Mobile Networks and Applications

, Volume 23, Issue 2, pp 368–375 | Cite as

Brain Intelligence: Go beyond Artificial Intelligence

  • Huimin LuEmail author
  • Yujie Li
  • Min Chen
  • Hyoungseop Kim
  • Seiichi Serikawa


Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan’s economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.


Brain intelligence Artificial intelligence Artificial life 



This work was supported by Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Grants-in-Aid for Scientific Research of JSPS (17 K14694), Research Fund of Chinese Academy of Sciences (No.MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1608), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1510), Research Fund of The Telecommunications Advancement Foundation, and Fundamental Research Developing Association for Shipbuilding and Offshore.


  1. 1.
    Siri, Accessed 20 April 2017
  2. 2.
    AlphaGo, Accessed 20 April 2017
  3. 3.
    IBM Watson, Accessed 20 April 2017
  4. 4.
    Microsoft Translator Speech API, Accessed 20 April 2017
  5. 5.
    Amazon Prime Air, Accessed 20 April 2017
  6. 6.
    Taigman Y, Yang M, Ranzato M, Wolf L (2014) “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2014), pp. 1–8Google Scholar
  7. 7.
    Stanford Artificial Intelligence Laboratory, Accessed 20 April 2017
  8. 8.
    MIT BigDog, Accessed 20 April 2017
  9. 9.
    The 4th Science and Technology Basic Plan of Japan, Accessed 20 April 2017
  10. 10.
    AI EXPO, Accessed 20 April 2017
  11. 11.
    2017 Will Be the Year of AI, Accessed 20 April 2017
  12. 12.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  13. 13.
    Bengio Y, Ducharme R, Vincent P, Janvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137–1155zbMATHGoogle Scholar
  14. 14.
    Mnih A, Hinton G (2007) “Three new graphical models for statistical language modelling,” In Proc of ICML07, pp. 641–648Google Scholar
  15. 15.
    T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, S. Khudanpur, (2010) “Recurrent neural network based language model,” In Proc of Interspeech 10, pp. 1045–1048Google Scholar
  16. 16.
    Sutskever I, Vinyals O, Le Q (2014) “Sequence to sequence learning with neural networks,” In Advances in Neural Information Processing Systems, pp. 3104–3112Google Scholar
  17. 17.
    Mei H, Bansal M, Walter M (2016) “What to talk about and how? Selective generation using LSTMs with coarse-to-fine alignment,” In NAACL-HLT, pp. 1–11Google Scholar
  18. 18.
    Luong M, Le Q, Sutskever I, Vinyals O, Kaiser L (2016) “Multitask sequence to sequence learning,” In Proc ICLR, pp. 1–10Google Scholar
  19. 19.
    Bourlard H, Morgan M (1994) Connnectionist speech recognition: a hybrid approach. Kluwer Academic Publishers, NetherlandsGoogle Scholar
  20. 20.
    Graves A, Mohamed A, Hinton G (2013) “Speech recognition with deep recurrent neural networks,” In ICASSP 2013, pp. 1–5Google Scholar
  21. 21.
    Wiederhold B, Riva G, Wiederhold M (2015) Virtual reality in healthcare: medical simulation and experiential interface. ARCTT 13:239Google Scholar
  22. 22.
    Bartsch G, Mitra A, Mitra S, Almal A, Steven K, Skinner D, Fry D, Lenehan P, Worzel W, Cote R (2016) Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder. J Urol 195:493–498CrossRefGoogle Scholar
  23. 23.
    Labonnote N, Hoyland K (2017) Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study. Intell Buildings Int 29(1):40–63CrossRefGoogle Scholar
  24. 24.
    Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E (2014) Cudnn: efficient primitives for deep learning, pp. 1–10, arXiv:1410.0759Google Scholar
  25. 25.
    Coates A, Huval B, Wang T, Wu D, Catanzaro B, Andrew N (2013) Deep learning with cots hpc systems, In Proc of the 30th International Conference on Machine Learning, pp. 1337–1345Google Scholar
