe 1/2017
Print ISSN: 2095-9184
Elektronische ISSN: 2095-9230
DOI: https://doi.org/10.1631/FITEE.1601883

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2017

A vision-centered multi-sensor fusing approach to self-localization and obstacle perception for robotic cars

Towards human-like and transhuman perception in AI 2.0: a review

Disambiguating named entities with deep supervised learning via crowd labels

Cross-media analysis and reasoning: advances and directions

Applications of artificial intelligence in intelligent manufacturing: a review

Coalition formation based on a task-oriented collaborative ability vector

Premium Partner