  26. 26.
    Lacey G, Taylor G, Areibi S (2016) Deep learning on FPGAs: past, present, and future, pp. 1–8, ar Xiv: 1602.04283Google Scholar
  27. 27.
    Lin W, Lin S, Yang T (2017) Integrated business prestige and artificial intelligence for corporate decision making in dynamic environments. Cybern Syst:1–22.
  28. 28.
    Ratnaparkhi A, Pilli E, Joshi R (2016) Survey of scaling platforms for deep neural networks, In Proc of International Conference on Emerging Trends in Communication Technologies, pp. 1–6Google Scholar
  29. 29.
    Raina R, Madhavan A, Ng A (2009) Large-scale deep unsupervised learning using graphics processors, In Proc of 26th Annual International Conference on Machine Learning, pp. 873–880Google Scholar
  30. 30.
    Catanzaro B (2013) Deep learning with COTS HPC systems, In Proc of the 30th International Conference on Machine Learning, pp. 1337–1345Google Scholar
  31. 31.
    Dean J, Corrado G, Monga R, Chen K, Devin M, Le Q, Mao M, Ranzato M, Senior A, Tucker P, Yang K, Ng A (2012) Large scale distributed deep networks, In Proc of Advances in Neural Information Processing Systems, pp. 1223–1231Google Scholar
  32. 32.
    Chilimbi T, Suzue Y, Apacible J, Kalyanaraman K (2014) Project adam: Building an efficient and scalable deep learning training system, In Proc of 11th USENIX Symposium on Operating Systems Design and Implementation, pp. 571–582Google Scholar
  33. 33.
  34. 34.
    Merolla P, Arthur J, Alvarez-lcaza R, Cassidy A, Sawada J, Akopyan F, Jackson B, Imam N, Guo C, Nakamura Y, Brezzo B, Vo I, Esser S, Appuswamy R, Taba B, Amir A, Flickner M, Risk W, Manohar R, Modha D (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197):668–673CrossRefGoogle Scholar
  35. 35.
    Khan M, Lester D, Plana L, Rast A, Jin X, Painkras E, Furber S (2008) SpiiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor, In Proc of IEEE International Joint Conference on Neural Networks, pp. 2849–2856Google Scholar
  36. 36.
    Lacity M, Willcocks L (2016) A new approach to automating services. MIT Sloan Manag Rev 2016:1–16Google Scholar
  37. 37.
    Mikolov T, Karafiat M, Burget L, Cernocky J, Khudanpur S (2010) Recurrent neural network based language model, In Proc of Interspeech 2010, pp. 1045–1048Google Scholar
  38. 38.
    Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681CrossRefGoogle Scholar
  39. 39.
    Graves A, Jaitly N, Mohamed A (2013) Hybrid speech recognition with deep bidirectional LSTM, In Proc of IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 1–4Google Scholar
  40. 40.
    Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architecutres. Neural Netw 18(5–6):602–610CrossRefGoogle Scholar
  41. 41.
    Mishra A, Desai V (2006) Drought forecasting using feed-forward recursive neural network. Ecol Model 198(1–2):127–138CrossRefGoogle Scholar
  42. 42.
    Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Li F (2014) Large-scale video classification with convolutional neural networks, In Proc of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732Google Scholar
  43. 43.
    LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551CrossRefGoogle Scholar
  44. 44.
    Bell R, Koren Y (2007) Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter 9(2):75–79CrossRefGoogle Scholar
  45. 45.
    Simonyan K, Zisserman A (2015) “Very deep convolutional networks for large-scale image recognition,” In Proc of IEEE ICLR2015, pp. 1–14Google Scholar
  46. 46.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions, In Proc of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–12Google Scholar
  47. 47.
    Arora S, Bhaskara A, Ge R, and Ma T (2013) “Provable bounds for learning some deep representations,” arXiv:abs/1310.6343Google Scholar
  48. 48.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, In Proc. Of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–12Google Scholar
  49. 49.
    Chen M, Ma Y, Li Y, Wu D, Zhang Y (2017) Wearable 2.0: enabling human-cloud integration in next generation healthcare systems. IEEE Commun Mag 54(12):3–9Google Scholar
  50. 50.
    Song J, Zhang Y (2016) TOLA: Topic-oriented learning assistance based on cyber-physical system and big data. Futur Gener Comput Syst.
  51. 51.
    Zhang Y (2016) Grorec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans Serv Comput 9(5):786–795CrossRefGoogle Scholar
  52. 52.
    Liu Q, Ma Y, Alhussein M, Zhang Y, Peng L (2016) Green data center with IoT sensing and cloud-assisted smart temperature controlling system. Comput Netw 101:104–112CrossRefGoogle Scholar
  53. 53.
    Chen D, Manning C (2014) A fast and accurate dependency parser using neural networks, In Proc of Empirical Methods in Natural Language Processing, pp. 740–750Google Scholar
  54. 54.
    Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences, In Proc of Annual Meeting of the Association for Computational Linguistics, pp. 655–665Google Scholar
  55. 55.
    Ciresan D, Meier U, Masci J, Schmidhuber J (2012) Multi-column deep neural network for traffic sign classification. Neural Netw 32:333–338CrossRefGoogle Scholar
  56. 56.
    Santos C, Xiang B, Zhou B (2015) Classifying relations by ranking with convolutional neural networks, In Proc of Annual Meeting of the Association for Computational Linguistics, pp. 626–634Google Scholar
  57. 57.
    Hu B, Tu Z, Lu Z, Chen Q (2015) Context-dependent translation selection using convolutional neural network, In Proc of Annual Meeting of the Association for Computational Linguistics, pp. 536–541Google Scholar
  58. 58.
    Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77CrossRefGoogle Scholar
  59. 59.
    Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Li J, Serikawa S (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput: Pract Experience 29(6):1–8CrossRefGoogle Scholar
  60. 60.
    Lu H, Li Y, Uemura T, Ge Z, Xu X, He L, Serikawa S, Kim H (2017) FDCNet: filtering deep convolutional network for marine organism classification, Multimedia Tools and Applications, pp. 1–14Google Scholar
  61. 61.
    Lu H, Li Y, Zhang L, Serikawa S (2015) Contrast enhancement for images in turbid water. J Opt Soc Am 32(5):886–893CrossRefGoogle Scholar
  62. 62.
    Serikawa S, Shimomura T (1998) Proposal of a system of function-discovery using a bug type of artificial life. Trans IEE Jpn 118-C(2):170–179Google Scholar
  63. 63.
    Stanley K, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evol Comput 10(2):99–127CrossRefGoogle Scholar
  64. 64.
    Schrum J, Miikkulainen R (2014) Evolving multimodal behavior with modular neural networks in Ms. Pac-Man, In Proc of the Genetic and Evolutionary Computation Conference, pp. 325–332Google Scholar
  65. 65.
    Stanley K, Ambrosio D, Gauci J (2009) A hypercube-based encoding for evolving large-scale neural networks. Artif Life 15(2):185–212CrossRefGoogle Scholar
  66. 66.
    Emmert-Streib F, Dehmer M, Haibe-Kains B (2014) Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks. Front Cell Dev Biol 2(38):1–7Google Scholar
  67. 67.
    Dinh H, Aubert M, Noman N, Fujii T, Rondelez Y, Iba H (2015) An effective method for evolving reaction networks in synthetic biochemical systems. IEEE Trans Evol Comput 19(3):374–386CrossRefGoogle Scholar
  68. 68.
    Hwang K, Chen M (2017) Big-data analytics for cloud, IoT and cognitive computing. Press, Wiley, 432 pagesGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Kyushu Institute of TechnologyKitakyushuJapan
  2. 2.Yangzhou UniversityYangzhouChina
  3. 3.Huazhong University of Science and TechnologyWuhanChina

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